Cowpea is a well-known nutrition rich African leafy vegetable that has potential to sustain food and nutrition insecurity in sub-Saharan Africa. Consumption of cowpea legumes is associated with reduced risk of type 2 diabetes mellitus. Therefore, the present study was designed to evaluate the (i) variation in phenolic metabolites in seven cowpea cultivars (VOP1, VOP2, VOP3, VOP4, VOP5, VOP7, and VOP8 using UHPLC coupled with high resolution Q-TOF-MS technique, (ii) in vitro antioxidant activity using ferric reducing/antioxidant capacity (FRAP) assay (iii) in vitro anti-diabetic effects and (iv) composition of carotenoids and amino acids of theses cowpea cultivars. The results of this study demonstrated that gentisic acid 5-O-glucoside, quercetin 3-(2G-xylosylrutinoside) and Quercetin 3-glucosyl-(1->2)-galactoside were highest in VOP1 VOP4 and VOP5, respectively. High inhibition (>50%) of α-glucosidase and α-amylase activities was shown by the leaf extracts (50 and 25 mg/mL) of VOP1 and VOP4. Cowpea cultivars VOP1 and VOP4 demonstrated the highest gene expression levels of regulation of glucose transporter GLUT4 in C2C12 skeletal muscle cells, similar to insulin. A positive correlation exited between the phenolic components and the inhibitory effect of antidiabetic enzymes and FRAP activity. Cytotoxic effect was not detected in vitro in any cowpea cultivar. Lutein (124.6 mg/100 g) and all-trans-beta-carotene (92.6 mg/100 g) levels were highest in VOP2 and VOP1, respectively. Cowpea cultivars VOP3 and VOP4 showed potential to fulfil the daily requirements of essential amino acids. Thus, based on this information, cowpea (leaves) genotypes/cultivars can be selected and propagated for the further development of supplementary foods or functional food ingredients.The inherent degradation property of most dental resins in the mouth leads to the long-term release of degradation by-products at the adhesive/tooth interface. The by-products increase the virulence of cariogenic bacteria, provoking a degradative positive-feedback loop that leads to physicochemical and mechanical failure. Photoinduced free-radical polymerization and sol‒gel reactions have been coupled to produce a novel autonomous-strengthening adhesive with enhanced hydrolytic stability. This paper investigates the effect of network structure on time-dependent mechanical properties in adhesives with and without autonomous strengthening. Stress relaxation was conducted under 0.2% strain for 8 h followed by 40 h recovery in water. The stress‒time relationship is analyzed by nonlinear least-squares data-fitting. The fitted Prony series predicts the sample’s history under monotonic loading. Results showed that the control failed after the first loading‒unloading‒recovery cycle with permanent deformation. EN450 supplier While for the experimental sample, the displacement was almost completely recovered and the Young’s modulus increased significantly after the first test cycle. The experimental polymer exhibited higher degree of conversion, lower leachate, and time-dependent stiffening characteristics. The autonomous-strengthening reaction persists in the aqueous environment leading to a network with enhanced resistance to deformation. The results illustrate a rational approach for tuning the viscoelasticity of durable dental adhesives.Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter. Today, the speech emotion recognition (SER) system evaluates the emotional state of the speaker by investigating his/her speech signal. Emotion recognition is a challenging task for a machine. In addition, making it smarter so that the emotions are efficiently recognized by AI is equally challenging. The speech signal is quite hard to examine using signal processing methods because it consists of different frequencies and features that vary according to emotions, such as anger, fear, sadness, happiness, boredom, disgust, and surprise. Even though different algorithms are being developed for the SER, the success rates are very low according to the languages, the emotions, and the databases. In this paper, we propose a new lightweight effective SER model that has a low computational complexity and a high recognition accuracy. The suggested method uses the convolutional neural network (CNN) approach to learn the deep frequency features by using a plain rectangular filter with a modified pooling strategy that have more discriminative power for the SER. The proposed CNN model was trained on the extracted frequency features from the speech data and was then tested to predict the emotions. The proposed SER model was evaluated over two benchmarks, which included the interactive emotional dyadic motion capture (IEMOCAP) and the berlin emotional speech database (EMO-DB) speech datasets, and it obtained 77.01% and 92.02% recognition results. The experimental results demonstrated that the proposed CNN-based SER system can achieve a better recognition performance than the state-of-the-art SER systems.In this study, numerical simulations of coupled solid-phase reactions (pyrolysis) and gas-phase reaction (combustion) were conducted. During a fire, both charring and non-charring materials undergo a pyrolysis as well as a combustion reaction. A three-dimensional computational fluid dynamics (CFD)-based fire model (Fire Dynamics Simulator, FDS version 6.2) was used for simulating the PMMA (non-charring), pine (charring), wool (charring) and cotton (charring) flaming fire experiments conducted with a cone calorimeter at 50 and 30 kW/m2 irradiance. The inputs of chemical kinetics and the heat of reaction were obtained from sample mass change and enthalpy data in TGA and differential scanning calorimetry (DSC) tests and the flammability parameters were obtained from cone calorimeter experiments. An iso-conversional analytical model was used to obtain the kinetic triplet of the above materials. The thermal properties related to heat transfer were also mostly obtained in house. All these directly measured fire properties were inputted to FDS in order to model the coupled pyrolysis-combustion reactions to obtain the heat release rate (HRR) or mass loss. The comparison of the results from the simulations of non-prescribed fires show that experimental HRR or mass loss curve can be reasonably predicted if input parameters are directly measured and appropriately used. Some guidance to the optimization and inverse analysis technique to generate fire properties is provided.