• Energy Research
• 2021-2025close
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Siderophores are low molecular weight secondary metabolites produced by microorganisms under low iron stress as a specific iron chelator. In the present study, a rhizospheric bacterium was isolated from the rhizosphere of sesame plants from Salem district, Tamil Nadu, India and later identified as Bacillus subtilis LSBS2. It exhibited multiple plant-growth-promoting (PGP) traits such as hydrogen cyanide (HCN), ammonia, and indole acetic acid (IAA), and solubilized phosphate. The chrome azurol sulphonate (CAS) agar plate assay was used to screen the siderophore production of LSBS2 and quantitatively the isolate produced 296 mg/L of siderophores in succinic acid medium. Further characterization of the siderophore revealed that the isolate produced catecholate siderophore bacillibactin. A pot culture experiment was used to explore the effect of LSBS2 and its siderophore in promoting iron absorption and plant growth of Sesamum indicum L. Data from the present study revealed that the multifarious Bacillus sp. LSBS2 could be exploited as a potential bioinoculant for growth and yield improvement in S. indicum.

IT technologies related to Industry 4.0 facilitate the implementation of the framework for sustainable manufacturing. At the same time, Industry 4.0 integrates IT processes and systems of production companies with IT solutions of cooperating companies that support a complete manufactured product life cycle. Thus, the implementation of sustainable manufacturing implies a rapid increase in interfaces between IT solutions of cooperating companies. This, in turn, raises concerns about security among manufacturing company executives. The lack of a recognized methodology supporting the decision-making process of choosing the right methods and means of cybersecurity is, in effect, a significant barrier to the development of sustainable manufacturing. As a result, the propagation of technologies in Industry 4.0 and the implementation of the sustainable manufacturing framework in companies are slowing down significantly. The main novelty of this article, addressing the above deficiencies, is the creation, using the combined DEMATEL and ANP (DANP) and PROMETHEE II methods, of a ranking of the proposed three groups of measures, seven dimensions and twenty criteria to be implemented in companies to ensure cybersecurity in Industry 4.0 and facilitate the implementation of the sustainable production principles. The contribution of Industry 4.0 components and the proposed cybersecurity scheme to achieve the Sustainable Development goals, reducing the carbon footprint of companies and introducing circular economy elements was also indicated. Using DANP and PROMETHEE II, it can be concluded that: (i) the major criterion of cybersecurity in companies is validation and maintaining electronic signatures and seals; (ii) the most crucial area of cybersecurity is network security; (iii) the most significant group of measures in this regard are technological measures.

A serious problem faced by the metal mineral mining industry is the challenge to the sustainable development of resource mining due to the continuous decline of ore geological grade. In the case of producing concentrates of the same quality, compared with using only high-grade raw ore, ore blending is a way to slow down the decline of ore geological grade by combining high- and low-grade raw ore. There are many ore blending models considering cost minimization or profit maximization as the target value, ignoring the fact that ore blending is intended to obtain a homogenized product. Moreover, the ore blending model cannot be solved by traditional operational research methods when blended grade stability of multiple elements is considered in the ore blending program. In this paper, a multi-objective ore blending optimization model is constructed for the comprehensive utilization of associated resources in ores. It minimizes the deviation of the grade of each metallic element in the blended associated ore from the beneficiation grade and the percentage of different types of rocks at the unloading point. To solve this multi-objective optimization model, an intelligent optimization method is proposed that is an improved multi-objective optimization algorithm based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III). The case study shows that the proposed model and algorithm can effectively solve the mixing problem of polymetallic ores and obtain a satisfactory ore blending solution.

More healthy and sustainable food are nowadays desirable to improve human health and protect the planet’s resources. From this perspective, the aim of this study is to investigate artichoke (Cynara scolymus L.) by-products as a potential source of phenolic compounds and to use these compounds to design new fresh egg pasta formulation. Sustainable extraction was carried out using ultrasound-assisted extraction (UAE) and chemometric techniques, such as the Response Surface Methodology (RSM). UAE process parameters (temperature and time) and solvent composition (ethanol aqueous mixtures) were optimized using a three-level Box–Behnken design, in order to carry out the maximum yield in phenols. Under the optimal conditions (temperature: 60 °C; time: 60 min; solvent: 50% ethanol:water), the amount of phenolics (TPC) was 22.4 ± 0.2 mg GAE g−1 d.w., characterized mainly by dicaffeoylquinic acid (32.8 ± 0.6 mg CAE g−1 d.w.) and chlorogenic acid (14.1 ± 0.2 mg CAE g−1 d.w.). Hence, the polyphenols extract was used as an ingredient to design a new formulation of functional fresh egg pasta. Four recipes with soft wheat and semolina (P1 and P2) and with soft wheat alone (P3 and P4) were prepared. Compared with control pastas (P1 and P3), the enriched ones (P2 and P4) showed a higher polyphenol content, especially for P4 (1.86 ± 0.03 mg GAE g−1 d.w. for P1, 2.05 ± 0.02 mg GAE g−1 d.w. for P2, 1.92 ± 0.03 mg GAE g−1 d.w. for P3, 2.04 ± 0.02 mg GAE g−1 d.w. for P4). A high decrease in TPC was observed as a result of the cooking process, especially for the two control formulations (−71% for P1 and −70% for P3) in comparison with P2 (−64%) and P4 (−55%). At last, to assess the antimicrobial effect of artichoke by-products on fresh pasta and to monitor its spoilage, we used image analysis. Corresponding to a higher TPC content, P2 and P4 showed an extended shelf life of 16% and 33%, respectively, probably due to the antioxidant activity of artichoke. The new fresh egg pasta enriched with polyphenols extracted from artichoke by-products showed very good nutritional and technological characteristics, even after cooking, confirming the good potentiality of artichoke by-products in the design of new, healthy, and sustainable food products.

Our study aims to analyze factors determining the green brand equity (GBE) based on a systematic literature review (SLR) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. We posed 3 research questions and searched five databases (Scopus, Web of Sciences, Google Scholar, EBSCO, and Elsevier) for studies containing the term ‘green brand equity’ and the combination of two terms: ‘brand equity’ and ‘green’. Additionally, the backward and forward snowballing methods were applied. In our SLR, we included empirical studies published between 2006 and 2021 as peer-reviewed papers in English. Exclusion criteria included studies with theoretical models, studies describing brand equity not related to GBE, Ph.D. thesis, short reports, workshop papers, practice guidelines, book chapters, reviews, and conference publications. Finally, 33 articles were analyzed as part of the SLR in two fields: general information (authorship, year of publication, type of study, research country or location, sample size, and product categories), and research specifications (factors or variables, number and type of hypotheses, scale or measurement items, type of statistical analysis, and selected indicators of statistical methods). Image, trust, value, satisfaction, and loyalty appeared to be the most studied determinants of GBE. Less frequently analyzed were quality, awareness, attributes, particular promotional activities, and the fact of purchase. The results obtained are important in practical terms, showing what to consider when creating GBE in different categories of products and services.

Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R2 were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

Virtual reality systems have been developed primarily for the entertainment sector. However, they are being increasingly considered as high potential tools for use in industry and education. In this context, schools are now facing a challenge to introduce virtual-reality-supported teaching into their processes. With this in mind, the authors, in their paper, focus on the possibility for using virtual excursions as part of vocational education and training. For this purpose, they analyze the suitability and usability of selected virtual reality systems, as well as relevant camera systems, for the creation of virtual reality software products designed for industrial practice in upper secondary vocational schools’ apprenticeships (vocational education and training). The main results of their analyses are summarized in the form of tabularized SWOT parameters.

Floods are among the most devastating disasters in terms of socio-economics and casualties. However, these natural disasters can be managed and their effects can be minimized by flood modeling performed before the occurrence of a flood. In this study, flood modeling was developed for the Göksu River Basin, Mersin, Türkiye. Flood hazard and risk maps were prepared by using GIS, HEC-RAS, and HEC-HMS. In hydraulic modeling, Manning’s n values were obtained from 2018 CORINE data, return period flow rates (Q25, Q50, Q100, Q500) were obtained from HEC-HMS, and the application was carried out on a 5 m resolution digital surface model. In the study area, the water depths could reach up to 10 m, and water speeds were approximately 0.7 m/s. Considering these values and the fact that the study area is an urban area, hazard maps were obtained according to the UK Department for Environment, Food and Rural Affairs (DEFRA) method. The results indicated that possible flood flow rates from Q25 to Q500, from 1191.7 m3/s to 1888.3 m3/s, were detected in the study area with HEC-HMS. Flooding also occurred under conditions of the Q25 flow rate (from 4288 km2 to 5767 km2), and the impacted areas were classified as extremely risky by the DEFRA method.