Abstract:Fermatean fuzzy set (FFS) is the generalization of both intuitionistic\nfuzzy set (IFS) and Pythagorean fuzzy set (PFS). FFS possesses higher\nprospect of applications because of its wider scope compare to IFS and PFS.\nThe notion of composite relation is a very important information measure in\ndetermining decision-making problems, and thus this paper proposes max-minmax\ncomposite relation under Fermatean fuzzy environment and discusses some\nproperties of FFS. We present an algorithm with its flowchart to aid the computation of Fermatean fuzzy max-min-max composition (FFMMMC). In terms\nof application, a hypothetical medical diagnostic reasoning is determined based\non the proposed FFMMMC where diseases and patients are presented as Fermatean\nfuzzy values in the feature space of some symptoms.
Abstract:In the Fourth Industrial Revolution, crime is hardly reported to the Police, or other law enforcement agencies. Most victims prefer to go on social media and vent, as this medium is easier for them to access and requires no paperwork or interrogations. This leaves policy makers and the law enforcement with skewed dataset, due to unreported crimes. Hence, it is paramount that one finds a way to “mine” the crime data reported on social media to comprehensively gain insights in crimes that have been committed for decision making. In this paper, we have attempted to estimate crime rates, using one microblogging and social networking service, Twitter as a data source. To do this, we have used a formal technique – Jumping Finite Automata (JFA), for the abstraction of a corpus of crime-related words and leveraged shuffle algorithms to establish semantic relationships between these words. Furthermore, we implemented JFA in a tool called “Crime-Ripper”. Through evaluation, we demonstrate that given real-world tweet dataset, Crime-Ripper is able to estimate crime rates and produce reports that are map annotations, showing areas of a city and their respective estimated crime-rates. Crime-Ripper is expected to find applications in law enforcement, policy making and public safety sensitization.
Abstract:The overactivation of fibroblasts, which results in their proliferation with overproduction of the extracellular matrix, is a pathophysiological hallmark of hypertrophic scar formation. The intermediate-conductance Ca2+-activated K+ channel (KCa3.1) is involved in fibroblast activation in multiple clinical conditions; however, its role in the post-burn hypertrophic scar formation remains to be determined. In this study, we aimed to investigate the role of KCa3.1 and anti-fibrotic potential of cyclohexadiene lactone, a KCa3.1 blocker, in post-burn hypertrophic scar formation. Cell proliferation and expression of hypertrophic markers were investigated in fibroblasts obtained directly from patients within 1–2 weeks after third-degree burns who consequently developed post-burn hypertrophic scars. The anti-fibrotic effects of cyclohexadiene lactone via KCa3.1 inhibition were assessed using in vitro fibroblasts and in vivo murine burn models. Increased cell proliferation and expression of hypertrophic markers were identified in burn-wound fibroblasts obtained from patients. The targeted inhibition of KCa3.1 by cyclohexadiene lactone markedly decreased cell proliferation along with the expression of hypertrophic markers in burn-wound fibroblasts from patients. In addition, the anti-scarring effect following cyclohexadiene lactone administration was confirmed using murine burn models in terms of molecular, histological, and visual aspects. This study demonstrated altered cellular and molecular responses of skin fibroblasts from patients after third-degree burns. In addition, this study confirmed an anti-fibrotic effect of KCa3.1 inhibition by cyclohexadiene lactone in both in vitro within burn fibroblasts and in vivo within murine burn models. These results suggest that selective inhibition of KCa3.1 by cyclohexadiene lactone has therapeutic potential to prevent hypertrophic scar formation following burns.
Abstract:Hypertrophic scars are the most common complication resulting from burn injury, and their pathological hallmark is excessive deposition of fibroblast-derived extracellular matrix proteins. Calpain, a calcium-dependent endopeptidase, mediates fibroblast activation and collagen synthesis, leading to the development of certain fibrotic diseases; however, its role in hypertrophic skin scar formation following a burn injury is yet to be determined. In this study, a detailed evaluation of the expression and activity of calpain in skin fibroblasts obtained directly from patients with third-degree burns, who subsequently developed post-burn hypertrophic scars, was performed. Furthermore, the antifibrotic effect of targeted inhibition of calpain by leupeptin on post-burn skin scarring was evaluated in vitro and in vivo. The mRNA and protein expression and activity of calpain were significantly higher in burn wound fibroblasts from patients than in normal cells. Selective inhibition of calpain by leupeptin significantly reduced the proliferation of burn wound fibroblasts as well as the mRNA and protein expression of calpain, transforming growth factor-beta 1, α-smooth muscle actin, type I and type III collagens, fibronectin, and vimentin in these cells. Furthermore, the molecular, histological, and visual effects related to post-burn scar suppression by leupeptin were verified in murine burn models. The results obtained herein highlight the pathophysiological role of calpain and indicate that calpain inhibition by leupeptin could serve as a new therapeutic strategy for preventing hypertrophic scar formation following burns.
Abstract:A total of 50 grain rice (Oryzae sativa) samples were tested to establish their mycological contamination and their aflatoxigenic potential. Rice is the most extensively consumed cereal grain by a substantial portion of the world\'s society, and in Asia predominantly. Under certain conditions, a variety of fungi may develop within rice grains; some of which have the capacity to synthesize mycotoxins. Thus, rice consumers are considered to be a high-risk population specially since this toxin has been linked to health problems and is also highly associated with liver cancer today. When compared to non-local samples, samples from Iraqi markets (of various origins), particularly imported ones, exhibited high quantities of fungus. From samples, three species of Aspergillus section Flavi (A. flavus, A. parasiticus, and A. tamarii) have been isolated and identified. Culture-based and ELISA approaches were used to detect aflatoxigenic A.strains. Fluorescence in response to UV long-wavelength (365 nm) light and pigment synthesis in response to ammonium hydroxide were used. By both methods, the ratio of aflatoxigenic A. flavus isolates to non-aflatoxigenic strains was higher. All the tested strains of A. parasiticus showed aflatoxigenic potential. Aflatoxigenic potential of selected strains by ELISA technique for A.parasiticus isolates ranged from 181.0 to 360 ppb, whilest, levels of aflatoxins production for A.flavus isolates ranged from 183 to 300 ppb. 9p
Abstract:This paper presents experimental and theoretical studies on the response of 6061-T6 aluminum alloy round-hole tubes (Al-RHTs) with five different hole sizes of 2, 4, 6, 8, and 10 mm and with five different hole positions of 0, 45, 90, 135, and 180 degree submitted to pure bending relaxation. Pure bending relaxation is defined as bending the tube to the desired curvature and maintaining that curvature for a period of time. It can be seen from experimental results of pure bending relaxation that the moment decreases sharply with time and tends to a stable value. In addition, larger held curvature or hole size results in larger drop of the moment. Due to the constant curvature of pure bending relaxation, the Al-RHT does not break. Finally, the empirical formula proposed by of Lee et al.  was improved to simulate the relaxation moment-time relationship for pure bending relaxation. After comparing with the experimental results, it is found that theoretical analysis has a good agreement with the experimental results.
Abstract:We propose CNNEnsem model, an ensemble model to efficiently classify respiratory diseases from respiratory sound data. The classification accuracy of an ensemble model is proposed based on the classification models and verified on application of CNNEnsem model. For CNNEnsem model, two convolutional neural networks are proposed and tested using MFCC (mel-frequency cepstrum coefficients) images and time-domain waveform images of respiratory sounds. These two types of images provide distinguishing features of different types of respiratory diseases. The proposed models are evaluated on the ICBHI 2017 dataset to classify respiratory diseases. To validate CNNEnsem model, its accuracy for disease classification is compared with that of both the individual convolutional neural networks and existing models. The CNNEnsem model achieves a classification accuracy of 99.46%, which outperforms similar methods.
Abstract:The inland container supply chain is vital for countries such as Malaysia where more than 50% of the country’s cargo movements are containerized. The hauliers provide the transport of containers and their performance affects the inland container supply chain performance and national logistics performance. New practices and new forms of information sharing have emerged due to the advancement of information systems. This study explores how information are shared among members in the inland container supply chain, how does it benefit the hauliers and subsequently their performance and how information sharing can be further improved. Twelve participants from the haulier companies were selected and interviewed. The transcribed data were analysed using Nvivo11. Findings indicate that information is mainly shared through the information systems of the hauliers and the information systems of other supply chain members. However, benefits to the hauliers in sharing the information was not present. Too much of data, duplication of data entry and information systems working in silos were identified as the main challenges. Information shared by the hauliers to the depots and the port terminals were not equitable to the hauliers and does not improve their performance. Data entry automation and information systems integration are expected to improve the information sharing. More importantly, opportunistic behaviour of organizations in the supply chain has to be curtailed and subsequently information asymmetry has to be reduced for the information shared to be mutually beneficial and equitable for the hauliers to realize the information sharing benefits.
Abstract:Objective: From a computerized image analysis prospective, early diagnosis of lung infections demonstrates the analysis of Corona Virus Disease based on a clinical Computed Tomography (CT) is the mainstay of diagnostic imaging evaluation of thoracic disorders. Methods: Sensitivity and specificity are the two most important indicators in selection of medical imaging devices for lunge screening. Lunge images taken Computed Tomography (CT) and CT collected from patients. Results: The statistical features extracted from the histograms of the regions of interest revealed that CT modality registered the highest scores, in the differentiation between normal, and lunge infections tissues. Conclusion: They were then studied and compared for sensitivity and specificity results. The sensitivity and specificity sensitivity increases on the expense of specificity, and vice versa. The data of this study revealed that, CT high sensitivity.
Abstract:COVID-19 have been known to the scientific community recently. The present study was aimed to explain the correlation between the body mass index and the levels of plasma lipid profile in patients with COVID-19 infection. The present study was carried out on 112 patients with COVID-19 infection. Patients were divided into two groups according to the infection severity (mild and severe). Also, each group was divided into two subgroups according to the patient’s body mass index. The body mass index of males and females with mild COVID-19 infection ranged from 31- 32. Also, the body mass index of males and females with severe COVID-19 infection ranged from 23- 25. All patients with mild COVID-19 infection had a normal level of D-dimer, lactate dehydrogenase, ferritin, C-reactive protein, serum creatinine, total cholesterol, triacylglycerols and high-density lipoprotein-cholesterol (HDL-C) when compared to normal range. However, levels of plasma, triacylglycerols, d-dimer, lactate dehydrogenase, ferritin, C-reactive protein and serum creatinine of patients with severe COVID-19 infection were significant increase when compared to normal range. Also, plasma total cholesterol and high-density lipoprotein-cholesterol (HDL-C) of patients with severe COVID-19 infection were significant decrease when compared to normal control (p< 0.05). Conclusion: The levels of the biochemical parameters was corelated with the patient body mass index and the infection severity.