Search results for: eligibility metric

Authors: Dorota Witkowska, Paweł Gosek, Lukasz Swiecicki, Wojciech Jernajczyk, Bruce J. West, Miroslaw Latka

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In clinical practice, the selection of an antidepressant often degrades to lengthy trial-and-error. In this work we employ a normalized wavelet power of alpha waves as a biomarker of antidepressant treatment response. This novel EEG metric takes into account both non-stationarity and intersubject variability of alpha waves. We recorded resting, 19-channel EEG (closed eyes) in 22 inpatients suffering from unipolar (UD, n=10) or bipolar (BD, n=12) depression. The EEG measurement was done at the end of the short washout period which followed previously unsuccessful pharmacotherapy. The normalized alpha wavelet power of 11 responders was markedly different than that of 11 nonresponders at several, mostly temporoparietal sites. Using the prediction of treatment response based on the normalized alpha wavelet power, we achieved 81.8% sensitivity and 81.8% specificity for channel T4.

Keywords: alpha waves, antidepressant, treatment outcome, wavelet

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Authors: Hummad Ch. Umar

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Most of the developing economies, including Pakistan, are confronted with high level of external debt which is adversely affecting their economic performance. The hypothesis of debt overhang is often used to assess the negative relationship between foreign debt and the economic growth of the indebted country. As first objective of the present study, this hypothesis is tested by using Pooled OLS (POLS), Generalized Method of Moment (GMM), Random Effect (RE), and Fixed effect (FE) techniques. As second objective, the study uses the concept of debt Laffer Curve to determine the eligibility condition of the indebted countries for the relief programs. According to this approach, countries lying on the right side of the Laffer Curve are stated to be trapped in the strong debt overhang making them unable to come out of the vicious circle of low growth and high foreign debt. The empirical analysis confirms that only two countries out of twenty two completely fulfill the conditions of being eligible for the debt relief. All other countries continue to face debt burden of different magnitudes. The study further confirms that the debt relief alone is not sufficient for overcoming the debt problem. Instead, sound economic policies and conducive investment decisions are required to lay the foundations of long-term growth and development. Debt relief should be the option for only those countries that meet a minimum measurable criterion of good governance, economic freedom, and consistency of policies.

Keywords: external debt, debt burden, debt overhang, debt laffer curve, debt relief, investment decisions

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Authors: M. Mohammed Raoof, A. Enkhtaivan, H. Aljuaid

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This study follows the design science research methodology to design and implement a smartphone application artifact. The developed artifact was evaluated through three phases. The System Usability Scale (SUS) metric was used for the evaluation. The designed artifact aims to spread awareness about reducing air pollution, decreasing lung cancer development, and checking the air quality status in Southern California Counties. Participants have been drawn for a pilot study to facilitate awareness of air pollution. The study found that smartphone applications have a beneficial effect on the study’s aims.

Keywords: air pollution, design science research, indoor air pollution, lung cancer, outdoor air pollution, smartphone application

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.

Keywords: pattern recognition, global terrorism database, Manhattan distance, k-means clustering, terrorism data analysis

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Authors: Maria Grazia Albanesi, Riccardo Amadeo

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This study presents a new model of the human image quality assessment process: the aim is to highlight the foundations of the image quality metrics proposed in literature, by identifying the cognitive/physiological or mathematical principles of their development and the relation with the actual human quality assessment process. The model allows to create a novel categorization of objective and subjective image quality metrics. Our work includes an overview of the most used or effective objective metrics in literature, and, for each of them, we underline its main characteristics, with reference to the rationale of the proposed model and categorization. From the results of this operation, we underline a problem that affects all the presented metrics: the fact that many aspects of human biases are not taken in account at all. We then propose a possible methodology to address this issue.

Keywords: eye-tracking, image quality assessment metric, MOS, quality of user experience, visual perception

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Authors: Nor Fazlina Iryani Abdul Hamid, Mohamad Khatim Hasan

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Evolution of software quality measurement has been started since McCall introduced his quality model in year 1977. Starting from there, several software quality models and software quality measurement methods had emerged but none of them focused on telecommunication industry. In this paper, the implementation of software quality measurement system for telecommunication industry was compulsory to accommodate the rapid growth of telecommunication industry. The quality value of the telecommunication related software could be calculated using this system by entering the required parameters. The system would calculate the quality value of the measured system based on predefined quality metrics and aggregated by referring to the quality model. It would classify the quality level of the software based on Net Satisfaction Index (NSI). Thus, software quality measurement system was important to both developers and users in order to produce high quality software product for telecommunication industry.

Keywords: software quality, quality measurement, quality model, quality metric, net satisfaction index

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Authors: Usamah Al-Ali

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We explore the effect of expanding space on the exoticity of the matter supporting a traversable Lorentzian wormhole of zero radial tide whose line element is given by ds2 = dt^2 − a^2(t)[ dr^2/(1 − kr2 −b(r)/r)+ r2dΩ^2 in the context of General Relativity. This task is achieved by deriving the Einstein field equations for anisotropic matter field corresponding to the considered cosmological wormhole metric and performing a classification of their solutions on the basis of a variable equations of state (EoS) of the form p = ω(r)ρ. Explicit forms of the shape function b(r) and the scale factor a(t) arising in the classification are utilized to construct the corresponding energy-momentum tensor where the energy conditions for each case is investigated. While the violation of energy conditions is inevitable in case of static wormholes, the classification we performed leads to interesting solutions in which this violation is either reduced or eliminated.

Keywords: general relativity, Einstein field equations, energy conditions, cosmological wormhole

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Authors: F. S. Irwansyah, I. Farida, Y. Maulana

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Studying chemistry requires the ability to understand three levels of understanding in the form of macroscopic, submicroscopic and symbolic, but the lack of emphasis on the submicroscopic level leads to the understanding of chemical concepts becoming incomplete, due to the limitations of the tools capable of providing visualization of submicroscopic concepts. The purpose of this study describes the stages of making augmented reality learning media on the concept of molecular geometry and analyze the feasibility test result of augmented reality learning media on the concept of molecular geometry. This research uses Research and Development (R & D) method which produces a product of AR learning media on molecular geometry concept and test the effectiveness of the product. Research stages include concept analysis and learning indicators, design development, validation, feasibility, and limited testing. The stages of validation and limited trial are aimed to get feedback in the form of assessment, suggestion and improvement on learning aspect, material substance aspect, visual communication aspect and software engineering aspects and media feasibility in terms of media creation purpose to be used in learning. The results of the overall feasibility test obtained r-calculation 0,7-0,9 with the interpretation of high feasibility value, whereas the result of limited trial got the percentage of eligibility with the average value equal to 70,83-92,5%. This percentage indicates that AR's learning media product on the concept of molecular geometry, deserves to be used as a learning resource.

Keywords: android, augmented reality, chemical learning, geometry

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Authors: Toledo-Fernandez Aldebaran

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Introduction: One of the least explored cognitive functions in relation with depression is the one related to musical stimuli. Music perception and memory can become impaired as well. The term amusia is used to define a type of agnosia caused by damage to basic processes that creates a general inability to perceive music. Therefore, the main objective is to explore performance-based and self-report deficits in music perception and memory on people with major depressive disorder (MDD). Method: Data was collected through April-October 2021 recruiting people who met the eligibility criteria and using the Montreal Battery of Evaluation of Amusia (MBEA) to evaluate performance-based music perception and memory, along with the module for depression of the Mini International Neuropsychiatric Interview, and the Amusic Dysfunction Inventory (ADI) which evaluates the participants’ self-report concerning their abilities in music perception. Results: 64 participants were evaluated. The main study, referring to analyzing the differences between people with MDD and the control group, only showed one statistical difference on the Interval subtest of the MBEA. No difference was found in the dimensions assessed by the ADI. Conclusion: Deficits in interval perception can be explained by mental fatigue, to which people with depression are more vulnerable, rather than by specific deficits in musical perception and memory associated with depressive disorder. Additionally, significant associations were found between musical deficits as observed by performance-based evidence and music dysfunction according to self-report, which could suggest that some people with depression are capable of detecting these deficits in themselves.

Keywords: depression, amusia, music, perception, memory

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Authors: Claudia Troccoli

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Childbearing constitutes one of the key factors affecting labour market differences between men and women, accounting for almost a quarter of the gender wage gap. Family leave policies, such as maternity, paternity, and parental leave, represent potential key policy tools to address these inequalities, as they can promote mothers' job continuity and career progression. This paper analyses four major reforms implemented in Finland between the 1960s and the early 1980s. It studies the effects of these maternity and parental leave extensions on mothers' short- and long-run labour market outcomes. Eligibility to longer leave was determined on the basis of the child's date of birth. Therefore, estimation of the causal effects of the reforms is possible by exploiting random variation in children's birthdates and comparing the outcomes of mothers giving birth just before and just after the reform cutoff date. Overall, the three maternity leave reforms did not significantly improve mothers' earnings or employment rates. On the contrary, the estimates, although imprecise, seem to indicate negative effects on women's labour market outcomes. The extension of parental leave is, on the other hand, the only reform that improved mothers' short- and long-term labour market outcomes, both in terms of earnings and employment rate. At the same time, fathers appeared to be negatively affected by the reform. These results provide suggestive evidence that shareable parental leave might have more beneficial effects on mothers' job continuity, as it weakens the connotation of childcare as a task reserved for mothers.

Keywords: family policies, Finland, maternal labour market outcomes, maternity leave

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Authors: Thomas Odaker, Dieter Kranzlmueller, Jens Volkert

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We present an approach to triangle mesh simplification designed to be executed on the GPU. We use a quadric error metric to calculate an error value for each vertex of the mesh and order all vertices based on this value. This step is followed by the parallel removal of a number of vertices with the lowest calculated error values. To allow for the parallel removal of multiple vertices we use a set of per-vertex boundaries that prevent mesh foldovers even when simplification operations are performed on neighbouring vertices. We execute multiple iterations of the calculation of the vertex errors, ordering of the error values and removal of vertices until either a desired number of vertices remains in the mesh or a minimum error value is reached. This parallel approach is used to speed up the simplification process while maintaining mesh topology and avoiding foldovers at every step of the simplification.

Keywords: computer graphics, half edge collapse, mesh simplification, precomputed simplification, topology preserving

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Authors: Blaise Fyama, Elie Museng, Grace Mukoma

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In this article we present a java implementation of video telephony using the SIP protocol (Session Initiation Protocol). After a functional analysis of the SIP protocol, we relied on the work of Italian researchers of University of Parma-Italy to acquire adequate libraries for the development of our own communication tool. In order to optimize the code and improve the prototype, we used, in an incremental approach, test techniques based on a static analysis based on the evaluation of the complexity of the software with the application of metrics and the number cyclomatic of Mccabe. The objective is to promote the emergence of local start-ups producing IP video in a well understood local context. We have arrived at the creation of a video telephony tool whose code is optimized.

Keywords: static analysis, coding complexity metric mccabe, Sip, uml

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Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions

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Authors: Seyed-Ali Sadegh-Zadeh, Kaveh Kavianpour, Hamed Atashbar, Elham Heidari, Saeed Shiry Ghidary, Amir M. Hajiyavand

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Evaluation metrics play a critical role in assessing the performance of machine learning models. In this review paper, we provide a comprehensive overview of performance measurement approaches for machine learning models. For each category, we discuss the most widely used metrics, including their mathematical formulations and interpretation. Additionally, we provide a comparative analysis of performance measurement approaches for metric combinations. Our review paper aims to provide researchers and practitioners with a better understanding of performance measurement approaches and to aid in the selection of appropriate evaluation metrics for their specific applications.

Keywords: evaluation metrics, performance measurement, supervised learning, unsupervised learning, reinforcement learning, model robustness and stability, comparative analysis

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Authors: Hyun Chul Koag, Hyun-Sik Ahn

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In this paper, we propose a design approach for the safety mechanism of an actuator used in a commercial vehicle’s EPS system. As the number of electric/electronic system in a vehicle increases, the importance of the functional safety has been receiving much attention. EPS(Electric Power Steering) systems for commercial vehicles require large power than passenger vehicles, and hence, dual motor can be applied to get more torque. We show how to formulate the development process for the design of hardware and software of an EPS system using dual motors. A lot of safety mechanisms for the processor, sensors, and memory have been suggested, however, those for actuators have not been fully researched. It is shown by metric analyses that the target ASIL(Automotive Safety Integrated Level) is satisfied in the point of view of hardware of EPS controller.

Keywords: safety mechanism, functional safety, commercial vehicles, electric power steering

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Authors: Kenny Raharjo, Ramon Lawrence

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Game designers have the challenging task of building games that engage players to spend their time and money on the game. There are an infinite number of game variations and design choices, and it is hard to systematically determine game design choices that will have positive experiences for players. In this work, we demonstrate how multi-arm bandits can be used to automatically explore game design variations to achieve improved player metrics. The advantage of multi-arm bandits is that they allow for continuous experimentation and variation, intrinsically converge to the best solution, and require no special infrastructure to use beyond allowing minor game variations to be deployed to users for evaluation. A user study confirms that applying multi-arm bandits was successful in determining the preferred game variation with highest play time metrics and can be a useful technique in a game designer's toolkit.

Keywords: game design, multi-arm bandit, design exploration and data mining, player metric optimization and analytics

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Authors: Alicja Reuben, Amira Annabi

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In this paper, we explore the relationship between the selection of valuation techniques by investment banks and the banks’ risk perceptions and performance in the context of the theory of performativity. We use inferential statistics to study these relationships by building a unique dataset based on the disclosure of 12 investment banks’ 2012-2015 annual financial statements. Moreover, we create two constructs, namely intensity of use and risk perception. We measure the intensity of use as a frequency metric of how often a particular bank adopts valuation techniques for a particular asset or liability. We measure risk perception based on disclosed ranges of values for unobservable inputs. Our results are twofold: we find a significant negative correlation between (1) intensity of use and investment bank performance and (2) intensity of use and risk perception. These results indicate that a performative process takes place, and the valuation techniques are enacting their environment.

Keywords: language, linguistics, performativity, financial techniques

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Authors: Junling Li, Fang Meng, Yichun Zhang

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Image saliency detection has been long studied, while several challenging problems are still unsolved, such as detecting saliency inaccurately in complex scenes or suppressing salient objects in the image borders. In this paper, we propose a new saliency detection algorithm in order to solving these problems. We represent the image as a graph with superixels as nodes. By considering appearance similarity between the boundary and the background, the proposed method chooses non-saliency boundary nodes as background priors to construct the background probability model. The probability that each node belongs to the model is computed, which measures its similarity with backgrounds. Thus we can calculate saliency by the transformed probability as a metric. We compare our algorithm with ten-state-of-the-art salient detection methods on the public database. Experimental results show that our simple and effective approach can attack those challenging problems that had been baffling in image saliency detection.

Keywords: visual saliency, background probability, boundary knowledge, background priors

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Authors: Tracey Bone

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Deaf inmates, whose first or preferred language is a Signed Language, experience barriers to accessing the necessary two-way communication with correctional staff, and the educational and social programs that will enhance their eligibility for conditional release from the federal prison system in Canada. The development of visual content to enhance the knowledge and skill development of correctional staff is a contemporary strategy intended to significantly improve the correctional experience for deaf inmates. This presentation reports on the development of two distinct training videos created to enhance staff’s understanding of the needs of deaf inmates; one a two-part simulation of an interaction with a deaf inmate, the second an interview with a deaf academic. Part one of video one demonstrates the challenges and misunderstandings inherent in communicating across languages without a qualified sign language interpreter; the second part demonstrates the ease of communication when communication needs are met. Video two incorporates the experiences of a deaf academic to provide the cultural grounding necessary to educate staff in the unique experiences associated with being a visual language user. Lack of staff understanding or awareness of deaf culture and language must not be acceptable reasons for the inadequate treatment of deaf visual language users in federal prisons. This paper demonstrates a contemporary approach to meeting the human rights and needs of this unique and often ignored inmate subpopulation. The deaf community supports this visual approach to enhancing staff understanding of the unique needs of this population. A study of its effectiveness is currently underway.

Keywords: accommodations, American Sign Language (ASL), deaf inmates, sensory deprivation

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Sung Ham, Chanho Song, Jiabin Wu

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Employees are commonly incentivized on both quantity and quality performance and much of the extant literature focuses on demonstrating that multitasking incentives lead to tradeoffs. Alternatively, we consider potential solutions to the tradeoff problem from both a theoretical and an experimental perspective. Across two field experiments from a call center, we find that tradeoffs can be mitigated when incentives are jointly enhanced across tasks, where previous research has suggested that incentives be reduced instead of enhanced. In addition, we also propose and test, in a laboratory setting, the implications of revising the metric used to assess quality. Our results indicate that metrics can be adjusted to align quality and quantity more efficiently. Thus, this alignment has the potential to thwart the classic tradeoff problem. Finally, we validate our findings with an economic experiment that verifies that effort is largely consistent with our theoretical predictions.

Keywords: incentives, multitasking, field experiment, experimental economics

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Authors: Sarina Sahmeddini, Fahimeh Safarpour, Forough Pazhuheian

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With the concepts of minimally invasive treatment and preventive dentistry gaining more and more recognition by dentists, there are many published clinical trials comparing the use of the erbium laser with traditional drilling for caries removal. However, the efficacy of the erbium laser is still controversial. The aim of this review study is to compare the effects of tooth preparation by laser irradiation and conventional preparation by bur to identify the best means for cavity preparation and reduction of recurrent caries. Randomized controlled trials, controlled clinical trials, and prospective, and retrospective cohort studies were included in this review. The eligibility criteria included studies in humans’ permanent teeth in which cavities were conducted in their cervical third and proximal surfaces. PubMed, Google scholar, and Scopus about Er:YAG laser and bur prepared cavities were carried out. The studies’ details were organized in four tables according to the groups: (1) Microleakage; (2) Morphological changes; (3) Microhardness; and (4) Bond strength. The initial search resulted in 134 articles, 12 studies published from 2012 up to March 2020 were included in this review. According to the risk of bias evaluation, all studies were classified as high quality. Clinical implications: Er:YAG lasers with the energy levels between 250 to 300 mJ can be proper alternatives to conventional burs, as minimal invasive instruments with no significant differences or better results in microleakage, microhardness, and bond strength compared with conventional burs. In conclusion, Er:YAG laser irradiations accompanied by phosphoric acid etching can reduce the chance of recurrent carries.

Keywords: lasers, drilling, caries, micro leakage

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Authors: Mohammadamin Hashemi, Mohammadreza Kashizadeh

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Droughts pose a significant threat to sustainable water resource management, agriculture, and socioeconomic sectors, particularly in the field of climate change. This study investigates drought simulation using rainfall-runoff modelling in the Cuneo district, Italy, over the past 60-year period. The study leverages the TUW model, a lumped conceptual rainfall-runoff model with a semi-distributed operation capability. Similar in structure to the widely used Hydrologiska Byråns Vattenbalansavdelning (HBV) model, the TUW model operates on daily timesteps for input and output data specific to each catchment. It incorporates essential routines for snow accumulation and melting, soil moisture storage, and streamflow generation. Multiple catchments' discharge data within the Cuneo district form the basis for thorough model calibration employing the Kling-Gupta Efficiency (KGE) metric. A crucial metric for reliable drought analysis is one that can accurately represent low-flow events during drought periods. This ensures that the model provides a realistic picture of water availability during these critical times. Subsequent validation of monthly discharge simulations thoroughly evaluates overall model performance. Beyond model development, the investigation delves into drought analysis using the robust Standardized Runoff Index (SRI). This index allows for precise characterization of drought occurrences within the study area. A meticulous comparison of observed and simulated discharge data is conducted, with particular focus on low-flow events that characterize droughts. Additionally, the study explores the complex interplay between land characteristics (e.g., soil type, vegetation cover) and climate variables (e.g., precipitation, temperature) that influence the severity and duration of hydrological droughts. The study's findings demonstrate successful calibration of the TUW model across most catchments, achieving commendable model efficiency. Comparative analysis between simulated and observed discharge data reveals significant agreement, especially during critical low-flow periods. This agreement is further supported by the Pareto coefficient, a statistical measure of goodness-of-fit. The drought analysis provides critical insights into the duration, intensity, and severity of drought events within the Cuneo district. This newfound understanding of spatial and temporal drought dynamics offers valuable information for water resource management strategies and drought mitigation efforts. This research deepens our understanding of drought dynamics in the Cuneo region. Future research directions include refining hydrological modelling techniques and exploring future drought projections under various climate change scenarios.

Keywords: hydrologic extremes, hydrological drought, hydrological modelling, machine learning, rainfall-runoff modelling

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Authors: Guang-Ho Cha

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This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results.

Keywords: context-sensitive search, image search, similarity ranking, similarity search

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Authors: Merve Kadioğlu, Nevin H. Şahin

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Purpose: This research is organized to determine the influence of the web-based learning program. The program has been developed to gain information about normal delivery skill that is one of the topics of nursing students who take the woman health and illness. Material and Methods: The methodology of this study was applied as pre-test post-test single-group quasi-experimental. The pilot study consisted of 28 nursing student study groups who agreed to participate in the study. The findings were gathered via web-based technologies: student information form, information evaluation tests, Web Based Training Material Evaluation Scale and web-based learning environment feedback form. In the analysis of the data, the percentage, frequency and Wilcoxon Signed Ranks Test were used. The Web Based Instruction Program was developed in the light of full learning model, Mayer's research-based multimedia development principles and Gagne's Instructional Activities Model. Findings: The average scores of it was determined in accordance with the web-based educational material evaluation scale: ‘Instructional Suitability’ 4.45, ‘Suitability to Educational Program’ 4.48, ‘Visual Adequacy’ 4.53, ‘Programming Eligibility / Technical Adequacy’ 4.00. Also, the participants mentioned that the program is successful and useful. A significant difference was found between the pre-test and post-test results of the seven modules (p < 0.05). Results: According to pilot study data, the program was rated ‘very good’ by the study group. It was also found to be effective in increasing knowledge about normal labor.

Keywords: normal delivery, web-based learning, nursing students, e-learning

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Authors: Naji Ali Albakay, Abdulrahman Alothaim, Isa Barshushi

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The most common quadrature amplitude modulator (QAM) applies two Mach-Zehnder Modulators (MZM) and one phase shifter to generate high order modulation format. The bias of MZM changes over time due to temperature, vibration, and aging factors. The change in the biasing causes distortion to the generated QAM signal which leads to deterioration of bit error rate (BER) performance. Therefore, it is critical to be able to lock MZM’s Q point to the required operating point for good performance. We propose a technique for automatic bias control (ABC) of QAM transmitter using BER measurements and gradient descent optimization algorithm. The proposed technique is attractive because it uses the pertinent metric, BER, which compensates for bias drifting independently from other system variations such as laser source output power. The proposed scheme performance and its operating principles are simulated using OptiSystem simulation software for 4-QAM and 16-QAM transmitters.

Keywords: automatic bias control, optical fiber communication, optical modulation, optical devices

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Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

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The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool

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Authors: Huda Algharib, Amal Algharib, Hanan Algharib, Ali Mohammad Alqudah

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Image registration is an important topic for many imaging systems and computer vision applications. The standard image registration techniques such as Mutual information/ Normalized mutual information -based methods have a limited performance because they do not consider the spatial information or the relationships between the neighbouring pixels or voxels. In addition, the amount of image noise may significantly affect the registration accuracy. Therefore, this paper proposes an efficient method that explicitly considers the relationships between the adjacent pixels, where the gradient information of the reference and scene images is extracted first, and then the cosine similarity of the extracted gradient information is computed and used to improve the accuracy of the standard normalized mutual information measure. Our experimental results on different data types (i.e. CT, MRI and thermal images) show that the proposed method outperforms a number of image registration techniques in terms of the accuracy.

Keywords: image registration, mutual information, image gradients, image transformations

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Authors: Marie Madeleine Sarzi Inacio, Ligiane Carolina Leite Dauzacker, Rodrigo Henriques Lopes Da Silva

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The climate changes resulting from human activity have increased interest in more sustainable production practices to reduce and offset pollutant emissions. Brazil, with its vast areas capable of carbon absorption, holds a significant advantage in this context. However, to optimize the country's sustainable potential, it is important to establish a robust carbon market with clear rules for the eligibility and validation of projects aimed at reducing and offsetting Greenhouse Gas (GHG) emissions. In this study, our objective is to conduct a feasibility analysis through a case study to evaluate the implementation of an urban forest credits standard in Brazil, using the Urban Forest Credits (UFC) model implemented in the United States as a reference. Thus, the city of Ribeirão Preto, located in Brazil, was selected to assess the availability of green areas. With the CO2 emissions value from the metalworking industry, it was possible to analyze information in the case study, considering the activity. The QGIS software was used to map potential urban forest areas, which can connect to various types of geospatial databases. Although the chosen municipality has little vegetative coverage, the mapping identified at least eight areas that fit the standard definitions within the delimited urban perimeter. The outlook was positive, and the implementation of projects like Urban Forest Credits (UFC) adapted to the Brazilian reality has great potential to benefit the country in the carbon market and contribute to achieving its Greenhouse Gas (GHG) emission reduction goals.

Keywords: carbon neutrality, metalworking industry, carbon credits, urban forestry credits

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Authors: N. Anjana

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Worldwide, the prevalence of mental illnesses is increasing. The issues of persons with mental illness and their caregivers have been well documented in the literature. However, data regarding the factors affecting the caregiving experience of children with parental mental illnesses is sparse. This systematic review aimed to examine the existing literature of the factors affecting the caregiving experience of children of parents with mental illnesses. A comprehensive search of databases such as PubMed, EBSCO, JSTOR, ProQuest Central, Taylor and Francis Online, and Google Scholar were performed to identify peer-reviewed papers examining the factors associated with caregiving experiences of children with parental mental illnesses such as schizophrenia and major depression, for the 10-year period ending November 2019. Two researchers screened studies for eligibility. One researcher extracted data from eligible studies while a second performed verification of results for accuracy and completeness. Quality appraisal was conducted by both reviewers. Data describing major factors associated with caregiving experiences of children with parental mental illnesses were synthesized and reported in narrative form. Five studies were considered eligible and included in this review. Findings are organized under major themes such as the impact of parental mental illness on children’s daily life, how children provide care to their mentally ill parents as primary carers, social and relationship factors associated with their caregiving, positive and negative experiences in caregiving and how children cope with their experiences with parental mental illnesses. Literature relating to the caregiving experiences of children with parental mental illnesses is sparse. More research is required to better understand the children’s caregiving experiences related to parental mental illnesses so as to better inform management for enhancing their mental health, wellbeing, and caregiving practice.

Keywords: caregiving experience, children, parental mental illnesses, wellbeing

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