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Psychocutaneous disorders

Hai TC

Med J Malaya, 1970 Dec;25(2):91-8.
PMID: 4251141
Fulltext A framework of evolutionary optimized convolutional neural network for classification of shang and chow dynasties bronze decorative patterns

Qi X, He X, Chen SW, Hai T

PLoS One, 2024;19(5):e0293517.
PMID: 38743798 DOI: 10.1371/journal.pone.0293517

As a UNESCO World Cultural Heritage, the aesthetic value of bronze artifacts from the Shang and Chow Dynasties has had a profound influence on Chinese traditional culture and art. To facilitate the digital preservation and protection of these Shang and Chow bronze artifacts (SCB), it becomes imperative to categorize their decorative patterns. Therefore, a SCB pattern classification method of differential evolution called Shang and Chow Bronze Convolutional Neural Network (SCB-CNN) is proposed. Firstly, the original bronze decorative patterns of Shang and Chow dynasties are collected, and the samples are expanded through image augmentation technology to form a training dataset. Secondly, based on the classical convolutional neural network structure, the recognition and classification of bronze patterns are implemented by adjusting the network parameters. Then, the initial parameters of the convolutional neural network are optimized by differential evolution algorithm, and the optimized SCB-CNN is simulated. Finally, comparative experiments were conducted between the optimized SCB-CNN, the unoptimized model, VGG-Net, and GoogleNet. The experimental results indicate that the optimized SCB-CNN significantly reduces training time while maintaining fast prediction speed, convergence speed, and high accuracy. This study provides new insights for the inheritance and innovation research of SCB patterns.
Reduction in environmental CO2 by utilization of optimized energy scheme for power and fresh water generations based on different uses of biomass energy

Hai T, Ali MA, Alizadeh A, Almojil SF, Almohana AI, Alali AF

Chemosphere, 2023 Apr;319:137847.
PMID: 36657576 DOI: 10.1016/j.chemosphere.2023.137847

Renewable energy sources are undoubtedly necessary, considering global electricity demand is expected to rise dramatically in the coming years. This research looks at a unique multi-generation plant from the perspectives of exergy, energy, and economics; also, an environmental evaluation is performed to estimate the systems' CO2 emissions. The unit is made up of a biomass digester and gasifier, a Multi effect Desalination unit, and a supercritical CO2 (SCO2) cycle. In this study, two methods for using biomass are considered: the first is using synthesis gas generated by the gasifier, and the second is utilizing a digester to generate biogas. A comprehensive parametric study is performed on the designed energy unit to assess the influence of compressor pressure ratio, Gas turbine inlet temperature, supercritical CO2 cycle pressure ratio, and the number of effects of multi-effect distillation on the system performance. Furthermore, the exergy study revealed that the exergy destruction in the digestion unit was 11,337 kW, which was greater than the exergy destruction in the gasification unit, which was 9629. Finally, when compared to the gasifier, the amount of exergy efficiency, net output power, and freshwater production in the digester was greater.
Performance assessment and multiobjective optimization of a biomass waste-fired gasification combined cycle for emission reduction

Hai T, Alshahri AH, Mohammed AS, Sharma A, Almujibah HR, Mohammed Metwally AS, et al.

Chemosphere, 2023 Sep;334:138980.
PMID: 37207897 DOI: 10.1016/j.chemosphere.2023.138980

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h.
Comparison of two newly suggested power, refrigeration, and hydrogen production, for moving towards sustainability schemes using improved solar-powered evolutionary algorithm optimization

Hai T, Abd El-Salam NM, Kh TI, Chaturvedi R, El-Shafai W, Farhang B

Chemosphere, 2023 Sep;336:139160.
PMID: 37327820 DOI: 10.1016/j.chemosphere.2023.139160

In the third millennium, developing countries will confront significant environmental problems such as ozone depletion, global warming, the shortage of fossil resources, and greenhouse gas emissions. This research looked at a multigenerational system that can generate clean hydrogen, fresh water, electricity, heat, and cooling. The system's components include Rankine and Brayton cycles, an Organic Rankine Cycle (ORC), flash desalination, an Alkaline electrolyzer, and a solar heliostat. The proposed process has been compared for two different start-up modes with a combustion chamber and solar heliostat to compare renewable and fossil fuel sources. This research evaluated various characteristics, including turbine pressure, system efficiency, solar radiation, and isentropic efficiency. The energy and exergy efficiency of the proposed system were obtained at around 78.93% and 47.56%, respectively. Exergy study revealed that heat exchangers and alkaline electrolyzers had the greatest exergy destruction rates, at 78.93% and 47.56%, respectively. The suggested system produces 0.04663 kg/s of hydrogen. Results indicate that at the best operational conditions, the exergetic efficiency, power, and hydrogen generation of 56%, 6000 kW, and 1.28 kg/s is reached, respectively. Also, With a 15% improvement in the Brayton cycle's isentropic efficacy, the quantity of hydrogen produced increases from 0.040 kg/s to 0.0520 kg/s.
Fulltext Pre-operative Factors Predicting Mortality in Six Months and Functional Recovery in Elderly Patients with Hip Fractures

Nam NH, Minh ND, Hai TX, Sinh CT, Loi CB, Anh LT

Malays Orthop J, 2023 Mar;17(1):10-17.
PMID: 37064636 DOI: 10.5704/MOJ.2303.002

INTRODUCTION: This study aimed to determine on-admission and perioperative factors predicting six-month mortality and functional recovery in Vietnamese patients with hip fracture.
MATERIALS AND METHODS: Between April 2020 and July 2021, 118 patients participated in this prospective study. Patients' data were collected from medical records. Harris hip score (HHS) was used to evaluate the functional recovery six months after fractures. The obtained data were analysed using a univariate and multivariate model.
RESULTS: The mean age of the participants was 79.5±9.4 years and 68.6% of the patients were female. The six-month mortality rate was 5.9% and independently associated with age (odds ratio (OR): 3.512, 95% confidence interval (CI) 1.538 - 8.019; P<0.001, patients aged >80 years vs those aged ≤80 years) and hypoproteinemia (OR: 2.859, 95% CI: 1.001 - 8.166, P=0.049). Among 111 survivors there were 66 (59.5%) of patients with a good functional recovery. Patients aged >80 years had a higher risk of poor functional outcome (OR: 3.167, 95% CI: 1.386 - 7.235, P: 0.006) compared to those aged ≤ 80 years. No significant correlations between other clinical (gender, body mass index, comorbidities, type of fractures or surgery, time until surgery) or laboratory parameters (anaemia, hyperglycemia, marked elevation of C reactive protein level, electrolyte abnormalities, elevated urea) and mortality or functional outcome were found.
CONCLUSION: Advanced age is the most important factor affecting both mortality and functional outcome while hypoproteinemia is associated with a higher risk of mortality in elderly patients with hip fractures.
Multiobjective optimization of a cogeneration system based on gas turbine, organic rankine cycle and double-effect absorbtion chiller

Hai T, Alsubai S, Yahya RO, Gemeay E, Sharma K, Alqahtani A, et al.

Chemosphere, 2023 Oct;338:139371.
PMID: 37442387 DOI: 10.1016/j.chemosphere.2023.139371

Combined cooling, heating and power (CCHP) is one of methods for enhancing the efficiency of the energy conversion systems. In this study a CCHP system consisting of a gas turbin (GT) as the topping cycle, and an organic Rankine cycle (ORC) associated with double-effect absorbtion chiller (DEACH) is decisioned as the bottoming cycle to recover the waste heat from GT exhaust gas. The considered CCHP system is investigated to maintain electricity, heating and cooling demand of a town. A parametric study is investigated and the effect decision variables on the performance indicators including exergy efficiency, total cost rate (TCR), cooling capacity, and ORC power generation is examined. Decision variables of the ORC system consist of HRVG pressure, and condenser pressure and the DEACH including evaporator pressure, condseser pressure, concentration of the concentrated solution, concentration of the weak solution, and solution mass flow rate. Finally a multi-objective optimization performed using Genetic Algorithm (GA) and the optimal design point is selected. It is observed at the optimum point the exergy efficiency, TCR, and sustainability index are 17.56%, 74.49 $/h, and 1.21, respectively.
Techno-economic optimization of a new waste-to-energy plant for electricity, cooling, and desalinated water using various biomass for emission reduction

Hai T, Ma X, Singh Chauhan B, Mahmoud S, Al-Kouz W, Tong J, et al.

Chemosphere, 2023 Oct;338:139398.
PMID: 37406939 DOI: 10.1016/j.chemosphere.2023.139398

A newly developed waste-to-energy system using a biomass combined energy system designed and taken into account for electricity generation, cooling, and freshwater production has been investigated and modeled in this project. The investigated system incorporates several different cycles, such as a biomass waste integrated gasifier-gas turbine cycle, a high-temperature fuel cell, a Rankine cycle, an absorption refrigeration system, and a flash distillation system for seawater desalination. The EES software is employed to perform a basic analysis of the system. They are then transferred to MATLAB software to optimize and evaluate the impact of operational factors. Artificial intelligence is employed to evaluate and model the EES software's analysis output for this purpose. By enhancing the flow rate of fuel from 4 to 6.5 kg/s, the cost rate and energy efficiency are reduced by 51% and increased by 6.5%, respectively. Furthermore, the maximum increment in exergetic efficiency takes place whenever the inlet temperature of the gas turbine rises. According to an analysis of three types of biomasses, Solid Waste possesses the maximum efficiency rate, work output, and expense. Rice Husk, in contrast, has the minimum efficiency, work output, and expense. Additionally, with the change in fuel discharge and gas turbine inlet temperature, the system behavior for all three types of biomasses will be nearly identical. The Pareto front optimization findings demonstrate that the best mode for system performance is an output power of 53,512 kW, a cost of 0.643 dollars per second, and a first law efficiency of 42%. This optimal value occurs for fuel discharge of 5.125 and the maximum inlet temperature for a gas turbine. The rates of water desalination and cooling in this condition are 18.818 kg/s and 2356 kW, respectively.
Techno-economic optimization and Nox emission reduction through steam injection in gas turbine combustion chamber for waste heat recovery and water production

Hai T, El-Shafay AS, Goyal V, Alshahri AH, Almujibah HR

Chemosphere, 2023 Sep 01.
PMID: 37660791 DOI: 10.1016/j.chemosphere.2023.139782

Considering the persistent human need for electricity and fresh water, cogeneration systems based on the production of these two products have attracted the attention of researchers. This study investigates a cogeneration system of electricity and fresh water based on gas turbine (GT) as the prime mover. The wasted energy of the GT exhaust gases is absorbed by a heat recovery steam generator (HRSG) and supplies the superheat steam required by the steam turbine (ST). In order to produce fresh water, a multi-effect desalination (MED) system is applied. The motive steam required is provided by extracting steam from the ST. In order to reduce the environmental pollution of this cogeneration system, the steam injection method is proposed in the GT's combustion chamber (CC). This system is optimized by a multi-objective optimization tool based on the Genetic Algorithm (GA). The design variables include pressure ratio of compressor (CPR), inlet temperature of gas turbine (TIT), steam injection mass flow rate in the CC, HRSG operating pressure, HRSG evaporator pinch point temperature difference (PPTD), steam pressure of the MED ejector, ejector motive steam flow rate, number of MED effects, and return effect. The goals are to minimize the total cost rate (TCR), which includes the cost of initial investment and maintenance of the system, the cost of consumed fuel, and the cost of disposing of CO and NO pollutants, as well as maximizing the exergy efficiency. In the end, it is observed that the steam injection in the CC leads to the reduction of the mentioned pollutant index, and it is proposed as a suitable solution to reduce the pollution of the proposed cogeneration system.
Pharmacogenomics of CYP2A6, CYP2B6, CYP2C19, CYP2D6, CYP3A4, CYP3A5 and MDR1 in Vietnam

Veiga MI, Asimus S, Ferreira PE, Martins JP, Cavaco I, Ribeiro V, et al.

Eur J Clin Pharmacol, 2009 Apr;65(4):355-63.
PMID: 18979093 DOI: 10.1007/s00228-008-0573-8

AIM: The aim of this study was to obtain pharmacogenetic data in a Vietnamese population on genes coding for proteins involved in the elimination of drugs currently used for the treatment of malaria and human immunodeficiency virus/acquired immunodeficiency syndrome.
METHOD: The main polymorphisms on the cytochrome P450 (CYP) genes, CYP2A6, CYP2B6, CYP2C19, CYP2D6, CYP3A4 and CYP3A5, and the multi-drug resistance 1 gene (MDR1) were genotyped in 78 healthy Vietnamese subjects. Pharmacokinetic metrics were available for CYP2A6 (coumarin), CYP2C19 (mephenytoin), CYP2D6 (metoprolol) and CYP3As (midazolam), allowing correlations with the determined genotype.
RESULTS: In the CYP2 family, we detected alleles CYP2A6*4 (12%) and *5 (15%); CYP2B6*4 (8%), *6 (27%); CYP2C19*2 (31%) and *3 (6%); CYP2D6*4, *5, *10 (1, 8 and 44%, respectively). In the CYP3A family, CYP3A4*1B was detected at a low frequency (2%), whereas CYP3A5 *3 was detected at a frequency of 67%. The MDR1 3435T allele was present with a prevalence of 40%. Allele proportions in our cohort were compared with those reported for other Asian populations. CYP2C19 genotypes were associated to the S-4'-OH-mephenytoin/S-mephenytoin ratio quantified in plasma 4 h after intake of 100 mg mephenytoin. While CYP2D6 genotypes were partially reflected by the alpha-OH-metroprolol/metoprolol ratio in plasma 4 h after dosing, no correlation existed between midazolam plasma concentrations 4 h post-dose and CYP3A genotypes.
CONCLUSIONS: The Vietnamese subjects of our study cohort presented allele prevalences in drug-metabolising enzymes that were generally comparable with those reported in other Asian populations. Deviations were found for CYP2A6*4 compared to a Chinese population (12 vs. 5%, respectively; P = 0.023), CYP2A6*5 compared with a Korean population (15 vs. <1%, respectively; P < 0.0001), a Malaysian population (1%; P < 0.0001) and a Chinese population (1%; P < 0.0001); CYP2B6*6 compared with a Korean population (27 vs. 12%; P = 0.002) and a Japanese population (16%; P = 0.021). Pharmacokinetic metrics versus genotype analysis reinforces the view that the predictive value of certain globally common variants (e.g. CYP2D6 single nucleotide polymorphisms) should be evaluated in a population-specific manner.
Fulltext Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs

Hai T, Basem A, Alizadeh A, Sharma K, Jasim DJ, Rajab H, et al.

Sci Rep, 2024 Aug 31;14(1):20271.
PMID: 39217234 DOI: 10.1038/s41598-024-71027-9

Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) of GPR are analyzed separately and classified into three groups based on their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and marine predators algorithm (MPA), are employed to optimize HPs. Optimizing the four most significant hyperparameters (covariance function, basis function, standardization, and sigma) within the first group using any of the three metaheuristic algorithms resulted in excellent outcomes. All algorithms achieved a reasonable R-value (0.9983), demonstrating their effectiveness in this context. The second group explored the impact of including additional, moderate-significant HPs, such as the fit method, predict method and optimizer. While the resulting models showed some improvement over the first group, the PSO-based model within this group exhibited the most noteworthy enhancement, achieving a higher R-value (0.99834). Finally, the third group was analyzed to examine the potential interactions between all twelve HPs. This comprehensive approach, employing the GA, yielded an optimized GPR model with the highest level of target compliance, reflected by an impressive R-value of 0.999224. The developed models are a cost-effective and efficient solution to reduce laboratory costs for various systems, from TES to thermal management.
Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems

Hai T, Basem A, Alizadeh A, Sharma K, Jasim DJ, Rajab H, et al.

Sci Rep, 2024 Nov 27;14(1):29524.
PMID: 39604527 DOI: 10.1038/s41598-024-81044-3

Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature. The optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), and thermal conductivity (TC). In the proposed hybrid approach, the powerful group method of data handling (GMDH)-type ANN technique is used to model TPPs in terms of input variables. The obtained models are integrated into the multi-objective particle swarm optimization (MOPSO) and multi-objective thermal exchange optimization (MOTEO) algorithms, forming a three-objective optimization problem. In the final step, the TOPSIS technique, one of the well-known multi-criteria decision-making (MCDM) approaches, is employed to identify the desirable Pareto points. Modeling results showed that the developed models for TC, DV, and SHC demonstrate a strong performance by R-values of 0.9984, 0.9985, and 0.9987, respectively. The outputs of MOPSO revealed that the Pareto points dispersed a broad range of MXene MFs (0-0.4%). However, the temperature of these optimal points was found to be constrained within a narrow range near the maximum value (75 °C). In scenarios where TC precedes other objectives, the TOPSIS method recommended utilizing an MF of over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt for an MF ranging from 0.15 to 0.17%. Also, when SHC becomes the primary concern, TOPSIS advised utilizing the base fluid without any MXene additive.
Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model

Hai T, Ahmadianfar I, Halder B, Heddam S, Al-Areeq AM, Demir V, et al.

Environ Sci Pollut Res Int, 2024 Apr 23.
PMID: 38653893 DOI: 10.1007/s11356-024-33027-0

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.

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