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Arsenotrophic bacteria play an essential role in lowering arsenic contamination by converting toxic arsenite [As (III)] to less toxic and less bio-accumulative arsenate [As (V)]. The current study focused on the qualitative and electrocatalytic detection of the arsenite oxidation potential of an arsenite-oxidizing bacteria A. xylosoxidans BHW-15 (retrieved from As-contaminated tube well water), which could significantly contribute to arsenic detoxification, accumulation, and immobilization while also providing a scientific foundation for future electrochemical sensor development. The minimum inhibitory concentration (MIC) value for the bacteria was 15 mM As (III). Scanning Electron Microscopy (SEM) investigation validated its intracellular As uptake capacity and demonstrated a substantial association with the MIC value. During the stationary phase, the strain’s As (III) transformation efficiency was 0.0224 mM/h. Molecular analysis by real-time qPCR showed arsenite oxidase ( aio A) gene expression increased 1.6-fold in the presence of As (III) compared to the untreated cells. The immobilized whole-cell also showed As (III) conversion up to 18 days. To analyze the electrochemical oxidation in water, we developed a modified GCE/P-Arg/ErGO-AuNPs electrode, which successfully sensed and quantified conversion of As (III) into As (V) by accepting electrons; implying a functional As oxidase enzyme activity in the cells. To the best of our knowledge, this is the first report on the electrochemical observation of the As-transformation mechanism with Achromobacter sp . Furthermore, the current work highlighted that our isolate might be employed as a promising candidate for arsenic bioremediation, and information acquired from this study may be helpful to open a new window for the development of a cost-effective, eco-friendly biosensor for arsenic species detection in the future.

This work aims to study the optimal policy of a sustainable supply chain problem under interval uncertainty. In this chain, the customers’ demand is fluctuated within an interval and its rate is shaped by three factors: the selling price, green level, and heat-resistance efficiency. To handle this fluctuation, all interval-valued demand parameters and cost components are presented in a parametric form. And this leads to the building of imprecise profit functions in parametric form for both the centralized and decentralized scenarios of the supply chain. This work differs from existing interval-valued models by jointly incorporating green-level and heat-resistance-dependent demand within a parametric optimization framework. Now, the core research question of this study is as follows: how can one find the optimal policy of the system under imprecise parametric settings? To answer this question, this work introduces a new optimization technique, named parametric optimization, to study the optimal policy of the imprecise parametrized profits of different scenarios of the system. Using this parametric optimization technique and game-theoretic approach, the formulae for finding optimal values of decision variables along with optimal profits in all scenarios are obtained. Then, numerical examples are provided to demonstrate the optimal policy of the proposed model. Finally, a post-optimality analysis is performed to examine the effects of variations in key system parameters on optimal policies of the system. This research offers practical insights to study how the supply chain’s partners can navigate uncertainty by meeting sustainability goals.

Discovering the compactness properties in generalized-type metric spaces opens up a fascinating area of research. The present study tries to develop a theoretical framework for compactness with key properties in the recently developed interval metric space. This work begins with explaining the covers and open covers to define compact interval metric spaces and their main features. Next, a similar definition of compactness using the finite intersection property is introduced. Then, the famous Heine–Borel theorem for compactness is extended in the case of interval metric spaces. Also, the concepts of sequential-type compactness and Bolzano–Weierstrass (BW)-type compactness for interval metric spaces are introduced with their equivalency relationship. Finally, the notion of total boundedness in interval metric spaces and its connection with compactness is introduced, providing new insights into these mathematical concepts.

This paper aims to introduce a new interval approach called the Semi-Type-2 interval to represent imprecise parameters in uncertain decision-making. The proposed work establishes arithmetic operations of Semi-Type-2 intervals with algebraic properties. Additionally, a new interval ranking is proposed in order to compare Semi-Type-2 interval numbers, and the corresponding properties of total order relations are also derived. All the definitions and properties related to Semi-Type-2 intervals are illustrated with the help of numerical examples. Numerical illustrations confirm the consistency of the framework and its effectiveness in extending classical interval mathematics. Finally, some probable applications of the Semi-Type-2 interval approach are demonstrated for future implementation.

Chromosomal co-existence of metal and antibiotic resistance genes in bacteria offers a new perspective to the bacterial resistance proliferation in contaminated environment. In this study, an arsenotrophic bacterium Achromobacter xylosoxidans BHW-15, isolated from Arsenic (As) contaminated tubewell water in the Bogra district of Bangladesh, was analyzed using high throughput Ion Torrent Personal Genome Machine (PGM) complete genome sequencing scheme to reveal its adaptive potentiality. The assembled draft genome of A. xylosoxidans BHW-15 was 6.3 Mbp containing 5,782 functional genes, 1,845 pseudo genes, and three incomplete phage signature regions. Comparative genome study suggested the bacterium to be a novel strain of A. xylosoxidans showing significant dissimilarity with other relevant strains in metal resistance gene islands. A total of 35 metal resistance genes along with arsenite-oxidizing aioSXBA , arsenate reducing arsRCDAB , and mercury resistance merRTPADE operonic gene cluster and 20 broad range antibiotic resistance genes including β-lactams, aminoglycosides, and multiple multidrug resistance (MDR) efflux gene complex with a tripartite system OM-IM-MFP were found co-existed within the genome. Genomic synteny analysis with reported arsenotrophic bacteria revealed the characteristic genetic organization of ars and mer operonic genes, rarely described in β-Proteobacteria. A transposon Tn21 and mobile element protein genes were also detected to the end of mer (mercury) operonic genes, possibly a carrier for the gene transposition. In vitro antibiotic susceptibility assay showed a broad range of resistance against antibiotics belonging to β-lactams, aminoglycosides, cephalosporins (1st, 2nd, and 3rd generations), monobactams and even macrolides, some of the resistome determinants were predicted during in silico analysis. KEGG functional orthology analysis revealed the potential of the bacterium to utilize multiple carbon sources including one carbon pool by folate, innate defense mechanism against multiple stress conditions, motility, a proper developed cell signaling and processing unit and secondary metabolism-combination of all exhibiting a robust feature of the cell in multiple stressed conditions. The complete genome of the strain BHW-15 stands as a genetic basis for the evolutionary adaptation of metal and the antibiotic coexistence phenomenon in an aquatic environment.

Due to the uncertainty of market economy as well as fluctuation of customer’s demand, appropriate modelling of an inventory problem is a challenging task to the researchers/OR practitioners. In order to overcome this type of challenging situation, in this article, using the parametric approach of interval a non-deterministic inventory model for deteriorating items with partially backlogged shortages and interval-valued deterioration rate has been developed. Here, the demand rate is considered to be interval-valued and dependent upon the selling price which is also interval-valued. Also, all the parameters (like ordering cost, holding cost, etc.) except the backlogging parameter have been considered as interval-valued. The corresponding problem has been formulated as a maximization problem with an interval-valued objective. Then, to solve the said problem, we have used different variants of the QPSO algorithm with interval fitness using interval ranking. Then, the computational results have been illustrated with the help of three numerical examples. Finally, the sensitivity of the solution has been analysed with the changes in the values of different parameters associated with the model and a fruitful conclusion has been drawn.

The modelling and optimization of a manufacturing systems in the context of sustainable production under uncertainty remain a pivotal focus in control theory. The goal of this research is to develop a robust decision-making framework for a production-inventory system characterised by imperfect production with reworking processes, and interval valued non-linear demand rate which is dependent on green level, selling price, warranty period, and time. This study also considers the impact of carbon emission regulation taxes to demonstrate how CO2 emission control influences the best-found policy of the proposed system. To fulfil the goal, an interval-valued optimal control problem (IVOCP) is constructed using generalised variational principle and corresponding highly nonlinear interval maximization problem is obtained. To tackle this interval optimisation problem, an improved c-r optimisation technique and the meta-heuristic algorithm Sparrow Search algorithm (SSA) are employed. The best-found solution for the corresponding problem is numerically illustrated through four distinct scenarios based on the presence of green investment levels and warranty periods in the demand rate. The obtained best found results are compared by some other metaheuristic algorithms. Additionally, statistical tests and non-parametric tests are conducted to assess the effectiveness, consistency, and stability of the algorithms. Furthermore, sensitivity analyses have been made to observe how inventory system parameters impact the optimal policy. Based on these analyses, managerial insights are derived to aid in decision-making processes.

The goal of this work is to propose a hybrid algorithm called real-coded self-organizing migrating genetic algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions. In RCGA, exponential ranking selection, whole-arithmetic crossover and non-uniform mutation operations have been used as different operators where as in SOMA, a modification has been done. The performance of the proposed hybrid algorithm has been tested by solving a set of benchmark optimization problems taken from the existing literature. Then, the simulated results have been compared numerically and graphically with existing algorithms. In the graphical comparison, a modified performance index has been proposed. Finally, the proposed algorithm has been applied to solve two real-life problems.

In the traditional nonlinear optimization theory, the Karush-Kuhn-Tucker (KKT) optimality conditions for constrained optimization problems with inequality constraints play an essential role. The situation becomes challenging when the theory of traditional optimization is discussed under uncertainty. Several researchers have discussed the interval approach to tackle nonlinear optimization uncertainty and derived the optimality conditions. However, there are several realistic situations in which the interval approach is not suitable. This study aims to introduce the Type-2 interval approach to overcome the limitation of the classical interval approach. This study introduces Type-2 interval order relation and Type-2 interval-valued function concepts to derive generalized KKT optimality conditions for constrained optimization problems under uncertain environments. Then, the optimality conditions are discussed for the unconstrained Type-2 interval-valued optimization problem and after that, using these conditions, generalized KKT conditions are derived. Finally, the proposed approach is demonstrated by numerical examples.