Search results for: application lifecycle management

Authors: Carlos F. Sanz-Navarro, Sonia Fereres

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Binary and ternary mixtures of molten salts are excellent thermal energy storage systems and have been widely used in commercial tanks both in nuclear and solar thermal applications. However, the energy density of the commercially used mixtures is still insufficient, and therefore, new systems based on latent heat storage (or phase change materials, PCM) are currently being investigated. In order to shed some light on the macroscopic physical properties of the molten salt phases, knowledge of the microscopic structure and dynamics is required. Several molecular dynamics (MD) simulations have been performed to model the thermal behavior of (Li,K)2CO3 mixtures. Up to this date, this particular molten salt mixture has not been extensively studied but it is of fundamental interest for understanding the behavior of other commercial salts. Molten salt diffusivities, the internal electrical ion mobility, and the physical properties of the solid-liquid phase transition have been calculated and compared to available data from literature. The effect of anion polarization and the application of a strong external electric field have also been investigated. The influence of electrical ion mobility on local composition is explained through the Chemla effect, well known in electrochemistry. These results open a new way to design optimal high temperature energy storage materials.

Keywords: atomistic simulations, thermal storage, latent heat, molten salt, ion mobility

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Authors: Bao Liu, Maarten Vanierschot, Frank Buysschaert

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The present work examines the wake dynamics of a rim-driven thruster (RDT) with Computational Fluid Dynamics (CFD). Unsteady Reynolds-averaged Navier-Stokes (URANS) equations were solved in the commercial solver ANSYS Fluent in combination with the SST k-ω turbulence model. The application of the moving reference frame (MRF) and sliding mesh (SM) approach to handling the rotational movement of the propeller were compared in the transient simulations. Validation and verification of the numerical model was performed to ensure numerical accuracy. Two representative scenarios were considered, i.e., the bollard condition (J=0) and a very light loading condition(J=0.7), respectively. From the results, it’s confirmed that compared to the SM method, the MRF method is not suitable for resolving the unsteady flow features as it only gives the general mean flow but smooths out lots of characteristic details in the flow field. By evaluating the simulation results with the SM technique, the instantaneous wake flow field under both conditions is presented and analyzed, most notably the helical vortex structure. It’s observed from the results that the tip vortices, blade shed vortices, and hub vortices are present in the wake flow field and convect downstream in a highly non-linear way. The shear layer vortices shedding from the duct displayed a strong interaction with the distorted tip vortices in an irregularmanner.

Keywords: computational fluid dynamics, rim-driven thruster, sliding mesh, wake dynamics

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Authors: Obsi Terfasa, Bhanupriya Das, Mithilish Passawan

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The large-scale application of microbial fuel cell (MFC) technology is significantly hindered by the high cost of the commonly used proton exchange membrane, Nafion. This has led to the recent development of ceramic membranes using various clay minerals. This study evaluates the characteristics and potential use of a new ceramic membrane made from potter’s clay © mixed with different proportions (0, 5, 10 wt%) of fly ash (FA), labeled as CFA0, CFA5, CFA10, for cost-effective and sustainable MFC use. Among these, the CFA10 membrane demonstrated superior quality with a fine pore size distribution (average 0.41 μm), which supports higher water uptake and reduced oxygen diffusion. Its oxygen mass transfer coefficient was 4.13 ± 0.13 × 10⁻⁴ cm/s, about 40% lower than the control. X-ray diffraction analysis revealed that the CFA membrane is rich in quartz, which enhances proton conductance and water retention. Electrochemical kinetics studies, including cyclic voltammetry and electrochemical impedance spectroscopy (EIS), also confirmed the effectiveness of the CFA10 membrane in MFC, showing a peak current output of 15.35 mA and low ohmic resistance (78.2 Ω). The novel CFA10 ceramic membrane, incorporating coal fly ash, a waste material, shows promise for high MFC performance at a significantly reduced cost (96%), making it suitable for sustainable scaling up of the technology.

Keywords: ceramic membrane, Coulombic efficiency, electro-chemical kinetics, fly ash, proton conductivity, microbial fuel cell

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Authors: Mariette Swanepoel, Terry Ellapen, Henriette Hammil, Juandre Williams, Timothy Qumbu

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The biomechanical consequence of pes planus is a topic seldom reviewed in regards to energy expenditure and predisposition to injury. However its comprehension in the field of foot rehabilitation, pre-and post-surgery is fundamental to successful patient management. This short communication unites the present literature to provide the reader with better insight on the consequence of pes planus, foot mechanics and its predisposition to injury at the foot and tibiofemoral joint. Further, the consideration of synergistic dominance of the foot invertors to compensate for the ineffective torque production of the fibularis longus due pes planus is presented.

Keywords: pes planus, fibularis longus, synergistic dominance, injury

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Authors: Adisu Mengesha Assefa

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The field of cardiovascular care is being transformed by the incorporation of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), collectively known as Extended Reality (XR), into medical education, procedural planning, and patient care. This review examines the state-of-the-art applications of XR in cardiology, emphasizing its role in enhancing the precision of interventional procedures and understanding complex anatomical structures. XR technologies complement conventional imaging methods by enabling immersive three-dimensional interaction that facilitates both preoperative planning and intraoperative guidance. Despite these promising developments, challenges such as harmonizing data, integrating various imaging systems, and addressing the prevalence of cybersickness remain. Ethical considerations, including maintaining physician focus and ensuring patient safety, are crucial when implementing XR in clinical settings. This review summarizes the existing literature and highlights the need for more rigorous future studies to validate therapeutic benefits and ensure safe application. By examining both the potential and the challenges, this paper aims to delineate the current and future roles of XR in cardiovascular care, emphasizing the necessity for continued innovation and ethical oversight to improve patient outcomes.

Keywords: virtual reality, augmented reality, mixed reality, cardiovascular care, education, preprocedural planning, intraoperative guidance, postoperative patient rehabilitation

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Authors: Xiang-Yao Zheng, Jen-Cheng Wang, Joe-Air Jiang

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Improving the line ampacity while using existing power grids is an important issue that electricity dispatchers are now facing. Using the information provided by the dynamic thermal rating (DTR) of transmission lines, an overhead power grid can operate safely. However, dispatchers usually lack real-time DTR information. Thus, this study proposes a long-short-term memory (LSTM)-based method, which is one of the neural network models. The LSTM-based method predicts the DTR of lines using the weather data provided by Central Weather Bureau (CWB) of Taiwan. The possible thermal bottlenecks at different locations along the line and the margin of line ampacity can be real-time determined by the proposed LSTM-based prediction method. A case study that targets the 345 kV power grid of TaiPower in Taiwan is utilized to examine the performance of the proposed method. The simulation results show that the proposed method is useful to provide the information for the smart grid application in the future.

Keywords: electricity dispatch, line ampacity prediction, dynamic thermal rating, long-short-term memory neural network, smart grid

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Authors: Umran Kurtan, Hamide Aydin, Sevim Unugur Celik, Ayhan Bozkurt

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In the present work, novel hBN/polyacrylonitrile composite nanofibers were produced via electrospinning approach and loaded with the electrolyte for rechargeable lithium-ion battery applications. The electrospun nanofibers comprising various hBN contents were characterized by using Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The influence of hBN/PAN ratios onto the properties of the porous composite system, such as fiber diameter, porosity, and the liquid electrolyte uptake capability were systematically studied. Ionic conductivities and electrochemical characterizations were evaluated after loading electrospun hBN/PAN composite nanofiber with liquid electrolyte, i.e., 1 M lithium hexafluorophosphate (LiPF6) in ethylene carbonate (EC)/ethyl methyl carbonate (EMC) (1:1 vol). The electrolyte loaded nanofiber has a highest ionic conductivity of 10−3 S cm⁻¹ at room temperature. According to cyclic voltammetry (CV) results it exhibited a high electrochemical stability window up to 4.7 V versus Li+/Li. Li//10 wt% hBN/PAN//LiCO₂ cell was produced which delivered high discharge capacity of 144 mAhg⁻¹ and capacity retention of 92.4%. Considering high safety and low cost properties of the resulting hBN/PAN fiber electrolytes, these materials can be suggested as potential separator materials for lithium-ion batteries.

Keywords: hexagonal boron nitride, polyacrylonitrile, electrospinning, lithium ion battery

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Authors: Tewelde Gebreslassie Gebreanenia, Frie Ayalew Yimam, Seada Hussen Adem

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Patient safety and quality of care are essential goals of health care delivery, but they are often compromised by human errors, system failures, or resource constraints. In a variety of healthcare contexts, artificial intelligence (AI), a quickly developing field, can provide fresh approaches to enhancing patient safety and treatment quality. Artificial Intelligence (AI) has the potential to decrease errors and enhance patient outcomes by carrying out tasks that would typically require human intelligence. These tasks include the detection and prevention of adverse events, monitoring and warning patients and clinicians about changes in vital signs, symptoms, or risks, offering individualized and evidence-based recommendations for diagnosis, treatment, or prevention, and assessing and enhancing the effectiveness of health care systems and services. This study examines the state-of-the-art and potential future applications of AI-based technologies for enhancing patient safety and care quality, as well as the opportunities and problems they present for patients, policymakers, researchers, and healthcare providers. In order to ensure the safe, efficient, and responsible application of AI in healthcare, the paper also addresses the ethical, legal, social, and technical challenges that must be addressed and regulated.

Keywords: artificial intelligence, health care, human intelligence, patient safty, quality of care

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Authors: Lin Shang Neng

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Due to rapid changes in the societal environment, career development and planning have become increasingly crucial, leading more individuals to seek the assistance of career consultations. However, the training process for career consultants often emphasizes the application of assessment tools and guidance in job-seeking behavior. The abilities of case conceptualization and consulting skills require further in-service supervision. This study aims to inquire about the supervised experiences of employment specialists at the Employment Service Center of the Taiwan Ministry of Labor or career consultants who held private clinics for at least three years. The research participants were continuously supervised by the Adlerian approach twice a month for at least one year, helping them integrate the whole picture of the client through Lifestyle Assessment (the qualitative way, specific diagnosis) and other Adlerian assessment tools (the quantitative way, general diagnosis.) The supervisor was familiar with Adlerian Psychology and certified by the North American Society of Adlerian Psychology. The research method involves semi-structured interviews and qualitative analysis. For the ethical considerations, the participants were invited to interview after the supervision sessions finished. The findings of this research were discussed with possible implications, like how they applied Adlerian Psychology to their career consultations, especially to case conceptualizations and consulting skills. Recommendations for further research and training for career consultants are also discussed.

Keywords: supervision, Adlerian psychology, case conceptualization, career consultant

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Authors: Rahul Paul, Kedar Nath Das

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The field of machine learning (ML) is experiencing rapid development, resulting in a multitude of theoretical advancements and extensive practical implementations across various disciplines. The objective of ML is to facilitate the ability of machines to perform cognitive tasks by leveraging knowledge gained from prior experiences and effectively addressing complex problems, even in situations that deviate from previously encountered instances. Reinforcement Learning (RL) has emerged as a prominent subfield within ML and has gained considerable attention in recent times from researchers. This surge in interest can be attributed to the practical applications of RL, the increasing availability of data, and the rapid advancements in computing power. At the same time, optimization algorithms play a pivotal role in the field of ML and have attracted considerable interest from researchers. A multitude of proposals have been put forth to address optimization problems or improve optimization techniques within the domain of ML. The necessity of a thorough examination and implementation of optimization algorithms within the context of ML is of utmost importance in order to provide guidance for the advancement of research in both optimization and ML. This article provides a comprehensive overview of the application of metaheuristic evolutionary optimization algorithms in conjunction with RL to address a diverse range of scientific challenges. Furthermore, this article delves into the various challenges and unresolved issues pertaining to the optimization of RL models.

Keywords: machine learning, reinforcement learning, loss function, evolutionary optimization techniques

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Authors: Ruma Rano, Firoza Sultana, Bishal Bhuyan, Nurul Alam Mazumder

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Fly ash collected from Cachar Paper Mill, Assam, India has been thoroughly characterized in respect of its physico-chemical, morphological and mineralogical features were concerned by using density, LOI, FTIR, XRD, SEM-EDS etc. The results reveal that there is a striking difference in the features and properties of the coarser and finer fractions .The high carbon ash consists of large unburnt carbon (chars), irregular carbonaceous particles in the coarser fraction, which appear to be porous and may be used as domestic fuel. The percentage of char albeit the carbon content decreases with decrease in size of particles. The various fractions essentially contain quartz and mullite as the main mineral phases. For suggesting the potential utilization channels, number of experiments were performed correlating the total characteristic features. Water holding capacities of different size classified fractions were determined, the coarser fractions have unexpectedly higher water holding capacities than the finer ones. An attempt has been made to correlate the results obtained with potential use in agriculture. Another potential application of coarser particles is used as adsorbent for effluents containing waste organic materials. Thus thorough characterization leads to not only a definite direction about the uses of the value added components but also gives useful information regarding the prevailing combustion process.

Keywords: chars, porous, water holding capacity, combustion process

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Authors: Zuzana Crhová, Karel Kolman, Drahomíra Pavelková

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Knowledge is considered as an important asset which can help organizations to create competitive advantage. The necessity of taking care of these assets is more important in these days – in days of turbulent changes in business environment. Knowledge could facilitate adaption to constant changes. The aim of this paper is to describe how the knowledge sharing can be supported in the manufacturing companies. The methods of case studies and grounded theory were used to present information gained by carrying out semi-structured interviews. Results show that knowledge sharing is supported in very similar ways in respondent companies.

Keywords: case study, human resource management, knowledge, knowledge sharing

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Authors: Dolhadi Bin Zainudin

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The study attempts to predict the relationship between influencing factors in the adoption of ISO 9000:2008 and to identify which how these factors play the main role in achieving ISO 9000 standard. A survey using structured questionnaire was employed. A total of 255 respondents from 255 small and medium enterprises participated in this study. With regards to influencing factors, a discriminant analysis was conducted and the results showed that three out of nine critical success factors is statistically significant between ISO 9000:2008 and non-ISO 9000 certified companies which are communication for quality, information and analysis and organizational culture.

Keywords: ISO 9000, quality management, factors, small and medium enterprise, Malaysia, influencing factors

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Authors: Zulfiye Ahan, Mustafa Dogu, Elcin Yilmaz

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The use of nonwoven textile materials in many application areas is rapidly increasing thanks to their versatile performance properties. The automotive industry is one of the largest sectors in the world, with a potential market of more than 2 billion euros for nonwoven textile materials applications. Lightweight materials having higher mechanical performance, better sound and heat insulation properties are of interest in many applications. Since the usage of nonwoven surfaces provides many of these advantages, the demand for this kind of material is gradually growing, especially in the automotive industry. Nonwoven materials used in lightweight vehicles can contain economical and high strength thermoplastics as well as durable components such as glass fiber. By bringing these composite materials into foam structure containing micro or nanopores, products with high absorption ability, light and mechanically stronger can be fabricated. In this respect, our goal is to produce thermoplastic composite nonwoven by using nonwoven glass fiber fabric reinforced polypropylene (PP). Azodicarbonamide (ADC) was selected as a foaming agent, and a thermal process was applied to obtain a porous structure. Various foaming temperature ranges and residence times were studied to examine the foaming behaviour of the thermoplastic composite nonwoven. Physicochemical and mechanical tests were applied in order to analyze the characteristics of composite foams.

Keywords: composite nonwoven, thermoplastic foams, foaming agent, foaming behavior

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Authors: Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane, Hassan Ouajji, Mohamed Ouadi Bensalah

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The aim of this paper is to present a distributed implementation of the Type-2 Fuzzy algorithm in a parallel and distributed computing environment based on mobile agents. The proposed algorithm is assigned to be implemented on a SPMD (Single Program Multiple Data) architecture which is based on cooperative mobile agents as AVPE (Agent Virtual Processing Element) model in order to improve the processing resources needed for performing the big data image segmentation. In this work we focused on the application of this algorithm in order to process the big data MRI (Magnetic Resonance Images) image of size (n x m). It is encapsulated on the Mobile agent team leader in order to be split into (m x n) pixels one per AVPE. Each AVPE perform and exchange the segmentation results and maintain asynchronous communication with their team leader until the convergence of this algorithm. Some interesting experimental results are obtained in terms of accuracy and efficiency analysis of the proposed implementation, thanks to the mobile agents several interesting skills introduced in this distributed computational model.

Keywords: distributed type-2 fuzzy algorithm, image processing, mobile agents, parallel and distributed computing

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Authors: Qianqian Chen

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The article intends to design a new research perspective on a linguistic landscape with the research background on multilingual urban tourism by analyzing the collected data, including a number of surveys on current urban tourism and the possibility of internationalization. The language usage analysis focuses on terms of English, Japanese and Spanish, which is based on the previous investigations. The analysis highlights the fact that contemporary tourism management and planning emphasizes cultural memories and heritage, and the combination between culture and tourism recalls the importance of "re-humanity" inhuman activities.

Keywords: multilingualism, culture, linguistic landscape, Hangzhou

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Authors: Andrey Vinajera-Zamora, Norge Coello-Machado, Elke Glistau

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The objective of this research is calculate the added value in operations of electric power meters (EPM) supply chains, specifically the EPM of 220v. The tool used is composed by six steps allowing at same time the identification of calibration of EPM as the bottleneck operation according the net added value being at same time the process of higher added value. On the other hand, this methodology allows calculate the amount of money to buy the raw material. The main conclusions are related to the analyze ‘s way and calculating of added value in supply chain integrated by the echelons procurement, production and distribution or any of these.

Keywords: economic value added, supply chain management, value chain, bottleneck detection

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Authors: Nicolò Vaiana, Mariacristina Spizzuoco, Giorgio Serino

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In this paper, the results of experimental tests performed on a Helical Wire Rope Isolator (HWRI) are presented in order to describe the dynamic and static behavior of the selected metal device in three different displacements ranges, namely small, relatively large, and large displacements ranges, without and under the effect of a vertical load. A testing machine, allowing to apply horizontal displacement or load histories to the tested bearing with a constant vertical load, has been adopted to perform the dynamic and static tests. According to the experimental results, the dynamic behavior of the tested device depends on the applied displacement amplitude. Indeed, the HWRI displays a softening and a hardening stiffness at small and relatively large displacements, respectively, and a stronger nonlinear stiffening behavior at large displacements. Furthermore, the experimental tests reveal that the application of a vertical load allows to have a more flexible device with higher damping properties and that the applied vertical load affects much less the dynamic response of the metal device at large displacements. Finally, a decrease in the static to dynamic effective stiffness ratio with increasing displacement amplitude has been observed.

Keywords: base isolation, earthquake engineering, experimental hysteresis loops, wire rope isolators

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: Ersumo Tessema, Bogale Girmaye Tamrat, Mohammed Burka

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Background: Hyperthyroidism is a common thyroid disorder especially in women and characterized by increased thyroid hormone synthesis and secretion. The disorder manifests predominantly as Graves’ disease in iodine-sufficient areas and has increasing prevalence in iodine-deficient countries in patients with nodular thyroid disease and following iodine fortification. In Ethiopia, the magnitude of the disorder is unknown and, in Africa, due to scarcity of resources, its management remains suboptimal. Objective: The aim of this study was to analyze the pattern and management of patients with hyperthyroidism at the United Vision Medical Services Center, Addis Ababa between August 30, 2013, and February 1, 2018. Patients and methods: The study was a retrospective analysis of medical records of all patients with hyperthyroidism at the United Vision Private Medical Services Center, Addis Ababa. A questionnaire was filled out; the collected data entered into a computer and statistically analyzed using the SPSS package. The results were tabulated and discussed with literature review. Results: A total of 589 patients were included in this study. The median age was 40 years, and the male to female ratio was 1.0:7.9. Most patients (93%) presented with goiter and the associated features of toxic goiter except weight loss, sweating and tachycardia were uncommon. Majority of patients presented more than two years after the onset of their presenting symptoms. The most common physical finding (91%), as well as diagnosis, was toxic nodular goiter. The most frequent (83%) derangement in the thyroid function tests was a low thyroid-stimulating hormone, and the most commonly (94%) used antithyroid drug was a propylthiouracil. The most common (96%) surgical procedure in 213 patients was a near-total thyroidectomy with a postoperative course without incident in 92% of all the patients. Conclusion: The incidence and prevalence of hyperthyroidism are apparently on the increase in Addis Ababa, which may be related to the existing severe iodine-deficiency and or the salt iodation program (iodine-induced hyperthyroidism). Hyperthyroidism predominantly affects women and, in surgical services, toxic nodular goiter is more common than diffuse goiter, and the treatment of choice in experienced hands is a near-total thyroidectomy.

Keywords: Ethiopia, grave’s disease, hyperthyroidism, toxic nodular goiter

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Authors: Rituraj Deka, Nomi Baruah

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The development of information technology during the past few years resulted in designing of more and more complex software. The outsourcing of software development makes a higher requirement for the management of software development project. Various software enterprises follow various paths in their pursuit of excellence, applying various principles, methods and techniques along the way. The new research is proving that CMMI and Agile methodologies can benefit from using both methods within organizations with the potential to dramatically improve business performance. The paper describes a mapping between CMMI key process areas (KPAs) and Feature-Driven Development (FDD) communication perspective, so as to increase the understanding of how improvements can be made in the software development process.

Keywords: Agile, CMMI, FDD, KPAs

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Authors: Anusha Bhardwaj, Shekhar Shinde

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Nitric oxide (NO•) has been documented in research papers as one of the most versatile player in the therapeutics. It is identified as a biological multifunctional messenger molecule which is synthesized by the action of nitric oxide synthase (NOS) enzyme from L-arginine. The protective and the toxic effect in conjunction form the complete picture of the biological function of nitric oxide in humans. The dual nature is because of various factors such as concentration of NO, the isoform of NOS involved, type of cells in which it is synthesized, reaction partners like proteins, reactive oxygen intermediates, prosthetic groups, thiols etc., availability of the substrate L-arginine, intracellular environment in which NO is produced and generation of guanosine 3, 5’- cyclic monophosphate (cGMP). Activation of NOS through infection or trauma leads to one or more systemic effects including enhanced immune activity against invading pathogens, vaso/bronchodilatation in the cardiovascular and respiratory systems and altered neurotransmission which can be protective or toxic. Hence, NO affects the balance between healthy signaling and neurodegeneration in the brain. In lungs, it has beneficial effects on the function of airways as a bronchodilator and acts as the neurotransmitter of bronchodilator nerves. Whereas, on the other hand, NO may have deleterious effects by amplifying the asthmatic inflammatory response and also act as a vasodilator in the airways by increasing plasma exudation. But NOS Inhibitors and NO donors hamper the signalling pathway and hence a therapeutic application of NO is compromised.

Keywords: nitric oxide, multifunctional, dual nature, therapeutic applications

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Authors: Paulino V. Tavares, Ana L. Romao

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Public governance represents an articulated arrangement, dynamic and interactive, present in the exercise of authority aimed at strengthening the decision-making procedure in public administration with transparency, accountability, responsiveness and capable of to emerge control and social empowerment, to pursue and achieve the objectives efficiently and with the effectiveness desired by the collective, respecting laws and providing social, institutional and economic equility in society. With this, using a multidimensional approach with the application of three questionnaires to a universe of twenty Counselors of the Courts of Auditors (Brazil), twenty professionals of public administration (Brazil), twenty Government/Provincial Counselors (South Africa), and twenty South African professionals of public administration, the present work aims to capture what is the perception about the efficiency and equility of public policies in Brazil and South Africa. With this, up until now, 30 responses have been obtained, and the results indicate that, in Brazil, 65% affirm due to the inefficiency of public policies, 70% point out that they do not believe in the equility of these same policies. In South Africa, the results indicate that 45% believe in government efficiency, and, with regard to the equility of public policies, 65% do not believe. In Brazil, the research reveals at least three reasons for this result, that is, lack of planning, lack of clear objectives of public policies, and lack of information on the part of society, while in South Africa, so far, research has not identified a specific reason for this result.

Keywords: efficiency, equility, governance, public policy

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Authors: Hui Zhang, Sherif Beskhyroun

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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

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Authors: Soojin Kim, Jiwon Yang, Sungzoon Cho

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As a seasonal product is only in demand for a short time, inventory management is critical to profits. Both markdowns and stockouts decrease the return on perishable products; therefore, researchers have been interested in the distribution of seasonal products with the aim of maximizing profits. In this study, we propose a data-driven seasonal product sales pattern analysis method for individual retail outlets based on observed sales data clustering; the proposed method helps in determining distribution strategies.

Keywords: clustering, distribution, sales pattern, seasonal product

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Authors: Princess Ncube

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In the context of South Africa's banking sector, the prevention of bank failures is of paramount importance to ensure financial stability and economic growth. This paper focuses on the role of regulatory frameworks in safeguarding the resilience of South African banks and mitigating the risks of failures. It aims to assess the effectiveness of existing regulatory measures and proposes strategies to enhance the resilience of financial institutions in the country. The paper begins by examining the specific regulatory frameworks in place in South Africa, including capital adequacy requirements, stress testing methodologies, risk management guidelines, and supervisory practices. It delves into the evolution of these measures in response to lessons learned from past financial crises and their relevance in the unique South African banking landscape. Drawing on empirical evidence and case studies specific to South Africa, this paper evaluates the effectiveness of regulatory frameworks in preventing bank failures within the country. It analyses the impact of these frameworks on crucial aspects such as early detection of distress signals, improvements in risk management practices, and advancements in corporate governance within South African financial institutions. Additionally, it explores the interplay between regulatory frameworks and the specific economic environment of South Africa, including the role of macroprudential policies in preventing systemic risks. Based on the assessment, this paper proposes recommendations to strengthen regulatory frameworks and enhance their effectiveness in bank failure prevention in South Africa. It explores avenues for refining existing regulations to align capital requirements with the risk profiles of South African banks, enhancing stress testing methodologies to capture specific vulnerabilities, and fostering better coordination among regulatory authorities within the country. Furthermore, it examines the potential benefits of adopting innovative approaches, such as leveraging technology and data analytics, to improve risk assessment and supervision in the South African banking sector.

Keywords: banks, resolution, liquidity, regulation

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Authors: Greg Turner, Bin Lu, Cheer-Sun Yang

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As computing technology advances, smartphone applications can assist in student learning in a pervasive way. For example, the idea of using a mobile apps for the PA Common Trees, Pests, Pathogens, in the field as a reference tool allows middle school students to learn about trees and associated pests/pathogens without bringing a textbook. In the past, some researches study the mobile software Mobile Application Software Development Life Cycle (MADLC) including traditional models such as the waterfall model, or more recent Agile Methods. Others study the issues related to the software development process. Very little research is on the development of three heterogenous mobile systems simultaneously in a small university where the availability of developers is an issue. In this paper, we propose to use a hybride model of Waterfall Model and the Agile Model, known as the Relay Race Methodology (RRM) in practice, to reflect the concept of racing and relaying for scheduling. Based on the development project, we observe that the modeling of the transition between any two phases is manifested naturally. Thus, we claim that the RRM model can provide a de fecto rather than a de jure basis for the core concept in the MADLC. In this paper, the background of the project is introduced first. Then, the challenges are pointed out followed by our solutions. Finally, the experiences learned and the future work are presented.

Keywords: agile methods, mobile apps, software process model, waterfall model

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Authors: Erica Cochran Hameen, Bobuchi Ken-Opurum, Mounica Guturu

Abstract:

The United Stated (U.S.) populous spends the majority of their time indoors in spaces where building codes and voluntary sustainability standards provide clear Indoor Environmental Quality (IEQ) metrics. The existing sustainable building standards and codes are aimed towards improving IEQ, health of occupants, and reducing the negative impacts of buildings on the environment. While they address the post-occupancy stage of buildings, there are fewer standards on the pre-occupancy stage thereby placing a large labor population in environments much less regulated. Construction personnel are often exposed to a variety of uncomfortable and unhealthy elements while on construction sites, primarily thermal, visual, acoustic, and air quality related. Construction site power generators, equipment, and machinery generate on average 9 decibels (dBA) above the U.S. OSHA regulations, creating uncomfortable noise levels. Research has shown that frequent exposure to high noise levels leads to chronic physiological issues and increases noise induced stress, yet beyond OSHA no other metric focuses directly on the impacts of noise on contractors’ well-being. Research has also associated natural light with higher productivity and attention span, and lower cases of fatigue in construction workers. However, daylight is not always available as construction workers often perform tasks in cramped spaces, dark areas, or at nighttime. In these instances, the use of artificial light is necessary, yet lighting standards for use during lengthy tasks and arduous activities is not specified. Additionally, ambient air, contaminants, and material off-gassing expelled at construction sites are one of the causes of serious health effects in construction workers. Coupled with extreme hot and cold temperatures for different climate zones, health and productivity can be seriously compromised. This research evaluates the impact of existing green building metrics on construction and risk management, by analyzing two codes and nine standards including LEED, WELL, and BREAM. These metrics were chosen based on the relevance to the U.S. construction industry. This research determined that less than 20% of the sustainability context within the standards and codes (texts) are related to the pre-occupancy building sector. The research also investigated the impact of construction personnel’s health and well-being on construction management through two surveys of project managers and on-site contractors’ perception of their work environment on productivity. To fully understand the risks of limited Environmental and Health Quality metrics for contractors (EHQ) this research evaluated the connection between EHQ factors such as inefficient lighting, on construction workers and investigated the correlation between various site coping strategies for comfort and productivity. Outcomes from this research are three-pronged. The first includes fostering a discussion about the existing conditions of EQH elements, i.e. thermal, lighting, ergonomic, acoustic, and air quality on the construction labor force. The second identifies gaps in sustainability standards and codes during the pre-occupancy stage of building construction from ground-breaking to substantial completion. The third identifies opportunities for improvements and mitigation strategies to improve EQH such as increased monitoring of effects on productivity and health of contractors and increased inclusion of the pre-occupancy stage in green building standards.

Keywords: construction contractors, health and well-being, environmental quality, risk management

Procedia PDF Downloads 128

Authors: Rujia Chen, Ajit Narayanan

Abstract:

Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

Procedia PDF Downloads 180

Authors: M. V. Rane, Tareke Tekia

Abstract:

Fresh Air Dehumidifier having a capacity of 1 TR has been developed by Heat Pump Laboratory at IITB. This fresh air dehumidifier is based on potassium formate liquid desiccant. The regeneration of the liquid desiccant can be done in two stages. The first stage of liquid desiccant regeneration involves the boiling of liquid desiccant inside the evacuated glass type solar thermal collectors. Further regeneration of liquid desiccant can be achieved using Low Temperature Regenerator, LTR. The coefficient of performance of the fresh air dehumidifier greatly depends on the performance of the major components such as high temperature regenerator, low temperature regenerator, fresh air dehumidifier, and solution heat exchangers. High effectiveness solution heat exchanger has been developed and tested. The solution heat exchanger is based on a patented aluminium extrusion with special passage geometry to enhance the heat transfer rate. Effectiveness up to 90% was achieved. Before final testing of the dehumidifier, major components have been tested individually. Testing of the solar thermal collector as hot water and steam generator reveals that efficiency up to 55% can be achieved. In this paper, the development of 1 TR fresh air dehumidifier with special focus on solution heat exchangers and solar thermal collector performance is presented.

Keywords: solar, liquid desiccant, dehumidification, air conditioning, regeneration, coefficient of performance

Procedia PDF Downloads 185