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Workplace Stress: Implications for Employees and Organizations
Get PDFStressors are ubiquitous in today’s society, impacting the personal and professional lives of most people on a daily basis. In terms of stress in organizational settings, employees must deal with an array of workplace stressors, including: Role conflict or ambiguity; Task overload; Difficult relationships with co-workers or supervisor; Harsh environmental conditions; Lack of clear communication or direction; Insufficient resources needed to perform a job; Inadequate pay and benefits; Hostile work environment, including bullying or harassment; Job instability or uncertainty of the organization’s future. It is in the best interest of an organization to establish practices and programs to help reduce or eliminate stressors from the work environment. Employees who are able to perform their jobs with minimal stressors will demonstrate greater engagement, as well as productivity. This will enable an organization to sustain a high level of performance for achieving short and long-term objectives and overall success
Robust desynchronization of Parkinson’s disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation
No full textThe hyperkinetic symptoms of Parkinson’s Disease (PD) are associated with the ensembles of interacting oscillators that cause excess or abnormal synchronous behavior within the Basal Ganglia (BG) circuitry. Delayed feedback stimulation is a closed loop technique shown to suppress this synchronous oscillatory activity. Deep Brain Stimulation (DBS) via delayed feedback is known to destabilize the complex intermittent synchronous states. Computational models of the BG network are often introduced to investigate the effect of delayed feedback high frequency stimulation on partially synchronized dynamics. In this study, we develop a reduced order model of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. This model is able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Train of high frequency pulses in a delayed feedback stimulation has shown deficiencies such as strengthening the synchronization in case of highly fluctuating neuronal activities, increasing the energy consumed as well as the incapability of activating all neurons in a large-scale network. To overcome these drawbacks, we propose a new feedback control variable based on the filtered and linearly delayed LFP recordings. The proposed control variable is then used to modulate the frequency of the stimulation signal rather than its amplitude. In strongly coupled networks, oscillations reoccur as soon as the amplitude of the stimulus signal declines. Therefore, we show that maintaining a fixed amplitude and modulating the frequency might ameliorate the desynchronization process, increase the battery lifespan and activate substantial regions of the administered DBS electrode. The charge balanced stimulus pulse itself is embedded with a delay period between its charges to grant robust desynchronization with lower amplitudes needed. The efficiency of the proposed Frequency Adjustment Stimulation (FAS) protocol in a delayed feedback method might contribute to further investigation of DBS modulations aspired to address a wide range of abnormal oscillatory behavior observed in neurological disorders.https://doi.org/10.1371/journal.pone.020776
Differentiation Of Mesenchymal Stem Cells (Mscs) To Functional Neuron On Graphene-Polycaprolactone Nanoscaffolds
No full textSpinal cord is an important part of the central nervous system that controls all activities of the body. It is a tubular bundle of nerve fibers and tissues connecting brain to nearly all parts of the body. Nerve cells in an adult human body do not divide and make copies of themselves. Therefore, in case of an injury or damage to any part of spinal cord causes permanent changes to strength, sensation and other body functions. The field of tissue engineering and regenerative medicine which aims to replace and repair damaged tissues, organs or cells entails for effective methods for fabricating biological scaffolds. Here we present synthesis of fibrous scaffolds by a process called electrospinning that can provide a microenvironment in-vitro for differentiation and proliferation of functional neurons from mesenchymal stem cells. These nanofibrous PCL scaffolds with graphene as filler materials are engineered in such a way so as to provide topological, biochemical as well as electrical cues that can enhance neurite extension and penetration. Poly(ε-caprolactone) (PCL) is a FDA approved synthetic biodegradable polyester extensively used in biomedical applications. Graphene, a single layer carbon crystal, based nanomaterials have recently gained considerable interest for tissue engineering applications including osteogenic, neural and differentiation in other lineages due to their favorable chemical, electrical and mechanical properties. Our final aim is that the functional tissues or organs developed in vitro shall be implanted inside body to rehabilitate the biological function that was lost due to injury, abnormality or loss
Data Collection from a Sensor Network using a Quadcopter
No full textThe purpose of this project is to implement an automated data collection drone that fly’s to wireless sensor nodes and collects measurement data. This is intended for sensor networks that are placed too far apart to communicate wirelessly or need to operate on very low power in remote locations. An off the shelf quad copter was outfitted with an Arduino and a ZigBee for collecting data from nodes and storing the data on a micro SD card through openlog. The drone uses a GPS module with provided coordinates for navigation. Each node is constructed using an Arduino, a ZigBee and a temperature sensor
Energy-Efficient Hybrid key Distribution Scheme for Wireless Sensor Networks
No full textKey distribution in Wireless Sensor Networks (WSNs) is challenging issue because a WSN is a network of resource-constrained nodes that carry limited-power batteries. Therefore, a key distribution scheme for WSNs must be an energy and memory efficient. In this poster, we proposed an energy-efficient hybrid key distribution scheme that is designed to suit the resource-constrained devices such as WSNs. We utilized Arduino UNO microcontroller and OPNET Modeler to investigate our proposed scheme in comparison to key distribution schemes in the literature. The findings show that our scheme achieves security and consumes less energy compared to other schemes in the literature. less energy
Standard Analytics Process - A step by step approach
No full textUB's analytics process is a step by step method that provides a template for how to approach and analyze data. It is also an example of how explicit knowledge can be documented and taught through process definition. Though the target audience for this process is business and data analysts, process details are provided to facilitate its application in other disciplines. This analytics process has been mapped into four major segments: "research question," "develop plan," "explore data" and "decision making." The four segments are broken into detailed steps. While variations to this process may be required in practice, this document depicts a standard process shared by the University of Bridgeport's Analytics and Systems team at the Ernest C. Trefz School of Business. While an introduction to basic algebra and statistics provides a strong foundation for this process, business acumen is a must as everything starts with a business need. This "living document" is intended to be dynamic. Subsequent changes are welcomed pursuant to version control
This Riddle Will Show You If You Are Gender Bias
No full textFaculty Research Day 2018: Graduate Student Poster 1st PlaceGender bias is prominent be it work or home. This bias was not even identified until it was pointed out after the survey. The purpose of this survey is to make people realize our brain overthinks or gets manipulated easily even when the answer is just stating the obvious
Enhancing Nurse Residency Programs with High Quality Simulation and Debriefing
No full textInadequate clinical judgment in newly graduated nurses has been positively linked to decreased patient outcomes and lower nursing job satisfaction, compelling staff nurse educators to employ innovative evidence-based methods to remedy this known clinical-practice gap. Use of quality simulation and theory-based debriefing has been shown in the evidence to positively contribute to clinical judgment formation in novice nurses. Incorporating quality simulation and theory-based debriefing into nurse residency programs has the potential to increase job satisfaction and decrease turnover, resulting in better outcomes and quality for patients and less cost for health care organizations. Methods: This was a quasi-experimental evidence-based change project with pretest/post-test design, aimed at increasing clinical judgment and learner satisfaction in novice nurses through evidence-based teaching measures. Debriefing for Meaningful learning, a theory-based debriefing pedagogy, and quality simulation scenarios were added to an existing nurse residency program. A pre and post learner satisfaction survey, the Debriefing Experience Scale was given to measure satisfaction with program changes. The scale is a two-part 5 point Likert scale which measures the participant experience as well as areas of perceived importance. Current evidence supported the validity and reliability of the scale with student nurses, although further testing had been encouraged with other participant types. Results: A paired samples T-test correlation was conducted in comparing the pre- and post-intervention experience and importance subsets completed by the program participants. The agreement portion of the scale measured a mean of 4.54 in June, 4.47 in July and 4.42 in August. The importance portion of the scale measured a mean of 4.39 in June, 4.25 in July and 4.23 in August revealing no statistical significance with program changes. Test-retest reliability was conducted to further validate the validity and reliability of the Debriefing Experience Scale with a post-graduate, post-licensure population. The Cronbach’s alpha coefficient remained consistently greater than .894 within each month of the program. These findings further validate that the DES is a valid and reliable tool to determine participant satisfaction following a simulation and debriefing experience. Conclusions: Simulation and Debriefing for Meaningful Learning are evidence-based teaching methodologies which have been shown in the literature to positively affect clinical judgment development in participants. The data which was collected looked at the satisfaction of participants involved in a simulation and theory-based debriefing pedagogy, as compared to simulation and informal debriefing measures. Although no statistically significant increase in participant satisfaction could be empirically determined, it can be surmised that learner experience was enhanced through implemented changes. Participants reported high satisfaction with the program both pre and post implementation. As participants remained satisfied despite program changes, use of simulation and DML within residency programs can be supported. In addition, the validity and reliability of the Debriefing Experience Scale were established with a postgraduate population. These findings further validate that the DES is a valid and reliable tool to determine participant satisfaction following a simulation and debriefing experience
A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware
No full textDespite the popularity of wireless sensor networks (WSNs) in a wide range of applications, the security problems associated with WSNs have not been completely resolved. Since these applications deal with the transfer of sensitive data, protection from various attacks and intrusions is essential. From the current literature, we observed that existing security algorithms are not suitable for large-scale WSNs due to limitations in energy consumption, throughput, and overhead. Middleware is generally introduced as an intermediate layer between WSNs and the end user to address security challenges. However, literature suggests that most existing middleware only cater to intrusions and malicious attacks at the application level rather than during data transmission. This results in loss of nodes during data transmission, increased energy consumption, and increased overhead. In this research, we introduce an intelligent middleware based on an unsupervised learning technique called the Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a discriminator (D) network. The G network generates fake data that is identical to the data from the sensor nodes; it combines fake and real data to confuse the adversary and stop them from differentiating between the two. This technique completely eliminates the need for fake sensor nodes, which consume more power and reduce both throughput and the lifetime of the network.
The D network contains multiple layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN.
The framework is implemented in Python with experiments performed using Keras. The results illustrate that the suggested algorithm not only improves the accuracy of the data but also enhances its security by protecting it from attacks. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques. Simulation results show that the proposed technique provides higher throughput and increases successful data rates while keeping the energy consumption low
Improving Human Face Recognition Using Deep Learning Based Image Registration And Multi-Classifier Approaches
No full textFace detection, registration, and recognition have become a fascinating field for researchers. The motivation behind the enormous interest in the topic is the need to improve the accuracy of many real-time applications. Countless methodologies have been acknowledged and presented in the past years. The complexity of the human face visual and the significant changes based on different effects make it more challenging to design as well as implementing a powerful computational system for object recognition in addition to human face recognition. Using supervised learning often requires extensive training for the computer which results in high execution times. It is an essential step in the face recognition to apply strong preprocessing approaches such as face registration to achieve a high recognition accuracy rate. Although there are exist approaches do both detection and recognition, we believe the absence of a complete end-to-end system capable of performing recognition from an arbitrary scene is in large part due to the difficulty in alignment. Often, the face registration is ignored, with the assumption that the detector will perform a rough alignment, leading to suboptimal recognition performance. In this research, we presented an enhanced approach to improve human face recognition using a back-propagation neural network (BPNN) and features extraction based on the correlation between the training images. A key contribution of this paper is the generation of a new set called the T-Dataset from the original training data set, which is used to train the BPNN. We generated the T-Dataset using the correlation between the training images without using a common technique of image density. The correlated T-Dataset provides a high distinction layer between the training images, which helps the BPNN to converge faster and achieve better accuracy. Data and features reduction is essential in the face recognition process, and researchers have recently focused on the modern neural network. Therefore, we used using a classical conventional Principal Component Analysis (PCA) and Local Binary Patterns (LBP) to prove that there is a potential improvement even using traditional methods. We applied five distance measurement algorithms and then combined them to obtain the T-Dataset, which we fed into the BPNN. We achieved higher face recognition accuracy with less computational cost compared with the current approach by using reduced image features. We test the proposed framework on two small data sets, the YALE and AT&T data sets, as the ground truth. We achieved tremendous accuracy. Furthermore, we evaluate our method on one of the state-of-the-art benchmark data sets, Labeled Faces in the Wild (LFW), where we produce a competitive face recognition performance. In addition, we presented an enhanced framework to improve the face registration using deep learning model. We used deep architectures such as VGG16 and VGG19 to train our method. We trained our model to learn the transformation parameters (Rotation, scaling, and shifting). By leaning the transformation parameters, we will able to transfer the image back to the frontal domain. We used the LFW dataset to evaluate our method, and we achieve high accuracy