Jasmonates (JAs) are phospholipid-derived hormones that regulate plant development and responses to environmental stress. The synthesis of JAs and the transduction of their signaling pathways are precisely regulated at multiple levels within and outside the nucleus as a result of a combination of genetic and epigenetic regulation. In this review, we focus on recent advances in the regulation of JA biosynthesis and their signaling pathways. The biosynthesis of JAs was found to be regulated with an autocatalytic amplification mechanism via the MYC2 regulation pathway and inhibited by an autonomous braking mechanism via the MYC2-targeting bHLH1 protein to terminate JA signals in a highly ordered manner. The biological functions of JAs mainly include the promotion of fruit ripening at the initial stage via ethylene-dependent and independent ways, the regulation of mature coloring via regulating the degradation of chlorophyll and the metabolism of anthocyanin, and the improvement of aroma components via the regulation of fatty acid and aldehyde alcohol metabolism in agricultural crops. JA signaling pathways also function in the enhancement of biotic and abiotic stress resistance via the regulation of secondary metabolism and the redox system, and they relieve cold damage to crops through improving the stability of the cell membrane. These recently published findings indicate that JAs are an important class of plant hormones necessary for regulating plant growth and development, ripening, and the resistance to stress in agricultural crops and products. Full article

Graphene is an important nanocarbon nanofiller for polymeric matrices. The polymer–graphene nanocomposites, obtained through facile fabrication methods, possess significant electrical–thermal–mechanical and physical properties for technical purposes. To overcome challenges of polymer–graphene nanocomposite processing and high performance, advanced fabrication strategies have been applied to design the next-generation materials–devices. This revolutionary review basically offers a fundamental sketch of graphene, polymer–graphene nanocomposite and three-dimensional (3D) and four-dimensional (4D) printing techniques. The main focus of the article is to portray the impact of 3D and 4D printing techniques in the field of polymer–graphene nanocomposites. Polymeric matrices, such as polyamide, polycaprolactone, polyethylene, poly(lactic acid), etc. with graphene, have been processed using 3D or 4D printing technologies. The 3D and 4D printing employ various cutting-edge processes and offer engineering opportunities to meet the manufacturing demands of the nanomaterials. The 3D printing methods used for graphene nanocomposites include direct ink writing, selective laser sintering, stereolithography, fused deposition modeling and other approaches. Thermally stable poly(lactic acid)–graphene oxide nanocomposites have been processed using a direct ink printing technique. The 3D-printed poly(methyl methacrylate)–graphene have been printed using stereolithography and additive manufacturing techniques. The printed poly(methyl methacrylate)–graphene nanocomposites revealed enhanced morphological, mechanical and biological properties. The polyethylene–graphene nanocomposites processed by fused diffusion modeling have superior thermal conductivity, strength, modulus and radiation- shielding features. The poly(lactic acid)–graphene nanocomposites have been processed using a number of 3D printing approaches, including fused deposition modeling, stereolithography, etc., resulting in unique honeycomb morphology, high surface temperature, surface resistivity, glass transition temperature and linear thermal coefficient. The 4D printing has been applied on acrylonitrile-butadiene-styrene, poly(lactic acid) and thermosetting matrices with graphene nanofiller. Stereolithography-based 4D-printed polymer–graphene nanomaterials have revealed complex shape-changing nanostructures having high resolution. These materials have high temperature stability and high performance for technical applications. Consequently, the 3D- or 4D-printed polymer–graphene nanocomposites revealed technical applications in high temperature relevance, photovoltaics, sensing, energy storage and other technical fields. In short, this paper has reviewed the background of 3D and 4D printing, graphene-based nanocomposite fabrication using 3D–4D printing, development in printing technologies and applications of 3D–4D printing. Full article

Melanoma is a cancer that threatens life and leads to death. Effective detection of skin lesion types by images is a challenging task. Dermoscopy is an effective technique for detecting skin lesions. Early diagnosis of skin cancer is essential for proper treatment. Skin lesions are similar in their early stages, so manual diagnosis is difficult. Thus, artificial intelligence techniques can analyze images of skin lesions and discover hidden features not seen by the naked eye. This study developed hybrid techniques based on hybrid features to effectively analyse dermoscopic images to classify two datasets, HAM10000 and PH2, of skin lesions. The images have been optimized for all techniques, and the problem of imbalance between the two datasets has been resolved. The HAM10000 and PH2 datasets were classified by pre-trained MobileNet and ResNet101 models. For effective detection of the early stages skin lesions, hybrid techniques SVM-MobileNet, SVM-ResNet101 and SVM-MobileNet-ResNet101 were applied, which showed better performance than pre-trained CNN models due to the effectiveness of the handcrafted features that extract the features of color, texture and shape. Then, handcrafted features were combined with the features of the MobileNet and ResNet101 models to form a high accuracy feature. Finally, features of MobileNet-handcrafted and ResNet101-handcrafted were sent to ANN for classification with high accuracy. For the HAM10000 dataset, the ANN with MobileNet and handcrafted features achieved an AUC of 97.53%, accuracy of 98.4%, sensitivity of 94.46%, precision of 93.44% and specificity of 99.43%. Using the same technique, the PH2 data set achieved 100% for all metrics. Full article

A series of computational fluid dynamics–discrete element method (CFD-DEM) simulations were applied to seed flow in horizontal-vertical 90-degree elbows. The performance of one-way and two-way CFD-DEM coupling methods was compared. Additionally, simulated seed velocities were compared to the current pneumatic conveying theory for each coupling method. Simulated field peas (Pisum sativum) were pneumatically conveyed to study the effect of air velocity (20, 25, and 30 m/s), seed rate (0.07, 0.21, and 0.42 kg/s), elbow diameter, D, (48.3, 60.3, and 72.4 mm), and elbow bend radius (1.5D, 2.5D, 3.5D, and 4.5D) on seed attributes (trajectory, velocity, and force). Results showed that seed velocity was significantly different between one-way and two-way coupling. Both methods resulted in nearly identical seed trajectory and force. Overall, simulated seed velocities had a strong correlation to values calculated through the current pneumatic conveyance theory. Dimensional analysis revealed that seed contact force was proportional to the elbow diameter to the power of 0.26 and inversely proportional to the elbow bend radius to the power of 0.5. Simulation results indicated that one-way coupling could be suitable to describe seed flow when two-way coupling may not be possible or practical. Full article

As third-generation semiconductors become commercial, SiC semiconductors are gradually becoming more widely used in LLC resonant converters. The efficiency of the LLC resonant converter is improved by employing soft switching. However, when designing LLC resonant converters, semiconductors are usually regarded as ideal devices, and their turn-on and turn-off times are neglected. Furthermore, the method of designing the dead time relies on engineering experience and lacks precise theoretical foundations. In order to overcome the shortcomings of the current empirical method and to improve the generality and practicality of the dead time design method, a novel method for calculating the dead time of full-bridge LLC converters is proposed through theoretical research based on the operating principle of full-bridge LLC converters and the conditions for implementing soft switching. The method takes into account the switching characteristics of semiconductors and the on-state delay time of their body diodes, stray inductance, drive circuits, and errors arising from the first harmonic approximation (FHA) and improves the accuracy of the dead time calculation. It can implement good soft switching with full-bridge LLC converters, reduce switching losses, and improve system efficiency. Finally, the simulation experiment and the 2 kW experimental prototype are built to verify the effectiveness of the proposed method. Full article

One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparameters achieved the highest testing accuracy of 87.91% for dataset-I and the extreme gradient boosting classifier with tuned hyperparameters achieved the highest testing accuracy of 99.03% for the comprehensive dataset-II. The novelty of this work was the use of grid search cross-validation to enhance the performance in the form of training and testing. The ideal parameters for predicting heart disease were identified through experimental results. Comparative studies were also carried out with the existing studies focusing on the prediction of heart disease, where the approach used in this work significantly outperformed their results. Full article

Conventional stirred-tank bioreactor (STR) designs are optimised for cultures of bacteria but not fungal cultures; therefore, a new Air-L-Shaped Bioreactor (ALSB) was fabricated. The ALSB was designed to eliminate the wall growth and clumping of fungal mycelium in STRs. Ganoderma lucidum was used as a fungal model and its biomass and exopolysaccharide (EPS) production were maximised by optimising the agitation rate, glucose concentration, initial pH, and aeration via response surface methodology (RSM). The ALSB system generated 7.8 g/L of biomass (biomass optimised conditions: 110 rpm, 24 g/L glucose, pH 5.6, and 3 v/v of aeration) and 4.4 g/L of EPS (EPS optimised conditions: 90 rpm, 30 g/L glucose, pH 4, and 2.5 v/v of aeration). In combination, for both optimised conditions, biomass (7.9 g/L) and EPS (4.6 g/L) were produced at 110 rpm, 30 g/L glucose, pH 4, and 3 v/v of aeration with minimal wall growth. The data prove that the ALSB is a blueprint for efficient economical fungal cultivation. Full article

Industrial processes often involve a long time delay, which adversely affects the stability of closed-loop control systems. The traditional Smith Predictor (SP) is a model-based controller used in processes with large time delays. The variation of system parameters and load disturbance situations are disadvantages of the traditional SP, and researchers have, therefore, proposed modified SP structures. In this paper, a design method based on the direct synthesis approach on a modified SP structure is discussed. In the design, an I-PD controller structure is used on the set-point tracking side of the SP, and a cascading PD lead–lag controller is used on the disturbance rejection side. In contrast with other studies in the literature, the use of simpler controllers enables the mathematical expressions that arise in the direct synthesis method to be significantly reduced. The proposed method is examined under the disturbance input effects for normal and parameter-changing conditions on system models with unstable second-order plus time-delay processes. The first plant model has two unstable poles, the second has one stable and one unstable pole, and the third has one unstable and one zero pole. When the results obtained using the proposed method were compared with other methods, significant improvements were achieved in terms of set-point tracking, disturbance rejection, and robustness conditions. Full article

Improved irradiance forecasting ensures precise solar power generation forecasts, resulting in smoother operation of the distribution grid. Empirical models are used to estimate irradiation using a wide range of data and specific national or regional parameters. In contrast, algorithms based on Artificial Intelligence (AI) are becoming increasingly popular and effective for estimating solar irradiance. Although there has been significant development in this area elsewhere, employing an AI model to investigate irradiance in Bangladesh is limited. This research forecasts solar radiation in Bangladesh using ensemble machine-learning models. The meteorological data collected from 32 stations contain maximum temperature, minimum temperature, total rain, humidity, sunshine, wind speed, cloud coverage, and irradiance. Ensemble machine-learning algorithms including Adaboost regression (ABR), gradient-boosting regression (GBR), random forest regression (RFR), and bagging regression (BR) are developed to predict solar irradiance. With the default parameters, the GBR provides the best performance as it has the lowest standard deviation of errors. Then, the important hyperparameters of the GRB are tuned with the grid-search algorithms to further improve the prediction accuracy. On the testing dataset, the optimized GBR has the highest coefficient of determination ($R^2$) performance, with a value of 0.9995. The same approach also has the lowest root mean squared error (0.0007), mean absolute percentage error (0.0052), and mean squared logarithmic error (0.0001), implying superior performance. The absolute error of the prediction lies within a narrow range, indicating good performance. Overall, ensemble machine-learning models are an effective method for forecasting irradiance in Bangladesh. They can attain high accuracy and robustness and give significant information for the assessment of solar energy resources. Full article

Energy fuels retrieved from biomass utilization are considered to be an economically and environmentally friendly source. In this day and age, bioenergy provides an alternative option to replace traditional fossil-based energy to accomplish energy demand with fewer greenhouse gas emissions into the environment. A huge amount of food waste is produced every year due to mass ethnographic activities. Their potential has been underused and this has led to waste ending up in the garbage. Bioenergy production by anaerobic digestion of cheap substrate provides an effectual approach to cope with this issue. The hydrolysis stage during anaerobic digestion is enhanced by various pretreatment methods, where the disintegration of the waste substrate leads to the enhancement of soluble organics and eases the production of bioenergy. The present review focuses on state-of-the-art knowledge about food waste, its utilization, and its valorization by the action of pretreatment, thereby enhancing anaerobic digestion. Additionally, this review further focuses on the major challenges during the pretreatment method and future recommendations. Full article

To improve the understanding of the mixing performance of double shaft, batch-type paddle mixers, the discrete element method (DEM) in combination with a Plackett–Burman design of experiments simulation plan is used to identify factor significance on the system’s mixing performance. Effects of several factors, including three material properties (particle size, particle density and composition), three operational conditions (initial filling pattern, fill level and impeller rotational speed) and three geometric parameters (paddle size, paddle angle and paddle number), were quantitatively investigated using the relative standard deviation (RSD). Four key performance indicators (KPIs), namely the mixing quality, mixing time, average mixing power and energy required to reach a steady state, were defined to evaluate the performance of the double paddle mixer. The results show that the material property effects are not as significant as those of the operational conditions and geometric parameters. In particular, the geometric parameters were observed to significantly influence the energy consumption, while not affecting the mixing quality and mixing time, showing their potential towards designing more sustainable mixers. Furthermore, the analysis of granular temperature revealed that the centre area between the two paddles has a high diffusivity, which can be correlated to the mixing time. Full article

In this study, the temperature and thermal stress fields of an internal combustion hot blast stove were calculated and analysed. Turbulent, species transport, chemical reaction, radiation, and porous media models were implemented in a computational fluid dynamics model. Thermal boundary conditions on the structure of the hot blast stove were calculated based on the analytic adiabatic Y-plus method. A method to interpolate the thermal boundary conditions to a finite element mesh was developed, and the boundary conditions were mapped through the proposed method. In the on-gas period, the vortex was generated in the dome, and it made the variation of the temperature field in the checker chamber. The maximum temperature of the flue gas reached 1841 K in the on-gas period. In the on-blast period, the flow was considerably even compared to the on-gas period, and the average blast temperature reached 1345 K. The outer region of the checker chamber is shown to be continuously exposed to a higher temperature, which makes the region the main domain in managing the deterioration of the refractory linings. The shell temperature did not change during the operation due to the lower thermal diffusivity of the refractory linings, where the inner surface of the refractory had a maximum temperature change from 1441 K to 1659 K. The maximum temperature of the shell was 418.4 K at the conical region of the checker chamber side. The conical region had the higher maximum and middle principal thermal stresses due to the presence of a large temperature gradient around the conical region, where the largest maximum and middle principal stresses were 300.6 MPa and 192.0 MPa, respectively. The conical region was found to be a significant area of interest where it had a higher temperature and thermal stress. Full article

With regard to speed, comfort, and a dense network of destinations, the popularity of air transport is on the rise. For this reason, jet fuel is a commodity with rapidly growing consumption and interesting refinery margins. At the same time, however, it is becoming a focus of attention in terms of reducing negative environmental impacts. As a response to these trends, it will be necessary to coprocess alternative petroleum fractions with sustainable aviation components in oil refineries. Six alternative jet fuel samples of different origin were used to investigate their jet fuel-specific properties, that is, aromatics (from 0 to 59.7 vol%), smoke point (from 12.2 to >50 mm), freezing point (from −49 to <−80 °C) and net specific energy (41.2–43.7 MJ·kg⁻¹), and these properties were compared to standard hydrotreated straight-run Jet A-1 kerosene. The properties of the components studied differed significantly with respect to each other and to the requirements of Jet A-1. Nevertheless, the properties could be well correlated. This provides an opportunity to study possible synergies in blending these components. It was also found that the current methods and instruments used do not always allow a precise determination of the smoke point (>50 mm) and freezing point (<80 °C). Full article

Asphaltenes are known to cause problems related to flocculation, precipitation, and plugging, either in the formation, production lines, and processing equipment. Different models have been proposed to predict the thermodynamic conditions under which asphaltenes precipitate over the past years. This work analyses the performance of various models on their capability to match the literature experimental data of precipitated asphaltene mass fractions. Twenty-five different models based on equation-of-state (EoS), polymer solution, and thermodynamic-colloidal theories were identified. The performance/test datasets were collected and classified according to their pressure/temperature conditions, CO₂, n-C₅/n-C₇ gas, and liquid titrations. Statistical analysis, including residuals, parity plots, and average absolute relative deviation (AARD, %), were used to compare the adequacy of selected models. Results confirmed the need for further model development for general applications over wide pressure, temperature, and composition intervals. Full article

This work seeks to contribute to the study of techniques for level control considering a nonlinear plant model. To achieve this goal, different approaches are applied to classical control techniques and their results are analyzed. Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Model Predictive Control (MPC) and Nonlinear Auto-Regressive Moving Average (NARMA-L2) controllers are designed for the level control of a spherical tank. Subsequently, several tests and scenarios similar to those present in industrial processes are established, while the transient response of the controllers, their performance indices for monitoring the reference value, the rejection of disturbances, the presence of parameter uncertainties and the effects of noise are analyzed. The results show good reference tracking, with a settling time of approximately 5 s for 5 cm and a rise time of less than 4 s. No evidence for steady-state error or overshoot was found and controllers behave positively in the diverse scenarios assessed. The FLC and ANN controllers showed the greatest limitations, while ANFIS, MPC and NARMA-L2 exhibited competitive results considering their transient response and the performance indices calculated. Full article