•The UCS of GSCE samples with Na2SiO3/NaOH ratio of 1.5 and 8% AAS had values in a range from 9.04 MPa to 10.06 MPa for the curing age from 14 to 180 days.•The compressive and splitting tensile ...strengths of GSCE samples decreased when the w-d cycles increased, except in the case of the samples in the pH = 4 solution: both compressive and splitting tensile strengths of GSCE samples increased until 6 w-d cycles, then these parameters decreased in the further w-d cycles.•SEM, XRD, and FTIR techniques were used to characterise the microstructure, mineralogical and chemical compositions of different samples.•GSCE with 8% AAS could satisfy the high exigences in the case of severe conditions.
The paper presents an experimental study on the durability of geopolymer stabilised compacted earth (GSCE) samples exposed to various wetting–drying (w-d) cycles at different pH and salt concentration conditions. Compressive strength, splitting tensile strength, weight loss, and moisture content were determined after 1, 3, 6, 9, and 12 w-d cycles. The microstructure, mineralogical and chemical compositions were characterised by SEM, XRD, and FTIR techniques. The results showed that after 12 w-d cycles, all samples tested had compressive strengths higher than 4.5 MPa. This value was higher than the minimum value required by the standards. The samples exposed to the pH = 4 solution had the highest performance. The results show the potential application of GSCE in severe conditions.
Spinal cord injury (SCI) is defined as temporary or permanent changes in spinal cord function and reflex activity. The objective of this study is to evaluate health-related quality of life (HRQoL) ...and activities of daily living (ADL) among postoperative surgery patients with complete cervical SCI in Vietnam and to explore the factors associated with these indices. A cross-sectional study was conducted on 88 adults in Vietnam from June 2018 to June 2019. The EQ-5D-5L, ADL, and instrumental activities of daily living (IADL) were applied. Multivariate Tobit regression was adopted to determine factors that were associated with HRQOL, ADL, and IADL. Results: Participants who were in American Spinal Cord Injury Association (ASIA) scale group A (ASIA-A) had the lowest ADL, IADL index, and HRQOL score (p < 0.001). HRQoL and ADL were affected by health insurance coverage, occupation, type of fracture, and IADL. Meanwhile, IADL was significantly associated with living areas and ASIA. Low HRQoL among patients suffering from SCI was observed. Attention should be given to outcomes related to a disability during clinical treatment and should be treated effectively in the recovery.
•Application of graphene and graphene oxide (Gr-GO) in compacted earth material.•Gr-GO used in combination with cement stabilisation.•198 specimens were tested under uniaxial compression and ...splitting tests.•Potential of Gr-GO addition in the improvement of the mechanical strengths.
Earth material is attracting renewed attentions in the context of sustainable development. In several cases, to improve the properties of earth material, cement is added. This paper presents an exploratory investigation on the addition of Graphene Oxide and Graphene (GO-Gr) solution in cement stabilised earth (CSE) material. The GO-Gr are the nanosheets which were developed in the present study. The main aim of the study is to assess the reduction of cement stabilisation by using nanomaterials. Different compositions of CSE with and without GO-Gr addition were investigated. The specimens were tested under uniaxial compression tests and splitting tests at 7, 14 and 28 days, respectively. Totally 198 specimens were manufactured and tested. The X-Ray diffraction (XRD) and Scanning Electron Microscopy (SEM) techniques were applied to investigate the microstructure of the specimens. The results showed that when the GO-Gr solution was added to 8% cement CSE specimens, significant improvements of compressive and tensile strengths were observed. For low cement content (less than 8% in mass), the efficiency of GO-Gr addition was limited; further optimizations should be necessary. This is the first time to our knowledge that this topic is explored.
The purpose of this study is to examine the effect of family ownership and other factors on firm performance in Vietnam and to determine the optimal level of family ownership required to maximize ...firm performance. The study employs the quantitative method of panel-corrected standard errors (PCSE) regression to analyze data on 31 nonfinancial enterprises listed on the Ho Chi Minh City Stock Exchange (HOSE) in Vietnam between 2011 and 2019. The firm performance is analyzed from both a market perspective (via Tobin's Q) and an accounting perspective (via return on assets ROA). The U-shaped curve illustrating this effect show that the relationship between family ownership and the performance of Vietnamese enterprises is negative. Tobin's Q decreases as the family ownership ratio increases. Firm performance reaches its lowest point when family ownership exceeds 42.53%; then, as family ownership increases, Tobin's Q increases as well. Similarly, as family ownership increases, ROA decreases, and firm performance reaches its lowest point at 65.89%; then, as family ownership increases, firm performance improves.
The study's goal is to determine how factors affecting tourism resilience during the COVID-19 pandemic affect Ho Chi Minh Tourism's ability to respond to changes and disruptions. The model and ...research hypotheses were tested using Multiple Regression Analysis Models. The statistical findings showed that the tourism resilience components have a significant influence on the tourism resilience in Ho Chi Minh city. The analyses revealed that tourism resilience consisted of four latent dimensions. There are 4 explanatory variables with a significance coefficient < 0.05. Therefore, the variables Economic resilience, Ecological resilience, Institutional resilience, and Social resilience all have a significant impact on tourist resilience, which is consistent with Jamaliah and Powell (2017). The findings have important managerial implications for local governments, as well as factors that contribute to tourism resilience, as they must attempt to adapt to changes and turbulences during a pandemic, ensuring that the tourism system rebounds in the future. The four components of tourist resilience are defined in the theoretical contribution. The findings of the study could serve as a starting point for developing future tourist resilience strategies. Because the application of tourist resilience theory is still relatively new, this study presents two theoretical and methodological contributions.
This study aims to investigate the relationship between foreign direct investment (FDI) and economic growth at the provincial level by using time-series data in Binh Dinh from 1997 to 2019. We ...applied the quantitative approaches Vector Autoregression (VAR) and Autoregressive Distributed Lags (ARDL) in the model, which includes economic growth, real foreign direct investment capital, ratio of trained workers, and infrastructure. The results show that all these variables are stationary at the first difference. In ARDL analysis, we found that the economic growth positively affects FDI attraction. However, there is no evidence of the effect of FDI on economic growth in the condition of low capital implemented. Moreover, findings also show that the impact of FDI on economic growth is influenced by two factors: infrastructure and human capital. The lack of human capital, which is trained personnel and infrastructure, is the main barrier hindering and inhibiting FDI's contribution to local economic growth. In order to improve the efficiency of FDI on economic growth in the future, it is suggested that the Binh Dinh government should have proper policies in terms of the infrastructure, the human capital investment. They would allow Binh Dinh to enhance the capital absorptive capacity and capital efficiency.
Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited ...training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in a low resource ICU.
This was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool.
The average accuracy of beginners' LUS interpretation was 68.7% 95% CI 66.8-70.7% compared to 72.2% 95% CI 70.0-75.6% in intermediate, and 73.4% 95% CI 62.2-87.8% in advanced users. Experts had an average accuracy of 95.0% 95% CI 88.2-100.0%, which was significantly better than beginners, intermediate and advanced users (p < 0.001). When supported by our AI tool for interpreting retrospectively acquired clips, the non-expert clinicians improved their performance from an average of 68.9% 95% CI 65.6-73.9% to 82.9% 95% CI 79.1-86.7%, (p < 0.001). In prospective real-time testing, non-expert clinicians improved their baseline performance from 68.1% 95% CI 57.9-78.2% to 93.4% 95% CI 89.0-97.8%, (p < 0.001) when using our AI tool. The time-to-interpret clips improved from a median of 12.1 s (IQR 8.5-20.6) to 5.0 s (IQR 3.5-8.8), (p < 0.001) and clinicians' median confidence level improved from 3 out of 4 to 4 out of 4 when using our AI tool.
AI-assisted LUS can help non-expert clinicians in an LMIC ICU improve their performance in interpreting LUS features more accurately, more quickly and more confidently.
Abstract Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual ...delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively ( p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998–0.999 vs. 0.982 95% CI 0.962–0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9–21.7) to 9.4 min (IQR 7.2–11.7) compared to when using the AI tool ( p < 0.001). AI-assisted muscle ultrasound removes the need for manual tracing, increases reproducibility and saves time. This system may aid monitoring muscle size in ICU patients assisting rehabilitation programmes.
Abstract Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other ...outcomes, especially when impacted by co-morbidities such as HIV infection. Brain magnetic resonance imaging (MRI) characterises the extent and severity of disease and may enable more accurate prediction of complications and poor outcomes. We analysed clinical and brain MRI data from a prospective longitudinal study of 216 adults with TBM; 73 (34%) were HIV-positive, a factor highly correlated with mortality. We implemented an end-to-end framework to model clinical and imaging features to predict disease progression. Our model used state-of-the-art machine learning models for automatic imaging feature encoding, and time-series models for forecasting, to predict TBM progression. The proposed approach is designed to be robust to missing data via a novel tailored model optimisation framework. Our model achieved a 60% balanced accuracy in predicting the prognosis of TBM patients over the six different classes. HIV status did not alter the performance of the models. Furthermore, our approach identified brain morphological lesions caused by TBM in both HIV and non-HIV-infected, associating lesions to the disease staging with an overall accuracy of 96%. These results suggest that the lesions caused by TBM are analogous in both populations, regardless of the severity of the disease. Lastly, our models correctly identified changes in disease symptomatology and severity in 80% of the cases. Our approach is the first attempt at predicting the prognosis of TBM by combining imaging and clinical data, via a machine learning model. The approach has the potential to accurately predict disease progression and enable timely clinical intervention.
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