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61.
Ali Eshaghi Mohammad Mashreghi Mahere Rezazade Bazaz Masoud HomayouniTabrizi Majid Darroudi 《IET nanobiotechnology / IET》2017,11(5):517
The aim of this study is to introduce natural‐based polymers, chitosan and starch, to design a remedial nanocomposite, comprising of cerium oxide nanoparticles and silver nanoparticles, to investigate their effects in accelerating wound healing and in wound microbial load. Cerium oxide nanoparticles synthesized in starch solution added to the colloidal dispersion of synthesized silver nanoparticles in chitosan to make a three‐component nanomaterial. Mice were anaesthetized and two parallel full‐thickness round wounds were excised under aseptic conditions with the help of sterile dermal biopsy punch. Furthermore, effects of silver‐chitosan and silver‐cerium‐chitosan nanocomposite had evaluated on rate of wound closure and collagen density and on microbial load of wound in full‐thickness model. Results showed that both silver chitosan and silver‐cerium‐chitosan had significant impact on acceleration of wound closure and collagen content and on reduction of wound microbial load in comparison with control group, which was, received no treatments. However, the silver‐cerium‐chitosan nanocomposite is more potent than silver‐chitosan group and control group in wound closure. The wound healing effects of silver‐cerium‐chitosan nanocomposite are due to unique features of its three components and this nanocomposite promises impressive remedies for clinical application.Inspec keywords: wounds, nanocomposites, nanomedicine, nanoparticles, proteins, cerium, silver, polymers, colloids, patient treatmentOther keywords: biopolymer‐based nanocomposite wound dressing, wound healing properties, wound microbial load, natural‐based polymers, chitosan, remedial nanocomposite, cerium oxide nanoparticles, nanoceria, silver nanoparticles, starch solution, three‐component nanomaterial, synthesised silver nanoparticles, ketamine intraperitoneal injection, silver‐cerium‐chitosan nanocomposite, wound closure, collagen density, wound healing effects, wound care, aseptic conditions, sterile dermal biopsy punch, Ag‐Ce 相似文献
62.
Seyed Mohammad Reza Derakhshandeh Seyed Mohammad Mehdi Hadavi Mohammad Javad Eshraghi Masoumeh Javaheri Masoud Mozafari 《IET nanobiotechnology / IET》2017,11(6):656
In this study, hydrogenated amorphous carbon thin films, structurally similar to diamond‐like carbon (DLC), were deposited on the surface of untreated and plasma nitrocarburised (Nitrocarburizing‐treated) stainless steel medical implants using a plasma‐enhanced chemical vapour deposition method. The deposited DLC thin films on the nitrocarburising‐treated implants (CN+DLC) exhibited an appropriate adhesion to the substrates. The results clearly indicated that the applied DLC thin films showed excellent pitting and corrosion resistance with no considerable damage on the surface in comparison with the other samples. The CN+DLC thin films could be considered as an efficient approach for improving the biocompatibility and chemical inertness of metallic implants.Inspec keywords: tissue engineering, bone, biomedical materials, electrochemistry, amorphous state, carbon, hydrogen, thin films, plasma CVD, adhesion, corrosion resistance, surface hardeningOther keywords: electrochemical performance, plasma nitrocarburised stainless steel medical implants, hydrogenated amorphous carbon thin films, bone tissue engineering, plasma‐enhanced chemical vapour deposition method, adhesion, corrosion resistance, biocompatibility, chemical inertness, metallic implants, C:H 相似文献
63.
Masoud Kamalahmadi 《国际生产研究杂志》2016,54(1):302-321
In this study, we examine the optimal allocation of demand across a set of suppliers in a supply chain that is exposed to supply risk and environmental risk. A two-stage mixed-integer programming model is used to develop a flexible sourcing strategy under disruptions. Our model integrates supplier selection and demand allocation with transportation channel selection and provides contingency plans to mitigate the negative impacts of disruptions and minimise total network costs. Finally, a numerical example is presented to illustrate the model and provide insights. The findings suggest that developing contingency plans using flexibility in suppliers’ production capacity is an effective strategy for firms to mitigate the severity of disruptions. We also show that flexibility and reliability of the suppliers and regions play a significant role in determining contingency plans for during disruption. Findings generally show that highly flexible suppliers receive less allocation, and their flexible capacity is reserved for disruptions. For firms that do not incorporate risk management into supplier selection and allocation, the recommendation is to source from fewer, more reliable suppliers with less risk of disruption. Our findings also emphasise that the type of disruption has important implications for supplier selection and demand allocation. This study highlights the supply chain risk management strategy of regionalising as a means for minimising the impact of environmental disruptions. 相似文献
64.
Munsif Ali Jatoi Nidal Kamel Aamir Saeed Malik Ibrahima Faye Jose M. Bornot Tahamina Begum 《International journal of imaging systems and technology》2016,26(1):55-64
Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others. 相似文献
65.
Zahra Sedighi Maman W. Wade Murphy Saeed Maghsoodloo Fatemah Haji Ahmadi 《Quality Engineering》2016,28(4):455-466
Process capability indices such as Cp are used extensively in manufacturing industries to assess processes in order to decide about purchasing. In practice, the parameter for calculating Cp is rarely known and is frequently replaced with estimates from an in-control reference sample. This article explores the optimal sample size required to achieve a desired error of estimation using absolute percentage error of different Cp estimates. Moreover, some practical tools are created to allow practitioners to find sample size in different situations. 相似文献
66.
Fahimeh Sadat?Tabatabaei Saeed?Tatari Ramin?Samadi Maryam?TorshabiEmail author 《Journal of materials science. Materials in medicine》2016,27(11):164
Bone autografts are often used for reconstruction of bone defects; however, due to the limitations of autografts, researchers have been in search of bone substitutes. Dentin is of particular interest for this purpose due to high similarity to bone. This in vitro study sought to assess the surface characteristics and biological properties of dentin samples prepared with different treatments. This study was conducted on regular (RD), demineralized (DemD), and deproteinized (DepD) dentin samples. X-ray diffraction and Fourier transform infrared spectroscopy were used for surface characterization. Samples were immersed in simulated body fluid, and their bioactivity was evaluated under a scanning electron microscope. The methyl thiazol tetrazolium assay, scanning electron microscope analysis and quantitative real-time polymerase chain reaction were performed, respectively to assess viability/proliferation, adhesion/morphology and osteoblast differentiation of cultured human dental pulp stem cells on dentin powders. Of the three dentin samples, DepD showed the highest and RD showed the lowest rate of formation and deposition of hydroxyapatite crystals. Although, the difference in superficial apatite was not significant among samples, functional groups on the surface, however, were more distinct on DepD. At four weeks, hydroxyapatite deposits were noted as needle-shaped accumulations on DemD sample and numerous hexagonal HA deposit masses were seen, covering the surface of DepD. The methyl thiazol tetrazolium, scanning electron microscope, and quantitative real-time polymerase chain reaction analyses during the 10-day cell culture on dentin powders showed the highest cell adhesion and viability and rapid differentiation in DepD. Based on the parameters evaluated in this in vitro study, DepD showed high rate of formation/deposition of hydroxyapatite crystals and adhesion/viability/osteogenic differentiation of human dental pulp stem cells, which may support its osteoinductive/osteoconductive potential for bone regeneration. 相似文献
67.
Masoud Ebrahimzadeh Sari Granroth Sami Vuori Marko Punkkinen Mikko Miettinen Risto Punkkinen Mikhail Kuzmin Pekka Laukkanen Mika Lastusaari Kalevi Kokko 《Advanced Engineering Materials》2023,25(19):2300762
Manufacturing a low-resistive Ohmic metal contact on p-type InP crystals for various applications is a challenge because of the Fermi-level pinning via surface defects and the diffusion of p-type doping atoms in InP. Development of wet-chemistry treatments and nanoscale control of p-doping for InP surfaces is crucial for decreasing the device resistivity losses and durability problems. Herein, a proper combination of HCl-based solution immersion, which directly provides an unusual wet chemical-induced InP(100)c(2 × 2) atomic structure, and low-temperature Mg-surface doping of the cleaned InP before Ni-film deposition is demonstrated to decrease the contact resistivity of Ni/p-InP by the factor of 10 approximately as compared to the lowest reference value without Mg. Deposition of the Mg intermediate layer on p-InP and postheating of Mg/p-InP at 350 °C, both performed in ultrahigh-vacuum (UHV) chamber, lead to intermixing of Mg and InP elements according to X-ray photoelectron spectroscopy. Introducing a small oxygen gas background (O2 ≈ 10−6 mbar) in UHV chamber during the postheating of Mg/p-InP enhances the indium outdiffusion and provides the lowest contact resistivity. Quantum mechanical simulations indicate that the presence of InP native oxide or/and metal indium alloy at the interface increases In diffusion. 相似文献
68.
Mahmoud Ragab Mohammed W. Al-Rabia Sami Saeed Binyamin Ahmed A. Aldarmahi 《计算机、材料和连续体(英文)》2023,74(2):2889-2904
With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches. 相似文献
69.
Muhammad Aadil Siddiqui M. H. Md Khir Zaka Ullah Muath Al Hasan Abdul Saboor Saeed Ahmed Magsi 《计算机、材料和连续体(英文)》2023,75(2):2859-2871
One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm−1. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm−1, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R2 value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology. 相似文献
70.
Saeed Mohsen Sherif S. M. Ghoneim Mohammed S. Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan 《计算机、材料和连续体(英文)》2023,75(3):5271-5286
Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers. 相似文献