Modified moth swarm algorithm for optimal economic load dispatch problem

In this study, optimal economic load dispatch problem (OELD) is resolved by a novel improved algorithm. The proposed modified moth swarm algorithm (MMSA), is developed by proposing two modifications on the classical moth swarm algorithm (MSA). The first modification applies an effective formula to replace an ineffective formula of the mutation technique. The second modification is to cancel the crossover technique. For proving the efficient improvements of the proposed method, different systems with discontinuous objective functions as well as complicated constraints are used. Experiment results on the investigated cases show that the proposed method can get less cost and achieve stable search ability than MSA. As compared to other previous methods, MMSA can archive equal or better results. From this view, it can give a conclusion that MMSA method can be valued as a useful method for OELD problem

Improved particle swarm optimization algorithms for economic load dispatch considering electric market

Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time

Rifampicin resistant 'Mycobacterium tuberculosis' in Vietnam, 2020–2022

Objective: We conducted a descriptive analysis of multi-drug resistant tuberculosis (MDR-TB) in Vietnam’s two largest cities, Hanoi and Ho Chi Minh city. Methods: All patients with rifampicin resistant tuberculosis were recruited from Hanoi and surrounding provinces between 2020 and 2022. Additional patients were recruited from Ho Chi Minh city over the same time period. Demographic data were recorded from all patients, and samples collected, cultured, whole genome sequenced and analysed for drug resistance mutations. Genomic susceptibility predictions were made on the basis of the World Health Organization’s catalogue of mutations in Mycobacterium tuberculosis associated with drug resistance, version 2. Comparisons were made against phenotypic drug susceptibility test results where these were available. Multivariable logistic regression was used to assess risk factors for previous episodes of tuberculosis. Results: 233/265 sequenced isolates were of sufficient quality for analysis, 146 (63 %) from Ho Chi Minh City and 87 (37 %) from Hanoi. 198 (85 %) were lineage 2, 20 (9 %) were lineage 4, and 15 (6 %) were lineage 1. 17/211 (8 %) for whom HIV status was known were infected, and 109/214 (51 %) patients had had a previous episode of tuberculosis. The main risk factor for a previous episode was HIV infection (odds ratio 5.1 (95 % confidence interval 1.3–20.0); p = 0.021). Sensitivity for predicting first-line drug resistance from whole genome sequencing data was over 90 %, with the exception of pyrazinamide (85 %). For moxifloxacin and amikacin it was 50 % or less. Among rifampicin-resistant isolates, prevalence of resistance to each non-first-line drug was < 20 %. Conclusions: Drug resistance among most MDR-TB strains in Vietnam’s two largest cities is confined largely to first-line drugs. Living with HIV is the main risk factor among patients with MDR-TB for having had a previous episode of tuberculosis

Functional outcome and muscle wasting in adults with tetanus.

BACKGROUND: In many countries, in-hospital survival from tetanus is increasing, but long-term outcome is unknown. In high-income settings, critical illness is associated with muscle wasting and poor functional outcome, but there are few data from resource-limited settings. In this study we aimed to assess muscle wasting and long-term functional outcome in adults with tetanus. METHODS: In a prospective observational study involving 80 adults with tetanus, sequential rectus femoris ultrasound measurements were made at admission, 7 days, 14 days and hospital discharge. Functional outcome was assessed at hospital discharge using the Timed Up and Go test, Clinical Frailty Score, Barthel Index and RAND 36-item Short Form Health Survey (SF-36) and 3 and 6 months after discharge using the SF-36 and Barthel Index. RESULTS: Significant muscle wasting occurred between hospital admission and discharge (p70 y of age, functional recovery at 6 months was reduced compared with younger patients. Hospital-acquired infection and age were risk factors for muscle wasting. CONCLUSIONS: Significant muscle wasting during hospitalization occurred in patients with tetanus, the extent of which correlates with functional outcome

The burden of tuberculosis in Ho Chi Minh City, Vietnam: a spatial analysis of drug-susceptible and multi-drug resistant cases between 2020 and 2023

We characterised the spatial distribution of drug-susceptible (DS) and multi-drug resistant (MDR) tuberculosis (TB) cases in Ho Chi Minh City (HCMC), a major South-East Asian metropolis, and explored demographic and socioeconomic factors associated with local TB burden. Hot spots of DS- and MDR-TB incidence were observed in the central parts of HCMC, with substantial heterogeneity observed across wards. Positive spatial autocorrelation was observed for both DS- and MDR-TB. Ward-level TB incidence was associated with HIV prevalence (incidence rate ratio [IRR] 1.77, 95% CI 1.54-2.03) and the male proportion of the population (IRR 1.05, 95% CI 1.02-1.08). No ward-level demographic and socioeconomic indicators were associated with MDR-TB case count relative to total TB case count. Our findings may inform spatially-targeted TB control strategies and provide insights for generating hypotheses about the nature of the relationship between DS- and MDR-TB in HCMC, Vietnam and the wider South-East Asia region

Jellyfish search algorithm for optimization operation of hybrid pumped storage-wind-thermal-solar photovoltaic systems

This study applies Jellyfish Search Algorithm and five other algorithms to minimize the electricity generation cost of two hybrid systems for one operating day. The first system comprises one pumped storage hydroelectric plant and two thermal power plants. The second system is expanded by integrating one wind and one solar photovoltaic power plant into the first system. For each system during one operating day, the pumped storage hydroelectric plant with only generation mode acts as a conventional hydroelectric plant in the first scenario. Still, it can run pumps to store water and produce electricity in the second scenario. As a result, JSA can reach smaller costs than all compared algorithms, from about 1 % to higher than 10 % for two scenarios in the two systems. The comparisons of generation cost indicate the second scenario with the pumped storage hydroelectric plant can reach a smaller cost than the first scenario with the conventional hydroelectric power plant by $53,359.7, corresponding to 7.4$39,472.8, corresponding to 6.95 % in the second system. Therefore, the water storage function of the pumped storage hydroelectric plant is very effective in reducing the electricity generation costs for hybrid power systems

Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam

Groundwater salinization is considered as a major environmental problem in worldwide coastal areas, influencing ecosystems and human health. However, an accurate prediction of salinity concentration in groundwater remains a challenge due to the complexity of groundwater salinization processes and its influencing factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting groundwater salinity and identify its influencing factors. We conducted a study for the coastal multi-layer aquifers of the Mekong River Delta (Vietnam), using a geodatabase of 216 groundwater samples and 14 conditioning factors. We compared the predictive performances of different ML techniques, i.e., the Random Forest Regression (RFR), the Extreme Gradient Boosting Regression (XGBR), the CatBoost Regression (CBR), and the Light Gradient Boosting Regression (LGBR) models. The model performance was assessed by using root-mean-square error (RMSE), coefficient of determination (R2), the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the CBR model has the highest performance with both training (R2 = 0.999, RMSE = 29.90) and testing datasets (R2 = 0.84, RMSE = 205.96, AIC = 720.60, and BIC = 751.04). Ten of the 14 influencing factors, including the distance to saline sources, the depth of screen well, the groundwater level, the vertical hydraulic conductivity, the operation time, the well density, the extraction capacity, the thickness of the aquitard, the distance to fault, and the horizontal hydraulic conductivity are the most important factors for groundwater salinity prediction. The results provide insights for policymakers in proposing remediation and management strategies for groundwater salinity issues in the context of excessive groundwater exploitation in coastal lowland regions. Since the human-induced influencing factors have significantly influenced groundwater salinization, urgent actions should be taken into consideration to ensure sustainable groundwater management in the coastal areas of the Mekong River Delta

Risk factors for poor treatment outcomes of 2266 multidrug-resistant tuberculosis cases in Ho Chi Minh City: a retrospective study

BACKGROUND: Multidrug resistant tuberculosis (MDR-TB) remains a serious public health problem with poor treatment outcomes. Predictors of poor outcomes vary in different regions. Vietnam is among the top 30 high burden of MDR-TB countries. We describe demographic characteristics and identify risk factors for poor outcome among patients with MDR-TB in Ho Chi Minh City (HCMC), the most populous city in Vietnam. METHODS: This retrospective study included 2266 patients who initiated MDR-TB treatment between 2011 and 2015 in HCMC. Treatment outcomes were available for 2240 patients. Data was collected from standardized paper-based treatment cards and electronic records. A Kruskal Wallis test was used to assess changes in median age and body mass index (BMI) over time, and a Wilcoxon test was used to compare the median BMI of patients with and without diabetes mellitus. Chi squared test was used to compare categorical variables. Multivariate logistic regression with multiple imputation for missing data was used to identify risk factors for poor outcomes. Statistical analysis was performed using R program. RESULTS: Among 2266 eligible cases, 60.2% had failed on a category I or II treatment regimen, 57.7% were underweight, 30.2% had diabetes mellitus and 9.6% were HIV positive. The notification rate increased 24.7% from 2011 to 2015. The treatment success rate was 73.3%. Risk factors for poor treatment outcome included HIV co-infection (adjusted odds ratio (aOR): 2.94), advanced age (aOR: 1.45 for every increase of 5 years for patients 60 years or older), having history of MDR-TB treatment (aOR: 5.53), sputum smear grade scanty or 1+ (aOR: 1.47), smear grade 2+ or 3+ (aOR: 2.06), low BMI (aOR: 0.83 for every increase of 1 kg/m2 of BMI for patients with BMI < 21). CONCLUSION: The number of patients diagnosed with MDR-TB in HCMC increased by almost a quarter between 2011 and 2015. Patients with HIV, high smear grade, malnutrition or a history of previous MDR-TB treatment are at greatest risk of poor treatment outcome

Medical-Waste Chain: A Medical Waste Collection, Classification and Treatment Management by Blockchain Technology

To prevent the spread of the COVID-19 pandemic, 2019 has seen unprecedented demand for medical equipment and supplies. However, the problem of waste treatment has not yet been given due attention, i.e., the traditional waste treatment process is done independently, and it is not easy to share the necessary information. Especially during the COVID-19 pandemic, the interaction between parties is minimized to limit infections. To evaluate the current system at medical centers, we also refer to the traditional waste treatment processes of four hospitals in Can Tho and Ho Chi Minh cities (Vietnam). Almost all hospitals are handled independently, lacking any interaction between the stakeholders. In this article, we propose a decentralized blockchain-based system for automating waste treatment processes for medical equipment and supplies after usage among the relevant parties, named Medical-Waste Chain. It consists of four components: medical equipment and supplies, waste centers, recycling plants, and sorting factories. Medical-Waste Chain integrates blockchain-based Hyperledger Fabric technology with decentralized storage of medical equipment and supply information, and securely shares related data with stakeholders. We present the system design, along with the interactions among the stakeholders, to ensure the minimization of medical waste generation. We evaluate the performance of the proposed solution using system-wide timing and latency analysis based on the Hyperledger Caliper engine. Our system is developed based on the hybrid-blockchain system, so it is fully scalable for both on-chain and off-chain-based extensions. Moreover, the participants do not need to pay any fees to use and upgrade the system. To encourage future use of Medical-Waste Chain, we also share a proof-of-concept on our Github repository

Medical-Waste Chain: A Medical Waste Collection, Classification and Treatment Management by Blockchain Technology

To prevent the spread of the COVID-19 pandemic, 2019 has seen unprecedented demand for medical equipment and supplies. However, the problem of waste treatment has not yet been given due attention, i.e., the traditional waste treatment process is done independently, and it is not easy to share the necessary information. Especially during the COVID-19 pandemic, the interaction between parties is minimized to limit infections. To evaluate the current system at medical centers, we also refer to the traditional waste treatment processes of four hospitals in Can Tho and Ho Chi Minh cities (Vietnam). Almost all hospitals are handled independently, lacking any interaction between the stakeholders. In this article, we propose a decentralized blockchain-based system for automating waste treatment processes for medical equipment and supplies after usage among the relevant parties, named Medical-Waste Chain. It consists of four components: medical equipment and supplies, waste centers, recycling plants, and sorting factories. Medical-Waste Chain integrates blockchain-based Hyperledger Fabric technology with decentralized storage of medical equipment and supply information, and securely shares related data with stakeholders. We present the system design, along with the interactions among the stakeholders, to ensure the minimization of medical waste generation. We evaluate the performance of the proposed solution using system-wide timing and latency analysis based on the Hyperledger Caliper engine. Our system is developed based on the hybrid-blockchain system, so it is fully scalable for both on-chain and off-chain-based extensions. Moreover, the participants do not need to pay any fees to use and upgrade the system. To encourage future use of Medical-Waste Chain, we also share a proof-of-concept on our Github repository