Search results for: variable precision rough sets theory

Authors: Said Abdullah Al Saifi

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The goal of this research is to examine the impact of trust, motivation, and national culture on knowledge sharing within the context of electronic mail. This study is quantitative and survey based. In order to conduct the research, 200 students from a leading university in New Zealand were chosen randomly to participate in a questionnaire survey. Motivation and trust were found to be significantly and positively related to knowledge sharing. The research findings illustrated that face saving, face gaining, and individualism positively moderates the relationship between motivation and knowledge sharing. In addition, collectivism culture negatively moderates the relationship between motivation and knowledge sharing. Moreover, the research findings reveal that face saving, individualism, and collectivism culture positively moderate the relationship between trust and knowledge sharing. In addition, face gaining culture negatively moderates the relationship between trust and knowledge sharing. This study sets out several implications for researchers and practitioners. The study produces an integrative model that shows how attributes of national culture impact knowledge sharing through the use of emails. A better understanding of the relationship between knowledge sharing and trust, motivation, and national culture differences will increase individuals’ ability to make wise choices when sharing knowledge with those from different cultures.

Keywords: knowledge sharing, motivation, national culture, trust

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Authors: Gule Teri

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The effective validation of analytical methods forms a crucial component of pharmaceutical manufacturing. However, traditional validation techniques can occasionally fail to fully account for inherent variations within datasets, which may result in inconsistent outcomes. This deficiency in validation accuracy is particularly noticeable when quantifying low concentrations of active pharmaceutical ingredients (APIs), excipients, or impurities, introducing a risk to the reliability of the results and, subsequently, the safety and effectiveness of the pharmaceutical products. In response to this challenge, we introduce an enhanced, tolerance-based Design of Experiments (DoE) approach for the validation of analytical methods. This approach distinctly measures variability with reference to tolerance or design margins, enhancing the precision and trustworthiness of the results. This method provides a systematic, statistically grounded validation technique that improves the truthfulness of results. It offers an essential tool for industry professionals aiming to guarantee the accuracy of their measurements, particularly for low-concentration components. By incorporating this innovative method, pharmaceutical manufacturers can substantially advance their validation processes, subsequently improving the overall quality and safety of their products. This paper delves deeper into the development, application, and advantages of this tolerance-based DoE approach and demonstrates its effectiveness using High-Performance Liquid Chromatography (HPLC) data for verification. This paper also discusses the potential implications and future applications of this method in enhancing pharmaceutical manufacturing practices and outcomes.

Keywords: tolerance-based design, design of experiments, analytical method validation, quality control, biopharmaceutical manufacturing

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Authors: Vasanth C. Shunmugasamy, Bilal Mansoor

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Magnesium alloys are increasingly being considered for structural systems across different industrial sectors, including precision components of biomedical devices, owing to their high specific strength, stiffness and biodegradability. However, Mg alloys exhibit a high corrosion rate that restricts their application as a biomaterial. For safe use as biomaterial, it is essential to control their corrosion rates. Mg alloy corrosion is influenced by several factors, such as grain size, precipitates and texture. In Mg alloys, microgalvanic coupling between the α-Mg matrix and secondary precipitates can exist, which results in an increased corrosion rate. The present research addresses this challenge by engineering the microstructure of a biodegradable Mg–Zn–RE–Zr alloy by friction stir processing (FSP), a severe plastic deformation process. The FSP-processed Mg alloys showed improved corrosion resistance and mechanical properties. FSPed Mg alloy showed refined grains, a strong basal texture and broken and uniformly distributed secondary precipitates in the stir zone. Mg, alloy base material, exposed to In vitro corrosion medium showed micro galvanic coupling between precipitate and matrix, resulting in the unstable passive layer. However, FS processed alloy showed uniform corrosion owing to stable surface film formation. The stable surface film is attributed to refined grains, preferred texture and distribution of precipitates. The research results show promising potential for Mg alloy to be developed as a biomaterial.

Keywords: biomaterials, severe plastic deformation, magnesium alloys, corrosion

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Authors: Rohollah Asadian Kohestani, Mustafa Hashemzadeh

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The present paper is the outcome of a field research that was conducted with the study objective of influencing factor's effect on the behavior of customers switching in the Saipa Emdad Khodro Company. To achieve this goal, six factors of service quality, service cost, waiting time to receive services, reputation of organization, costs of switching and the way to respond the needs of customers as the independent variables of research and their effect on the customer switching was studied as the variable related to the research. The statistical society of this research included all customers of the Saipa Emdad Khodro company that possess the vehicles of automobile manufacturing group of Saipa throughout the country and the statistical sample included 150 persons of such customers. The results of this research indicated that all under study factors excluding the reputation factor effect on the behavior of customer switching.

Keywords: customer services, switching cost, service price, customer switching behavior

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

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Authors: Fereshteh Zarei, Mahdi Kohandel

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The aim of this study was investigate and compare plyometric and vibration exercises on muscle explosive power in non-athlete female students. For this purpose, 45 female students from non-athletes selected target then divided in to the three groups, two experimental and one control groups. From all groups were getting pre-tested. Experimental A did whole-body vibration exercises involved standing on one of machine vibration with frequency 30 Hz, amplitude 10 mm and in 5 different postures. Training for each position was 40 seconds with 60 seconds rest between it, and each season 5 seconds was added to duration of each body condition, until time up to 2 minutes for each postures. Exercises were done three times a week for 2 month. Experimental group B did plyometric exercises that include jumping, such as horizontal, vertical, and skipping .They included 10 times repeat for 5 set in each season. Intensity with increasing repetitions and sets were added. At this time, asked from control group that keep a daily activity and avoided strength training, explosive power and. after do exercises by groups we measured factors again. One-way analysis of variance and paired t statistical methods were used to analyze the data. There was significant difference in the amount of explosive power between the control and vibration groups (p=0/048) there was significant difference between the control and plyometric groups (019/0 = p). But between vibration and plyometric groups didn't observe significant difference in the amount of explosive power.

Keywords: vibration, plyometric, exercises, explosive power, non-athlete

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Authors: Lisa Fox, Jill Aylott

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There is a global need to identify ways to engage doctors in non-clinical matters such as medical leadership, service improvement and health system transformation. Using the core principles of Realistic Evaluation (RE), this study examined what works, for doctors of different grades, specialities and experience in an acute NHS Hospital Trust in the UK. Realistic Evaluation is an alternative to more traditional cause and effect evaluation models and seeks to understand the interdependencies of Context, Mechanism and Outcome proposing that Context (C) + Mechanism (M) = Outcome (O). In this study, the context, mechanism and outcome were examined from within individual medical leaders to determine what enables levels of medical engagement in a specific improvement project to reduce hospital inpatient mortality. Five qualitative case studies were undertaken with consultants who had regularly completed mortality reviews over a six month period. The case studies involved semi-structured interviews to test the theory behind the drivers for medical engagement. The interviews were analysed using a theory-driven thematic analysis to identify CMO configurations to explain what works, for whom and in what circumstances. The findings showed that consultants with a longer length of service became more engaged if there were opportunities to be involved in the beginning of an improvement project, with more opportunities to affect the design. Those that are new to a consultant role were more engaged if they felt able to apply any learning directly into their own settings or if they could use it as an opportunity to understand more about the organisation they are working in. This study concludes that RE is a useful methodology for better understanding the complexities of motivation and consultant engagement in a trust wide service improvement project. The study showed that there should be differentiated and bespoke training programmes to maximise each individual doctor’s propensity for medical engagement. The RE identified that there are different ways to ensure that doctors have the right skills to feel confident in service improvement projects.

Keywords: realistic evaluation, medical leadership, medical engagement, patient safety, service improvement

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Authors: Mehena Fedoul

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This paper examines the intersection of race, gender, and disability through a Critical disability race theory and black feminist disability perspective in Keah Brown's memoir, "The Pretty One." The background of the study highlights the significance of intersectionality in understanding the multifaceted experiences of individuals who navigate multiple marginalized identities. The study contributes to the underrepresented field of disability studies from Critical race and black feminist perspectives, shedding light on the unique challenges and resilience of black disabled women. The study employs a qualitative analysis of Keah Brown's memoir as a primary text. Drawing on intersectionality theory and black feminist disability scholarship, the analysis focuses on how Brown's memoir illuminates the ways in which her race, gender, and disability intersect and shape her lived experiences. The analysis reveals how Brown's memoir challenges traditional notions of disability, beauty, and empowerment through the unapologetic celebration of her blackness, femaleness, and disability. The major findings of the study indicate that Brown's memoir provides a powerful narrative of the complexity, uniqueness and richness of the lived experiences of black disabled women. It demonstrates how the intersectionality of race, gender, and disability shapes Brown's identity, body image, relationships, and societal interactions. The paper also highlights how Brown's memoir emphasizes the importance of inclusivity and intersectionality in understanding and addressing the challenges faced by black disabled women. In conclusion, this study offers a critical analysis of the intersection of race, gender, and disability in Keah Brown's memoir, "The Pretty One," from a black feminist disability perspective. It contributes to the growing body of literature that recognizes the significance of intersectionality in understanding the experiences of marginalized individuals in the disability community. The study underscores the need for more inclusive and intersectional perspectives in disability studies and advocates for greater recognition of the voices and experiences of black disabled women in academic and societal discourse.

Keywords: disability studies, intersectionality, black feminism, Keah Brown

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Authors: Surajit Bhattacharya, Daniel Veltri, Atit A. Patel, Daniel N. Cox

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miRNAs have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel tool in R, Intra-miR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithms, using statistical methods including Pearson and Distance Correlation on microarray data, to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using Drosophila melanogaster as a model organism for bioinformatics analyses and functional validation. A number of putative targets were obtained which were also validated using qRT-PCR analysis. Additional features of the tool include downloadable text files containing GO analysis from DAVID and Pubmed links of literature related to gene sets. Moreover, we are constructing interaction maps of intragenic miRNAs, using both micro array and RNA-seq data, focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development.

Keywords: miRNA, miRNA:mRNA target prediction, statistical methods, miRNA:mRNA interaction network

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Authors: Robin Lok Wang Ma

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The purpose of this paper is to investigate how engineering students’ motivation and interests are maintained in their journeys. In recent years, different pedagogies of teaching, including entrepreneurship, experiential and lifelong learning, as well as dream builder, etc., have been widely used for education purposes. University advocates hands-on practice, learning by experiencing and experimenting throughout different courses. Students are not limited to gaining knowledge via traditional lectures, laboratory demonstrations, tutorials, and so on. The capability to identify both complex problems and their corresponding solutions in daily life are one of the criteria/skill sets required for graduates to obtain their careers at professional organizations and companies. A project-based course, namely Mechatronic Design and Prototyping, was developed for students to design and build a physical prototype for solving existing problems in their daily lives, thereby encouraging them as an entrepreneur to explore further possibilities to commercialize their designed prototypes and launch them to the market. Feedbacks from students show that they are keen to propose their own ideas freely with guidance from the instructor instead of using either suggested or assigned topics. Proposed ideas of the prototypes reflect that if students’ interests are maintained, they acquire the knowledge and skills they need, including essential communication, logical thinking, and, more importantly, problem solving for their lifelong learning journey.

Keywords: problem solving, lifelong learning, entrepreneurship, engineering

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Authors: Chitsanzo Isaac

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Across the world, the ongoing Covid-19 pandemic has challenged urban systems and policy frameworks, highlighting societal vulnerabilities and systemic inequities among many communities. Measures of confinement and social distancing to contain the Covid-19 virus have fragmented the physical and social fabric of cities. This has caused urban dwellers to reassess how they engage with their urban surroundings and maintain social ties. Urbanists have presented strategies that would allow communities to survive and even thrive, in extraordinary times of crisis like the pandemic. Tactical Urbanism, particularly the 15-Minute City, has gained popularity. It is considered a resilient approach in the global north, however, it’s transferability to the global south has been called into question. To this end, this paper poses the question: to what extent is the 15-Minute City framework integral sustainable design, and are there processes that make it adoptable by cities in the global south? This paper explores four issues using secondary quantitative data analysis and convergence analysis in the Paris and Blantyre urban regions. First, it questions how the 15-Minute City has been defined and measured, and how it impacts urban dwellers. Second, it examines the extent to which the 15-minute city performs under the lens of frameworks such as Wilber’s integral theory and Fleming’s integral sustainable design theory. Thirdly this work examines the processes that can be transferred to developing cities which foster community resilience through the perspectives of experience, behaviors, cultures, and systems. Finally, it reviews the principal ways in which a multi-perspective reality can be the basis for resilient community design and sustainable urban development. This work will shed a light on the importance of a multi-perspective reality as a means of achieving sustainable urban design goals in developing urban areas.

Keywords: 15-minute city, developing cities, global south, community resilience, integral sustainable design, systems thinking, complexity, tactical urbanism

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Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

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Authors: Alaa Abdullah Altaee

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This paper presents an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on violation of received data symbols with minimum detection requirements at the clock and data recovery (CDR) stage. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The effectiveness of the proposed architecture is validated using the simulation results of a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Spectre from Cadence Design Systems with BSIM4 device models.

Keywords: adaptive DFE, CMOS equalizer, error detection, serial links, timing jitter, wire-line communication

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Authors: Seyedeh Fatemeh Nabavi, Hamid Dalir, Anooshiravan Farshidianfar

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Laser welding is pivotal in modern manufacturing, offering unmatched precision, speed, and efficiency. Its versatility in minimizing heat-affected zones, seamlessly joining dissimilar materials, and working with various metals makes it indispensable for crafting intricate automotive components. Integration into automated systems ensures consistent delivery of high-quality welds, thereby enhancing overall production efficiency. Noteworthy are the safety benefits of laser welding, including reduced fumes and consumable materials, which align with industry standards and environmental sustainability goals. As the automotive sector increasingly demands advanced materials and stringent safety and quality standards, laser welding emerges as a cornerstone technology. A comprehensive model encompassing thermal dynamic and characteristics models accurately predicts geometrical, metallurgical, and mechanical aspects of the laser beam welding process. Notably, Model 2 showcases exceptional accuracy, achieving remarkably low error rates in predicting primary and secondary dendrite arm spacing (PDAS and SDAS). These findings underscore the model's reliability and effectiveness, providing invaluable insights and predictive capabilities crucial for optimizing welding processes and ensuring superior productivity, efficiency, and quality in the automotive industry.

Keywords: laser welding process, geometrical characteristics, mechanical characteristics, metallurgical characteristics, comprehensive model, thermal dynamic

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Authors: Adda Ali-Pacha, Naima Hadj-Said

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Nowadays, the security of information is primarily founded on the calculation of algorithms that confidentiality depend on the number of bits necessary to define a cryptographic key. In this work, we introduce a new chaotic cryptosystem that we call evolutional substitution cipher on a chaotic attractor. In this research paper, we take the Henon attractor. The evolutional substitution cipher on Henon attractor is based on the principle of monoalphabetic cipher and it associates the plaintext at a succession of real numbers calculated from the attractor equations.

Keywords: cryptography, substitution cipher, chaos theory, Henon attractor, evolutional substitution cipher

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Authors: Raoul Ouambo Tobou, Alexis Kuitche, Marcel Edoun

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The FWM (Finite Window Method) is a new numerical meshfree technique for solving problems defined either in terms of PDEs (Partial Differential Equation) or by a set of conservation/equilibrium laws. The principle behind the FWM is that in such problem each element of the concerned domain is interacting with its neighbors and will always try to adapt to keep in equilibrium with respect to those neighbors. This leads to a very simple and robust problem solving scheme, well suited for transfer problems. In this work, we have applied the FWM to an unsteady scalar convection-diffusion equation. Despite its simplicity, it is well known that convection-diffusion problems can be challenging to be solved numerically, especially when convection is highly dominant. This has led researchers to set the scalar convection-diffusion equation as a benchmark one used to analyze and derive the required conditions or artifacts needed to numerically solve problems where convection and diffusion occur simultaneously. We have shown here that the standard FWM can be used to solve convection-diffusion equations in a robust manner as no adjustments (Upwinding or Artificial Diffusion addition) were required to obtain good results even for high Peclet numbers and coarse space and time steps. A comparison was performed between the FWM scheme and both a first order implicit Finite Volume Scheme (Upwind scheme) and a third order implicit Finite Volume Scheme (QUICK Scheme). The results of the comparison was that for equal space and time grid spacing, the FWM yields a much better precision than the used Finite Volume schemes, all having similar computational cost and conditioning number.

Keywords: Finite Window Method, Convection-Diffusion, Numerical Technique, Convergence

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Authors: Margarida Matos, João Pereira dos Santos, José Tavares

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Anti-minority discrimination is a persistent phenomenon with long-run effects. While there is a vast literature in economics and psychology that shows that personality and beliefs are not fixed and can be altered by experience, particularly in the so-called impressionable years in early adulthood, less is known about the long-lasting impacts of major events occurring during this time on minority attitudes. In this paper, we study the impact of serving in the military on long-term attitudes towards minorities. For many, military conscription and serving in war are unique life-shaping events. In the context of military service, individuals from different socioeconomic backgrounds interact and learn with each other, potentially changing their views and attitudes in a persistent manner. A prominent theory about the change of attitudes is the contact theory. It suggests that prejudice can be decreased if members of the groups interact with one another. The present paper adds to the literature by providing evidence from a more complicated setting involving the exposure to combat. We study the attitudes of veterans of the Portuguese Colonial War between 1961 and 1974, what was the latest war between African independence movements and Europeans. More than 70 percent of military age Portuguese men were drafted every year and sent to fight in Africa in the widest draft in post-World War II Western Europe. The contact between Portuguese and African soldiers was both cooperative as well as adversarial. Portuguese fought against but also alongside locally recruited African men, who represented half of the Portuguese contingent for substantial periods. We use data from the European Social Survey to identify Portuguese citizens likely to have been drafted and were send to fight in the former Portuguese colonies in Africa. We show that men likely to have fought in African wars are more accepting of immigrants than women of their same cohort, as well as than males from younger and older cohorts. The use of corresponding cohorts from Spain as placebo tests confirms our results. Our findings also hold in a regression discontinuity design setting.

Keywords: attitudes, immigration, war in Africa, veterans, portugal

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Authors: K. Tahir, C. Belfedal, T. Allaoui, C. Gerard, M. Doumi

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In this paper, we presented an optimized RST controller using Particle Swarm Optimization (PSO) meta-heuristic technique of the active and reactive power regulation of a grid connected wind turbine based on a wound field synchronous generator. This regulation is achieved below the synchronous speed, by means of a maximum power point tracking algorithm. The control of our system is tested under typical wind variations and parameters variation, fault grid condition by simulation. Some results are presented and discussed to prove simplicity and efficiency of the WRSG control for WECS. On the other hand, according to simulation results, variable speed driven WRSG is not significantly impacted in fault conditions.

Keywords: wind energy, particle swarm optimization, wound rotor synchronous generator, power control, RST controller, maximum power point tracking

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Authors: Alev Yüksel Aydar, Tuncay Yılmaz, Melisa Özçeli̇k, Tuba Aydın, Elif Karabaş

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In this study, response surface methodology (RSM) was used to obtain the optimum conditions for the freeze-drying of Gemlik variety olive seeds of to achieve the desired quality characteristics. The Box Behnken Design (BBD) was applied with three-variable and three replications in the center point. The effects of the different drying parameters including initial temperature of olive seed, pressure and time for freezing on the DPPH activity, total phenolic contents, and oleuropein absorbance value of the samples were investigated. Temperature (50 – 82 °C), pressure (0.2-0.5 mbar), time (6-10 hours) were chosen as independent variables. The analysis revealed that, while the temperature of the product prior to lyophilization and the drying time had no statistically significant effect on DPPH activity (p>0.05), the pressure was more important than the other two variables , and the quadratic effect of pressure had a significant effect on DPPH activity (p<0.05). The R2 and Adj-R2 values of the DPPH activity model were calculated to be 0.8962 and 0.7045, respectively.

Keywords: olive seed, gemlik variety, DPPH, phenolics, optimization

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Authors: A. I. Khalafalla, K. A. Al-Busada, I. M. El-Sabagh

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Pox and pox-like diseases of camels are a group of exanthematous skin conditions that have become increasingly important economically. They may be caused by three distinct viruses: camelpox virus (CMPV), camel contagious ecthyma virus (CCEV) and camel papillomavirus (CAPV). These diseases are difficult to differentiate based on clinical presentation in disease outbreaks. Molecular methods such as PCR targeting species-specific genes have been developed and used to identify CMPV and CCEV, but not simultaneously in a single tube. Recently, multiplex PCR has gained reputation as a convenient diagnostic method with cost- and time–saving benefits. In the present communication, we describe the development, optimization and validation a multiplex PCR assays able to detect simultaneously the genome of the three viruses in one single test allowing for rapid and efficient molecular diagnosis. The assay was developed based on the evaluation and combination of published and new primer sets, and was applied to the detection of 110 tissue samples. The method showed high sensitivity, and the specificity was confirmed by PCR-product sequencing. In conclusion, this rapid, sensitive and specific assay is considered a useful method for identifying three important viruses in specimens from camels and as part of a molecular diagnostic regime.

Keywords: multiplex PCR, diagnosis, pox and pox-like diseases, camels

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Authors: Anne McCarthy, Anna Kim, Yin Song, Kyoo Jo, Donald Cropek, Sungmin Hong

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Corrosion prevention remains an indispensable concern across a spectrum of industries, demanding inventive and adaptable methodologies to effectively tackle the ever-evolving obstacles presented by corrosive surroundings. This abstract introduces a pioneering approach to corrosion prevention that amalgamates the distinct attributes of magneto-responsive polymers with finely adjustable hydrophobicity inspired by the structure of cicada wings, effectively deterring bacterial proliferation and biofilm formation. The proposed strategy entails the creation of an innovative array of magneto-responsive nanostructures endowed with the capacity to dynamically modulate their hydrophobic characteristics. This dynamic control over hydrophobicity facilitates active repulsion of water and corrosive agents on demand. Additionally, the cyclic motion generated by magnetic activation prevents the biofilms formation and rejection. Thus, the synergistic interplay between magneto-active nanostructures and hydrophobicity manipulation establishes a versatile defensive mechanism against diverse corrosive agents. This study introduces a novel method for corrosion prevention, harnessing the advantages of magneto-active nanostructures and the precision of hydrophobicity adjustment, resulting in water-repellency, effective biofilm removal, and offering a promising solution to handle corrosion-related challenges. We believe that the combined effect will significantly contribute to extending asset lifespan, improving safety, and reducing maintenance costs in the face of corrosion threats.

Keywords: magneto-active material, nanoimprinting, corrosion prevention, hydrophobicity

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In robotics literature, the simultaneous localization and mapping (SLAM) is commonly associated with a priori-posteriori problem. The autonomous vehicle needs a neutral map to spontaneously track its local position, i.e., “localization” while at the same time a precise path estimation of the environment state is required for effective route planning and obstacle avoidance. On the other hand, the environmental noise factors can significantly intensify the inherent uncertainties in using odometry information and measurements obtained from the robot’s exteroceptive sensor which in return directly affect the overall performance of the corresponding SLAM. Therefore, the current work is primarily dedicated to provide a diagnostic analysis of six SLAM algorithms including FastSLAM, L-SLAM, GraphSLAM, Grid SLAM and DP-SLAM. A SLAM simulated environment consisting of two sets of landmark locations and robot waypoints was set based on modified EKF and UKF in MATLAB using two separate maps for indoor and outdoor route planning subject to natural and artificial obstacles. The simulation results are expected to provide an unbiased platform to compare the estimation performances of the five SLAM models as well as on the reliability of each SLAM model for indoor and outdoor applications.

Keywords: route planning, obstacle, estimation performance, FastSLAM, L-SLAM, GraphSLAM, Grid SLAM, DP-SLAM

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Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: Zhang Qingzhong

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Though there exists an increasing number of research studies on the headquarters-subsidiary relationship, and within this context, there is a focus on subsidiaries' contributory role to multinational corporations (MNC), subsidiary autonomy, and the conditions under which autonomy exerts an effect on subsidiary performance still constitute a subject of debate in the literature. The objective of this research is to study the MNC subsidiary autonomy and performance relationship and the effect of subsidiary entrepreneurial orientation and trust on subsidiary autonomy in the China environment, a phenomenon that has not yet been studied. The research addresses the following three questions: (i) Is subsidiary autonomy associated with MNC subsidiary performance in the China environment? (ii) How do subsidiary entrepreneurship and its trust in headquarters affect the level of subsidiary autonomy and its relationship with subsidiary performance? (iii) Does subsidiary autonomy have a mediating effect on subsidiary performance with subsidiary’s entrepreneurship and trust in headquarters? In the present study, we have reviewed literature and conducted semi-structured interviews with multinational corporation (MNC) subsidiary senior executives in China. Building on our insights from the interviews and taking perspectives from four theories, namely the resource-based view (RBV), resource dependency theory, integration-responsiveness framework, and social exchange theory, as well as the extant articles on subsidiary autonomy, entrepreneurial orientation, trust, and subsidiary performance, we have developed a model and have explored the direct and mediating effects of subsidiary autonomy on subsidiary performance within the framework of the MNC. To test the model, we collected and analyzed data based on cross-industry two waves of an online survey from 102 subsidiaries of MNCs in China. We used structural equation modeling to test measurement, direct effect model, and conceptual framework with hypotheses. Our findings confirm that (a) subsidiary autonomy is positively related to subsidiary performance; (b) subsidiary entrepreneurial orientation is positively related to subsidiary autonomy; (c) subsidiary’s trust in headquarters has a positive effect on subsidiary autonomy; (d) subsidiary autonomy mediates the relationship between entrepreneurial orientation and subsidiary performance; (e) subsidiary autonomy mediates the relationship between trust and subsidiary performance. Our study highlights the important role of subsidiary autonomy in leveraging the resource of subsidiary entrepreneurial orientation and its trust relationship with headquarters to achieve high performance. We discuss the theoretical and managerial implications of the findings and propose directions for future research.

Keywords: subsidiary entrepreneurial orientation, trust, subsidiary autonomy, subsidiary performance

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Authors: Thirsa Sherry, Utkarsh Shrivastav, Kannan B. T., Iynthezhuton K.

Abstract:

The field of aerodynamics and aeroacoustics remains one of the most poignant and well-researched fields of today. The current paper aims to investigate the predominant problem concerning the effects of noise of varying frequencies and waveforms on airflow surrounding an airfoil. Using a single speaker beneath the airfoil at different positions, we wish to simulate the effects of sound directly impinging on an airfoil and study its direct effects on airflow. We wish to study the same using smoke visualization methods with incense as our smoke-generating material in a variable-speed subsonic wind tunnel. Using frequencies and wavelengths similar to those of common engine noise, we wish to simulate real-world conditions of engine noise interfering with airflow and document the arising trends. These results will allow us to look into the real-world effects of noise on airflow and how to minimize them and expand on the possible relation between waveforms and noise. The parameters used in the study include frequency, Reynolds number, waveforms, angle of attack, and the effects on airflow when varying these parameters.

Keywords: engine noise, aeroacoustics, acoustic excitation, low speed

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Authors: Merve Akca

Abstract:

Retrieval-induced forgetting (RIF) refers to the phenomenon that selective retrieval of some information impairs memory for related, but not previously retrieved information. Despite age differences in retrieval-induced forgetting regarding retrospective memory being documented, this research aimed to highlight age differences in RIF of the prospective memory tasks for the first time. By using retrieval-practice paradigm, this study comparatively examined RIF effects in retrospective memory and event-based prospective memory in young and old adults. In this experimental study, a mixed factorial design with age group (Young, Old) as a between-subject variable, and memory type (Prospective, Retrospective) and item type (Practiced, Non-practiced) as within-subject variables was employed. Retrieval-induced forgetting was observed in the retrospective but not in the prospective memory task. Therefore, the results indicated that selective retrieval of past events led to suppression of other related past events in both age groups but not the suppression of memory for future intentions.

Keywords: prospective memory, retrieval-induced forgetting, retrieval inhibition, retrospective memory

Procedia PDF Downloads 314

Authors: Hui Zhang, Sherif Beskhyroun

Abstract:

Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

Procedia PDF Downloads 98

Authors: Ersin Sener, Ibrahim Demir, Hasan Aykut Karaboga, Kadir Kilinc

Abstract:

Bayesian methods, which have very wide range of applications, are implemented to the data obtained from the production of F class fly ash-based geopolymer bricks’ experimental design. In this study, dependent variable is compressive strength, independent variables are treatment type (oven and steam), treatment time, molding time, temperature, water absorbtion ratio and density. The effect of independent variables on compressive strength is investigated. There is no difference among treatment types, but there is a correlation between independent variables. Therefore, hierarchical Bayesian path analysis is applied. In consequence of analysis we specified that treatment time, temperature and density effects on compressive strength is higher, molding time, and water absorbtion ratio is relatively low.

Keywords: experimental design, F class fly ash, geopolymer bricks, hierarchical Bayesian path analysis

Procedia PDF Downloads 386

Authors: Dominic McGann

Abstract:

Applying Kuhn’s model of paradigm shifts in science to cases of doctrinal change in religion has been a common area of study in recent years. Few authors, however, have sought an explanation for the ease with which this model of theory change in science can be applied to cases of religious change. In order to provide such an explanation of this analytic phenomenon, this paper aims to answer one central question: Why is it that a theory that was intended to be used in an analysis of the history of science can be applied to something as disparate as the doctrinal history of religion with little to no modification? By way of answering this question, this paper begins with an explanation of Kuhn’s model and its applications in the field of religious studies. Following this, Massa’s recently proposed explanation for this phenomenon, and its notable flaws will be explained by way of framing the central proposal of this article, that the operative parts of scientific and religious changes function on the same fundamental concept of changes in understanding. Focusing its argument on this key concept, this paper seeks to illustrate its operation in cases of religious conversion and in Kuhn’s notion of the incommensurability of different scientific paradigms. The conjecture of this paper is that just as a Pagan-turned-Christian ceases to hear Thor’s hammer when they hear a clap of thunder, so too does a Ptolemaic-turned-Copernican-astronomer cease to see the Sun orbiting the Earth when they view a sunrise. In both cases, the agent in question has undergone a similar change in universal understanding, which provides us with a fundamental connection between changes in religion and changes in science. Following an exploration of this connection, this paper will consider the implications that such a connection has for the concept of the division between religion and science. This will, in turn, lead to the conclusion that religion and science are more alike than they are opposed with regards to the fundamental notion of understanding, thereby providing an answer to our central question. The major finding of this paper is that Kuhn’s model can be applied to religious cases so easily because changes in science and changes in religion operate on the same type of change in understanding. Therefore, in summary, science and religion share a crucial similarity and are not as disparate as they first appear.

Keywords: Thomas Kuhn, science and religion, paradigm shifts, incommensurability, insight and understanding, philosophy of science, philosophy of religion

Procedia PDF Downloads 169