Limonene is a valuable monoterpenoid that with diverse applications. Microbial biosynthesis offers a sustainable alternative to plant-based extraction, which is prone to hinderance by climate change and acreage. In the present study, the effects on limonene production of carbon source, inoculum, working volume, temperature, pH, dodecane coverage, and media were investigated with engineered strains of Rhodosporidium toruloides. Through single-factor and orthogonal experiments, the limonene titer was improved from 52.2 to 358.1 mg/L on 250-mL flask, a 586% increase. The optimized medium was MM, with a working volume of 50 mL, inoculum OD600=0.6, 22 °C, pH 6, and dodecane coverage (v/v) of 20%. The results here demonstrated that the R. toruloides could be explored as an efficient limonene producer and facilitate other monoterpenoids biosynthesis with R. toruloides.

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

To study the fermentation process of biocatalyzing decanoic acid to synthesize trans-2-decenoic acid, this study was based on the engineering E.coli constructed in the laboratory in the early stage and studied the effect of seed liquid culture time, culture temperature, inoculum amount, induction temperature, dissolution effects of substrate solvent, metal ions, and substrate loading on the yield of trans-2-decenoic acid. Based on single-factor experimental optimization, the Box–Behnken design was used to conduct response surface testing using the substrate feeding concentration, inducer concentration, and MnCl2 concentration as response variables and trans-2-decenoic acid production as the response value. The optimal fermentation process was determined through experiments: seed culture time 20 h, culture temperature 37°C, inoculation amount 1%, induction temperature 30°C, substrate flow amount 0.15 g/L, inducer concentration 5.60 g/L, MnCl2 concentration was 0.10 mM. Under these conditions, the average production of trans-2-decenoic acid was 1.982 ± 0.110 g/L. Compared with the basic LB medium, the production of trans-2-decenoic acid increased by 1.042 g/L; compared with the previous period, the yield of trans-2-decenoic acid studied increased by 1.501 ± 0.110 g/L, which provides a reliable basis for further research on the fermentation process of biocatalytic decanoic acid synthesis of trans-2-decenoic acid.

In order to improve the yield of the therapeutic agent N-methylsansalvamide (SA), optimization of stationary liquid fermentation conditions was carried out on an endophytic strain, Fusarium sp. R1, at flask level. Using a One-Factor-At-a-Time approach, the fermentation conditions for SA production were determined as follows: fermentation time of 13 d, 24 oC, initial pH of 6.5, seed age of 24 h, inoculum size of 5.0% (v/v), loading volume of 50% (v/v), 20.0 g/L salinity, and sucrose, tryptone, and yeast extract were found to be the best sources of carbon and nitrogen. Using response surface methodology, the optimal medium compositions consist of 22.5 g/L sucrose, 16.5 g/L tryptone, and 0.024 g/L yeast extract. Verification tests suggested that the SA yield under these optimal conditions reached up to 536.77 ± 2.67 mg/L, which was increased by almost ten times the initial yield (54.05 ± 3.45 mg/L). The findings indicate that a high SA production yield can be achieved by static culture of strain R1 under the proper fermentation conditions using a low-cost medium. This study paves the way towards industrial-scale SA production by strain R1 for new drug development.

Green synthesis of silver nanoparticles (AgNPs) using raw Whey has never been reported. Silver nanoparticle formation involves a reduction reaction of silver salt solution with Whey extract. Extract and metal salt concentrations, and temperature can control this reaction. In this work, incubation period, extract, AgNO3, and NaOH concentrations were used as the independent factors using central composite design under response surface methodology. The aim was to elucidate the influence these factors have on the measured UV-Vis absorption spectra fitted with a Voigt function, and to optimize the conditions for nanoparticle formation. The fitting parameters, peak wavelength (λ0), peak area (A), and Full Width at Half Maximum (FWHM), were the responses. Silver nanoparticle formation was only possible in an alkaline environment (pH 10). The peak wavelength and the FWHM are influenced mainly by extract and AgNO3 concentrations. In contrast, the peak area is influenced by a total of 33.54 % from the interaction terms of AgNO3 and extract concentrations with NaOH. Metallic AgNPs were formed for the following parameter ranges: a) Whey (0.4 – 1.6 % v/v), b) AgNO3 (0.15 – 0.6 mM), c) NaOH (10 – 50 mM), and d) Time (20 – 60 min). Silver oxide nanoparticles were also observed.

Objective: The aim of this study was to optimize the fermentation process of Radix Ranunculi ternate via microbial fermentation and analyze the changes in the contents of the main components, the antioxidant and hypoglycemic capacities of the extract before and after fermentation. Methods: The solid-state fermentation process was optimized using single-factor tests and the response surface method, with the yield of the alcohol extract of R. ternate as an evaluation index. Results: The best fermentation process was optimized using solid-state endophytic fungus fermentation technology as follows: strain addition ratio of Chaetomium globosum/Fusarium equiseti =1:1, fermentation for 5 d, sieve size of 40 mesh, liquid/material ratio of 0.8:1 mL·g−1, fermentation temperature of 31 °C, and inoculation amount of 7.5%. Under the optimized conditions, the contents of the water-soluble extract and total polysaccharides decreased by 12.71% and 12.95%, respectively, in the fermentation, The contents of ethanol-soluble-extract, flavonoids, saponins, polyphenols, organic acids, and total amino acids of fermented R. ternate increased by 19.77%, 57.14%, 79.67%, 14.29%, 17.63% and 3.82%, respectively, and the scavenging rate for DPPH, ABTS+, ·OH free radicals and inhibitory rate for α-amylase also increased by 19.02%, 14.17%, 7.53% and 34.54%, respectively, compared with unfermented R. ternate. Conclusions: Solid-state fermentation opens new avenues for the development and application of R. ternate as natural antioxidant and hypoglycemic food.

In the process of oil and gas development and production, when the flow rate in the wellbore is large, the materials underground will be eroded, causing the operation to be unable to proceed smoothly and reducing the efficiency of oil and gas production. Therefore, optimizing the anti-erosion coating under high-speed flow conditions in wellbore is a very interesting thing. It can greatly improve operational efficiency. In this paper, laser cladding was used to prepare the new coating used in the PDC drill bit. Then, the effects of tungsten carbide and multi-layer graphene on the microstructure and properties of the tungsten carbide coating were studied. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding WC, the hardness has significantly improved. However, when the WC content further increases more than 30%, the increase in hardness is relatively small. In addition, when the content of graphene is more than 0.3%, the branched structure became thicker, and not only the segregation of Cr, Si and W became very obvious again, but also the segregation of Fe occurred at the Ni enrichment site. The research results contribute to improving oil and gas production and operational efficiency under high-speed fluid flow conditions in the wellbore.

Atta capiguara is a grass-cutting ant species frequently found in Cerrado biome. However, little is known about the giant nest architecture of this ant. In this study, we investigated the architecture of three A. capiguara nests from the fragment of cerrado in Botucatu, São Paulo, Brazil. Casts were made of the nests by filling them with cement to permit better visualization of internal structures such as chambers and tunnels. After excavation, the depth and dimensions (length, width, and height) of the chambers were measured. The results showed the typical shape of Atta capiguara nests consisting of mounds of loose soil with unique features resembling a conic section. The fungus chambers were found outside the apparent main part of the nest and were spaced apart and distributed laterally at ground level. The waste chambers were located beneath the largest mound of loose soil. Both the fungus and waste chambers exhibited a sectoral distribution. Our study contributes to a better understanding of the so far unknown nest architecture of the grass-cutting ant A. capiguara.

Single cell proteins are the dead dried cells of microorganisms or purified protein isolated from microorganism’s cell culture, used as a food supplement to humans’ food and animals feed. World suffer from malnutrition particularly developing countries, due to rapid increase in population, increased the demand for protein and nutrients requirement. Bacteria is potential microorganism for SCP production due to high protein content, fast generation time, bio-active secondary metabolites production and can grow on various substrates. Actinobacteria species and strain have capability to produce biological active compounds, produced about two-thirds of antibiotics available in the market, actively used as antibiotics, antiprotozoal, antifungal, antiviral, anticancer, anticholesterol, antihelminth and immunosuppressant. Actinomycetes can be used as probiotic as well as single cell protein that will deal with antibiotic as well as protein source.

A portable laser photoacoustic sensor based on a Field-Programmable Gate Array (FPGA) is reported for methane detection. A tunable DFB diode laser in the 1654 nm wavelength range is used as an excitation source. The photoacoustic signal processing was implemented by a FPGA device. A small resonant photoacoustic cell is designed. The minimum detection limit (1σ) of 10 ppm for methane (CH₄) is demonstrated.

More than 800 million tons of fish are utilizing in a year and 25-30% become waste. The waste amount is beneficial source for extraction of collagen. But procedure of extraction is still to be optimized. The current study was designed to extract collagen from fish through fish waste fermentation under various conditions. For collagen extraction we did lactic acid fermentation in which yogurt and Dough bacteria was added with fish sample and placed it in incubator at 30ºC for one month. In yogurt and dough culture have at least 10 type of lactic acid bacterial species. After every week we check PH of each sample and take soup of that sample. After centrifugation of that sample’s TCA (Trichloroacetic acid) precipitation was done of yogurt and dough sample and kept at -20ºC. then did SDS-PAGE) using 6% resolving and 5% stacking gel of already stored sample. Then HaCaT cells (1 × 10⁴ cells/well) were cultured in 96-well flat-bottom culture plates and treated with appropriate doses of FFCP (fermented fish collagen peptide) for 24 and 48 hours.The SDS-PAGE revealed that the collagen protein of fish had doublet pattern for α1 and α2 chains at corresponding to 145 kDa and 132 kDa respectively. The density for α1 twice as compared to α2. Our result agrees that The fish collagen consists mostly of α-chain as well as little amount of inter and intra molecular cross-linked components of α-chains; b (dimmer) and c (trimer). Different biochemical tests were done for identification of lactic acid bacteria catalase positive, citrate positive, urease negative. Our fermented fish collagen and peptides mixture were test for cellular cytotoxicity and proliferative effect on HaCaT cells. Current result shows that fermented extracted collagen are nontoxic and induce the proliferation of HaCaT cells. Current study is supported by various studies that revealed the medical application of fish extracted collagen as underline.

A Brewer Spent Grain (BSG), the most abundant by-product of the brewing process, is mainly used in animal feed and biogas production. The aim of the present work is BSG valorization by yeast fermentation to produce carotenoids. R. mucilaginosa CBS316 was cultured on BSG syrups, supplemented or not with YE and PEP (s-Fsa and Fsa). The biomass and carotenoid production were optimized in flasks and then scaled-up in bioreactor. In flask tests, the maximum biomass concentration was 5.3 and 6.2 g/L for Fsa and s-Fsa cultures, respectively. The maximum carotenoid production was 833 μg/L in Fsa and 1159 μg/L in s-FSA. Considering that the highest specific productivity value (P 7.16 μg/g*h) was obtained with Fsa syrup, this medium was selected for the process scale-up. In bioreactor tests, the maximum concentrations of biomass (4.29 g/L) and carotenoid (396 ug/L) was reached at 72 hours of fer-mentation, 24 h later than in flask. The total carotenoid produced was 524 and 150 ug in bioreactor and flask cultures, respectively. The carotenoids production by R. mucilaginosa was obtained both in Fsa and s-Fsa syrups. The results confirm the possibility to use the syrup as it is, Fsa, thus limiting the process costs.

Strong-aroma Baijiu is a traditional Chinese spirit distilled from grains that undergo solid-state fermentation. At the end of each fermentation cycle, a significant quantity of Huangshui accumulates at the bottom of the fermentation pit, constituting one of the primary by-products in the brewing process of strong-flavor liquor. In this report, the chemical component analysis of Huangshui obtained from a strong-flavor liquor factory revealed a diverse array of organic acids, alcohols, esters, starch, reducing sugars, amino acids, and other compounds present in Huangshui. Additionally, two specialized solid-state fermentation models were developed to investigate the formation process of Huangshui. Our findings demonstrate a positive correlation between the yield of Huangshui and the initial moisture content of fermented grains. It is concluded that Huangshui is the percolate produced during the solid-state brewing process of strong-flavor liquor, with its water content originating from the initial moisture of fermented grains.

In this study, four experimental treatments were evaluated: (T1) alfalfa hay + concentrate, (50:50%, DM); (T2) alfalfa hay + Leucaena leucocephala + concentrate, (30:20:50%, DM); (T3) alfalfa hay + prickly pear + concentrate, (30:20:50%, DM); and (T4) alfalfa hay + Leucaena leucocephala + prickly pear + concentrate, (30:10:10:50%, DM). NH3-N concentrations in T2 and T4 decreased when replaced alfalfa hay in 20 and 10 %, respectively. Treatments did not affect the concentration of total volatile fatty acids (TVFA) between T3 and T4 (p>0.05); while the concentrations among T1 and T2 were different (P<0.05). T2 showed a reduction of 25.5 % in the methane production when compared to T1(p < 0.05).The lowest concentrations of protozoa were observed in T2 and T4, which contained Leucaena leucocephala (T2) and Leucaena leucocephala + prickly pear (T4) (p<0.05). The highest concentration of total methanogens was recorded in T1 and were different to T2, T3 and T4 (p<0.05). Leucaena leucocephala at a inclusion percentage of 20 % decreased the methane when compared to T1; whereas prickly pear there was not a positive effect the methane production.

Being one of the most critical phases of a flight, landing deserves specific attention, especially when the aircraft is subject to external disturbances such as wind. A notable concern associated with touchdown events, especially when crosswind is present is tire wear. This work is aimed first at developing a nonlinear flight simulator able to handle the entire landing maneuver in non-null wind conditions, considering the airborne phase, ground run, and the transition between them. Then, the simulator is included in an optimal process to define the landing technique associated with the minimum tire wear. The methodology is tested in a simulation environment with a realistic model of a reference aircraft, showing that a significant reduction of tire wear can be obtained by optimizing the sideslip angle at touchdown and the lateral-directional controls after the airplane touches the runway with both legs of the main landing gear.

Vanilla planifolia is grown as a high-value orchid spice for its odor and savor attributes that increase due to the curing process associated with microbial colonization. This tends to influence the aromatic properties of vanilla. Hence, 11 Bacillus sp. strains were isolated from V. planifolia and identified with 16S rRNA gene sequencing. The liquid culture (1 mL of 107 CFU mL-1) of selected Bacillus vallismortis NR_104873.1:11-1518, B. velezensis ZN-S10, and B. tropicus KhEp-2 effectively fermented green-blanched vanilla pods kept at 10 ℃ during the sweating stage. GC-MS analysis showed that the methanol extract of non-coated, and B. vallismortis treated vanilla detected three (3) volatile compounds, whereas seven (7) components were obtained in B. tropicus and B. velezensis treatment. 4H-pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl was found in B. velezensis ZN-S10, B. tropicus KhEp-2, B. vallismortis while it was not present in the control samples. This ketone compound suggested a Maillard reaction resulting in brown-increased aroma pods. Linoleic acid and Hexadecanoic acid ethyl esters were detected only in ZN-S10 strain-coated vanilla. A novel 3--Deoxy-d-mannoic lactone was detected only in B. vallismortis treated vanilla characterized as a new compound in V. planifolia which suggested that the new compound can be altered with the coating of bacteria in vanilla during fermentation. Thus, the Bacillus strains improved the volatile profile and exhibited a new aroma and flavor profile of vanilla owing to bacteria fermentation during the curing process.

The global food production system faces several challenges, including significant environmental impacts due to traditional agricultural practices. The rising demands of consumers for food products that are safe, healthy, and have animal welfare standards have led to an increased interest in alternative proteins and the development of the cellular agriculture field. Within this innovative field, precision fermentation emerges as a promising technological solution to produce proteins with reduced ecological footprints. This review provides a summary of the environmental impacts related to the current global food production, and explore how precision fermentation can contribute to address these issues. Additionally, we will report on the main animal-derived proteins produced by precision fermentation, with a particular focus on those used in the food and nutraceutical industries. The general principles of precision fermentation will be explained, including strain and bioprocess optimization. Examples of efficient recombinant protein production by bacteria and yeasts, such as milk proteins, egg-white proteins, structural and flavoring proteins, will also be addressed, along with case examples of companies producing these recombinant proteins in a commercial scale. Through these examples, we will explore how precision fermentation supports sustainable food production and holds the potential for significant innovations in the sector.

This study presents a groundbreaking approach in the field of gaming AI, focusing on the classic game” Pacman” through the lens of DRL integrated with advanced optimization techniques. The core of the research involves adapting DRL models for” Pacman”, utilizing the ESO for hyper- parameter tuning. These novel adaptations significantly enhance the AI agent’s performance, demonstrating a remarkable improvement in adaptability, responsiveness, and efficiency within the game environment. A pivotal aspect of this research is the innovative integration of metaheuristic optimization techniques into the DRL framework, a first in the domain of Atari gaming AI. This integration has proven essential in fine-tuning the DRL models, leading to a more effective and dynamic gaming experience. The study thoroughly evaluates and compares the performance of these algorithms, providing empirical support for their effectiveness and setting a new benchmark in AI-driven game development. The implications of this research extend beyond gaming AI, opening up possibilities for future exploration in several directions. Expanding DRL models to other complex gaming environments, continuous algorithmic enhancements, real-time learning adaptations, and applying these principles to robotics and autonomous vehicles are examples. The research also emphasizes ethical and responsible AI use in gaming to address fairness and addiction issues.

Currently, agro-industrial residues have been used to obtain bioactive compounds. The fermentation process is an alternative for the use of these residues and to be able to accumulate metabolites that may be of interest to different industrial sectors. The aim of this study was to evaluate solid-state fermentation conditions using pineapple waste as substrate with Aspergillus niger spp., to release polyphenolic compounds using a Plackett-Burman exploratory design. Temperature, humidity, inoculum, NaNO3, MgSO4, KCl and KH2PO4 conditions in the fermentation process were evaluated. The results revealed that the Aspergillus niger HT3 strain reached a hydrolyzable tannins accumulation of 10.00 mg/g, while Aspergillus niger Aa20 reached a condensed tannins release of 82.59 mg/g. The KH2PO4 affect the release of condensed tannins with A. niger Aa20 and MgSO4 affect the release of hydrolyzable tannins with A. niger HT3. In addition, a positive antioxidant activity was demonstrated for the DPPH, ABTS and FRAP technique. The HPLC analysis was able to identify compounds in the fermented pineapple peel such as 3-feruloylquinic acid, caffeic acid, lariciresinol and 3-hydroxyphloretin 2'-O-xylosyl-glucoside, among others. The solid-state fermentation process is a biotechnological alternative for the release of bioactive compounds.

This study aimed to explore the fermentation behavior of selected potential probiotic strains during sorghum fermentation, considering their microbial counts, pH levels, and antibacterial activity, their ability to break down starch. The fermentation processes were conducted in the presence and absence of glucose, and the lactic acid bacterial strains underwent controlled fermentation to assess microbial counts, amylase activity, pH, and lactic acid levels in the presence and absence of glucose. In addition, the inhibitory activity of these potential probiotic strains against pathogenic bacteria (Salmonella typhimurium, Escherichia coli, and Staph-ylococcus aureus) was examined through co-culturing. The results demonstrated that all nine Lactobacillus strains exhibited robust growth in both sugar and sugar-free fermentation experiments. Glucose supple-mentation significantly enhanced lactic acid yield which was in a range of 0.19-0.52 % compared to fer-mentation without glucose with a range of 0.03-0.29 %. The selected Lactobacillus strains effectively lowered the pH to less than 4 after 24 hours, producing substantial lactic acid. Notably, in the absence of glucose, only Lb. helveticus D7 and Lb. amylolyticus D12 achieved pH levels below 4 after 8 hours, producing the highest lactic acid amounts of 0.27 and 0.29 % after 24 hours, respectively. Amylase activity was detected on two strains D7 and D12. Furthermore, most of the tested Lactobacillus strains demonstrated complete inhibition of pathogen growth after 24 hours of co-culturing, suggesting their potential for enhancing the safety quality of sorghum-based fermented products.

The objective of this study was to investigate the effects of ethyl nitroacetate, ethyl 2-nitropropionate and 3-nitropropionic acid on ruminal production of CH4, H2, volatile fatty acids (VFAs) and on dry matter digestibility of ground alfalfa under in vitro conditions. In vitro incubations were conducted as consecutive batch cultures. Total gas and amounts of methane produced by ruminal microbes were decreased by all nitrocompounds (P &lt; 0.05). Total production of VFAs was significantly reduced for all the nitrocompounds (P &lt; 0.05) except for NPA. DM disappearance was not affected (P &gt;0.05) by treatment.

The DEAD-box RNA helicase DDX3X is a multifunctional protein involved in RNA metabolism and stress responses. In this study, we investigated the role of RG/RGG motifs in the dynamic process of liquid-liquid phase separation (LLPS) of DDX3X using cell-free assays and explored their potential link to cancer development through bioinformatic analysis. Our results demonstrate that the number, location, and composition of RG/RGG motifs significantly influence the ability of DDX3X to undergo phase separation and form self-aggregates. Mutational analysis revealed that the spacing between RG/RGG motifs and the number of glycine residues within each motif are critical factors in determining the extent of phase separation. Bioinformatic analysis of cancer genomic datasets uncovered a significant enrichment of DDX3X mutations in RG/RGG motifs across multiple cancer types, particularly in the N-terminal region of the protein. Furthermore, we found that DDX3X is co-expressed with the stress granule protein G3BP1 in several cancer types and can undergo co-phase separation with G3BP1 in a cell-free system, suggesting a potential functional interaction between these proteins in phase-separated structures. DDX3X and G3BP1 may interact through their RG/RGG domains and subsequently exert important cellular functions under stress situation. Collectively, our findings provide novel insights into the role of RG/RGG motifs in modulating DDX3X phase separation and their potential contribution to cancer pathogenesis.

This study compared growth performance, serum biochemical indicators, rumen fermentation parameters, rumen bacterial structure, and fecal bacterial structure of cattle and yaks, fed for two months, given a feed containing concentrate to roughage ratio of 7: 3 on dry matter basis. Compared with cattle, yak showed better growth performance. The serum biochemical results showed that the albumin: globulin ratio in yaks serum was significantly higher than that in cattle. Aspartate aminotransferase, indirect bilirubin, creatine kinase, lactate dehydrogenase and total cholesterol were significantly lower in yaks than that in cattle. Rumen pH, acetate to propionate ratio, and acetate were lower in yaks than in cattle while the lactate in yaks was higher than in cattle. There were significant differences in the structure of ruminal as well as fecal bacteria between cattle and yaks. The prediction of rumen bacterial function showed that there was metabolic difference between cattle and yaks. In general, the metabolic pathway of cattle was mainly riched in de novo synthesis of nucleotides, while that of yaks was mainly riched in the metabolic utilization of nutrients. This study provides a basis for understanding the rumen ecology under the condition of high concentrate diet.

The yeast Saccharomyces cerevisiae ensures successful fermentation in winemaking, although the persistent use of commercial strains lead the loss of aroma complexity of wines. Hence, the research of indigenous S. cerevisiae with proper oenological features and well adapted to specific wine-growing area become of great interest for winemakers. Here, 206 S. cerevisiae strains were isolated from two wineries during two-year sampling campaign and bio-typed with the aim to evaluate the occurrence and persistence of S. cerevisiae wild population linked to each winery. Both wineries belonging to the same Verdicchio DOC wine area (Castelli di Jesi), and never used commercial yeasts during fermentation. Results showed a specific and varied population of S. cerevisiae in each winery without cross-contamination each other and with commercial starter strains. Moreover, inside each winery it was observed a persistent of some dominant biotypes over the time, indicating a sort of “winery-effect”. The evaluation of S. cerevisiae populations for the oenological characters showed a proper and well distinct aromatic imprinting on the resulted wines confirming the “winery effect”.

Global optimization is widely adopted nowadays in a variety of practical and scientific problems. In this context, a group of techniques that is widely used is that of evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, a number of modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide - series test functions, available from the relevant literature and it was compared against other evolutionary methods.

Broad bean paste (BBP) is a traditional fermented soy food, and its high salt content not only prolongs the fermentation time but also threatens human health. In this study, three BBP-meju with different salt concentrations were prepared, and the effects of varying salinity on the fermentation process were comprehensively compared. The results showed that salt-reduced fermentation contributed to the accumulation of amino acid nitrogen, reducing sugars, free amino acids, and organic acids. Alcohols, esters, aldehydes, and acids were the main volatile flavor compounds in BBP-meju, and the highest total volatile flavor compounds were found in medium-salt meju. Bacillus, Staphylococcus, Aspergillus, and Mortierella were the dominant microbial communities during fermentation, and there were also three opportunistic pathogens, Enterobacter, Pantoea, and Brevundimonas, respectively. According to Spearman correlation analysis, Wickerhamomyces, Bacillus, Staphylococcus, and Mortierella all showed highly significant positive correlations with ≥3 key flavor compounds, which may be the core functional flora. Furthermore, the dominant microbial genera worked synergistically to promote the formation of high-quality flavor compounds and inhibit the production of off-flavors during salt-reduced fermentation. This study provides a theoretical reference for the quality and safety control of low-salt fermented soy foods.

Thromboembolic conditions, such as stroke and myocardial infarction [MI], are the primary causes of mortality globally. High efficiency was demonstrated by fibrinolytic enzymes isolated from microbial sources in dissolving various blood clots. The study aimed to investigate and produce bacterial fibrinolytic enzymes from various Egyptian soil conditions using recombinant DNA technology. Fifty soil samples were examined to determine the production of fibrinolytic enzymes. The plasmin plate technique assessed the thrombolytic activities of the strong fibrinolytic enzyme-producing bacterial isolates. In addition to determining the molecular mass using a mass spectrometer and the western blot method, the activity and thermostability of the enzymes were characterized. An affinity chromatography approach was used to purify the fibrinolytic enzymes. However, bacterial recombinant DNA technology produced the pure bacterial fibrinolytic enzyme. Several dosage formulations for the formulation of fibrinolytic enzymes were tested. By using affinity chromatography and the SDS-PAGE test, the isolated bacterial fibrinolytic enzyme in this investigation was almost 83% pure. Bacillus cereus ATCC 4342 was identified as the predominant bacterial isolate secreting fibrinolytic enzymes. It was discovered that the fibrinolytic enzyme activity of fifty soil samples ranged from 0.167 U/ml to 1.853 U/ml. The extracellular enzyme extracted from Bacillus cereus culture supernatant demonstrated a relatively stable fibrinolytic activity over 18 hours at a pH range of 7.4–10, as well as stability with maximum productivity at 43℃ for over 60 minutes. In this investigation, the enzyme was referred to as Fibrin-proteinase[ FP]. The fibrin clots were directly thrombolyzed by the FP. The calculated values for the specific activity and molecular mass of the FP were approximately 40.71 units/mg of protein and approximately 30 KDa, respectively. It was observed that the presence of Fe+3 marginally boosted protease activity, but the presence of EDTA decreased it. On the other hand, adding monovalent cations like K+1 and Na+1 significantly increased the fibrinolytic activity of FP. The enzyme exhibited activity throughout the temperature range of 20 to 65℃, peaking at 55℃. The ideal range for the generation of protease was 35–43℃ and 8.0–10. The thermostable fibrinolytic enzyme FP, which was isolated from Bacillus cereus ATCC 4342 in various soil environments in Egypt during the current study, was a promising fibrinolytic enzyme because it demonstrated a more extensive and effective fibrinolytic activity and fewer side effects than the fibrinolytic enzymes that are currently available worldwide. It is advised that further research be done on optimizing the right formulation of bacterial fibrinolytic enzymes in the future to help dissolve different types of blood clots and, as a result, lower the high death rate that exists worldwide.

This study investigated the effect of replacing part of the dietary soybean meal with either polymer-coated urea or gelatinized starch urea on the performance, nutrient digestibility, blood biochemical indexes, ruminal fermentation and rumen microflora of Angus heifers. The urea release profiles of two kinds of slow-release urea were evaluated in vitro and indicated a superior nitrogen retention with the polymer-coated urea. 210 purebred Angus cattle (BW = 314.26 kg) were divided into three groups: no urea group (CON), polymer-coated urea group (PCU), gelatinized starch urea group (GSU), 20g/kg polymer-coated urea or 25g/kg gelatinized starch urea was used to replace part of soybean meal in the concentrate feed respectively according to the principle of iso-metabolic energy and iso-nitrogenous. During the whole experiment, the ADG of PCU group was higher than both the GSU group and the CON group. The feed-weight ratio of the PCU group was significantly lower than both the GSU group and the CON group during the 90-day feeding period. The ADF apparent digestibility in the PCU group was significantly higher than that of the CON group, and extremely significantly higher than that of the GSU group. On the 86th day, the ALB content in the PCU group was significantly higher than that in the CON group. The contents of acetic acid and butyric acid, total volatile fatty acid and propionic acid were significantly higher in the PCU group than in the CON group. Bacterial diversity analysis found that the abundance of Firmicutes was higher in the PCU group than that in the GSU group and the CON group at the phylum level, and inverse result was observed in Bacteroidetes. The abundance of Paraprevotella was higher in the PCU group, whereas higher abundance of Prevotella was found in the GSU group at the genus level. These results indicate that both polymer-coated urea and gelatinized starch urea can replace part of soybean meal in the diet, and the amount of substitution in this trial had no diverse effect on the performance of Angus heifers, and the effect and economic benefits brought by polymer-coated urea were better than gelatinized starch urea.

The fermentation of syngas is an attractive technology that can be integrated with gasification of lignocellulosic biomass. The coupling of these two technologies allows for treating a great variety of raw materials. Lignin usually hinders microbial fermentations; thus, the thermal decomposition of the whole material into small molecules allows for the production of fuels and other types of molecules using syngas as substrate, a process performed at mild conditions. Syngas contains mainly hydrogen, carbon monoxide and carbon dioxide in varying proportions. These gases have a low volumetric energy density, resulting more interesting its conversion into higher energy density molecules. Syngas can be transformed by microorganisms, thus avoiding the use of expensive catalysts, which may be subject to poisoning. However, the fermentation is not free of suffering from inhibitory problems. The presence of trace components in syngas may cause a decrease in fermentation yields or cause a complete cessation of bacteria growth. The presence of tar and hydrogen cyanide are just examples of this fermentation's challenges. Syngas cleaning impairs significant restrictions in technology deployment. The technology may seem promising, but it is still far from large-scale application due to several aspects that still need to find a practical solution.

This study aims to explore anaerobic co-digestion (AcoD) of cassava (EUM) and poultry (FP) ef-fluents using one inoculum-substrate ratio (30%) and three EUM vs FP substrate composition ra-tios (25 :75, 50 :50 and 75 :25). The AcoD process was therefore designed for 20 L batch digesters, under mesophilic conditions, with less than 5% total solids for 66 days. The results showed that EUMs were highly resistant to degradation, while FPs were the most easily degradable. Kinetic analysis indicated specific organic matter reduction rates of 0.28% per day for EUM and 0.76% per day for FP. EUM alone produced 45.47 mL/g MOV, while the 50 :50 substrate produced 1184.60 mL/g MOV. The main factors contributing to EUM inefficiency were the inability to tame acidic conditions, and the accumulation of volatile fatty acids. AcoD produced 23 to 50 times more methane than EUM alone, 2 to 5 times more than FP alone and 2 to 4 times more than ino-culum. Consequently, AcoD of both types of waste had a positive effect on gas formation in terms of quantity and quality, with CH4 content increasing from around 2 to 75% as a function of organic nitrogen input

The production of butanol, acetone and ethanol by Clostridium acetobutylicum is a biphasic fer-mentation process. In the first phase the carbohydrate substrate is metabolized to acetic and bu-tyric acid, in the following second phase the product spectrum is shifted towards the economi-cally interesting solvents. Here we present a cascade of six continuous stirred tank reactors (CCSTR), which allows performing the time dependent metabolic phases of an ace-tone-butanol-ethanol (ABE) batch fermentation in a spatial domain. Experimental data of steady states under four operating conditions - with variations of the pH in the first bioreactor between 4.3 and 5.6 as well as the total dilution rate between 0.042 1/h and 0.092 1/h - were used to optimize and validate a corresponding mathematical model. Beyond a residence time distribution representation and substrate, biomass and product kinetics this model also includes the differen-tiation of cells between the metabolic states. Model simulations predict a final butanol product concentration of 8.2 g/L and a butanol productivity of 0.75 g/(L h) in the CCSTR operated at a pH in bioreactor 1 of 4.3 and D = 0.092 1/h, while 31 % of the cells are differentiated to the solventogenic state. Aiming at an enrichment of solvent-producing cells, a feedback loop was introduced into the cascade - sending cells from a later state of the process (bioreactor 4) back to an early stage of the process (bioreactor 2). In agreement with the experimental observations, the model accurately predicted an increase of butanol formation rate in bioreactor stages 2 and 3, resulting in an overall butanol productivity of 0.76 g/(L h) for the feedback loop cascade. The here presented CCSTR and the validated model will serve to investigate further ABE fermentation strategies for a controlled metabolic switch.

The goal of this research is to determine the optimal microwave-assisted sol-gel synthesis pa-rameters of TiO2 nanoparticles with the achievement of the crystalline TiO2 nanoparticles with the highest specific surface area and the lowest energy band gap. Titanium isopropoxide was used as a precursor to prepare the sol (colloidal solution) of TiO2, isopropanol as a solvent, acetylacetone for complexation, and nitric acid as a catalyst. Four samples of titanium dioxide were synthesized from the prepared colloidal solution in a microwave reactor at a temperature of 150 °C for 30 minutes and at a temperature of 200 °C for 10, 20, and 30 minutes. The phase composition of the prepared samples was determined by X-ray diffraction analysis (XRD) and Fourier-transform infrared spectroscopy (FTIR). Nitrogen adsorption/desorption isotherms were used to determine the spe-cific surface area and pore size distributions by Brunauer-Emmett-Teller (BET) method. The band gap energy values of the TiO2 samples were determined by diffuse reflectance spectroscopy (DRS). CrystallineTiO2 nanoparticles with the highest specific surface area and the lowest energy band gap were obtained for the TiO2 sample synthesized at 200 °C for 10 minutes.

In this research the efficiency of degradation of different organic contaminant classes, including pesticides (tembotrione, clomazone), pharmaceuticals (ciprofloxacin, 17α-ethinyl estradiol) and mycotoxins (zearalenone, deoxynivalenol, fumonisin B1), by subcritical water treatment, was studied in model systems. All experiments were conducted in a house-made batch type pilot reactor. The research was focused on the optimization of the treatment parameters by using moderate treatment conditions. Optimization of the remediation processes of water contaminated with 17α-ethinyl estradiol, tembotrione, clomazone, ciprofloxacin, was conducted by testing different homogeneous and heterogeneous catalysts, as well as different gas atmospheres (nitrogen and carbon-dioxide) for pressurization of the process system. Mycotoxins in water were degraded without catalysts and all experiments were conducted in nitrogen atmosphere. Optimization was conducted by defining the optimal combination of the treatment temperature and time, oriented towards energy saving and minimization of the technical requirements. The degradation efficiency in all tested samples was determined by HPLC analysis.

Studies have demonstrated the critical role that safety plays in preserving favourable working conditions in the construction industry, which is necessary to accomplish goals. The aim of this research was to inform stakeholders in the construction industry in developing nations about the value of safety and possible strategies for influencing their opinions regarding safety protocols. The importance of safety to the construction sector, which is crucial to the advancement of the country, has also been emphasized. However, due to a lack of adequate safety understanding among stakeholders in the construction sector, the construction industry is characterized by a great deal of instability and hazard. To determine what factors, affect productive working conditions in the construction production, this study examined safety. A conceptual framework for safe working conditions in the construction sector was developed by considering several aspects, including the person dimension, environment factor, safety behaviour, organization features, technology, and incentives. It is now imperative to bring in these dimensions to improve the safe working conditions in the construction industry, particularly in the developing countries. By putting these safety factors into practice, the construction industry can reduce safety risks, lower the number of accidents and fatalities, cut expenses related to subpar safety performance, safeguard the reputation of construction companies, boost employee morale and satisfaction with their work, enhanced employee retention, reduced absenteeism, and enhanced sustainability goals. Additionally, the proposed conceptual framework is assured to be new and to be widely accepted in the developing nations. Based on this assumption the conceptual framework is designed.

We investigate molecular beam epitaxy growth conditions of micrometers-thick In0.52Al0.48As designed for waveguide of InGaAs/InAlAs/InP quantum cascade lasers. Effect of growth temperature and V/III ratio on the surface morphology and defect structure were studied. The growth conditions which were developed for the growth of cascaded In0.53Ga0.47As/In0.52Al0.48As active region, e.g. growth temperature of TG=520°C and V/III ratio of 12, turned out to be not optimum for the growth of thick In0.52Al0.48As waveguide layers. It has been observed that after exceeding ~1µm thickness the quality of In0.52Al0.48As layers deteriorates. The in-situ optical reflectometry showed increasing surface roughness caused by defect forming, which was further confirmed by High Resolution X-Ray reciprocal space mapping, optical microscopy and atomic force microscopy. The presented optimization of growth conditions of In0.52Al0.48As waveguide layer led to the growth of defect free material, with good optical quality. This has been achieved by decreasing the growth temperature to TG=480 °C with appropriate increasing V/III ratio. At the same time the growth conditions of the cascade active region of the laser were left unchanged. The lasers grown using new recipe have shown lower threshold currents and improved slope efficiency. We relate this performance improvement to reduction of the electron scattering on the interface roughness and decreased waveguide absorption losses.

This paper proposes a novel approach called Multi-strategy Improved Chaotic Whale fused with Perturbation Observation method (IWOA-PO) for Maximum Power Point Tracking (MPPT) con-trol, to address the challenge of low efficiency in photovoltaic (PV) power generation under local shadows, according to the features of whale optimization algorithm (WOA) and perturbation observation (P&O) method. In the early stage of control algorithm, IWOA is used for global search to quickly locate the position of the Maximum Power Point (MPP). In the later stage of control algorithm, P&O is used for fine-grained local search to quickly, accurately and low oscillation track the position of the global maximum power point (GMPP). In order to verify the correctness, the tracking performance of IWOA-PO is comprehensively compared with WOA-PO、WOA、PSO under four conditions including uniform illumination, static partial shade, dynamic irradiance mutation and sudden changes in temperature and irradiance. In the above four conditions, the IWOA-PO algorithm has faster convergence and less power oscillations compared to the re-maining three algorithms, indicating that the IWOA-PO algorithm is effective. Finally, a physical verification experiment process is built to verify the correctness and effectiveness of the algorithm proposed in this paper.

Lignin, and its by-products in the effluent of most agrochemical-based industries like pulp and paper, increase environmental pollution. There is, therefore, an increasing demand to degrade lignin and its derivatives using bacteria-produced ligninolytic enzymes. This study focused on optimizing growth and lignin peroxidase production of ligninolytic potential bacterial strains (E. coli accession no: LR0250096.1 and P. aeruginosa accession no: CP031449.2) for the first time. A submerged fermentation process was used to simultaneously optimize growth and crude lignin peroxidase production using kraft lignin as the substrate. The E. coli and P. aeruginosa were separately cultured under different pH, temperature, lignin (substrate) concentration and incubation periods. It was observed that the enzyme production was growth dependent. The optimum conditions for E. coli were pH 7.5, temperature 30 °C and lignin concentration 1 g/L. For P. aeruginosa the best conditions were pH 6.5, temperature 40 °C and lignin concentration 1 g/L. The enzyme production peaked after 7 days of incubation for both organisms were 21.53 ± 3.11 mg/ml and 18.13 ± 2.1 mg/ml respectively. It is apparent that both E. coli (LR0250096.1) and P. aeruginosa (CP031449.2) are mesophiles bacteria. E. coli function best in alkaline pH while P. aeruginosa prefers a slightly acidic environment. Hence, they are promising candidates in the reduction of lignocellulosic wastes.

For the first time to optimize the creation of new neuroprotective agents based on bioflavonoids, we applied information technologies - docking analysis to calculate the binding of candidate molecules to the pharmacological target protein transthyretin, as well as the program of virtual screening of NO scavengers. As a result of this approach, the substance catechin was isolated from candidate molecules - quercetin, catechin, Epicatechin gallate, Epicatechin, Procyanidin B1, Procyanidin B2, Procyanidin B3, Catechin-3-gallate according to docking analysis. As a result of virtual screening, catechin was identified as a potential NO scavenger (55.15% prediction). The results of the prediction were confirmed by in vitro experiments. Course administration of catechin to animals with experimental multiple sclerosis (MS) against the background of methylprednisolone administration completely eliminated lethal cases, reduced the number of diseased animals by 20%, as well as prevented the development of severe neurological symptoms by 20% (compared to the methylprednisolone group) and by 60% compared to the control group. Course administration of catechin with methylprednisolone leads to decrease of neurodegradation markers in the cytosol of rats with EAE: NSE by 37%, and S-100 - by 54.8%. The combined administration of methylprednisolone significantly exceeds the combination of methylprednisolone with the reference drug mexidol by the degree of NSE reduction. The obtained results indicate a significant neuroprotective effect of ocular combinations of methylprednisolone and catechin. The above-mentioned confirms the correctness of bioflavonoid selection with the help of virtual screening program.

This study, conducted with meticulous attention to detail, comprehensively examines the state of plants and their adaptation to Kazakhstan's extra-arid climatic zone of the Mangistau. It uses the collections of woody plants from the Mangyshlak Experimental Botanical Garden in Aktau as a case study and extrapolates the results to the entire region. The study area is arid and unsuitable for many plants, so examining physiological adaptation mechanisms and strategies for preserving plants in unfavourable conditions is crucial. The study employed a range of methods, including the measurement of the intensity of transpiration, leaf hydration, and the content of chlorophyll A, B, and carotenoids. These methods were chosen for their proven effectiveness in similar studies and their ability to provide accurate and reliable data. The samples were also assessed using a point system for resistance to drought, phytophagy, gas tolerance, salt tolerance, winter hardiness, and soil fertility. The results obtained and entered into the IEBG platform “DinTseR” to assess the value of the introduction using a diagnostic scale revealed a high variability (up to 51.7-52.2%) of physiological indicators of growth and development. The highest values of transpiration intensity were observed in deciduous trees and shrubs, rose varieties (205-243 mg/g fresh leaf mass per hour), leaf water content - in climbing plants (68.9%), chlorophyll A and B content - in deciduous plants, and plants - old trees, fruit trees and varietal roses (3.46-3.93 and 1.34-1.84%). Their concentration in coniferous species is almost two times lower (0.63%). A correlation was also made between various significant factors. Twenty regression equations and forecasts have been identified that can be used to diagnose and assess resistance, thereby reducing the cost of determining the introduction value in the conditions of Mangistau. These findings are essential in understanding and preserving the unique flora of the Mangistau region.

To facilitate the computation of the uniaxial compressive strength value of the cemented paste backfill under the condition of high sand-cement ratio, and to analyze the size effect of the cemented paste backfill with high sand-cement ratio under varied aspect ratios, the cemented paste backfill specimens with sand-cement ratios of 8:1-24:1 and aspect ratios of 0.5, 1, and 2 were prepared. Through response surface experiments and finite element analysis, the influence of three aspect ratios on the cemented paste backfill strength was explored, and the failure characteristics and size effect mechanisms of cemented paste backfill with various aspect ratios were analyzed. The model of strength conversion between cemented paste backfill with different aspect ratios was established. The results show that the compressive strength of the cemented paste backfill decreases with the increase of the aspect ratio under the same mass concentration and sand-cement ratio. When the aspect ratio is 0.5 and 1, the splitting failure is the main factor; when the aspect ratio is 2, the shearing failure is the main factor. The three-dimensional stress caused by the end effect of the cemented paste backfill is the main reason for the high strength when the aspect ratio is 0.5. Due to the one-dimensional stress zone generated in the middle of the cemented paste backfill with an aspect ratio of 2 in the laboratory, its strength value is closer to the actual uniaxial compressive strength value of the cemented paste backfill. The strength values of the cemented paste backfill with aspect ratios of 0.5 and 1 were higher than the actual uniaxial compressive strength values in the laboratory. The rationality of the theoretical analysis was verified by the ABAQUS analysis results. According to the experimental results, a strength conversion model between different aspect ratios is established, which can provide a reference for the design of the uniaxial compressive strength of the cemented paste backfill.

The expansion of renewable energies is progressing strongly. The influence on the power supply networks by the volatility of the infeed must be met with new concepts. In this paper we investigate the possibilities of integrating microgrids as a cooperating unit in the power supply network to support further expansion of RES power plants. In this paper a differentiation of microgrids from similar network structures is established, a classification of proposed groups is made. Then, after the description of simulation of components in a microgrid, with practical advice, an example model is shown, which aids the dimensioning of the components within a microgrid to achieve a specified goal.

The olive tree (Olea europaea L.) is an evergreen tree that occupies 19% of the woody crop area and is cultivated in 67 countries on five continents. The largest olive production is concentrated in the Mediterranean basin, where the olive tree has had an enormous economic, cultural and environmental impact since the 7th century BC. In the Mediterranean region, salinity stands out as one of the main abiotic stress factors significantly affecting agricultural production. Moreover, climate change is expected to lead to increased salinisation in this region, threatening olive productivity. Salt stress causes combined damage by osmotic stress and ionic toxicity, restricting olive growth and interfering with multiple metabolic processes. A large variability in salinity tolerance among olive cultivars has been described. This paper aims to synthesize information from the published literature on olive adaptations to salt stress and its importance in salinity tolerance. The morphological, physiological, biochemical, and molecular mechanisms of olive tolerance to salt stress are reviewed.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

Based on the dynamic strength variation mechanism of concrete under complex loading conditions, two approaches were devised: numerical testing and CT scanning, guided by the principle of minimizing energy dissipation. A methodology for calculating concrete's failure area and measuring its strength was developed. The analysis delves into the mechanisms behind failure patterns and variations in concrete specimen strength, focusing on crack initiation, development, and the resultant failure surface formed by crack propagation, along with the stress distribution in concrete specimens subjected to diverse loading conditions. Through evaluating the strength and computed failure area of concrete specimens, it is evident that the loading method influences both the strength and failure area of concrete specimens. In general, a larger failure area correlates with a higher measured strength.

The majority of existing numerical simulations of the effect of bridge piers on water movement are based on a limited number of bridge piers at a laboratory scale. Furthermore, some 2D numerical simulations for actual bridge projects have deficiencies, including the use of overly large mesh sizes and an inadequate treatment of bridge piers. In this study, we compare three methods of structural mesh encryption, suspension mesh, and nonstructural mesh based on Delft3D, and apply the optimization scheme to a real bridge project. It is demonstrated that optimal results can be achieved by utilising a grid size comparable to the pier diameter (Dp) in the region away from the piers. In the vicinity of the piers, the grid cell size should be no larger than 1/9 Dp. The suspended grid technique (DD Boundary) can yield results consistent with those obtained using a full-area high-resolution grid, provided that the total number of grids can be reduced and the computational time is considerably reduced. In this study, the unstructured mesh (Deflt3D Flexible Mesh) scheme was unable to capture the oscillations of the wake flow behind the bridge piers. However, the application of the optimized scheme in bridge engineering has demonstrated its practical value. The findings of this study on mesh resolution and suspension mesh scheme can be applied to the Delft3D software and are also useful for other numerical simulation work.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

The integration of diverse distributed generation (DG) units, encompassing both Renewable Energy Sources (RES) and non-RES technologies delves into voltage levels, current distributions, and power flows, aiming to optimize and effectively manage the distribution network. An RES-based DGs such as photovoltaic systems and wind turbines, alongside non-RES units like fuel cells and micro turbines. This study proposes a hybrid approach the Multi-Objective Genetic Algorithm - Grey Wolf Optimizer (MOGA-GWO). By merging the strengths of multi-objective genetic algorithms with the efficiency of the Grey Wolf Optimizer, this advanced algorithm seeks semi-optimal solutions for multi-objective optimization problems. The algorithm aims to improve convergence speed without compromising solution quality, offering a promising solution for optimizing the characteristics of DG and RES integration in distribution networks compared to Cuckoo Search and Simulation Annealing Techniques. Key algorithmic operations include individual representation, initialization, fitness function computation, selection, crossover, mutation, and ending conditions. The DG optimal power flow problem is explored with a focus on minimizing generation costs, improving voltage profiles, minimizing losses, enhancing reliability, integrating renewable energy, reducing environmental impact, managing congestion, and ensuring compliance with operational constraints. Beyond traditional considerations, the study emphasizes neglected objectives and constraints, such as cyber-security concerns, real-time operation, regulatory frameworks, social acceptance, lifecycle environmental impact, grid resilience, data privacy, aging infrastructure compatibility, and economic viability.

The lactic acid bacteria Streptococcus thermophilus and Lactobacillus helveticus are commonly used as starter cultures in dairy product production. This study aimed to investigate the characteristics of fermented milk using different ratios of these strains and analyze the changes in volatile compounds during fermentation and storage. The 10:1 ratio of Streptococcus thermophilus CICC 6063 to Lactobacillus helveticus CICC 6064 showed optimal fermentation time (4.2 h), viable cell count (9.64 log10 colony-forming units/mL), and sensory evaluation score (79.1 points). In total, 56 volatile compounds were identified and quantified by solid-phase microextraction and gas chromatography–mass spectrometry (SPME-GC-MS), including aldehydes, ketones, acids, alcohols, esters, and others. Among these, acetaldehyde, 2,3-butanedione, 2,3-pentanedione, 2-heptanone, acetoin, and 1-heptanol had the highest odor activity values and contributed to the characteristic flavor of fermented milk. The concentrations and types of key flavor compounds reached their peak at the end of fermentation. Throughout the fermentation and storage process, the levels of 2,3-pentanedione and acetoin exhibited synergistic dynamics. These findings enhance our understanding of the chemical and molecular characteristics of milk fermented with Streptococcus thermophilus and Lactobacillus helveticus, providing a basis for improving the flavor and odor of dairy products during fermentation and storage.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

Nowadays circular economy trend drives researchers in the recovery of various bioactive compounds from agri-food by-products. Enzyme-assisted extraction (EAE) is shown as an innovative green technology for the effective extraction of various phytochemicals from agri-food section by-products, therefore this study aimed to evaluate the application of EAE as green technology to obtain extracts from olive leaves (Olea europaea) for potential industrial production. EAE was conducted under various enzyme dose combinations, and incubation time (120 min). Obtained extracts were characterized in terms of total polyphenols (TP) and Total Antioxidant Activity (AA). Firstly, the enzyme synergistic effect in enzymatic extraction of polyphenols was evaluated. TP optimal extraction conditions (468.19 mg GAE/L) were achieved after EAE (50-50%) and for AA (69.85 AA%). According to the above results, a second experimental part occurred for the incubation time (min.) and enzyme dose (mL) of optimal extraction conditions of olive leaves. Results indicated EAE as an excellent choice for green extraction of polyphenols from the olive leaves.

Phased injection of natural gas into internal combustion marine engines is a promising solution for optimizing performance and reducing harmful emissions, particularly unburned methane, a potent greenhouse gas. This innovative practice distinguishes itself from continuous injection because it allows for more precise control of the combustion process with only a slight increase in system complexity. By synchronizing the injection of natural gas with the intake and exhaust valve opening and closing times while also considering the gas path in the manifolds, methane release into the atmosphere is significantly reduced, making a substantial contribution to efforts to address climate change. Moreover, phased injection improves the efficiency of marine engines, resulting in reduced overall fuel consumption, lower fuel costs, and increased ship autonomy. This technology has been tested on a single-cylinder, large-bore, four-stroke research engine designed for marine applications, operating in dual-fuel mode with diesel and natural gas. Performance was compared with that of the conventional continuous feeding method. Evaluation of the effect on equivalent CO2 emissions indicates a potential reduction of up to approximately 20%. This reduction effectively brings greenhouse gas emissions below those of the diesel baseline case, especially when injection control is combined with supercharging control to optimize the air-fuel ratio.

Solar photovoltaic (PV) generation suffers from multiple serious issues, for example shading effects directly affect the performance of installed PV arrays. By studying these issues, it can play a key role in improving the limitations and performance of PV panels. The study of PV arrays under partial shading conditions is mentioned in many papers as a part of other studies and as an independent phenomenon. Therefore, it is essential to improve these studies and update them with new research and study this problem in detail to know how to limit the effects of partial shading irradiance conditions on PV arrays performances, including economical aspects. This paper proposes to study the effects of different partial shading irradiance conditions for a PV array in various configurations. The analysis is proved in detail through the simulation of the proposed PV array in the Matlab/Simulink environment. The obtained results show the performances of interconnected PV arrays under different partial shading irradiance and temperature conditions, including the solar array I-V and P-V characteristics. In addition, different PV array configurations with and without bypass and blocking protection diodes improve and minimize the effect of partial shading conditions and avoid the reductions in output power.

This article investigates the effectiveness of feature extraction and selection techniques for enhancing the performance of classifiers accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data by employing three Feature Extraction (FE) methods are used namely, Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques such as Non-Linear Regression - NLR, Linear Regression - LR, Gaussian Mixture Model - GMM, Expectation Maximization - EM, Logistic Regression - LoR, Softmax Discriminant Classifier - SDC, and Support Vector Machine with Radial Basis Function kernel - SVM-RBF are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.

According to the requirement of 20kg load of casting barrel, we design a three-dimensional model of the robot and analyze the three stages of liquid level change in the casting barrel during the casting process. Through the analysis, it is determined that the time and angle function is an implicit function, and it is impossible to find the analytical expression of time about angle. The kinetic function of the casting barrel is simulated by MATLAB, and it is found that the function image has mutation points, and when the original function is fitted and approximated by fifth-degree polynomial interpolation, the fifth-degree polynomial function image of the time versus the angular acceleration has a large amplitude of oscillation, especially the mutation points. A PID algorithm based on the discretization of the time and acceleration function is introduced for correction, and the results show that this method effectively reduces the oscillation amplitude of the image and improves the coincidence with the original function.

The degree of ore rock fragmentation of mine blasting is an important index for evaluating the effect of engineering blasting. In order to study the effect of millisecond delayed blasting on the internal fragmentation of the ore rock, LS-DYNA numerical simulation software was used to obtain the damage characteristics of the ore rock in the blasting area using the RHT model. The Mohr-Coulomb strength damage criterion was applied to analyze the internal stress state of the ore rock, and the effects of 25ms, 50ms and 75ms delay time conditions on ore rock fragmentation were studied. The results show that, within a certain range of delay time, the degree of rock fragmentation increases with the increase of delay time. The stress state in the ore rock due to blasting is strengthened with the increase of delay time in the direction of the blast hole, which shows that the stress concentration is strengthened from the excavation face to the interior of the ore rock. The damage and the degree of fragmentation show a characteristic relationship of 25ms＜50ms＜75ms, which is verified by carrying out blasting experiments on site.

This study evaluated batch fermentation modes, namely, separate hydrolysis and fermentation (SHF), Quasi-simultaneous saccharification and fermentation (Q-SSF), and simultaneous saccharification and fermentation (SSF), and fermentation conditions, i.e., enzyme and yeast loadings, nutrient supplementation and sterilization, on high titer bioethanol production from SPORL-pretreated Douglas-fir forest residue without detoxification. The result indicated Q-SSF and SSF were obviously superior to SHF operation in terms of ethanol yield. The enzyme loading showed a strong positive correlation between enzyme loading and the ethanol yield. The nutrient supplementation and sterility was not necessary for ethanol production from SPORL-pretreated Douglas-fir. The yeast loading showed no significant influence on the ethanol yield for typical SSF conditions. The terminal ethanol titer of 43.2 g/L, or 75.1% theoretical based on glucose, mannose, and xylose theoretical was achieved when SSF was conducted at the condition of following: whole slurry solids loading of 15%, enzyme loading of 20 FPU/g glucan, 1.8 g/kg (wet) yeast loading, without nutrition supplementation and sterilization, at 38°C, on shake flask at 150 rpm for 96h. It is believed that with mechanical mixing, enzyme loading can be substantially reduced with affect ethanol yield by using a long fermentation time.

The construction industry has a notable negative impact on the environment; thus, the promotion of the use of timber structures is an alternative to mitigate its effects. The research develops an artificial intelligence-based decision approach in the calculation of timber structures focused on the enhancement of the sustainability of roof structures. Based on the optimization carried out through genetic algorithms and the framework established in Eurocode 5, a general set of equations has been proposed for a laminated timber roof structure. The tool, which determines the most suitable roof structure for each strength class of laminated timber, allows for the determination of the dimensions of beams and purlins and their respective separations in order to minimize wood consumption. The ultimate goal is to offer multiple solutions regarding strength classes and structural designs in order to foster informed sustainable choices that promote efficient use of resources in construction.

Long COVID, a name often given to the persistent symptoms following acute SARS-CoV-2 infection, poses a multifaceted challenge for health. This review explores the intrinsic relationship between comorbidities and autoimmune responses in shaping the trajectory of long COVID. Autoantibodies have emerged as significant players in COVID-19 pathophysiology, with implications for disease severity and progression. Studies show immune dysregulation persisting months after infection, marked by activated innate immune cells and high cytokine levels. The presence of autoantibodies against various autoantigens suggests their potential as comorbid factors in long COVID. Additionally, the formation of immune complexes may lead to severe disease progression, highlighting the urgency for early detection and intervention. Furthermore, long COVID is highly linked to cardiovascular complications and neurological symptoms, posing challenges in diagnosis and management. Multidisciplinary approaches, including vaccination, tailored rehabilitation, and pharmacological interventions, are used for mitigating long COVID's burden. However, numerous challenges persist, from evolving diagnostic criteria to addressing the psychosocial impact and predicting disease outcomes. Leveraging AI-based applications holds promise in enhancing patient management and improving our understanding of long COVID. As research continues to unfold, unravelling the complexities of long COVID remains paramount for effective intervention and patient care.

This study investigated the efficiency of scale-propellers, typically used on small drones. A scale-propeller is accepted as having a diameter of 7 to 21 inches. The Ukraine War has demonstrated the utility of relatively small, low-cost first-person view (FPV) drones, which are attritable. This investigation outlines the development of a MATLAB optimisation code, based on minimum induced loss propeller theory, which calculates the optimal chord and twist distribution for a chosen propeller operating in known flight conditions. The MATLAB code includes a minimum Reynolds number functionality, which provides the option to alter the chord distribution to ensure the entire propeller is operating above a set threshold value of Reynolds (&gt;100,000), as this has been found to be a transition point between low and high section lift-to-drag ratios. Additional functions allow plotting of torque and thrust distributions along the blade. The results have been validated on experimental data taken from an APC ‘Thin Electric’ 10” x 7” propeller, where it was found that both the chord and twist distributions were accurately modelled. The MATLAB code resulted in a 16% increase in the maximum propulsive efficiency. Further work will investigate a direct interface to SolidWorks to aid rapid propeller manufacturing capability.

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

Dairy products are an important source of protein and other nutrients in the Mediterranean diet. In these countries the most common source of milk for producing dairies are cow, goat, sheep and buffalo. Andalusia is traditionally the largest producer of goat milk in Spain. Kefir is a fermented product made from bacteria and yeasts which has health benefits beyond its nutritional properties. There is a lack of knowledge about the molecular mechanisms and metabolites that bring about these benefits. In this work, the combination of analytical techniques (GC-FID, UHPLC-MS-QToF, GC-QqQ-MS, and GC-ToF-MS) resulted in the detection of 105 metabolites in kefir produced with goat milk from two different thermal treatments (raw and pasteurized) fermented at four times (12, 24, 36, and 48 h, using 0 h as control). Of these, 27 metabolites differed between kefir produced with raw and pasteurized milk. These changes could be possibly caused by the effect of pasteurization on the microbial population in the starting milk. Some interesting molecules were identified, as shikimic acid, dehydroabietic acid, GABA and tyramine that could be related to antibacterial properties, enhance of immune system and arterial pressure. Moreover, a viability assay of the NIRS technique was performed to evaluate its use on monitoring the fermentation and classification of samples, which had resulted in a 90 % of correct classified samples according to their fermentation time. This study represents the most comprehensive metabolomic analysis of goat milk kefir so far, revealing the intricate changes in metabolites during fermentation and the impact of milk treatment.

This paper investigates an innovative photovoltaic cell configuration, denoted as “Au/CuSCN/RbGeI3/CdTe/ZnO/TiO2/FTO", using SCAPS-1D simulation. The research focuses on optimizing device performance by analyzing key components, including ZnO as the electron transport layer (ETL), RbGeI3 as the perovskite absorber layer, CuSCN as the hole transport layer (HTL), and CdTe as the active buffer layer. A comparison is made with the standard “Au/CuSCN/CH3NH3SnI3/CdTe/ZnO/TiO2/FTO" configuration under dark and illuminated conditions. Through an in-depth investigation involving temperature effects, generation rate analysis, RbGeI3 absorber layer thickness, Nd level, and interface defect levels, an optimized design for a unit area is achieved. The proposed solar cell demonstrates outstanding performance metrics, including an open circuit voltage (Voc) of 0.94V, a short circuit current density (Jsc) of 34.52mA/cm2, a fill factor (FF) of 77.77%, and an energy conversion efficiency (η) of 25.40%. These values significantly outperform the reference models with energy conversion efficiencies of 10.11% and 21.09%. Real-world applicability is validated by implementing these optimized characteristics in a photovoltaic system for Pakistan’s geographical location using PVSyst software. The suggested solar cell achieves a remarkable performance ratio (PR) of 85.6%, surpassing the PR of the reference basic solar cell model at 83%. The findings of this research offer a promising and superior solar cell design, incorporating innovative materials and optimization techniques to achieve exceptional energy conversion efficiency and overall performance. This study’s outcomes have the potential to advance photovoltaic technology and contribute to sustainable energy solutions worldwide.

This study optimizes the placement and farm layout of Vertical Axis Wind Turbines (VAWTs) using a neural fuzzy network, an advanced machine learning system, and meta-heuristic algorithms. The methodology introduces a novel approach to enhance turbine efficiency and overall farm performance. The objective is to determine the optimal configuration of vertical axis wind turbines using a neural-fuzzy system and meta-heuristic optimizers. Turbine efficiency and power generation rates are assessed through three-dimensional numerical simulations based on the Reynolds-averaged Navier-Stokes equations. This innovative design, featuring straight blades with bevel-angle vortex generators, represents a significant advancement over traditional straight-blade VAWTs. Comparative analyses demonstrate that the oblique configuration exhibits higher average output moment and power. A novel concept integrates the strengths of Darrieus VAWTs, enhancing efficiency through vortex generator integration. Simulation results are validated with empirical data, confirming the superior aerodynamic efficiency and increased power generation of the oblique configuration. Inspired by the optimal group movements of birds, this study recommends adopting the oblique arrangement for improved wind turbine performance.

The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in MATLAB/SIMULINK evaluated these algorithms’ performance under various shading scenarios. The results indicate that while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. The study concludes that integrating AI and ML into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and support the green energy transition.

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront and the research interest in microgrids which rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper proposes an optimization algorithm based on reinforcement learning in an industrial microgrid that is capable of trading energy with the main grid, providing significant cost savings. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared to logic-based optimization and 86% savings compared to not using any optimization. These findings contribute to digitalization and decarbonization of energy technology and support fundamental goals and policies of the European Green Deal.

The paper presents a proposal for an in-house mathematical model of a liquid flat-plate solar collector. The proposed model is a one-dimensional distributed parameter model enabling simulations of the collector operation under arbitrarily variable boundary conditions. The model is based on the solution of energy balance equations for all components of the collector. The formulated differential equations are solved iteratively using an implicit difference scheme. In order to obtain a stable numerical solution, it is necessary to use appropriate steps of the time and spatial division. These were found by comparing the results obtained from the model with the results of the analytical solution available in the literature for the transient state, which constitutes the novelty of the present study. The accuracy of the results obtained from the model was verified experimentally by comparing the measured and the calculated history of the fluid temperature at the outlet of the collector. The calculation of the collector time constant is proposed in the paper as an example of the model practical application. The results of the time constant calculation were compared with the values obtained experimentally on the test stand. This is another novelty of the presented research. The analysed collector instantaneous efficiency was then calculated for selected outdoor conditions. The presented mathematical model can also be used to verify the correctness of the collector operation. Comparing, on an ongoing basis, the measured and the calculated values of the fluid temperature at the collector outlet, conclusions can be drawn about the process of solar glass fouling or glycol gelling. The model's simplicity and the low computational demands enable such comparisons in an online mode.

Lactoperoxidase (LP), which is a high-value minor whey protein, has recently drawn extensive attention from research scientists and industry due to its multi-function and potential therapeutic applications. In this study, the separation and optimization of two similar-sized proteins, LP and lactoferrin (LF) were investigated using strong cation exchange column chromatography. Optimization was started with central composite design based experiments to characterize the importance of different decision variables. The three variables used in the optimization were flow rate, length of gradient and final salt concentration in the linear elution gradient step. The obtained empiric functional model represented the effect of the significant factors on the yield as the objective function. Afterwards, the calibrated mechanistic model was employed to predict accurate optimal set of variables. The optimal operating points were found and the results were compared with validation experiments. Predictions respecting yield confirmed a very good agreement with experimental results while keeping purity, a product quality characteristic, equal or above to a predefined value.

The purpose of this article is to present an IoT architecture implemented in an intelligent environment, focusing on the optimization of initial conditions and device management. A general control system was developed using an open-source platform, integrating aWi-Fi network and YAML coding to enable communication and control of the IoT architecture devices. The implementation includes actuators, sensors, and control devices that, through a graphical interface, allow the visualization of the historical record of activities and real-time measurements of connected devices. The design includes specific mechanisms and the analysis of materials through tensile tests on specimens, following the ASTM E8 standard, and deformation test simulations, in which PLA demonstrated better performance compared to materials like ABS and resin. The significance of the implemented IoT system lies in an energy saving of 58% compared to conventional structures. Additionally, the study of response times for network connections showed that using an Ethernet cable resulted in a response time of 1.21 seconds, representing a reduction of 1.65 seconds compared to the second lowest value obtained.

The Fuzzy Transportation Problem (FTP) represents a significant extension of the Classical Transportation Problem (TP) by introducing uncertainly and imprecision into the parameters involved. Various algorithms have been proposed to solve the FTP, including fuzzy linear programming, metaheuristic algorithms and fuzzy mathematical programming techniques combined with Artificial Neural Networks. This paper presents the application of Trigonometric Acceleration Coefficients-PSO (TrigAc-PSO), a variation of the Classical Particle Swarm optimization algorithm, which is an innovative algorithm originally developed for solving the TP. TrigAC-PSO, has demonstrated remarkable success in optimizing various problem domains in crisp environments.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the Quality of Experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a Traveling Salesman Problem (TSP). Subsequently, we develop a coordinate descent (CD) based method and an alternating direction method of multipliers (ADMM) based method for network bandwidth and computing resource allocation in the MEC system. The CD-based method is simple to implement and has a low computational complexity, while the ADMM-based method can further enhance the optimization result through joint optimization. Extensive simulations demonstrate that our proposed algorithms perform well in large-scale edge networks and outperform other representative benchmark methods.

Society considers wine as a special product among food and beverages because of its high gastronomical value and its positively distinctive quality. In recent years, philosophies of the agricultural techniques and development of the oenological technology have been focused on the reduction of wasteful, "polluting" elements, and trends are moving towards an environmental friendly approach. Due to the stricter regulations and rules (with the limited amount and selection of the permitted chemicals) resistant, also known as interspecific or innovative grape varieties can be the ideal basic materials of alternative cultivation technologies. In terms of variety selection, innovative varieties can be equivalent to international varieties, although organically their quality could not compete with them. These grapes are more resistant to various fungal diseases and infections than international varieties. Well-founded analytical and organoleptic results have to provide the scientific background of resistant varieties, as these cultivars with the environmental friendly cultivation techniques, could be the raw material of the future.

Extraction techniques of important bioactive molecules from the major concern of Seafood byproducts are getting emphasis for better valorization. Employing green extraction technologies for efficient and quality production of these bioactive molecules is also strictly required. Hence, understanding the extraction process parameters to effectively design an applicable optimization strategy could make this real. In this review, statistical optimization strategies applied for the extraction process parameters of bioactive molecules from Seafood byproducts are focused. The type of experimental designs, and techniques applied to criticize and validate the effects of independent variables on the extraction output was addressed. Dominant parameters studied were enzyme/substrate ratio, pH, time, temperature and power of extraction instruments. The yield of bioactive compounds, chitin and chitosan, proteins and peptides, enzymes and carotenoids (Astaxanthins) were the most studied responses. Efficiency and/or economic and quality considerations and their selected optimization strategies that favour the production of potential bioactive molecules were also reviewed.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

The over use of pharmaceutical compounds, essentially antibiotics, led to an increase of their concentrations in aquatic ecosystems. In this study, the white-rot fungus Coriolopsis gallica (high-laccase-producing fungus) was investigated for the biodegradation of ampicillin (AMP) under different cultivation conditions. The biotransformation of the antibiotic was confirmed by means of high-performance liquid chromatography (HPLC), and the antibacterial activity was evaluated via the bacterial growth inhibition agar well diffusion method using Escherichia coli as an ampicillin-sensitive test strain. The results showed that AMP (50 mg L-1) was totally removed by C. gallica after 6 days of incubation in a liquid medium. The antibiotic removal rate was maximal with a fungal culture aged 9 days. Such a culture achieved the removal of a higher concentration of up to 500 mg L-1 of ampicillin in 3 days. This higher antibiotic removal rate was concomitant with the maximal laccase production in culture supernatant. In addition, four consecutive doses of 500 mg L-1 of ampicillin were transformed by the same fungal culture in 24 days. After that, the fungus was unable to remove the antibiotic. The measurement of ligninolytic enzyme activity showed that laccase of C. gallica might participate in the biotransformation of AMP.

Blockchain technology (BCT) has the potential for sustainable development, enhancing business efficiency, reducing costs, and strengthening data security. Despite its potential, adoption among small and medium-sized enterprises (SMEs) is low due to the need for significant changes in technology infrastructure and the challenge of interoperability. A cross-sectional survey was done with 226 Hong Kong SMEs. The PLS-SEM results highlighted favorable transformation conditions as the most critical factor for SMEs considering the adoption of BCT. These conditions address both internal and external readiness for adopting the disruptive strategy. Other contributing factors to BCT adoption include trust in BCT developers, perceived usefulness and ease of use, government support, and perceived security. The research results contributed to the literature on disruptive technology acceptance, adoption, transformation conditions, technology readiness, and SMEs’ technology adoption. Government-supported policies and initiatives can promote BCT adoption, including educational programs, case studies, and resources for readiness. Financial support and regulatory frameworks can further encourage adoption. BCT developers can demonstrate the value of their systems, effectively communicate potential ROI and cost savings, and provide user-friendly interfaces to increase SMEs’ intention to adopt BCT.

The Circular Bioeconomy (CBE) combines the concepts of bioeconomy and circular economy. As an alternative concept to the current fossil-based, linear economy, it de-scribes an economy based on the efficient valorization of biomass. It is regional in nature and aims to improve sustainability. An analysis of the transition process, by identifying its success criteria and assessing its impacts through the modelling of technology-specific scenarios is necessary to ensure that CBE concepts are sustainable. However, a compre-hensive consideration of regional influences on both is lacking. Based on extensive literature research and an expert survey, we (i) present a compre-hensive catalog of CBE success criteria and discuss their region-specific character and (ii) develop a methodology based on evaluation matrices that enable to match CBE technolo-gies with regions. The matrices support the evaluation of technological and regional characteristics influencing the successful CBE implementation. The results show that the success criteria "biomass resources", "technological", and "social" are perceived as highly important, and that most of the success criteria are both region- and technology-specific, highlighting the relevance of developing matrices to match them. We describe such matrices indicatively for the two broadest and most im-portant success criteria clusters “social acceptance” and “biomass supply chain”. With this, we substantiate the regional nature of CBE and raise the awareness on the importance of considering regional conditions in CBE transition processes. Furthermore, we provide practical guidance on how regional conditions can be reflected in the selection of technologies, e.g. in regional CBE technology scenarios.

The intranasal route of drug administration is characterized by high bioavailability and is considered promising for rapid delivery of drugs with systemic action to the central nervous system, bypassing the blood-brain barrier. This is particularly important for the use of neuroprotective drugs in the treatment of brain tissue damage in infants caused by the effects of intrauterine hypoxia. Creation of new dosage forms for neonatology with the use of software and mathematical technologies in pharmaceutical development allows to create cerebroprotective drugs with controlled pharmaco-technological properties, thus reducing time and resources for necessary research. We developed a formulation of a new nasal gel formulation with Angiolin using Box-Benken experiment design for the therapy of prenatal CNS damage. It was found that the consistency properties of the nasal gel were significantly influenced by the gelling agent and mucoadhesive component - sodium salt of carboxymethylcellulose. We have optimized the composition of nasal gel formulation with Angiolin using the formed models and relationships between the factors. The optimized composition of the obtained nasal gel has satisfactory parameters of thixotropic properties. The developed 1% gel for neuroprotection with Angiolin meets all the requirements for harmlessness and safety to drug forms of this group - low toxicity, absence of local irritant and allergic effect.

Selenium nanoparticles (SeNPs) has higher bioavailability and safety than inorganic selenium, and was widely used in medical, agricultural, nutritional supplements, and antibacterial fields. The present study screened a strain L11 producing SeNPs from a selenium rich dairy cow breeding base in Hubei Province, China. The strain was identified as Bacillus subtilis through physiological, biochemical, and molecular biology analysis. Through optimization of cultivation conditions, the optimal cultivation parameters for L11 to produce SeNPs were pH=6, cultivation temperature of 37 °C, 4 mmol/L Na2SeO3, and cultivation for 48 hours. XPS, SEM-EDS, and TEM were used to verify that the Se particles produced by L11 are SeNPs with diameters ranging from 50 to 200 nm. The combination of protein analysis of different cell components and TEM analysis showed that L11 mainly produces SeNPs through the transformation of the cell's periplasmic space, cell membrane, and cell wall. Adding L11 SeNPs complex to sheep feed can significantly enhance the antioxidant activity and immunity of sheep, and increase the Se content in the neck muscles, liver, and spleen tissues.

We primarily investigate the existence of solutions for fractional neutral integro-differential equations subjected to Neumann-type boundary conditions, which is crucial for understanding natural phenomena. Taking into account factors such as neutral type, fractional-order integrals, and fractional-order derivatives, we employ probability density functions, Laplace transforms, and resolvent operators to formulate a well-defined concept of a mild solution for the specified equation. Following this, by integrating fixed point theorems, we establish the existence of mild solutions under more relaxed conditions.

Microorganisms present in fermented foods and beverages play a vital role in enhancing both nutritive and non-nutritive components. During the fermentation, microorganisms can either naturally occur in the food through spontaneous fermentation or be intentionally added as starter cultures with a variety of species and strains. In this review, the focus is placed on micronutrients, such as vitamins and minerals, and three major macromolecules – prebiotics, bioactive peptides, and functional fatty acids. During fermentation, non-digestible carbohydrates like fructooligosaccharides and galactooligosaccharides are synthesized, imparting prebiotic properties that support gut health. Enzymatic hydrolysis of proteins by microorganisms yields bioactive peptides with diverse health benefits. Furthermore, fermentation facilitates the formation of fatty acids, including short-chain fatty acids and conjugated linoleic acid, potentially exerting physiological effects. By harnessing the power of microbial fermentation, nutrient-rich and bioactive compounds can be enhanced, offering a promising approach to address global malnutrition concerns.

This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

(1) Background: a study was undertaken to determine the prevalence of chronic diseases and health conditions that affect oral health and oral health self-care among independent living older adults in rural areas of Victoria, Australia; (2) Methods: The study analysed data from the Crossroads-II project, with a total of 1407 participants aged 55 years and older. Participants self-completed questionnaires about health conditions, service use, and demographics. 572 attended a 2-hour health screening that included an assessment of prescribed medications; (3) Results: Most participants were female (55.3%), with a mean age of 69.1 (SD=9.2) years. Participants had a high prevalence of medical conditions, the most common were high blood pressure, arthritis, high lipids, hearing loss, and chronic pain. Almost all participants reported taking medications, with an average of 4.3 medications per person. Among those who attended the health screening, 78.1% were taking medications that could cause xerostomia (dry mouth); (4) Conclusions: This study provides initial information on the prevalence of medical conditions, medication usage, with potential impact on oral health of older adults living in rural Victoria. This information would be valuable for healthcare and dental workforce planning, policy development, and improving the overall well-being of this population.

This study provides a comprehensive investigation of lactic acid bacteria (LAB) isolated from Argentinean Capsicum annum L. The research covers important aspects, including genotypic characterization, bacterial stress tolerance, adhesion ability, safety evaluation, and functional and technological properties. The predominant isolates were identified as Lactilactobacillus curvatus and Lactiplantibacillus plantarum. Rep-PCR analysis grouped the isolates into 11 clonal groups. Lp. plantarum LVP 40 and LV 46; Levilactobacillus brevis LVP 41, Pediococcus pentosaseus LV P43, and Lt. curvatus LVP44, displayed both safety and resilience against adverse conditions such a slow pH, bile, and simulated gastric and intestinal juices. Moreover, the LAB strains exhibited high hydrophobicity and auto-aggregation percentages, NaCl tolerance, and substantial acidifying capacity. LAB supernatants demonstrated promising surfactant and emulsifying properties. Likewise, they differentially inhibited Staphylococcus aureus and Pseudomonas aeruginosa biofilms, showcasing their potential as antipathogenic agents. Noteworthy some strains displayed considerable co-aggregation with these pathogens, and several isolates showed an effective antimutagenic and detoxifying power, further emphasizing their multifaceted capabilities. Five pepper bacterial strains showcased probiotic properties, suggesting their potential for gut health enhancement. In summary, these LAB strains hold promise as vegetable fermentation starters, contributing to food safety and versatile applications in food science.

Arthrospira platensis, commercially known as Spirulina, is a fresh-water cyanobacterium that is gaining even more attention in the last years due to its high biological and nutritional value. For this reason, it has been employed in several food applications, to obtain or enhance functional and technological properties of cheese, yogurt, bread, cookies or pasta. The aim of this work was to evaluate the potential boosting effect of two different concentrations (0.25% and 0.50%) of Arthrospira platensis on the fermentation capability of several starter LAB strains, 1 probiotic and 4 commercial mix culture. These strains were used to ferment three different substrates and their fermentation behaviors were evaluated by impedance analyses together with rheological and color measurements. It was demonstrated that the booster effect took place, but it was variable and dependent not only on the strain or mix culture used, but also on the substrate and Arthrospira platensis concentration. Also, rheological and color modifications were found to be dependent of these factors.

In GNSS/IMU integrated navigation systems, factors such as satellite occlusion and non-line-of-sight conditions can lead to degradation of satellite positioning results, Subsequently affecting the overall accuracy of the integrated navigation system. To address this issue and ef-fectively utilize historical pseudorange information from satellites, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints are con-structed using satellite ‘s position from previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint can serve as an alternative solution for individual satellites in case of signal anomalies, ensuring the integrity and continuity of the graph optimi-zation model. Additionally, this paper conducts an analysis of the graph optimization model constructed using these virtual constraints, comparing it with traditional integrated navigation graph optimization model, and analyzes and reconstructs the marginalization process based on these virtual constraints. The experimental results, compared with tightly coupled Kalman fil-tering and the original graph optimization method on a set of real-world data, demonstrate that the introduction of virtual pseudoranges maintains high positioning accuracy. The RMSE error between it and the original graph optimization remains within 5%, affirming the high feasibility of this approach.

Particulate organic carbon (POC) is a pivotal component within the marine carbon cycle, actively involved in diverse biogeochemical processes. This study focuses on Mediterranean as the research region, selecting the most influential factors from 47 potential ones influencing surface POC. Using geographic detector for factor identification, ten primary influencers were identified. Preliminarily optimized random forest (BRF), backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Notably BRF exhibited superior performance in estimating the sea surface POC. Further optimization of RF using the tuneRanger R package resulted in R² of 0.868, mean squared error of 1.119 (mg/m³) ², and mean absolute error of 1.041 (mg/m³). Surface POC concentrations in the Mediterranean for May and June 2017 were estimated. Spatial distribution analysis unveiled higher concentrations in the west, lower in the east, higher in the north, lower in the south, with higher levels near the coast and lower far from the coast. Additionally, the study deliberated on the impact of human activities on surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in the area.

Present study on land use land cover analysis and taluka wise combination of cropped area, carried out in Beed district of Maharashtra state in India and studied 11 talukas of the district. Secondary data on land use land cover and area in various crops has been collected, compiled and analyzed from various secondary sources. Simple descriptive statistics used to examine the agricultural land use land cover and J.C. Weaver’s crop combination technique was used for taluka wise analysis of crop combinations and on the basis of highest to smallest area engaged in particular crop, crops have been ranked. The study showed that, in the district as a whole, percentage of uncultivated land other than fallow was about 46 percent as compare to net sown area is only 35 percent in total geographical area (1063855ha) of Beed district. The study further revealed that about 42 percent area engaged in food crops and about 58 percent area occupied by nonfood crops, which includes cereals (25%), cotton (19%), pulses (13%), oilseed (12%), sugarcane (5%) and fruits and vegetables (3%). Results of Weaver’s crop combination technique showed that the Wadwani taluka demarcated as most diversified taluka by means of maximum number (sixteen) of crops combinations with the lowest variance value (14) found in the present investigation. Further, the study revealed that the high crop diversity found in Dharur taluka with eighteen crop combinations and the taluka has given first rank amongst others. To derive the crop diversity ranking, taluka wise maximum to minimum number of crop combinations derived in different talukas and given ranks accordingly. The study concluded that, agricultural land use analysis is essential for solving and optimizing the land use problem, maintaining socio-ecological balance, best utilization of scarce resources. The authors enlightened that the rational use of agricultural land has important for food security and biodiversity which increase the productivity and profitability of the agriculture sector and it indicates the decision making ability of farmers in study area.

(1) Background: Heat stress detrimentally restricted the economic in dairy farming production, therefore, this study was designed to investigate how automatic spraying method on alleviating heat stress on cows. (2) Methods: A total of 1208 multiparous dairy cows were randomly allocated into 6 barns, three barns were equipped with automatic sprinklers (SP), each barn was considered as a replicate. The other three barns were considered as control treatments (CON). Each barn was considered as a replicate. (3) Results: body temperature and milk somatic cell count were significantly decreased, while DMI, milk yield, and milk fat content significantly increased with spraying treatment. ICR was improved from 30% to 35%. Rumen fermentation was enhanced by the increased levels of total VFA, acetate, propionate, and butyrate with the spraying treatment. Rumen microbiota results showed at the phylum level, rumen Fibrobacters abundance significantly increased by the spraying treatment. At the genus level, Eubacterium, Shuttleworthia, Saccharofermentans, Lachnospira, Pseudobutyrivibrio, Selenomonas, and Succinivibrio abundance were significantly increased, while Ruminococcaceae and Rikenellaceae abundance were significantly decreased in the rumen with spraying treatment. (4) Conclusion: this study demonstrated automatic spraying modulated the rumen microbiota composition and fermentation function, and effectively meliorated side-effects of heat stress on dairy cows.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

The Ionian Sea is a crucial intersection for various water masses in the Mediterranean. Its hydrography and dynamics play a significant role in the seawater budgets and biogeochemistry of the neighboring sub-basins. Multiple theories have been formulated to gain a better understanding of the Ionian dynamics. These theories primarily attribute the variability of the near-surface Ionian circulation to internal processes. Here, we utilize horizontal currents and temperature-salinity profiles from the Copernicus reanalysis to examine the contribution of baroclinic modes to the variability of the basin horizontal circulation. Our findings demonstrate that, although the basin vertical structure is characterized by three layers, the primary patterns of the Ionian circulation can be attributed to the first baroclinic mode. This mode, along with the barotropic mode, accounts for over 85% of the overall variability in the Ionian circulation, suggesting that only one of the three interfaces separating the different water masses in the basin is dynamically active. We estimate the depth of this interface to be about 490 m. Additionally, our analysis shows that more than 90% of the kinetic energy over the water column is localized above this interface, indicating that the deep layer of the Ionian is dynamically nearly inert.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

Fermented foods play an important role in the human diet and particularly so in under-resourced environments where cold preservation is not attainable due to irregular supply of electricity. Fermented foods are reported to support gut health by contributing probiotics and the purpose of this study was to investigate the microbial diversity and metabolic potential of spontaneous millet fermentation. The literature in the field was reviewed and analysis conducted on publicly available Sequence Read Archive (SRA) datasets. Quality analysis was performed with FastQC, and operational taxonomic units (OTUs) generated using Quantitative Insights Into Microbial Ecology (QIIME2) and Divisive Amplicon Denoising Algorithm (DADA2) pipeline with Greengenes as the reference database. Metagenomics and pathways analysis were performed with Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2). Statistical analysis and visualization were accomplished with Statistical Analysis of Metagenomic Profiles (STAMP). At the family taxonomic level, there were significant differences in the relative abundances of bacteria involved in the spontaneous fermentation of millet namely, Lactobacillaceae, Leuconostocaceae, and Clostridiaceae in one dataset. The incidence of Lactobacillaceae and Bifidobacteriaceae suggest the probiotic characteristic of fermented millet. The datasets were collected with fermentations mediated by autochthonous microorganisms and the presence of some potential pathogens such as Enterobacteriaceae, Clostridiaceae, Aeromonadaceae, Microbacteiaceae, Pseudomonadaceae, and Neisseriaceae suggest the need for standardization of fermentation approaches. The genomes show the potential to synthesize metabolites such as vitamins suggesting that the respective fermented foods can be further optimized to enhance nutritional benefits.

The incremental sheet forming represents a relatively recent technology that guarantees high customization, thanks to the layered manufacturing principle typical of rapid prototyping, and cost-effectiveness, because it does not require dedicated equipment. Research has initially shown that this process is effective in metal materials capable of withstanding plastic deformation but, in recent years, the interest in this technique has been increasing for the manufacture of complex polymer sheet components as an alternative to the conventional technologies, based on heating-shaping-cooling manufacturing routes. Conversely, incremental formed polymer sheets can suffer from some peculiar defects, for example twisting. To reduce the risk of this phenomenon and the occurrence of failures, a viable way is to choose toolpath strategies that make the tool/sheet contact conditions less severe, and this represents the main goal of the present research. Polycarbonate sheets were worked by incremental forming; in detail, cone frusta with a fixed wall angle were manufactured with a reference and a stair toolpath strategy, and both the forming forces and the twist angle were monitored. The analysis of the results highlighted that a stair toolpath involving an alternation of diagonal up and vertical down steps represents a useful strategy to mitigate the forming forces and, with them, the occurrence of the twisting phenomenon in incremental formed thermoplastic sheets.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

The agronomic characteristics of Cenchrus purpureus were assessed through a randomized block experimental design with seven accessions (Taiwan grass, King grass, Elephant grass, Merkeron, CT-115, Purpule grass and Maralfalfa) as treatments and four replications research was carried out at the Instituto Tecnológico Superior de Tantoyuca, in 2019 and 2020, under rainfed conditions. Sowing density was established at 36 tillers in 25 m2. Green and dry matter yield, daily rates of dry matter accumulation in leaves, leaf/stem ratio, height, leaf area index and cutting frequency were assessed. Rainfall amount, temperature, relative humidity and global radiation levels were recorded to assess their effect on the evaluated variables. One-way ANOVA and multivariate analysis techniques, such as multiple linear regression (MLR), Clustering, and Principal component analysis (PCA) with the Statistica V7 software, were used. The leaf area index was greatest (p &lt; 0.05) on the dry season. Green and dry matter yield were greatest in Maralfalfa (p &lt; 0.05). Variation in the results is attributed to weather phenomena; nonetheless, Elephant grass and Maralfalfa showed the highest yields. Quality parameters must be evaluated to identify those grasses with the best attributes.

To curb the impacts arising from the agricultural sector the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint (EF) 3.0 life cycle impact assessment (LCIA) method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is constructed utilizing neighborhood interactions, through which a parameter known as farmer's green consciousness (GC) is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e. when farmers’ GC is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as EF “single score”) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried using the EF single score impact in the objective function, though this results in an 8% reduction in profitability over ten years.

The nutrient absorption and utilization efficiency in the roots of seedlings are beneficial for increasing crop yield. Creating marker assisted selection for wheat root traits can assist wheat breeders in choosing robust roots to maximize nutrient uptake. Exploring and identifying the effect of different nitrogen applications on RSA is of great significance for breeding nitrogen efficient wheat varieties. In this study, A total of 243 wheat varieties native to the Yellow and Huai Valley regions of China were utilized for genome-wide association studies (GWAS). Furthermore, a recombinant inbred line (RIL) population of 123 lines derived from the cross between Avocet and Chilero was utilized for linkage examination and confirmation of the hereditary loci distinguished by GWAS. Five RSA traits include the relative number of root tips (RNRT); relative total root length (RTRL); relative total root surface area (RTRS); relative total root volume (RTRV) and relative average root diameter (RARD) where evaluated under two N conditions—normal N (NN) supply and low N (LN)—to characterize the NUE of each accession. GWAS and linkage analysis were performed by integrating data from the wheat 660k single nucleotide polymorphism (SNP) chip and diversity arrays technology (DArT) to identify genetic loci associated with RSA. The results showed that based on the ratio of RSA related traits under two N supply conditions, a total of 598 SNP markers, which are significantly associated with RSA related traits, were detected at 160 genetic loci by GWAS. A total of 10 QTL loci related to RSA were dis-covered and identified by linkage mapping. In which, three significant RSA related Quantitative Trait Locus (QTL) were identified, namely qRtrl-3D, qRnrt-3D, and qRtrl-7D. The study has the potential to enhance the effectiveness of selecting root traits in wheat breeding programs, offering valuable insights into the genetic underpinnings of nitro-gen use efficiency (NUE) in wheat. This could lead to the creation of NUE wheat varieties by implementing focused breeding strategies