The present study reports for first time the blending of psyllium husk (PH) powder/gelatin (G) in the polymer-rich composition of polyvinyl alcohol (PVA) to make an electrospinnable solution. The composite was prepared in 3 different ratios viz., 100% (wt/wt) (PVA + PH), 75% + 25% (PVA + 75PH + 25G) (wt/wt) and 50% + 50% (PVA + 50PH + 50G) (wt/wt) in 6% PVA solution. Optimum electrospinning parameters were evaluated for all the prepared blends. The fabricated nanofibers were characterized by scanning electron microscopy (SEM), attenuated total reflectance-Fourier transform infrared, differential scanning calorimetry, porosity percentage, and fiber orientation using ImageJ software. A qualitative in vitro degradation study at room temperature is supported by SEM images. The cellular interactions were characterized by MTT assay of NIH-3T3 fibroblast cells for 2 and 4 days with an optimum cell growth of >50% by fourth day of culture and long-term cultivation of L929-RFP cells was observed for 10 days. The nanofibers were formed in the range of 49–600 nm. PVA + 75PH + 25G when cultured with L929-RFP cells exhibited highest fluorescence intensity and thus supported cellular proliferation significantly. Based on the results obtained from various analyses, we anticipate that fabricated psyllium-based nanofiber can be used as a promising candidate for wound healing and other biomedical applications. 相似文献
Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The present work takes the example of dynamic TSP to compare greedy approach, genetic algorithms and DGAs. The work also implements a greedy genetic approach for the problem. In the experiments carried out, the three variants of dominance were implemented and 115 runs proved the point that none of them outperforms the other. 相似文献
Barrages are hydraulic structures constructed across rivers to divert flow into irrigation canals or power generation channels.
The most of these structures are founded on permeable foundation. The optimum cost of these structures is nonlinear function
of factors that cause the seepage forces under the structure. There is, however, no procedure to ascertain the basic barrage
parameters such as depth of sheet piles or cutoffs and the length and thickness of floor in a cost–effective manner. In this
paper, a nonlinear optimization formulation (NLOF), which consists of an objective function of minimizing total cost, is solved
using genetic algorithm (GA). The mathematical model that represents the subsurface flow is embedded in the NLOF. The applicability
of the approach has been illustrated with a typical example of barrage profile. The results obtained in this study shows drastic
cost savings when the proposed NLOF is solved using GA than that of using classical optimization technique and conventional
method. A parametric analysis has also been performed to study the effect of varying soil and hydrological conditions on design
parameters and on over all cost. 相似文献
This special issue showcases articles written from five of the best presentations at the Hot Chips 19 conference, held in August 2007. The guest editors give highlights of the conference and introduce the articles, which cover the mobile-optimized northbridge of AMD's Griffin microprocessor family; the IBM z10 next-generation mainframe microprocessor; fault tolerance in IBM's Power6 microprocessor; NVIDIA's Tesla unified graphics and computing architecture; and SiBEAM's 4-Gbps 1080p-capable uncompressed HD A/V wireless 60-GHz transceiver chipset. 相似文献
For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process. 相似文献
Wireless Personal Communications - This paper presents the design and development of a flexible Ultra Wide Band (UWB) antenna using polydimethylsiloxane as a substrate. Copper foil having a... 相似文献
Wireless Personal Communications - A Greedy Perimeter Coordinator Routing and Mobility Awareness (GPCR-MA) vehicular routing is a widely accepted routing protocol for VANET (Vehicular Ad hoc... 相似文献
The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.