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Transportation is a critical contributor to carbon emissions, with road transportation playing a dominant role due to its dense network and versatility. However, the overreliance on road transportation has led to congestion, impacting reliability. As international trade grows, the demand for sustainable logistics practices intensifies. Intermodal transportation systems have emerged as a promising solution, harnessing different modes to reduce emissions and environmental impact while optimizing costs. It is important to underscore the significance of mode combinations in achieving environmental goals, aligning with the broader concept of environmental sustainability that encompasses economic and social dimensions. This article contributes to this evolving landscape by presenting a bi-objective intermodal transportation problem focusing on carbon emission reduction. Leveraging machine learning algorithms, including multiple linear regression, support vector regression, decision tree, and random forest, we predict transportation-based CO 2 emissions, offering environmentally friendly logistics plans. Our research responds to the call for green intermodal transportation, addresses financial incentives, emphasizes profit maximization, and reflects the growing influence of government policies. This paper outlines our methodology, presents a real-world case study, and offers computational results, underscoring the significance of sustainable intermodal transportation in the context of global climate goals and government initiatives. [ABSTRACT FROM AUTHOR]

Abstract: Third party logistics service providers (3PLs) have an important role in supply chain management. Increasing cooperation with 3PLs is expanding in today’s business environment. Hence, 3PLs need to have an efficient distribution network to meet customer demands. Nevertheless, few researches have tried to propose a solution for distribution network problems of 3PLs. The optimization problem which is discussing in our study is solved in two stages. At the first stage, the assignment problem which includes assigning the order of the vehicles is solved with mixed integer programming by using GAMS 21.6/CPLEX. The output of the first stage is used as an input in the second stage. In this stage routes are determined for vehicles by developing a genetic algorithm by using C#. [Copyright &y& Elsevier]

Abstract: Benzoylformate decarboxylase (BFD, EC 4.1.1.7) is a homotetrameric thiamine diphosphate (ThDP)-dependent enzyme which catalyzes the synthesis of chiral 2-hydroxyketones accepting a broad range of aldehydes as substrates. In this study the synthesis of 2-hydroxypropiophenone (2-HPP) from benzaldehyde and acetaldehyde was catalyzed by three BFD variants namely BFD F464I, BFD A460I and BFD A460I–F464I. This paper reports the effect of hydrostatic pressure up to 290MPa when the reactions were carried out at different benzaldehyde concentrations (5–40mM) as well as at different pH values (7.0–8.5). Acetaldehyde concentration was fixed at 400mM in all biotransformations. Reactions performed at high benzaldehyde concentrations and at high hydrostatic pressures showed an increase in (R)-2-HPP formation catalyzed by all BFD variants. For BFD A460I–F464I we observed an increase in the ee of (R)-2-HPP up to 80%, whereas at atmospheric conditions this variant synthesizes (R)-2-HPP with an ee of only 50%. Alkaline conditions (up to pH 8.5) and high hydrostatic pressures resulted in an increase of (R)-2-HPP synthesis, especially in the case of BFD A460I and BFD F464I. [Copyright &y& Elsevier]

Abstract: This paper proposes an integrated methodology and its solution for supplier selection problem. A two-stage stochastic programming model and fuzzy TOPSIS methods are consolidated in this methodology. After pre-research phase, in the second part of the methodology, fuzzy TOPSIS is used for ranking potential suppliers considering qualitative data under fuzzy environment. In the third part of the methodology a group of ranked potential suppliers are included in a two-stage stochastic programming model for evaluation. In the two-stage stochastic programming model demand is assumed as uncertain and different scenarios are generated for this parameter. With this methodology, supplier evaluation procedure can be done in unknown environment. Our methodology is interested with a problem in multi-product, multi-period and multi-sourcing environment. [ABSTRACT FROM AUTHOR]

Abstract: Paper is an example of a valuable material that can be recycled and recovered. In this study, a two-stage stochastic revenue-maximization model is presented to determine a long-term strategy under uncertainty for a large-scale real-world paper recycling company. This network-design problem includes optimal recycling center locations and optimal flow amounts between the nodes in the multi-facility environment. The proposed model is formulated with two-stage stochastic mixed-integer and robust programming approaches. The models are solved by commercial software GAMS 21.6/CPLEX 9.0 and the results are compared. The study is followed by the analyses of the results. [Copyright &y& Elsevier]

Abstract: With the globalization and the emergence of the extended enterprise of interdependent organizations, there has been a steady increase in the outsourcing of parts and services. This has led firms to give more importance to the purchasing function and its associated decisions. Since these decisions require a long term investment for the telecommunication industry especially and affect the strategic positioning of the companies in the sector, the selection of the proper supplier is one of the most important problems. Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. This paper develops a supplier evaluation approach based on the analytic network process (ANP) and the technique for order performance by similarity to ideal solution (TOPSIS) methods to help a telecommunication company in the GSM sector in Turkey under the fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. Contrary to conventional Fuzzy ANP (FANP) methodology in the literature, we use triangular fuzzy numbers in all pairwise comparison matrices in the FANP. Hence, criteria weights are calculated as the triangular fuzzy numbers and then these fuzzy criteria weights are inserted to the fuzzy TOPSIS methodology to rank the alternatives. This approach is demonstrated with a real world case study involving six main evaluation criteria that the company has determined to choose the most appropriate supplier. The study was followed by the sensitivity analyses of the results. [Copyright &y& Elsevier]

'Ideal' solvents in biocatalysis have to fulfill a large number of requirements, such as high substrate solubility, high enzyme activity and stability, and positive effects on reaction equilibrium. In the past decades, many enzymatic synthesis routes in water-based and nonaqueous (organic solvents, ionic or supercritical fluids) reaction media have been developed. However, no solvent meets every demand for different reaction types at the same time, and there is still a need for novel solvents suited for different reaction types and applications. Deep eutectic solvents (DESs) have recently been evaluated as solvents in different biocatalytic reactions. They can improve substrate supply, conversion, and stability. The best results were obtained when the DES is formed by the substrates of an enzymatic reaction. A wide range of inexpensive renewables can be used as components of deep eutectic solvents; therefore, the solvents are often biodegradable, nontoxic, nonvolatile, and nonflammable. A broad range of enzymatic and chemo-enzymatic synthesis reactions can be performed in deep eutectic solvents. Most of the reactions, covering transesterifications, epoxidations, and C–C bond formations, are catalyzed by lipases. Deep eutectic solvents can be used to enable new biocatalytic synthesis routes that cannot be realized in conventional reaction media (i.e., aqueous buffers). Components of deep eutectic solvents can be used as substrate and solvent at the same time; this virtually solvent-free approach enables processes with high substrate conversion and high atom efficiency. [ABSTRACT FROM AUTHOR]

In this review, we focus on the holistic continuous enzymatic production and put special emphasis on process intensification by up- and downstream processing in continuous flow biocatalysis. After a brief introduction, we provide an overview of current examples of enzyme immobilization as an upstream process for flow biocatalysis. Thereafter, we provide an overview of unit operations as downstream processing strategies, namely continuous (i) liquid–liquid extraction, (ii) adsorptive downstream processing, and (iii) crystallization and precipitation. Eventually, we present our perspectives on future trends in this research field. • Process intensification through upstream and downstream processing is an important tool to increase the overall productivity of continuous biocatalysis. • Immobilization is an important factor for upstream processing in flow biocatalysis. • Unit operations such as liquid-liquid extraction, adsorptive downstream processing, and crystallization are important instruments for downstream processing strategies. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this study is to model shopping center site selection problem for a real world application in Istanbul which is the most populated city in Turkey. Since Turkish metropolitan cities have been attracting a large population from smaller cities and rural areas, it has caused a considerable increase in the population of the big cities of Turkey. The growth in population and the enormous shift of people from old to newly developed districts in Istanbul also create new spending demand areas. This is the most powerful motivation to generate new sites in the city for locating attractive shopping centers. A number of conflicting qualitative and quantitative criteria exist for evaluating alternative sites. Qualitative criteria are often accompanied by ambiguities and vagueness. This makes fuzzy logic a more natural approach to this kind of multi criteria decision making (MCDM) problems. The paper proposes a combined MCDM methodology. Fuzzy AHP (analytic hierarchy process) is utilized for assigning weights of the criteria for site selection and fuzzy TOPSIS (technique for order preference by similarity to ideal solution) is used to determine the most suitable alternative using these criteria weights. The study was followed by the sensitivity analysis of the results. [Copyright &y& Elsevier]

• Whole-cell two-step biocatalytic conversion of cyclohexanol to ε-caprolactone. • Fed-batch process parameter optimisation for cyclohexanone monooxygenase variants. • The optimised process gave 98% conversion and 20 g L–1 product titre. ε-Caprolactone is an industrially important intermediate produced in multi-10,000 ton scale annually with broad applications. We report on a whole-cell biocatalytic conversion of cyclohexanol to ε-caprolactone using the combination of alcohol dehydrogenase (ADH) with two stability-improved variants (QM and M15) of the Baeyer-Villiger monooxygenase CHMO with a special focus on process development at the 200 mM scale. Influence of parameters such as volumetric mass transfer co-efficient, stirrer speed and catalytic loading (amount of E. coli whole-cells expressing ADH and CHMO) on the process efficiency were studied and optimised. This resulted in over 98% conversion, a product titer of 20 g L–1 and an isolated product amount of 9.1 g (80%). This corresponds to a space-time yield of 1.1 g L–1 h−1 and a reaction yield (mole of product per mole substrate) of 0.9. Comparing the two CHMO variants a significant difference in catalytic yield (weight of product to weight of catalyst; 0.6 vs 0.3) was observed without any inherent changes in the process. Hence, the reported process can accommodate in the future improved variants of the CHMO. [ABSTRACT FROM AUTHOR]

FAD-dependent Baeyer-Villiger monooxygenases (BVMOs) have proven to be useful biocatalysts in the selective and specific oxygenation of various ketones. Despite the cloning, heterologous expression and characterization of close to 80 members of this enzyme family, some sub-groups of BVMOs still remain underrepresented and their evolutionary relationship uncertain. Until recently, very few fungal BVMOs have been described. Our previous investigations into BVMOs from the fungus Aspergillus flavus , yielded very little activity on simple cyclic ketones. Here we report on another four BVMOs from A. flavus that are more closely related to cyclohexanone monooxygenase (CHMO) from Acinetobacter sp. NCIMB 9871. Evolutionary analysis with other characterized BVMOs show their closest relationship to be with either cycloalkanone monooxygenase (CAMO) or 2-oxo-Δ 3 -4,5,5-trimethylcyclopentenylacetyl-coenzyme A monooxygenase (OTEMO). The OTEMO-related BVMO AFL706 and BVMO AFL334 were heterologously expressed in E. coli , purified and shown to be able to convert a range of cyclic and substituted cyclic ketones. Of the unsubstituted cyclic ketones, cyclohexanone showed the highest conversion with maximum turnover frequencies reaching 4.3 s −1 for BVMO AFL706 . Unlike CHMO acinet , and many of the closely related BVMOs, no substrate inhibition was observed with cyclohexanone to a concentration of up to 30 mM, creating the possibility for applications requiring high substrate loading. Aliphatic ketones were also readily converted with excellent regioselectivity. Similar to CHMO acinet , acetophenones were not converted and the oxidation of rac - cis -bicyclo[3.2.0]hept-2-en-6-one occurs enantiodivergently, with the (1 R ,5 S ) isomer converted to the “normal” lactone and the (1 S ,5 R ) isomer to the “abnormal” lactone. [ABSTRACT FROM AUTHOR]

A two-step Design of Experiments (DoE) strategy followed by a two-liquid-phase system (2LPS) was applied to enhance the ɛ-caprolactone yield in the cyclohexanone monooxygenase (CHMO)-alcohol dehydrogenase (ADH) convergent cascade system. The key reaction parameters were identified and optimized for the determination of an optimal operational window for the aqueous media. In the 2LPS system, high partitioning of the lactone product was observed in 2-MeTHF and in toluene; however, these solvents led to drastically reduced enzymatic activity. Dodecane was chosen as the non-miscible organic phase owing to the enzymes' high residual activity, despite the low partitioning of the lactone. Cyclohexanone concentrations up to 75 mM were applied in the aqueous media. The turnover numbers for the nicotinamide cofactor and for the ADH reached up to 980 and 392,000, respectively whereas a turnover number value of 5600 was achieved for the CHMO. By employing a 2LPS, whereby 91 mM of cyclohexanone was applied in the second phase, turnover numbers were slightly increased. [ABSTRACT FROM AUTHOR]

In this study, a new methodology was applied to estimate the coal moisture content during the drying process. Adaptive-network-based fuzzy inference system (ANFIS) was applied to predict the coal moisture content at any time during the drying process. The experiments were carried out for different drying air temperatures (70, 100, 130 and 160 °C), drying air velocities (0.4, 0.7 and 1.1 m/s), bed heights (80, 130 and 150 mm) and sample sizes (20, 35 and 50 mm), and the experimental results were used to validate applicability of the ANFIS in the coal drying process. The ANFIS network achieves quite satisfying scientific results with acceptable deviations. The MSE and R 2 values were calculated as 1.899 and 0.998, respectively, for the testing stage. The results of this study show the applicability of the ANFIS in the coal drying processes to predict the coal moisture content at any time. Therefore, it is not necessary to carry out all the experiments: by using the ANFIS, the drying curves of some other cases which are not performed can be estimated easily. Herewith, the necessary number of the experiments decreases. [ABSTRACT FROM AUTHOR]

Peroxygenases are an emerging new class of enzymes allowing selective oxyfunctionalisation reactions in a cofactor-independent way different from well-known P450 monooxygenases. Herein, we focused on recent developments from organic synthesis, molecular biotechnology and reaction engineering viewpoints that are devoted to bring these enzymes in industrial applications. This covers natural diversity from different sources, protein engineering strategies for expression, substrate scope, activity and selectivity, stabilisation of enzymes via immobilisation, and the use of peroxygenases in low water media. We believe that peroxygenases have much to offer for selective oxyfunctionalisations and we have much to study to explore the full potential of these versatile biocatalysts in organic synthesis. [ABSTRACT FROM AUTHOR]