In the present world, there are many two-stage systems which provide information of inputs, outputs and intermediate measures which are imprecise, such as, (stochastic, fuzzy, interval etc). In these conditions, a two-stage data envelopment analysis or a (two-stage DEA method) cannot evaluate the e, ciencies of these systems. In several two-stage systems, the simultaneous presence of stages is necessary for the , nal product. Hence, in this paper, we shall propose the stochas-tic multiplicative model and the deterministic equivalent, to measure the e, ciencies of these systems, primarily, in the presence of stochastic data, under the constant returns to scale (CRS) assumption, by using the non-compensatory property of the multiplication operator. Then, we will use the reparative property of the additive operation to propose the additive models as well as the deterministic equivalents, to calculate the e, ciencies of two-stage systems, in presence of stochastic data, under the constant returns to scale (CRS) and variable returns to scale (VRS) assumptions. This is to illustrate that a simultaneous presence of the stages is not necessary for the , nal product and one stage compen-sates the shortcomings of another stage. Likewise, we shall convert each of these deterministic equivalents to quadratic programming prob-lems. Based on the proposed stochastic models, the whole system is e, cient if and only if, the , rst and the second stages are e, cient. Ul-timately, in the proposed multiplicative model, we will illustrate the proposed multiplicative model, by employing the data of the Taiwanese non-life insurance companies, which has been extracted from the extant literature.

The cross-e ciency ranking method is a well-known method in DEA which is frequently used under the constant returns to scale assumption; while various applications exist based on the variable returns to scale (VRS). This is due to the presence of negative input-oriented VRS cross-e ciencies. In this paper, each cross-e ciency is replaced by an equivalent distance measure as ine ciency measure. Then, the cross-ine ciency method is developed under the VRS assumption.

Data envelopment analysis as an e ective approach to evaluate the relative e ciency of decisionmaking units, recently applied for developing production plans in terms of resource allocation and target setting in many production and operation systems. Most of these systems are of network structures where the outputs from a given stage are intermediate measures which form the inputs for the next stage. This paper proposes a new k-stage DEA based multi objective linear programming model for target setting in the serial network structures, considering undesirable outputs. Since the proposed model takes the size of under evaluation units into account when developing the inputs/outputs arrangements, the new idea ensures the possibility of the results. An empirical example of a production system is used to illustrate the applicability of the proposed model. The results show that based on the new plan, all of the e ciency scores are improved.

The compound parabolic concentrator (CPC) is designed and its optical and thermal analysis is performed in ANSYS. The CPC sizing and the optimal mass flow rate by maximum power point tracking (MPPT) method in MATLAB are determined. The radiative transfer equation is solved by Discrete Ordinate (DO) and Mont Carlo (MC) models and the deduced radiative flux divergence is applied as a source term in Navier-Stokes equations to model heat transfer. Results indicate that MC is faster than DO with lower computational cost and higher accuracy. The optimal mass flow rate at each time-variable solar radiation is calculated from MPPT control and entered as the inlet boundary condition for 3D CFD model. The absorbed useful power by MPPT is about 4% higher than the constant mass flow rate case. Reduction of the convective heat transfer by locating the evacuated tube collectors inside a cavity leads to 12% more power and 25% temperature enhancement in 3D model with respect to MPPT-based analytical results. Then, the evacuated collector in a cavity with MPPT control has about 16% power gain.

Background: Nanoparticles have been applied to medicine, hygiene, pharmacy and dentistry, and will bring significant advances in the prevention, diagnosis, drug delivery and treatment of disease. Green synthesis of metal nanoparticles has a very important role in nanobiotechnology, allowing production of non-toxic and eco-friendly particles.Objectives: Green synthesis of silver nanoparticles (AgNPs) was studied using pine pollen as a novel, cost-effective, simple and non-hazardous bioresource. The antifungal activity of the synthesized AgNPs was investigatedin vitro.Materials and Methods: Biosynthesis of AgNPs was conducted using pollen of pine (as a novel bioresource) acting as both reducing and capping agents. AgNPs were characterized using UV-visible spectroscopy, X-ray diffraction and transmission electron microscopy. In evaluation for antifungal properties, the synthesized AgNPs represented significantin vitro inhibitory eff ects onNeofusicoccum parvum cultures.Results: Pine pollen can mediate biosynthesis of colloidal AgNPs with an average size of 12 nm. AgNPs were formed at 22oC and observed to be highly stable up to three months without precipitation or decreased antifungal property. AgNPs showed significant inhibitory effects against Neofusicoccum parvum.Conclusion: The first report for a low-cost, simple, well feasible and eco-friendly procedure for biosynthesis of AgNPs was presented. The synthesized AgNPs by pine pollen were nontoxic and eco-friendly, and can be employed for large-scale production. The nanoparticles showed strong eff ect on quantitative inhibition and disruption of antifungal growth.

Data envelopment analysis (DEA) evaluates the cost e,-ciency of systems with multiple inputs and multiple outputs in two scenarios, one where the input prices are the same in all decision-making units (DMUs) (competitive space), and the other where the input prices di, er from one DMU to another (non-competitive space). In many situations, the DMUs could have a multi-stage network structure with intermediate measures. Although a model has been presented for calculating the network cost e, ciency in a competitive space in such cases, no method has been proposed so far to calculate the cost e, ciency of such networks in a non-competitive space. The present article focuses on the concept of cost e, ciency in DEA for DMUs with network structures and varying input prices. To this end, a cost-based production possibility set (PPS) is , rst introduced for the network systems and their sub-systems, and after calculating the network cost e, ciency, it is decomposed into e, ciencies such as technical, price, and allocative e, ciencies, and the reasons behind the occurrence of extra costs due to various types of ine, ciency are explained.