A robust AHP-DEA method for measuring the relative efficiency: An application of airport industry

Measuring the relative efficiency of similar units has been an important topic of research among many researchers. Data envelopment analysis has been one of the most important techniques for measuring the efficiency of different units. However, there are some limitations on using such technique and some people prefer to use other methods such as analytical hierarchy process to measure the relative efficiencies. Besides, uncertainty in the input data is another issue, which makes some misleading results. In this paper, we present an integrated robust DEA-AHP to measure the relative efficiency of similar units. The proposed model of this is believed to capable of presenting better results in terms of efficiency compared with exclusive usage of DEA or AHP. The implementation of the proposed model is demonstrated for a real-world case study of Airport industry and the results are analyzed

Hydrogels Fibers

With the ever increasing demand for suitable tissue engineering and drug delivery systems, hydrogel fiber spinning has drawn increasing attention due to its ability to create three-dimensional (3D) structures using biomaterials. Hydrogel materials have shown a great promise to be used as templates for tissue engineering and implantable devices. Among the many production techniques available, advanced fiber processing, such as coaxial and triaxial spinning of natural hydrogels, has attracted a great deal of attention because the basic core-sheath structure provides a drug delivery system capable of delivering high concentrations of drug for localized drug delivery and tissue engineering applications. Encapsulating the drug and bioactive cores with a more bio-friendly coating allows for a versatile system for producing devices with appropriate mechanical, chemical and biological properties that can mimic the native extracellular matrix, better supporting cell growth and maintenance. This chapter presents a novel fabrication method using a wet-spinning process that allows for the routine production of multifunctional coaxial hydrogel fibers that take advantage of the encapsulating properties of a hydrogel core while also promoting good cell growth and biocompatibility via the use of bio-friendly material in the sheath

Dutasteride plus Tamsulosin therapy versus Tamsulosin Monotherapy in the treatment of lower urinary tract symptoms: A Cost-utility analysis

Introduction: Lower Urinary Tract symptoms (LUTS)? impacts the quality of life of about 23.8% of the male population in Iran, diagnosed with Benign Prostatic Hyperplasia, annually . The current pharmacological treatment protocol for LUTS are α-blockers and 5-alpha reductase inhibitors (such as Dutasteride). This study was designed to estimate the cost-utility of dutasteride plus tamsulosin therapy for LUTS from the perspective of the Iran Health System. Methods and Results: A Markov model was developed to estimate healthcare costs and patient outcomes, measured by quality-adjusted life years (QALYs), for patients with moderate to severe LUTS. The model, compared four mutually exclusive health states in two alternative treatment options: tamsulosin (0.4 mg/day) and dutasteride plus tamsulosin (0.5mg+0.4 mg/day). time horizon was 35 years, with the duration of one year per cycle. The discount rates for utilities and costs were 3% and 5% respectively. A meta-analysis was conducted to estimate advese drug reactions (ADRs) and After Surgery Events (ASEs) probabilities. Total Cost consists of the direct costs of medications, as well as inpatient and outpatient services (general practice and urology specialist examinations, hospitalizations, laboratory services, diagnostic procedures, TURP surgical procedures, treatment of AUR, and treatment in emergency care services). One-way sensitivity testing and Probabilistic Sensitivity Analyses (PSA) were performed for virtual cohort of 1,000 patients with LUTS. Utility weights for each health states were obtained from a meta-analysis of published studies with EQ5D method. These weights are calculated 0.86, 0.79, 0.72 and 0 in mild, moderate, severe and death states, respectively. The probability of ASEs (CI 95%) were calculated as: TUR syndrome (0-0.0109), Blood transfusion (0.0296-0.0676), Urinary incontinence (0.0198-0.1894), urethral stricture (0.0392-0.0769) and UTI (0.0169-0.0787). After 35 years, the incremental cost-effectiveness ratio for combination therapy was $5159, well within the threshold range typically applied in Iran. PSA showed that the probability of being cost-effective in combination therapy is 89% to 94%, also the model showed the most sensitivity to dutasteride unit price and surgery incidence with monotherapy. Conclusions: Combination therapy has a high probability of being cost-effective in comparison to tamsulosin monotherapy in Iran

Nanostructured electrically conducting biofibres produced using a reactive wet-spinning process

Electrically conducting, robust fibres comprised of both an alginate (Alg) biopolymer and a polypyrrole (PPy) component have been produced using reactive wet-spinning. Using this approach polypyrrole-biopolymer fibres were also produced with single-walled carbon nanotubes (CNTs), added to provide additional strength and conductivity. The fibres produced containing CNTs show a 78% increase in ultimate stress and 25% increase in elongation to break compared to PPy-alginate fibre. These properties are essential for studies involving the use of electrical stimulation to promote nerve regrowth and/or muscle regeneration. The resultant a novel fibres had been evaluated to develop a viable system in incorporating biological entities in the composite biomaterial. These results indicated fibres are biocompatible to living cells

A reactive wet spinning approach to polypyrrole fibres

Electrically conducting, robust fibres comprised of both an alginate (Alg) biopolymer and a polypyrrole (PPy) component have been produced using reactive wet-spinning. Using this approach polypyrrole-biopolymer fibres were also produced with single-walled carbon nanotubes (CNTs), added to provide additional strength and conductivity. SEM images of the PPy-Alg composite fibres clearly show the tubular multifilament form of the alginate fibre impregnated with PPy nanoparticles. The fibres produced containing CNTs show a 78% increase in ultimate stress and 25% increase in elongation to break compared to PPy-alginate fibre. Young\u27s modulus obtained for the PPy-Alg-CNT fibres showed a 30% increase compared to the PPy-alginate fibre. The fibres produced were electrochemically active and capable of electromechanical actuation with a strain of 0.7% produced at a scan rate of 100 mV s-1 of the potential. C 2011 The Royal Society of Chemistry

Fabrication of a graphene coated nonwoven textile for industrial applications

A cost effective electrically conductive textile for large scale applications would revolutionise numerous industries. Herein, we demonstrate a novel processing approach to produce conductive textiles for industrial applications. A conductive nonwoven textile was successfully fabricated using a simple dip coating method. The nonwoven polyester was coated with liquid crystallite graphene oxide with subsequent non-toxic chemical reduction. The process is readily scalable. The graphene coated fabric has been characterized by electron microscopy as well as by electrical, mechanical, thermal and abrasion resistance measurements. It was found that the electrical surface resistivity of the prepared polyester-graphene composite fabric was 330 Ω □-1. The electrical surface resistivity was 3 and 150 times lower than that of polypyrrole coated woven polyester fabric and graphene coated nonwoven fabrics, respectively, in previously published reports. The hybrid polyester-graphene textile prepared here should find applications in high-performance geotextiles or as heating elements

Stretchable and highly conductive carbon nanotube-graphene hybrid yarns for wearable systems

Carbon Nanotubes (CNTs) have emerged as potential candidates for replacement of conventional metals due to their significant mechanical, electrical, thermal properties and non-oxidizing abilities [1, 2]. The density of CNT composites is about five times lower than copper and around half that of aluminium. Moreover, their thermal conductivity is about ten times that of copper. With the above mentioned distinguishing features, CNTs have been of interest in medical, electronics and antenna applications [3]. CNTs are drawn into yarns by pulling and twisting them from CNT forests. Previously we have presented microwave characterization of CNT yarns [4]. Our results have shown that the CNT yarns exhibits frequency independent resistive behavior and is beneficial for wideband applications such as ultra-wideband (UWB) and wireless body area networks [4]. Electrical conductivity of a CNT yarn depends on the properties, loading and aspect ratio of the CNTs. It also depends upon the twist angle and the characteristics of the conductive network. By doping or adding materials, such as gold, silver or NiCr, electrical conductivity of CNTs can by varied. In [5], highly conductive carbon nanotube-graphene hybrid yarns are reported. They are obtained by drawing vertically aligned multi-walled carbon nanotubes (MWCNT) into long MWCNT sheets. Then graphene flakes are deposited onto the MWCNT sheet to form a composite hybrid structure that is transformed into yarns by twisting. The electrical conductivity of these composite MWCNT-graphene hybrid yarns is over 900 S/cm. In this work, we have modeled this hybrid material as a potential data transmission line and compared it with a transmission line made out of copper on the same substrate. The results are tabulated in Table-I. They show a good agreement between copper based and composite MWCNT-graphene hybrid material based transmission lines. The hybrid material is high conductive, flexible and stretchable. This makes it suitable to use as transmission lines and connecting wires in systems that require stretching and flexibility, such as wearable systems

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.

Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNN architectures to analyze 27,815 hematoxylin and eosin scanned images from The Cancer Genome Atlas for tumor/normal, cancer subtype, and mutation classification. Our CNNs are able to classify TCGA pathologist-annotated tumor/normal status of whole slide images (WSIs) in 19 cancer types with consistently high AUCs (0.995 ± 0.008), as well as subtypes with lower but significant accuracy (AUC 0.87 ± 0.1). Remarkably, tumor/normal CNNs trained on one tissue are effective in others (AUC 0.88 ± 0.11), with classifier relationships also recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45 ± 0.16 between classifier pairs. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. Patterns for TP53 mutations can also be detected, with WSI self- and cross-tissue AUCs ranging from 0.65-0.80. Finally, we comparatively evaluate CNNs on 170 breast and colon cancer images with pathologist-annotated nuclei, finding that both cellular and intercellular regions contribute to CNN accuracy. These results demonstrate the power of CNNs not only for histopathological classification, but also for cross-comparisons to reveal conserved spatial behaviors across tumors

Highly conductive carbon nanotube-graphene hybrid yarn

An efficient procedure for the fabrication of highly conductive carbon nanotube/graphene hybrid yarns has been developed. To start, arrays of vertically aligned multi-walled carbon nanotubes (MWNT) are converted into indefinitely long MWNT sheets by drawing. Graphene flakes are then deposited onto the MWNT sheets by electrospinning to form a composite structure that is transformed into yarn filaments by twisting. The process is scalable for yarn fabrication on an industrial scale. Prepared materials are characterized by electron microscopy, electrical, mechanical, and electrochemical measurements. It is found that the electrical conductivity of the composite MWNT-graphene yarns is over 900 S/cm. This value is 400% and 1250% higher than electrical conductivity of pristine MWNT yarns or graphene paper, respectively. The increase in conductivity is asssociated with the increase of the density of states near the Fermi level by a factor of 100 and a decrease in the hopping distance by an order of magnitude induced by grapene flakes. It is found also that the MWNT-graphene yarn has a strong electrochemical response with specific capacitance in excess of 111 Fg-1. This value is 425% higher than the capacitance of pristine MWNT yarn. Such substantial improvements of key properties of the hybrid material can be associated with the synergy of MWNT and graphene layers in the yarn structure. Prepared hybrid yarns can benefit such applications as high-performance supercapacitors, batteries, high current capable cables, and artificial muscles