335 research outputs found
Investigation of electrical properties for cantilever-based piezoelectric energy harvester
In the present era, the renewable sources of energy, e.g., piezoelectric materials are in great demand. They play a vital role in the field of micro-electromechanical systems, e.g., sensors and actuators. The cantilever-based piezoelectric energy harvesters are very popular because of their high performance and utilization. In this research-work, an energy harvester model based on a cantilever beam with bimorph PZT-5A, having a substrate layer of structural steel, was presented. The proposed energy scavenging system, designed in COMSOL Multiphysics, was applied to analyze the electrical output as a function of excitation frequencies, load resistances and accelerations. Analytical modeling was employed to measure the output voltage and power under pre-defined conditions of acceleration and load resistance. Experimentation was also performed to determine the relationship between independent and output parameters. Energy harvester is capable of producing the maximum power of 1.16 mW at a resonant frequency of 71 Hz under 1g acceleration, having load resistance of 12 k Omega. It was observed that acceleration and output power are directly proportional to each other. Moreover, the investigation conveys that the experimental results are in good agreement with the numerical results. The maximum error obtained between the experimental and numerical investigation was found to equal 4.3%
PERAN INTERACTION CAPABILITY DALAM MENINGKATKAN CUSTOMER PARTICIPATION DAN HOLISTIC PERCEIVED VALUE
Studi ini bertujuan menguji peran Interaction Capability dalam mendorong Customer Participation menuju terciptanya Perceived Holistic Value pada human transformative service, yaitu jenis jasa yang bertujuan untuk menciptakan perubahan yang menyenangkan dan peningkatan kesejahteraan pelanggan. Religious Interaction Capability merupakan dimensi baru sebagai penguat dimensi Interaction Capability yang sebelumnya hanya terdiri dari Individuated Interaction, Relational Interaction, Empowered Interaction dan Ethical Interaction Capability. Responden dalam penelitian ini adalah pasien rawat jalan rumah sakit Islam di Propinsi Jawa Tengah Indonesia yang berjumlah 192 pasien. Pengambilan sampel menggunakan purposive sampling. Structural Equation Model (SEM) digunakan untuk menganalisis data dan menguji model empiric. Studi ini berhasil membuktikan peran Religious Interaction Capability bersama dengan kapabilitas interaksi lainnya dalam mendorong pasien religiocentric untuk melakukan information seeking, information sharing dan responsible behaviour. Semua komponen pembentuk Customer Participation itu juga terbukti mampu menciptakan Perceived Holistic Value, yaitu persepsi nilai yang tidak hanya pada level transaksional yang diukur dari process value dan outcome value saja, namun sudah pada tataran yang lebih holistic dengan meningkatnya religious value. Hasil studi ini bermanfaat bagi penguatkan Resource Based View Theory
A Review of Applicability of Banning (Hajre) in Jurisprudence from the Perspective of Forensic Medicine
The topic of ward is among the important topics in jurisprudence and Forensic Medicine. Banning literally means prohibiting. The term of prohibition means preventing someone from being involved in financial or non-financial affairs and is called being banned. A person is banned from all affairs such as an unlettered person or some of the affairs such as a dying person. Banning has two forms; it sometimes is in the interests of the one banned from and his/her rights and sometimes for the benefit and the right of others. The objective of this brief article is an evaluation of extent banning applicability in Forensic Medicine topics and its relation with concepts and logics of the Islamic law
GREEN, RAPID, SIMPLE, AND AN EFFECTIVE ONE-POT MULTICOMPONENT STRATEGY FOR SYNTHESIS OF NOVEL DIHYDROPYRANO[2,3-C]PYRAZOL-6-AMINES IN AQUEOUS MEDIUM
Objectives: A series of new dihydropyrano [2,3-c]pyrazol-6-amines in this investigation study, are synthesized by the strategy of one-pot four component reaction in an aqueous medium.
Methods: After getting optimum conditions the reaction of different substituted aromatic aldehydes, ethyl acetoacetate, hydrazine hydrate, and phenylacetonitrile catalysed by Sodium hydrogen sulphate NaHSO4 (0.5 m mol) by refluxing in ethanol-water (1:1) (5 ml) and heated at 80 oC. The synthesized molecules product was filtered, washed with water to get nearly pure product in excellent yield.
Results: The pure products which were got in a short duration, with excellent yield (80 – 87%). Those new compounds were identified by using spectroscopic techniques FT-IR, 1H-NMR and 13C-NMR.
Conclusion: In conclusion, the usefulness of this one-pot multicomponents protocol include easily available starting materials, facile, simple and environment-friendly procedure, and easier separation of products by filtration.
Peer Review History:
Received 5 March 2020; Revised 11 April; Accepted 9 May, Available online 15 May 2020
Academic Editor: Dr. Asia Selman Abdullah, Al-Razi university, Department of Pharmacy, Yemen, [email protected]
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Received file: Reviewer's Comments:
Average Peer review marks at initial stage: 5.0/10
Average Peer review marks at publication stage: 7.0/10
Reviewer(s) detail:
Prof Cyprian Ogbonna ONYEJI, Obafemi Awolowo University, Ile-Ife, Nigeria, [email protected]
Dr. Md. Shahidul Islam, USTC, Chittagong, Bangladesh, [email protected]
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A Gaussian Mixture PHD Filter for Jump Markov System Models
The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets
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Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.
BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection
Comparative evaluation of commercially available supplementary sources of inorganic phosphorus in broiler feed
Comparative evaluation of commercially available supplementary sources of inorganic phosphorus in broiler feed
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Brewing sustainability and strength: biocomposite development through tea waste incorporation in polylactic acid
This study aims to enhance the mechanical properties of polylactic acid by incorporating black tea waste as an economical and sustainable additive capable of being recycled. The black tea waste, after the hot water color removal process, is milled to a fine and uniform powder size. The combination of these particles with polylactic acid is carried out using a twin-screw extruder. Specimens of pure polylactic acid and biocomposites containing 3 and 5 wt% black tea waste are manufactured using a hot press machine at a temperature of 200 °C. The filaments are also successfully extruded for three-dimensional printing purposes. Scanning electron microscopy images reveal that in the polylactic acid-3% tea biocomposite, the tea particles are appropriately dispersed within the polylactic acid matrix. The tensile test results indicate that biocomposite polylactic acid-3% tea has the highest mechanical properties, with a tensile strength of 67 MPa, demonstrating a 34% increase compared to polylactic acid. Furthermore, the biocomposite of polylactic acid-3% tea waste exhibits the best performance with a fracture energy of 2.5 kJ/m 2 in the impact test. Hence, polylactic acid-3% tea biocomposite can be recommended as a suitable sustainable substitute. Finally, numerical simulations are performed on biocomposites containing double keyhole notches. This analysis is conducted to observe the behavior of biocomposite models with double keyhole notch under mixed-mode loading. The critical fracture load of the models is calculated using the strain energy density criterion. It is observed that an increase in the notch inclination angle and notch radius leads to a decrease in the fracture load in the models
A critical analysis of mobility management related issues of wireless sensor networks in cyber physical systems
Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems; its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area
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