Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.     

Rapid growth of angle-confined large-domain graphene bicrystals

In the chemical vapor deposition growth of large-area graphene polycrystalline thin films,the coalescence of randomly oriented graphene domains results in a high density of uncertain grain boundaries (GBs).The structures and properties of various GBs are highly dependent on the misorientation angles between the graphene domains,which can significantly affect the performance of the graphene films and impede their industrial applications.Graphene bicrystals with a specific type of GB can be synthesized via the controllable growth of graphene domains with a predefined lattice orientation.Although the bicrystal has been widely investigated for traditional bulk materials,no successful synthesis strategy has been presented for growing two-dimensional graphene bicrystals.In this study, we demonstrate a simple approach for growing well-aligned large-domain graphene bicrystals with a confined tilt angle of 30° on a facilely recrystallized single-crystal Cu (100) substrate.Control of the density of the GBs with a misorientation angle of 30° was realized via the controllable rapid growth of subcentimeter graphene domains with the assistance of a cooperative catalytic surface-passivation treatment.The large-area production of graphene bicrystals consisting of the sole specific GBs with a tunable density provides a new material platform for fundamental studies and practical applications.     

Truncated O-Glycan-Bearing MUC16 Enhances Pancreatic Cancer Cells Aggressiveness via α4β1 Integrin Complexes and FAK Signaling

Elevated levels of Mucin-16 (MUC16) in conjunction with a high expression of truncated O-glycans is implicated in playing crucial roles in the malignancy of pancreatic ductal adenocarcinoma (PDAC). However, the mechanisms by which such aberrant glycoforms present on MUC16 itself promote an increased disease burden in PDAC are yet to be elucidated. This study demonstrates that the CRISPR/Cas9-mediated genetic deletion of MUC16 in PDAC cells decreases tumor cell migration. We found that MUC16 enhances tumor malignancy by activating the integrin-linked kinase and focal adhesion kinase (ILK/FAK)-signaling axis. These findings are especially noteworthy in truncated O-glycan (Tn and STn antigen)-expressing PDAC cells. Activation of these oncogenic-signaling pathways resulted in part from interactions between MUC16 and integrin complexes (α4β1), which showed a stronger association with aberrant glycoforms of MUC16. Using a monoclonal antibody to functionally hinder MUC16 significantly reduced the migratory cascades in our model. Together, these findings suggest that truncated O-glycan containing MUC16 exacerbates malignancy in PDAC by activating FAK signaling through specific interactions with α4 and β1 integrin complexes on cancer cell membranes. Targeting these aberrant glycoforms of MUC16 can aid in the development of a novel platform to study and treat metastatic pancreatic cancer.     

Nanorod heterostructures showing photoinduced charge separation

Size- and shape-dependent property modifications of semiconductor nanocrystals have been a subject of intense interest because of their potential for future engineering devices. The bandgap and related optical-property tuning of these materials are mainly governed by the nature of their band edges. In addition, fusing one type of nanocrystal over another enables further control of material properties that are dependent on the relative alignments of their energy levels. On a molecular scale, the synthesis of supramolecular compounds has inspired advances in theories for photoinduced charge transfer. Heterostructured nanocrystals potentially provide a nanoscale analog of such systems. A method for preparing heterostructured nanocrystals of complex morphologies showing photoinduced charge separation is presented. It is shown that the energy and lifetime of the charge-transfer photoluminescence band can be tuned by changing the relative alignment of band edges in CdSe/CdTe heterostructure nanorods. The long-lived charge transfer states in these type II semiconductors may make them attractive for photovoltaic applications.     

A Petri Net based approach to determine the disassembly strategy of a product

In order to protect the environment and regain value added to products, a process known as disassembly has come into the limelight. This process is to be applied to reuse abandoned goods and materials. Manufacturers are being forced to establish disassembly plants and to develop their products' designs so as to maintain the government's dictate to dispose off their products in an environmentally responsible manner. This research presents a cost-based heuristic analysis for a circuit board assembly. Various components of the product and their assembly relationships are represented by a Petri Net diagram. Firing the transitions of the disassembly Petri Net is integrated with cost-based indices to develop an effective disassembly strategy. The methodology discussed here simplifies the decisionmaking process involved in disassembly planning. A comprehensive disassembly process planning system is proposed here and is exemplified by a case study of circuit board assembly.     

Multiple Decision Expert Systems for Performance Analysis of a Boiler System

Artificial neural networks (ANN)-based multiple decision expert systems (MDES) were developed for assessing the performance of a boiler system. Different configurations of ANN were used with different decision combination methods, including a neural combiner, to propose the model. The model was developed using the plant data collected over a period of five months to predict steam temperature, pressure, and mass flow rate, using feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature as the input parameters. The predictive capability of the model is evaluated in terms of both correlation coefficient (R) and mean absolute percentage error (MAPE). The results observed from this work demonstrate that neural combiner and ANN-based MDES can efficiently predict the data on steam properties consistently, and that the model can serve as an efficient tool for monitoring boiler behavior under real-time conditions. Superiority of the proposed model over others under various scenarios is also demonstrated.