Prediction and optimization of medium-chain carboxylic acids production from food waste using machine learning models

Machine learning models were developed in this study to predict and optimize the medium-chain carbolic acids (MCCAs) production from food waste. All three selected prediction algorithms achieved decent performance (accuracy > 0.85, R² > 0.707). Three optimization algorithms were applied for MCCA production optimization based on the prediction algorithms. The maximum MCCA production rate (0.68 g chemical oxygen demand per liter per day) was achieved by simulated annealing coupled with random forest under the optimal conditions of pH 8.3, temperature 50 °C, retention time 4 days, loading rate 15.8 g volatile solid per liter per day, and inoculum to food waste ratio 70:30 with semi-continuous mode. Further experiments validated (18 % error) that the MCCA production rate was 113 % higher than the highest production rate of current lab experiments and 60 % higher than the statistical optimization using response surface methodology. This study demonstrates the potential of using machine learning for MCCA production prediction and optimization.

On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control

The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot.

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory.

Methods

The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues.

Results

In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells.

Conclusions

In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07911-8.

Experimental study on optimization models for evaluation of fireball characteristics and thermal hazards induced by LNG vapor Cloud explosions based on colorimetric thermometry

In order to facilitate transport, natural gas is cooled down by a cycle process of compression, condensation, expansion, and evaporation that transforms the gas into a liquid form, as known as Liquefied Natural Gas (LNG). However, once any leak happens in the transportation pipeline, it will result in serious thermal radiant damage due to the explosion fireball induced by LNG Vapor cloud explosions. In this work, an optimization fireball model is proposed by introducing the atmospheric transmission rate τ into the original TNO dynamic model. Based on the colorimetric thermometry technology, a full-scale LNG pipeline explosion experiment has been conducted and a series of testing data for the thermal radiant by VCEs' fireball have been obtained. It is found that theoretical predictions by using optimization model agree well with experimental data. According to the thermal radiant damage criterion, it is concluded that a near 100% fatality radius is expected within the range of 266.3 m and there is a safety area with an ellipse diameter of 1180.1 m. This work attempts to develop optimization fireball models to predict the thermal radiant damage more accurately, and improve the performance of risk assessment on LNG transport and storage industrial process.

Motion planning strategies in human control of non-rigid objects with internal degrees of freedom

The paper deals with modeling of human-like reaching movements in dynamic environments. A simple but not trivial example of reaching in a dynamic environment is the rest-to-rest manipulation of a multi-mass flexible object with the elimination of residual vibrations. Two approaches to the prediction of reaching movements are formulated in position and force actuation settings. In the first approach, either the position of the hand or the hand force is specified by the lowest order polynomial satisfying the boundary conditions of the reaching task. The second approach is based on the minimization of either the hand jerk or the hand force-change, with taking into account the dynamics of the flexible object. To verify the resulting four mathematical models, an experiment on the manipulation of a ten-masses flexible object of low stiffness is conducted. The experimental results show that the second approach gives a significantly better prediction of human movements, with the minimum hand force-change model having a slight but consistent edge over the minimum hand jerk one.

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

Recent developments in synthetic biology and metabolic engineering in microalgae towards biofuel production

In the wake of the uprising global energy crisis, microalgae have emerged as an alternate feedstock for biofuel production. In addition, microalgae bear immense potential as bio-cell factories in terms of producing key chemicals, recombinant proteins, enzymes, lipid, hydrogen and alcohol. Abstraction of such high-value products (algal biorefinery approach) facilitates to make microalgae-based renewable energy an economically viable option. Synthetic biology is an emerging field that harmoniously blends science and engineering to help design and construct novel biological systems, with an aim to achieve rationally formulated objectives. However, resources and tools used for such nuclear manipulation, construction of synthetic gene network and genome-scale reconstruction of microalgae are limited. Herein, we present recent developments in the upcoming field of microalgae employed as a model system for synthetic biology applications and highlight the importance of genome-scale reconstruction models and kinetic models, to maximize the metabolic output by understanding the intricacies of algal growth. This review also examines the role played by microalgae as biorefineries, microalgal culture conditions and various operating parameters that need to be optimized to yield biofuel that can be economically competitive with fossil fuels.