313 research outputs found
Vanadium‐Substituted Tungstosulfate Polyoxometalates as Peroxidase Mimetics and Their Potential Application in Biosensing
No full textThis article reports on the peroxidase-like catalytic activity of polyoxometalates (POMs) and their potential use as natural peroxidases for developing a simple and efficient colorimetric glucose sensor. Two Keggin-type vanadium-substituted tungstosulfates, [SVW11O40]3− (SVW11) and [SV2W10O40]4− (SV2W10), were tested for their potential as natural enzyme mimetics and exhibited strong peroxidase-like catalytic activity. The catalysis reaction was found to be in accordance with Michaelis-Menten and Lineweaver-Burk kinetics models. Michaelis-Menten constant (Km) and maximum velocity (Vmax) parameters were calculated to be 0.0759 mM and 0.329×10−8 Ms−1 for SVW11, and 0.0543 mM and 2.67×10−8 Ms−1 for SV2W10, respectively, indicating a high catalytic activity and a strong affinity of POMs towards 3,3,5,5-tetramethylbenzidine (TMB). In the case of H2O2, these values were found to be 57.1 mM and 0.325×10−8 mMs−1 for SVW11, and 47.7 mM and 2.72×10−8 mMs−1 for SV2W10. The peroxidase-like catalytic activity of these POMs was used to develop colorimetric glucose sensors as a proof-of-concept model for the POM-based naked-eye detection of biomolecules. The limit of detcetion (LOD) of glucose for SVW11 and SV2W10 was 1.14 μM and 1.24 μM, respectively. Our findings propose broad-ranging potential applications of these novel POMs in biosensing and bioanalytical chemistry.Full Tex
MONEY CREATION IN AN ISLAMIC MONETARY SYSTEM: A COMPARATIVE STUDY, BETWEEN MUHAMMAD UMER CHAPRA AND MABID ALI AL-JARHI
Get PDFThe creation of money is the most important part of monetary policy, it determines
the supply and circulation of money with main goals to main the stability in the value of
money. The prevalent money creation system is still regulated on debt basis, and interest
rate is the most important instrument of it. This particular system is clearly against the
principle of Islamic economics which prohibits the use of riba and interest rate of any kind.
The use of interest rate in money creation system would cause inflation and instability
value of money. And yet most Muslim majority countries are still implementing such
policy that includes Indonesia.
Acknowledging the previous background of research, the author tends to conduct
its undergraduate thesis on money creation in an Islamic monetary system by comparing
the two renown Muslim economists Muhammad Umer Chapra and Mabid Ali Al-Jarhi.
The research aims to know and understand the concept and mechanism of money creation
according to Muhammad Umer Chapra and Mabid Ali-Jarhi and then to compare the
similarities and differences of their thoughts.
The research is a qualitative and literature-based research, the author would use
descriptive comparative method to analyse and understand the thoughts and ideas of
Muhammad Umer Chapra and Mabid Ali Al-Jarhi on money creation in Islamic monetary
system. The author would descript their thoughts and ideas and then compare the differences
and similarities of both.
The research revealed the similarities and differences between Chapra’s and Al-Jahri’s
thoughts on the concept and mechanism of money creation. The fundamental difference
could be found in the concept of money creation, where Chapra proposed and suggested
the use of monetary targeting framework, meanwhile Al-Jahri proposed inflation targeting
framework. However, they both in agreement that Islamic banks have the capability to
create a significant amount of broad money. Nevertheless, they have different approach of
implementation of Islamic monetary policy instruments to control the creation of narrow
by central bank and broad money by Islamic banks.
Keywords: Money creation, reserve requirement, money suppl
INTELLIGENT MACHINE LEARNING APPROACHES TOWARDS SENSOR FAULT DIAGNOSTIC IN WIRELESS SENSOR NETWORKS
No full textWireless Sensor Network (WSN) being highly diversified Cyber-Physical System makes it vulnerable to numerous failures. These failures due to abnormal behaviors in the network can cause serious threat towards safety, economy, and reliability of systems. Abnormal behaviors of sensors are primarily triggered by low-quality production, electromagnetic interference, and complex environments. The precise detection and diagnosis of abnormal behaviors in WSN is a challenging issue due to the diversity of deployment and limitations in the resources.
In this dissertation, a data-driven supervised machine learning-based techniques are considered to scrutinize the behavior of sensors through their data for the timely detection and diagnosis of abnormal behaviors (faults or anomaly). In this study, most of the faults that commonly occur in WSN are considered such as drift, hard-over, spike, erratic, data-loss, stuck, and random fault.
A trusted dataset published by the researchers at the University of North Carolina composed of temperature and humidity sensor healthy measurements of multi-hop scenario was acquired and the aforementioned faults were injected in the non-faulty (healthy) sensor measurements. This practice is common among researchers due to the lack in availability of defective datasets.
Events from fault occurrences were generated to replicate realistic scenarios of WSN. For instance, fault may occur in WSN for a short length as well as long, or it may occur in the combination of both. To detect and diagnose the faults in timely manner, an ensemble learning-based lightweight machine learning classification technique is adopted, which is known as Extremely Randomized Trees or Extra-Trees.
Furthermore, multiple data labelling approaches such as multi-label/multi-class were utilized in order to get the best performance out of machine learning classifiers. In this study, the proposed Extra-Trees-based detection and diagnosis scheme has shown the ability of robustness towards signal noise and strong reduction of bias and variance error.
The performance of the proposed scheme was compared with those of the state-of-the-art machine learning algorithms such as support vector machine, neural network, random forest, and decision tree. Performance evaluation shows the efficiency of the proposed scheme in terms of lightweightness and detection/diagnosis accuracy, precision, F1-score, and area value under the ROC curve. To achieve the lightweight measure, the proposed scheme training time was compared to the aforementioned state-of-the-art machine learning classifiers.Maste
Comparative Analysis of a Current-Source and a Voltage-Source Converter for Three-Phase Grid-Connected Battery Energy Storage System
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Haar Adomian Method for the Solution of Fractional Nonlinear Lane-Emden Type Equations Arising in Astrophysics
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Haar wavelet operational matrix method for system of fractional nonlinear differential equations
No full text In this paper, we present a reliable method for solving system of fractional nonlinear differential equations. The proposed technique utilizes the Haar wavelets in conjunction with a quasilinearization technique. The operational matrices are derived and used to reduce each equation in a system of fractional differential equations to a system of algebraic equations. Convergence analysis and implementation process for the proposed technique are presented. Numerical examples are provided to illustrate the applicability and accuracy of the technique. </jats:p
Characterizing the role of mitochondria in the toxicity of trichothecenes produced by Fusarium graminearum
No full textFusarium graminearum is a toxigenic fungal pathogen infecting economically significant cereal crops. Trichothecenes are a large family of low molecular weight sesquiterpenoid mycotoxins synthesized by F. graminearum and other fungi and are among the most toxic compounds known to man. These mycotoxins and their producers are encountered worldwide in the environment as natural contaminants of cereal grains presenting a high food safety risk for humans and cattle and threaten the global food supply. Trichothecene mycotoxicosis was primarily associated with their inhibitory effects on translation. However, these highly stable toxins also inhibit other cellular processes which contribute to their toxicity. In this work, using yeast as a model organism, a genome wide approach has been applied to obtain a comprehensive understanding of the molecular mechanism of the type A and B trichothecene toxicity. Due to their prevalence and impact, T-2 toxin and diacetoxyscirpenol (DAS) are used as representative type A toxins while trichothecin (Tcin) and deoxynivalenol (DON) are used as representative type B toxins. The yeast knockout collection of nonessential genes was initially used to identify mutant strains that exhibited increased resistance or susceptibility to trichothecenes. This screening led to identification of the role of mitochondria during trichothecene toxicity. The largest group of mutants exhibiting resistance was affected in their mitochondrial functions. Mitochondrial translation was directly inhibited, independent of total translation, and the trichothecene-treated cells exhibited severe fragmentation of mitochondrial membrane. Furthermore, actively respiring cells with functional mitochondria were essential for trichothecene cytotoxicity suggesting a critical role for mitochondria. A large fraction of the highly susceptible strains exhibited very high levels of reactive oxygen species (ROS) upon trichothecene treatment. Antioxidants increased cell survival and reduced mitochondrial membrane damage in trichothecene-treated cells. The direct role for ROS in mediating trichothecene cytotoxicity was confirmed when two novel Arabidopsis nonspecific lipid transfer proteins that mediated resistance to trichothecenes in A. thaliana exhibited antioxidant property and rescued trichothecene-treated yeast cells. Rapamycin-induced mitophagy reduced ROS levels and increased survival in trichothecene-treated cells suggesting mitophagy as a novel prosurvival cellular mechanism during oxidative stress in trichothecene-treated cells.Ph. D.Includes bibliographical referencesby Mohamed Anwar Bin Ume
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