Bovidae-based gelatin: Extractions method,physicochemical and functional properties,applications, and future trends

Gelatin is one of the most important multifunctional biopolymers and is widely used as an essential ingredient in food, pharmaceutical, and cosmetics. Porcine gelatin is regarded as the leading source of gelatin globally then followed by bovine gelatin. Porcine sources are favored over other sources since they are less expensive. However, porcine gelatin is religiously prohibited to be consumed by Muslims and the Jewish community. It is predicted that the global demand for gelatin will increase significantly in the future. Therefore, a sustainable source of gelatin with efficient production and free of disease transmission must be developed. The highest quality of Bovidae-based gelatin (BG) was acquired through alkaline pretreatment, which displayed excellent physicochemical and rheological properties. The utilization of mammalian- and plant-based enzyme significantly increased the gelatin yield. The emulsifying and foaming properties of BG also showed good stability when incorporated into food and pharmaceutical products. Manipulation of extraction conditions has enabled the development of custom-made gelatin with desired properties. This review highlighted the various modifications of extraction and processing methods to improve the physicochemical and functional properties of Bovidae-based gelatin. An in-depth analysis of the crucial stage of collagen breakdown is also discussed, which involved acid, alkaline, and enzyme pretreatment, respectively. In addition, the unique characteristics and primary qualities of BG including protein content, amphoteric property, gel strength, emulsifying and viscosity properties, and foaming ability were presented. Finally, the applications and prospects of BG as the preferred gelatin source globally were outlined.     

Silver-substituted (Ag1-xCux)2ZnSnS4 solar cells from aprotic molecular inks

To battle the high open-circuit voltage deficit (V_OC,def) in kesterite (Cu₂ZnSnS₄ or CZTS) solar cells, a current field of research relates to point defect engineering by cation substitution. For example, by partly replacing Cu with an element of a larger ionic radius, such as Ag, the degree of Cu/Zn disorder decreases, and likewise does the associated band tailing. In this paper, solution-processed (Ag_1-xCu_x)₂ZnSnS₄ (ACZTS) samples are prepared through the aprotic molecular ink approach using DMSO as the solvent. The successful incorporation of silver into the CZTS lattice is demonstrated with relatively high silver concentrations, namely Ag/(Ag+Cu) ratios of 13% and 26%. The best device was made with 13% Ag/(Ag+Cu) and had an efficiency of 4.9%. The samples are compared to the pure CZTS sample in terms of microstructure, phase distribution, photoluminescence, and device performance. In the XRD patterns, a decrease in the lattice parameter c/a ratio is observed for ACZTS, as well as significant peak splitting with Ag addition for several of the characteristic kesterite XRD reflections. In addition to the improvement in efficiency, other advantageous effects of Ag-incorporation include enhanced grain growth and an increased band gap. A too high concentration of Ag leads to the formation of secondary phases such as SnS and Ag₂S as detected by XRD.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Structural silicone sealants after exposure to laboratory test for durability assessment

During the service life of structural sealant glazing (SSG) facades, the load-bearing capacity of the silicone bonds needs to be guaranteed. Laboratory tests can assess the durability of SSG-systems based on mechanical characteristics of the bond after simultaneous exposure to both climatic and mechanical loads. This article studies how the material characteristics of two common structural sealants are affected by laboratory and field exposure. Dynamic mechanical analysis (DMA) confirms a reduction in the dynamic modulus of exposed silicone samples. Results from thermogravimetric analysis, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and small-angle X-ray scattering/wide-angle X-ray scattering show differences between the two sealants and indicate no/minor changes in the composition and morphology of the laboratory and field exposed sealants. Mechanical characterization methods, such as DMA, and tensile and shear testing of the structural bond, are shown to be sensitive toward the combined climatic and mechanical loadings, and are hence suitable for studying degradation mechanisms of structural sealants.     

Operation principles for hydrogen spark ignited direct injection engines for passenger car applications

The sustainable reduction of greenhouse gas emissions from road transport requires solutions to achieve net-zero carbon dioxide emissions. Therefore, in addition to vehicles with electrified powertrains, such as those implemented in battery electric of fuel cell vehicles, internal combustion engines fueled with e-fuels or biofuels are also under discussion. An e-fuel that has come into focus recently, is hydrogen due to its potential to achieve zero tank-to-wheel and well-to-wheel carbon dioxide emissions when the electrolysis is powered by electricity from renewable sources. Due to the high laminar burning velocity, hydrogen has the potential for engine operation with high cylinder charge dilution by e.g. external exhaust gas recirculation or enleanment, resulting in increased efficiency. On the other hand, the high burning velocity and high adiabatic flame temperatures pose a challenge for engine cooling due to increased heat losses compared to conventional fuels. To further evaluate the use of hydrogen for small passenger car engines, a series production 1 L 3 cylinder gasoline engine provided by Ford Werke GmbH was modified for hydrogen direct injection. The engine was equipped with a high pressure external exhaust gas recirculation system to investigate charge dilution at stoichiometric operation. Due to limitations of the turbocharging system, very lean operation, which can achieve nitrogen oxides raw emissions below 10 ppm, was limited to part load operation below BMEP = 8 bar. Thus, a reduction of the nitrogen oxides emission level at high loads compared to stoichiometric operation was not possible. At stoichiometric operation with external exhaust gas recirculation engine efficiency can be increased significantly. The comparison of stoichiometric hydrogen and gasoline operation shows a reduced indicated efficiency with hydrogen with significant faster combustion of hydrogen at comparable centers of combustion. However, higher boost pressures would allow to achieve even higher indicated efficiencies by charge dilution compared to gasoline engine operation.     

Novel approaches to purifying bacteriocin: A review

ABSTRACT

Bacteriocin is a proteinaceous biomolecule produced by bacteria (both Gram-positive and Gram-negative) that exhibits antimicrobial activity against closely related species, and food-borne pathogens. It has recently gained importance and attracted the attention of several researchers looking to produce it from various substrates and bacterial strains. This ushers in a new era of food preservation where the use of bacteriocin in food products will be an alternative to chemical preservatives, and heat treatment which are understood to cause unwanted side effects, and reduce sensory and nutritional quality. However, this new market depends on the success of novel downstream separation schemes from various types of crude feedstocks which are both effective and economic. This review focuses on the downstream separation of bacteriocin from various sources using both conventional and novel techniques. Finally, recommendations for future interesting areas of research that need to be pursued are highlighted.     

A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

Redundancy allocation problem (RAP) is one of the best-developed problems in reliability engineering studies. This problem follows to optimize the reliability of a system containing s sub-systems under different constraints, including cost, weight, and volume restrictions using redundant components for each sub-system. Various solving methodologies have been used to optimize this problem, including exact, heuristic, and meta-heuristic algorithms. In this paper, an efficient multi-objective meta-heuristic algorithm based on simulated annealing (SA) is developed to solve multi-objective RAP (MORAP). This algorithm is knowledge-based archive multi-objective simulated annealing (KBAMOSA). KBAMOSA applies a memory matrix to reinforce the neighborhood structure to achieve better quality solutions. The results analysis and comparisons demonstrate the performance of the proposed algorithm for solving MORAP.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.