In order to increase the quality and quantity of agricultural products from greenhouse cultivation, and to cope with a very competitive market, it is necessary to have an optimal climate inside the greenhouse. To achieve this, the farmer uses expensive and very power-consuming heating and cooling systems. In order to solve this problem, a new system has been developed with a solar thermal collector and a specific heat transfer fluid. The experimental study of this new system has shown that this new system was able to keep the temperature inside the greenhouse in an optimal range for the development of the plants.

Methylimidazolium N-ethylamine (MIEA) is synthesized successfully with good yield in the high performances and green chemical process, using N-methylimidazole and tert-butyl N-(2-bromoethyl) carbamate as starting materials. Following the mechanism of reductive amination, this ionic liquid was a suitable ligand for reductive amination with the carbonyl group of the carbohydrates, and the activity of an ionic liquid is disclosed by mass spectrometric techniques. This work illustrates that methylimidazolium N-ethylamine as an ionic liquid labelling carbohydrate including GlcNAc and its derivatives, for example bovine lactoferrin (BL), and horseradish peroxidase (HRP) have been selected as examples of glycoproteins. The detection of profiling labels of oligosaccharides and glycoproteins is performed by using UPLC/ESI-QTOF and by MALDI-TOF mass spectrometry. Moreover, the ionic synthesis as a multifunctional oligosaccharide label for analysis carbohydrate was investigated, using as comparable with the others ligands such 2-AB labelled GlcNAc and its derivatives. Labelling glycoproteins by IL-MIEA has improving ESI ionization efficiency, even better than 2-AB derivatives. Relevantly, this ionic liquid is applicable as advancement and development in catalytic methods of modification of small molecules as potential drugs for anti-viruses and microbes’ infections.

Solar Photovoltaic (PV), a variable renewable energy, has continuously penetrated and increased its share in the global energy mix. However, due to its variability, its large-scale integration into the electrical network poses some technical and economic issues. Centralized Battery Energy Storage Systems (BESS) have been seen as one of the techniques to integrate more decentralized and distributed variable renewable energy systems into the grid and will thus facilitate the greater democratization of the energy systems. However, BESS has to be correctly allocated to work in the electrical network. This paper provides a practical process for evaluating the proper allocation of a centralized BESS in three types of communities with rooftop Solar PV, namely low-cost, medium-cost, and high-end community, by considering the mean and maximum energy consumption and peak demand profiles of each residential unit. Such values are used to allocate for the Solar PV system per residential unit and the centralized BESS at mean, 75% of maximum, maximum, and 125% of maximum energy consumption, to be used for the community and excess energy to support the electrical networks reliability improvement. The result shows that allocating the centralized BESS using the maximum and 125% of maximum energy consumption can provide more than enough energy capacity for electrical networks reliability improvement by supplying for the energy not Supplied (ENS). The result provides that for a High-end community, the stored energy in the BESS can reach up to 24,578kWh using 125% of maximum sizing at either mean or maximum energy consumption. While for Medium-cost and Low-cost communities, the stored energy can reach up to 17,549kWh and 14,527kWh using 125% of maximum sizing at either mean or maximum energy consumption, respectively.

In the production of wine, most fermentations are completed in successive batches during the 2 month harvest period. Red wine fermentations are usually completed in 10 to 14 days while white wine fermentations are completed in 21 to 24 days. The demand for resources - equipment, water, energy, labor, etc. - in a short time motivates the precise, automated control of the fermentation process. While much research has been performed to evaluate the effect of fermentation temperature on wine chemistry, the ability to research the effect of fermentation kinetics on the resulting wine chemistry and reproduce the results at commercial volumes requires advanced control strategies. In this work, a nonlinear model predictive controller (NMPC) was developed to determine the temperature to achieve a desired fermentation rate. The controller was combined with a pulse cooling and heating strategy to improve energy efficiency of temperature control. The potentials of this approach are demonstrated in simulations and 15 L fermentations.

The study of wind energy conversion systems (WECS) has gained attention in the field of renewable energy sources. This is partly because wind generator size has increased quickly, and also because power electronics have developed and can be used to extract wind energy. This article offers a thorough analysis of both historical and contemporary converter topologies that are related to permanent magnet generators and doubly-fed induction generators. Price, power consumption, efficiency, control complexity, and the diverse generator-converter combinations are contrasted on the basis of topology. The attributes of each generator-converter setup are considered from the viewpoint of wind turbine systems.

It is known that the immobilization of microorganisms on carriers of various nature increases their safety. The aim of the study was to identify a rational inorganic carrier for the immobilization of Lysobacter sp. Inorganic matrices: sodium carboxymethylcellulose technical brand "KMC 85/500"; colloidal silicon dioxide in the form of a commercial preparation "Polysorb"; sodium form of montmorillonite from the Podgorenskoye deposit, Voronezh region. The immobilization of bacterial cells was carried out by adding Lysobacter sp. solid sterile carrier with constant mechanical stirring in the ratio "carrier:biomass", equal to 1:(2-4), at T=30°C; subjected to freeze drying at T=-40-45°C for 24 hours to a moisture level of 3-7%. Survival of microorganisms Lysobacter sp. after immobilization on solid carriers: the samples were suspended in Petri dishes using molten agar, incubated for 24 hours and the number of colony forming units was determined by the method of serial dilutions. After three months of storage of lyophilizates, the following results were obtained: lyophilization of the bacterial culture of Lysobacter sp. without immobilization on the matrix leads to a decrease in safety to 37.5%; cell immobilization on sodium carboxymethyl cellulose allows to increase safety up to 65%; for colloidal silicon dioxide - up to 50%; when immobilized on the mineral montmorillonite, not only the preservation of microorganisms is manifested, but also an increase in the number of cells by 18%. Thus, it has been established that the mineral montmorillonite is a promising material for the immobilization of bacterial cells in order to obtain biocompositions based on them, since a positive trend in the preservation of bacterial cells has been revealed.

Intensive research in the field over the past decades highlighted the complexity of aroma partition. Still, no general model for predicting aroma matrix interactions could be described. The vision outlined here is to discover the blueprint for the prediction of aroma partitioning behavior in complex foods by using machine learning techniques. Therefore, known physical relationships governing aroma release are combined with machine learning to predict the Kmg value of aroma compounds in foods of different compositions. The approach will be optimized on a data set of a specific food product. Afterward, the model should be transferred using explainable artificial intelligence (XAI) to a different food category to validate its applicability. Furthermore, we can transfer our approach to other relevant questions in the food field like aroma quantification, extraction processes, or food spoilage.

Cheese whey (CW) is the residual-liquid waste from the cheese-manufacturing industries, which is rich in diverse nutrients with the potential for the usage as a growth-matrix for sustaining (LAB) fermentation. Lactic acid (LA) and phenyllactic acid (PLA), and their derivatives are green chemicals that can be produced by LAB metabolism by revalorization of CW. LA and PLA are known for their antimicrobial properties, immunoregulatory functions, and production of biobased polymers (bio-degradable plastics) like poly lactic acid and poly-phenyl lactic acid; hence they find numerous applications in agricultural /food-based, pharmaceutical, bio-chemistry, or medical fields, and as antibiotic supplements in livestock feeds in animal husbandry. Herewith, we discuss our experimental strategy /concept (that can be implemented) for the microbial fermentation of cheese whey streams using robust LAB cocultures to produce PLA through sequential steps, adding a note upon their possible applications hereof. It is proposed that various food matrices like raw cow milk, fermented cow milk, fermented table olives, would be screened for the isolation of robust lactic acid bacteria that can be used as starter cultures for fermentation of cheese whey liquids for producing augmented levels of LA and/or PLA. Moreover, the feasibility of practically producing PLA using an orchestrated assemblage of simple procedures viz., isolating robust LAB strains from natural food matrices, tailoring LAB growth using selective medium sustenance, adopting adaptive evolution procedures for improving resistance to higher temperatures and tolerance to lactic acid and/or cheese whey (low-cost substrate), and using FTIR and HPLC tools for analysing the PLA content produced, is discussed. Two Lactobacillus isolates (CM30_001 and CMW_10-3) sourced from raw cow milk and fermented cow milk whey, were found to produce PLA contents of 39mg/L and 32mg/L in batch-stage fermentation, using this proposed strategy.

Plant growth promoting Rhizospheric bacteria (PGPR) are beneficial soil bacteria that enhance plant growth against biotic and abiotic stress. For the last three decades many studies had been conducted on isolation and characterization of plant growth promoting bacteria from the rhizosphere of different plants. However, no study was done on chalky soil rhizospheric bacteria.The purpose of this study was to assess the abundance (number) of culturable bacteria and to characterize plant growth promoting traits of some bacteria isolated from the rhizosphere of the wild legume plant Chamacytisus ruthenicus(Russian broom) that growing on chalky soil. To estimate the number of colony forming units (cfu), three soil samples were collected in January at a temperature of 2-4^oC from the place where the Russian broom plants were grown. The first sample was taken from top soil, the second sample from the soil 15 cm deep and the third sample was from the rhizosphere. The result of the study revealed that the number of bacteria in the first, second and third soil sample were 4.25 x 10⁸ cfu/g, 3.58 x 10⁸ cfu/g and 10.1 x 10⁷ cfu/g respectively. In the present study a total of 23 rhizospheric bacteria were isolated. On the ongoing work, by chance three strains (Z11, Z12 and Z15) were shown microbial inhibition against fungi. Including the above three, six strains (Z11, Z12, Z15, Z24, Z26 and Z44) were chosen for further investigation since they differ in colony colour, morphology and anti-microbial activity. As a conclusion, the present study is the first report of chalky soil associated bacteria found in the rhizosphere of Russian broom in the Belgorod region of Russia.

The medical industry has made significant strides thanks to the use of many autonomous systems to identify various ailments in this day, surrounded by numerous technology. A crucial medical practice is the visual evaluation and counting of white blood cells in microscopic peripheral blood smears. It may offer helpful details about the patient’s health, such as the identification of Acute Lymphatic Leukemia or other serious illnesses.
In this study, a framework for recognizing acute lymphoblastic leukemia from a white blood cell's microscopic picture is proposed. Microscopic images must first undergo a thorough pre-processing step in order to be classified. In this study, a collection of textural, geometrical, and statistical features are retrieved from the segmented region following the segmentation of WBCs from blood smear pictures and morphological procedures.
In order to compare these algorithms in terms of various performance measures, four distinct machine learning techniques—namely, Random Forest (RF), Support Vector Machine (SVM), Naive Bayes classifier (NB), and K nearest neighbor (KNN) are also deployed. After careful comparison, it can be seen that the SVM is effective at classifying and identifying the acute lymphoblastic cell that causes leukemia malignancy.
A single classifier is nearly useless given the variety of blood smear pictures. As a result, we thought about using an EMC-SVM to classify leukocytes. The suggested method properly separates WBCs from blood smear images, according to experimental findings, and correctly classifies each segmented cell into its relevant category, which includes neutrophil, eosinophil, basophil, lymphocyte, and monocyte.