Experimental investigation of propeller wake velocity field

Propeller wake wash has been used as an operational ice management technique for many years, particularly in managing small and medium sizes ice floes in arctic and sub-arctic regions. Propeller wake wash is a complicated flow with axial, tangential and radial components of velocity. The jet velocity of a wash has a significant component that is directed upwards towards the free surface of the water. The component of the jet interacting with a free surface of water can be used for ice management, and this is of practical interest for the present investigation. The current study is an experiment on the propeller wake velocity field to investigate the influence of factors affecting propeller wake wash. The experiment was done on a steady wake wash, in the absence of ice, to measure fluid velocity components downstream of the propeller. The investigation was done by varying the major factors affecting propeller wake wash, which were: the power delivered by the propeller, the inclination of the propeller, and the depth of submergence of the propeller. The power delivered by the propeller was measured as propeller shaft rotational speed. The response variables of interest were the mean velocity in the wake, the spatial distribution of velocity, and the variability of the wake flow. The experiment was designed by following the Central Composite Design (CCD) of Response Surface methodology, testing at five levels for each of the three factors. All the experimental data, and the results that were analyzed, are presented in an OERC test report (Amin et al., 2017), and in this thesis

Impact of Work from Home on Employee Performance in Context of Bangladesh

This study investigates the effects of work-from-home on job performance and its mediating factors. This is certainly relevant in the aftermath of the coronavirus outbreak. While acknowledging the need of investigating employees' perspectives in order to create a productive work-from-home environment, this study focuses on the elements that influence job performance. It suggests job satisfaction and motivation as mediating variables to explain how working from home influences employee performance. The study's questionnaire-based data, which has been tailored to the changes caused by current pandemic, was acquired through multiple in-person and online survey of Bangladeshi employees. A total of 260 people actively participated in the assessment. In its further processing, the study utilized structural equation-based framework to address the research questions. In the study, employees reported more job satisfaction and motivation as a result of working from home, resulting in improved job performance. While the relevance of this study is constrained to how these advantages are manifested in Bangladesh, it may have external validity in other pandemic-affected nations. Keywords: Information Technology, Job Motivation, Performance Appraisal, Strategic Human Resource Management, Work Environment. DOI: 10.7176/EJBM/14-22-02 Publication date: November 30th 202

Qalbun Saliim: The Concept of A Clean Heart as A Foundation for Mental Health According to Ibnu Qayyim al Jauziyah

The topic of mental health is being campaigned hotly. It's quite difficult to care for human rights over their mentality because things that are alleged to be strengthening factors for mental health such as wealth, throne, and family on several occasions play the opposite role. This paper tries to offer a solution to the initial phenomenon, by elaborating on the concept of qalbun saliim, or a clean heart in Islam as the foundation for achieving mental health. None other because the heart has an important role in human beings, because if this heart is damaged then the whole human being is damaged. By using qualitative methods (library research) and documentation techniques in data collection, this paper will examine the literature of Ibn Qayyim al Jauziyyah regarding the nature of the creation of the heart, the types of conditions it is in, the diseases that infect it, the healing steps to it, to its urgency for human life. This discussion is interesting to study because the concept of qalbun saliim has not been widely used as a reference by mental health activists

Effect of Alpha-Type external input on annihilation of self-sustained activity in a two population neural field model

In the present work, we investigate the annihilation of persistent localized activity states (bumps) in a Wilson-Cowan type two-population neural field model in response to $\alpha$ -type spatio-temporal external input. These activity states serves as working memory in the prefrontal cortex. The impact of different parameters involved in the external input on annihilation of these persistent activity states is investigated in detail. The $\alpha$ -type temporal function in the external input is closer to natural phenomenon as observed in Roth et. al . ( Nature Neuroscience , vol. 19 (2016), 229–307). Two types of eraser mechanism are used in this work to annihilate the spatially symmetric solutions. Initially, if there is an activity in the network, inhibitory external input with no excitatory part and over excitation with no inhibition in the external input can kill the activity. Our results show that the annihilation of persistent activity states using $\alpha$ -type temporal function in the external input is more roubust and more efficient as compare to triangular one as used by Yousaf et al. ( Neural networks. , vol. 46 (2013), pp. 75–90). It is also found that the relative inhibition time constant plays a crucial role in annihilation of the activity. Runge-Kutta fourth order method has been employed for numerical simulations of this work.publishedVersio

Directly printable compact chipless RFID tag for humidity sensing

In this letter, 8-bit paper based printable chipless tag is presented. The tag not only justifies the green electronic concept but also it is examined for sensing functionality. The compact tag structure comprises of seven L-shaped and one I-shaped dipole structure. These conducting tracks/dipole structures are of silver nano-particle based ink having a conductivity of 1.1 × 107 S/m. Each conducting track yields one bit corresponding to one peak. The tag design is optimized and analyzed for three different flexible substrates i.e. paper, Kapton® HN, and PET. The tag has ability to identify 28 = 256 objects, by using different binary combinations. The variation in length of particular conducting strip results in a shift of peak for that specific conducting track. This shift corresponds to logic state-1. The response of the tag for paper, Kapton® HN, and PET substrates is observed in the frequency band of 2.2–6.1 GHz, 2.4–6.3 GHz, and 2.5–6.5 GHz, respectively. The tag has an attractive nature because of its easy printability and usage of low-cost, flexible substrates. The tag can be deployed in various low-cost sensing applications

Characterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations

Silicon-photonic neural networks (SPNNs) have emerged as promising successors to electronic artificial intelligence (AI) accelerators by offering orders of magnitude lower latency and higher energy efficiency. Nevertheless, the underlying silicon photonic devices in SPNNs are sensitive to inevitable fabrication-process variations (FPVs) stemming from optical lithography imperfections. Consequently, the inferencing accuracy in an SPNN can be highly impacted by FPVs -- e.g., can drop to below 10% -- the impact of which is yet to be fully studied. In this paper, we, for the first time, model and explore the impact of FPVs in the waveguide width and silicon-on-insulator (SOI) thickness in coherent SPNNs that use Mach-Zehnder Interferometers (MZIs). Leveraging such models, we propose a novel variation-aware, design-time optimization solution to improve MZI tolerance to different FPVs in SPNNs. Simulation results for two example SPNNs of different scales under realistic and correlated FPVs indicate that the optimized MZIs can improve the inferencing accuracy by up to 93.95% for the MNIST handwritten digit dataset -- considered as an example in this paper -- which corresponds to a <0.5% accuracy loss compared to the variation-free case. The proposed one-time optimization method imposes low area overhead, and hence is applicable even to resource-constrained design

Development of a mathematical model for the prediction of surface roughness in end milling of stainless steel SS 304

The use of advanced computer-based systems for the selection of optimum conditions of mechanical components during process planning becomes essential for today’s complex products. Computer aided manufacturing (CAM) has widely been implemented to obtain more accurate machining data and to ensure that optimum production is achieved. Machinability of a material provides an indication of its adaptability to be manufactured by a machining process. In general, machinability can be defined as an optimal combination of factors such as low cutting force, high material removal rate, good surface integrity, accurate and consistent workpiece geometrical characteristics, low tool wear rate and good curl or breakdown of chips. In machinability studies investigations, statistical design of experiments is used quite extensively. Statistical design of experiments refers to the process of planning the experiment so that the appropriate data can be analyzed by statistical methods, resulting in valid and objective conclusions [1]. Design and methods such as factorial design, response surface methodology (RSM) and Taguchi methods are now widely use in place of one-factor-at-a-time experimental approach which is time consuming and exorbitant in cost. A machinability model may be defined as a functional relationship between the input of independent cutting variables (speed, feed, depth of cut) and the output known as responses (tool life, surface roughness, cutting force, etc) of a machining process [2]. Response surface methodology (RSM) is a combination of experimental and regression analysis and statistical inference. RSM is a dynamic and foremost important tool of design of experiment (DOE), wherein the relationship between response(s) of a process with its input decision variables is mapped to achieve the objective of maximization or minimization of the response properties [3]. Many machining researchers have used response surface methodology to design their experiments and assess results. Kaye et al [4] used response surface methodology in predicting tool flank wear using spindle speed change. A unique model has been developed which predicts tool flank wear, based on the spindle speed change, provided the initial flank wear at the beginning of the normal cutting stage is known. An empirical equation has also been derived for calculating the initial flank wear, given the speed, feed rate, depth of cut and workpiece hardness. Alauddin et al [5] applied response surface methodology to optimize the surface finish in end milling of Inconel 718 under dry condition A dimensional-accuracy model for the peripheral milling of aluminum alloys under dry and down-milling conditions was presented by Fuh and Chang [6]. Gu et al. [7] presented a new model for the prediction of surface flatness errors in face milling. Their method called equivalent flexibility influence coefficient method. In this chapter, the technique is used to develop a mathematical model that utilizes the response surface roughness methodology and method of experiments to predict the surface roughness when milling stainless steel SS 304 using TiN coated Tungsten carbide inserts