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Small and medium-sized enterprises (SMEs) play a crucial role in the global economy. This article examines SMEs’ financial management attitudes in the Visegrad Four countries, aiming to understand how these attitudes impact the firms’ long-term sustainability. The empirical study, conducted in June 2022, involved 1,398 respondents, comprising owners or top managers of SMEs from V4 countries. The research explores various facets of the long-term viability of companies over the next five years. Central to the investigation is understanding how managerial comprehension of critical financial management aspects, the willingness to embrace financial risks, and effective risk management contribute to a company’s enduring success. Additionally, the study assesses how a positive evaluation of a company’s financial performance correlates with its overall longevity in the business landscape.Results indicate that financial management significantly influences SME stability in the medium term. Factors positively affecting SME stability include a proper understanding and management of financial risks, along with a positive assessment of the company’s financial activities. Correlation analysis suggests that the perception of business continuity over a 5-year horizon moderately depends on understanding key financial management aspects, accepting financial risks as part of daily operations, and adeptly managing them. A positive vision of V4 SME management regarding their own future emerged as a crucial factor in the study. In conclusion, the article underscores the importance of effective financial management practices, risk comprehension, and positive self-assessment for SMEs to achieve sustained stability in the dynamic business landscape. AcknowledgmentThis work is supported by Tomas Bata University in Zlin through IGA/FaME/2023/014: CSR as an important factor in obtaining bank loans and external financial sources for SMEs.

Abstract Background The COVID-19 pandemic has made devastating impacts on public health and global economy. While most people experience mild symptoms, it is highly transmissible and deadly in at-risk populations. Telemedicine has the potential to prevent hospitalization and provide remote care. Methods This retrospective study included 336 people with COVID-19, among which 141 (42%) and 195 (58%) were in Delta and Omicron dominant groups, respectively. Patients were confirmed to have COVID-19 by PCR or rapid test and were cared for via telemedicine. Severe cases were hospitalized for more intensive treatment. Results The majority of individuals recovered at home (97.02%), while 2.98% required hospitalization. All hospital admissions were in Delta dominant group. No deaths were reported. Delta dominant group was more likely to develop loss of taste and smell, decreased appetite and need longer treatment time than those in Omicron dominant group. Conclusions Telemedicine is a safe measure to provide at-home care for people with COVID-19 infections caused by both Delta and Omicron variants. Trial registration This study was approved by the Institutional Review Board Committee of University of Medicine and Pharmacy at Ho Chi Minh City (IRB No: 22115–DHYD).

Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted customers. The RFM model and machine learning combination have been widely applied in various areas.

The experiment was carried out to establish diet formulations for beef cattle, with the main ingredients being sweet potato vines (SPV) and sweet potato tubers (type 3) (SPT). The experiment was arranged in a completely randomized design with three treatments and three replications with seven evaluation time points. The formulations with the same ratio of SPV and SPT (according to DM) were mixed with other ingredients such as rice straw, copra meal, extracted soybean, corn kernel, rice bran, salt, mineral premix, and urea. Treatments were evaluated for sensory and chemical composition at 1, 14, 28, 42, 56, 70, and 84 days after ensiling. The parameters to assess the nutritional content such as dry matter (DM), ash, crude protein (CP), crude fiber (CF), acid detergent fiber (ADF), neutral detergent fiber (NDF), and ether extract (EE). The pH values starting from 14 to 84 days of storage were all satisfactory for silage since there is a pH in the range of 4-4.5. The NH3 content in the same formulation over the fermentation times was not statistically significant (P>0.05). The assessment results of changes in nutritional content and sensory evaluation over time showed little change in the formulation; all three formulations met the standards of Fermented Total Mixed Ration (FTMR).

Rice is the most important crop in Vietnam’s agriculture sector for its contribution to economic stability and food security. Improving the efficiency of rice farming and expanding production towards sustainability has become one of the national economic development goals. This study aimed to empirically evaluate technical efficiency of rice farms based on their farming practices using observational data from face-to-face interviews with 152 rice farm households in Long An Province in the Mekong Delta, Vietnam. The data envelopment analysis model was applied to evaluate the technical and scale efficiencies. Results show that except for farmers applying one practice, the average technical efficiency exceeded 81 %. Farmers who adopted more sustainable practices obtained higher technical efficiency and scale efficiency with scores of 90 % and 91 % respectively. Many farmers with different combinations of farming practices operated under increasing returns to scale. The Tobit model was also applied to estimate the determinant factors of technical efficiency. The results of the Tobit model revealed that the farmer’s education level and farming experience had a significant impact on rice farming efficiency. The study also found an inverted U-shaped relationship between education level and efficiency, which indicates that if farmers had higher education levels beyond the threshold point, their farming efficiency will decrease. The significance of adopting sustainable agriculture practices in rice-producing households was emphasized by the positive correlation observed between the number of sustainable agriculture practices applied and efficiency.

The teaching staff has a very important position and role, is a factor that plays a decisive role in the quality of teaching National Defense and Security Education at universities. However, the quality of the teaching staff in charge of National Defense and Security Dducation, besides the advantages, still exists some certain limitations. Through analyzing and explaining theoretical and practical issues about the quality of the teaching staff, some key solutions have been proposed to improve the quality of these lecturers, meeting the current requirements of education and training innovation.

The purpose of this paper is to research the elements that affect the decision to implement Artificial Intelligence (AI) in supply chain management at SMEs in Da Nang, Vietnam. The application of new technologies in supply chain management is extremely important during the development period of industry 4.0, and a more in-depth examination of the issues in implementing AI to the supply chain will provide a more objective perspective on the challenges and benefits of adopting AI to supply chain management in SMEs. This paper investigates the effect factors in the implementation of Artificial Intelligence (AI) technology by constructing a Technology-Organization-Environment (TOE) framework. In our research, we use qualitative research to build hypotheses. A structured questionnaire with a five-point Likert scale was distributed to 120 SMEs in Da Nang to collect data for quantitative research, analytical techniques, first to check the reliability and validity of the tool used, then perform exploratory factor analysis EFA, Pearson correlation, and multiple regression analysis. The results indicated that the complexity factor, compatibility factor, cost factor, and external pressure factor are not important factors that influence AI implementation. Referring to those technical factors (relative advantages), organization factors (top management support, organization readiness) and environment factor (government support) have a positive influence on the technology implementation of AI in supply chain management.

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer and one of the leading causes of death. Previous research found a link between LUAD and Aldehyde Dehydrogenase 2 (ALDH2), a member of aldehyde dehydrogenase gene (ALDH) superfamily. In this study, we identified additional useful prognostic markers for early LUAD identification and targeting LUAD therapy by analyzing the expression level, epigenetic mechanism, and signaling activities of ALDH2 in LUAD patients. The obtained results demonstrated that ALDH2 gene and protein expression significantly downregulated in LUAD patient samples. Furthermore, The American Joint Committee on Cancer (AJCC) reported that diminished ALDH2 expression was closely linked to worse overall survival (OS) in different stages of LUAD. Considerably, ALDH2 showed aberrant DNA methylation status in LUAD cancer. ALDH2 was found to be downregulated in the proteomic expression profile of several cell biology signaling pathways, particularly stem cell-related pathways. Finally, the relationship of ALDH2 activity with stem cell-related factors and immune system were reported. In conclusion, the downregulation of ALDH2, abnormal DNA methylation, and the consequent deficit of stemness signaling pathways are relevant prognostic and therapeutic markers in LUAD.

Abstract O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was shown in many studies to be an important predictive biomarker for temozolomide (TMZ) resistance and poor progression-free survival in glioblastoma multiforme (GBM) patients. However, identifying the MGMT methylation status using molecular techniques remains challenging due to technical limitations, such as the inability to obtain tumor specimens, high prices for detection, and the high complexity of intralesional heterogeneity. To overcome these difficulties, we aimed to test the feasibility of using a novel radiomics-based machine learning (ML) model to preoperatively and noninvasively predict the MGMT methylation status. In this study, radiomics features extracted from multimodal images of GBM patients with annotated MGMT methylation status were downloaded from The Cancer Imaging Archive (TCIA) public database for retrospective analysis. The radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two-stage feature selection method, including an eXtreme Gradient Boosting (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. The cross-validation results suggested that the GA-based wrapper model achieved the high performance with a sensitivity of 0.894, specificity of 0.966, and accuracy of 0.925 for predicting the MGMT methylation status in GBM. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0.780, specificity 0.620, and accuracy 0.750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. The performance indicated that our model may potentially confer significant improvements in prognosis and treatment responses in GBM patients.

The use of electronic devices based on electromagnetic waves is promising for inspecting additives in structural concrete. However, the existing commercial devices are high-cost and do not publicly provide circuit design information. To overcome this issue, this study designed a low-cost nondestructive testing device with a radar sensor, using an HB-100 radar sensor module to generate and receive the radar wave. A suitable bandpass filter was used to suppress electrical noise in the received signal, an Arduino board was used for signal processing, and the measured data were displayed on a computer. The output at the IF pin of the sensor module presents the Doppler frequency and absorbance of the target materials. The device was tested to detect additives inside the concrete. An additive material can be recognized by the fact that the obtained signal magnitudes are different with different additive materials. The findings in this study can contribute to making a low-cost nondestructive testing device based on radar technology for structural concrete inspection.