Background: Mayonnaise is a widely used emulsion-like food that is popular for its flavor properties. However, the modern trend of healthy eating requires a reduction in the calorie content of this product, which means a decrease in the oil content. Such emulsion systems require the solution of increased problems associated with the stabilization of their structure. It is known that the size of droplets as a microstructural characteristic depends on the stability of the emulsion and correlates with the rheological properties of emulsions. Thus, the study of these characteristics becomes one of the important factors in predicting the properties of emulsions being developed with different natures of the main ingredients.

Objective: The purpose of this study was a preliminary chemometric analysis of data on acidity, rheological and microstructural characteristics of commercial mayonnaises and mayonnaise sauces containing from 25 to 67% oils (sunflower, rapeseed and olive) in order to predict the effect of the main ingredients of the recipe on textural characteristics.

Methods: Microstructural and rheological characteristics of the samples were determined by laser diffraction and rotational viscometry with coaxial cylinders, respectively. Rheological data were analyzed within the framework of a structural representation based on the generalized Casson’s model. The nine standardized parameters were grouped using multivariate statistical methodology techniques such as principal component analysis and hierarchical cluster analysis.

Results: The experimental flow curves demonstrated pseudoplastic behavior, which is typical for such emulsion systems. The three factors of multivariate factor analysis can explain 72.5% of the variability. In the first factor, the most important variables (with the highest loads) were the Casson’s model coefficient of the aggregation degree, the static yield stress and the average droplet size. In the second factor, the highest loadings were the oil content and the Casson model coefficient, which indicates a tendency to form an infinitely large droplet aggregate. The pattern captured by PCA is confirmed by HCA analysis data. This approach made it possible to identify five clusters that unite objects that have a similar effect of acidity, rheological and microstructural characteristics on texture. The influence of the nature of the food ingredients of the mayonnaise-type emulsion on the results of multivariate analysis is discussed.

Conclusion: Rheology combined with microstructural characteristics can be used as a tool to evaluate the effect of ingredients in mayonnaises and mayonnaise sauces on textural properties. This information is important for formularies when using alternative ingredients.

A significant number of foods are manufactured in the form of emulsions. However, the resulting macroemulsion is a thermodynamically unstable system. The physical instability associated with processes such as flocculation, coalescing and Ostwald ripening eventually leads to separation of the oil and water phases over time. Predicting this behavior is important for food technology development purposes and especially for reduced oil systems as the most challenging task in terms of system stabilization. In the first approximation according to the Stokes model, the stability of an emulsion is affected by such a microstructural characteristic as the droplet size and the rheological properties of the emulsions. The combination of this approximation with the generalized Casson rheological model for structured liquid systems allows us to propose an approach for estimating the rate of emulsion cremation. The parameters used for calculations are determined from rheology and laser diffraction data. The approach is devoid of empirical variables, since all parameters have a physical meaning on the basis of the kinetic model of destruction-recovery of the structural aggregates of the system.

Calculations were carried out for real food-like emulsions with different nature of the oil phase and different microstructure. The results make it possible to evaluate the physical stability of emulsions, as well as the influence of rheological and microstructural characteristics on the texture of the final product during its shelf life as an important development characteristic.

Throughout history, waste has consistently posed concerns for civilizations, serving as a reflection of societal progress through the accumulation of solid waste in our environment. However, the ever-growing global population, combined with the industrial revolution and increased consumerism, has led to an exponential surge in waste generation. Scientists and waste management professionals have diligently sought solutions to address this issue and establish sustainable, suitable, and efficient approaches to solid waste management. The implementation of integrated solid waste management (ISWM) is crucial for addressing the challenges posed by increasing waste generation and limited landfill space. Remote sensing (RS) and Geographic Information Systems (GIS) have emerged as powerful tools to support ISWM strategies through their diverse applications. This short review explores the novel applications of RS and GIS in ISWM and highlights their potential for enhancing waste management practices. RS techniques, such as satellite imagery and aerial photography, enable the accurate mapping and monitoring of waste generation, disposal sites, and recycling facilities. GIS facilitates spatial analysis and decision-making, allowing for optimized waste collection routes, landfill site selection, and the identification of suitable locations for waste-to-energy projects. Furthermore, RS and GIS provide valuable insights into waste composition analysis, landfill stability assessment, and environmental impact evaluation. This review underscores the importance of leveraging RS and GIS technologies to improve waste management practices and offers valuable recommendations for future research in this field.

Food image classification and recognition is an emerging research area due to its growing importance in the medical and health industries. As India is growing digitally rapidly, an automated Indian food image recognition system will help in the development of diet tracking, calorie estimation, and many other health and food consumption-related applications. In recent years many deep learning techniques evolved. Deep learning is a robust and low-cost method for extracting information from food images, though, challenges lie in extracting information from real-world food images due to various factors affecting image quality such as photos from different angles and positions, several objects appearing in the photo, etc. In this paper, we use CNN as our base model to build our system, which gives an accuracy of 86% to 89% of the system. After that, we deployed the transfer learning technique with MobileNetV3 for improvement in accuracy, which resulted in an improvement in accuracy of up to 94%. Furthermore, we applied data augmentation techniques in pre-processing phase and we train our model using transfer learning with MobileNetV3 and we got an accuracy of up to 96%. So, the accuracy of the model increases by applying the data augmentation technique on top of transfer learning.

Numerical investigation eases the development and exploitation phase of a coal mine, considering geological settings by forecasting the overall stability. This research aims to manifest a generalized numerical simulation calibration approach to anticipate the probable geotechnical hazards in an explored coal mine; focusing on the Khalashpir coal basin, in Bangladesh. This study inspects the feasibility of initiating mining at the currently proposed central block associated with major faults by the finite element method (FEM), which is a valuable tool to understand the variations of stress distribution in the rock mass. The study verifies the findings of the FEM by further assessing the seam convergence, vertical stress, and strain safety factor by boundary element method (BEM), in which numerical discretization is done at reduced spatial dimension. Both results depict that there will be significant displacements in the formation, which infer subsidence and increases vastly along the major faults. The numerical investigation approach in this study will provide a reference for future research regarding newly explored coal mines, particularly ones in the Gondwana basin.

Nowadays, lithium-ion batteries (LiBs) have emerged as the most popular type of Energy Storage Systems (ESS) in electric vehicles (EVs). Accurately estimating the parameters for the equivalent circuit model (ECM) of LiBs, especially those that are not provided in the manufacturer's datasheets, is crucial for improving their behavior modeling and understanding. Therefore, this study focuses on investigating a precise method named Rao-1 algorithm, for extracting the optimal values of the ECM's parameters. The Rao-1 technique is a simple metaphor less algorithm that involves only arithmetic operation, such as addition and multiplication. The primary objective is to minimize the difference between the estimated voltage derived from the ECM and the measured voltage of the battery. To evaluate the effectiveness of this approach, a real-world driving data-based test profile is employed. Moreover, a comparative analysis is conducted against state-of-the-art optimization algorithms. Simulation results show that the applied method is capable of accurately estimating the parameters of the ECM and surpasses other methods in terms of accuracy and convergence speed. Finally, the Rao-1 approach can be suggested to improve the accuracy of battery models used in various applications since it offers a compromise between simplicity and precision.

Insulin is a hormone whose efficacy in wound healing was recognised in the late 1920s. Research in subsequent years has confirmed its significant contribution to reducing inflammation, regulating oxidative processes, fibroblast proliferation, and enhanced collagen deposition and vascularization in a variety of experimental wound models. Intensive research is currently underway to develop materials that will provide effective stabilisation of insulin and allow its controlled diffusion rate. The aim of this review was to bring together research on the development of innovative wound care strategies based on insulin-enriched bioactive dressings. An analysis of the literature contained in bibliographic databases (Embase, Medline, PubMed, Cochrane Library) and published up to 30 June 2023 was performed. Keywords used were: dressing, polymers, insulin, stability, topical, local treatment, sustained release, diabetic ulcers, wound healing, and chronic wounds. The literature was analysed, which met the established conditions of the review. To date, there have been many promising studies on the development of insulin dressings. The results of the included basic and preclinical studies confirm that engineered polymeric matrices/scaffolds with insulin show high efficacy and good tolerability in topical wound treatment.

The anti-inflammatory drug ketoprofen has shown promising results in the field of drug repurposing for the treatment of brain cancer, but currently developed formulations are for invasive administration (intravenous) and have very limited drug strength. Hence, the purpose of this work was to develop an intranasal oil-in-water nanoemulgel, with drug strength maximization, for non-invasive, more effective and safer treatment of glioma. The developed formulations were made of Capryol® 90 (hydrophobic surfactant), Tween® 80 (hydrophilic surfactant), Transcutol® (co-solvent and permeation enhancer), Pluronic® F-127 (surfactant and gelling agent) and ketoprofen. Droplet size, polydispersity index, in vitro drug release and accelerated stability were measured. Results showed that the addition of Pluronic to a preliminary optimized nanoemulsion led to a significant droplet size and PDI reduction (176 to 22 nm, and 0.3 to 0.1, respectively). Achieved drug strength was 4 mg/mL, which is more than 50 times higher than ketoprofen’s aqueous solubility. The developed formulations also appeared to have high stability, with instability indexes between 0.130 and 0.265, and high cumulative drug release percentage, varying between 78 to 93% after 24h. Formulations also showed a controlled release profile, fitting a Korsmeyer-Peppas kinetic model, with low AIC (43.84 to 54.67) and high R² (0.9725 to 0.9971) values, depicting non-Fickian diffusion (n between 0.7 and 0.8). Hence, high drug strength, high stability and high drug release ketoprofen-loaded nanoemulgels were successfully prepared. Future in vitro cytotoxicity evaluation in glioma cells will assess the true potential of the developed formulations for the treatment of brain cancer.

Carbon nanotubes (CNTs) are cylindrical nanostructures fabricated from carbon atoms that seem like seamless cylinders composed of rolled sheets of graphite. Owing to the unique properties of single-walled carbon nanotubes (SWCNTs), they are a promising candidate in various fields such as chemical sensing, hydrogen storage, catalyst support, electronics, nanobalances, and nanotubes. Because of their small size, large surface area, high sensitivity, and reversible behavior at room temperature, CNTs are ideal for measuring gas. They also show improved electron transfer when used as electrodes in electrochemical reactions and serve as solid media for Protein immobilization on biosensors. SWCNTs can be metallic or semi-conductive, counting on their structural properties. In this study, we use the atomic force microscope (AFM) as a powerful tool to manipulate and disaggregate SWCNTs. By precisely controlling the AFM probe, we were able to manipulate individual SWCNTs and separate them from the bundle structures. Next, the electrical transport of disaggregated SWCNTs was studied using the conductive atomic force microscope (cAFM) technique. Through careful measurement and analysis, we were able to observe the triggering effect and confirm the presence of both the semiconductor and the metallic SWCNT in the sample.

In oil well cementing, cement must flow through the casing before reaching the target annulus hence, a retarder must be added to provide the cement with sufficient time to reach the target depth before the cement sets. At the same time, in support of the Paris Agreement, the prospect of substituting ordinary Portland cement (OPC) with geopolymer cement as the well cement material has to be further explored. Although previous studies have found that retarders can delay the strength development of the cement, the studies were conducted either at ambient conditions or using OPC hence, the findings do not apply to geopolymer cement that is exposed to wellbore conditions. In order to address the shortcomings of the studies, the addition of a hydrocarboxylic‑acid-based retarder to a fly-ash-based geopolymer cement, at concentrations of up to 3% by weight of fly ash, was performed. Slurry of the cement was cured at 100 °C and 20.7 MPa for 8, 24 and 48 hours. Compressive strength tests were conducted on samples of the cement. At the 8-hour curing duration, retarder concentrations of 0.5–2.0% led to strength increases of 112.7–129.4% relative to that of 0%, or the control sample, whereas that of 3.0% led to a strength decrease of 84.2%. At the 24-hour curing duration, all retarder concentrations led to strength decreases of 16.4–22.5%. At the 48-hour curing duration, retarder concentrations of 1.0–3.0% led strength increases of 18.1–24.4%, whereas that of 0.5% led to a strength decrease of 16.7 %.