Search results for: wear particle analysis

Authors: N. Boontian

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Biological conversion of biomass to methane has received increasing attention in recent years. Grasses have been explored for their potential anaerobic digestion to methane. In this review, extensive literature data have been tabulated and classified. The influences of several parameters on the potential of these feedstocks to produce methane are presented. Lignocellulosic biomass represents a mostly unused source for biogas and ethanol production. Many factors, including lignin content, crystallinity of cellulose, and particle size, limit the digestibility of the hemicellulose and cellulose present in the lignocellulosic biomass. Pretreatments have used to improve the digestibility of the lignocellulosic biomass. Each pretreatment has its own effects on cellulose, hemicellulose and lignin, the three main components of lignocellulosic biomass. Solid-state anaerobic digestion (SS-AD) generally occurs at solid concentrations higher than 15%. In contrast, liquid anaerobic digestion (AD) handles feedstocks with solid concentrations between 0.5% and 15%. Animal manure, sewage sludge, and food waste are generally treated by liquid AD, while organic fractions of municipal solid waste (OFMSW) and lignocellulosic biomass such as crop residues and energy crops can be processed through SS-AD. An increase in operating temperature can improve both the biogas yield and the production efficiency, other practices such as using AD digestate or leachate as an inoculant or decreasing the solid content may increase biogas yield but have negative impact on production efficiency. Focus is placed on substrate pretreatment in anaerobic digestion (AD) as a means of increasing biogas yields using today’s diversified substrate sources.

Keywords: anaerobic digestion, lignocellulosic biomass, methane production, optimization, pretreatment

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Authors: Othman Mohamed Altheni, Abdurrahman Abusaada

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This study presents optimization of multiple performance characteristics [material removal rate (MRR), surface roughness (Ra), and overcut (OC)] of hardened AISI D3 tool steel in electrical discharge machining (EDM) using Taguchi method and Grey relational analysis. Machining process parameters selected were pulsed current Ip, pulse-on time Ton, pulse-off time Toff and gap voltage Vg. Based on ANOVA, pulse current is found to be the most significant factor affecting EDM process. Optimized process parameters are simultaneously leading to a higher MRR, lower Ra, and lower OC are then verified through a confirmation experiment. Validation experiment shows an improved MRR, Ra and OC when Taguchi method and grey relational analysis were used

Keywords: edm parameters, grey relational analysis, Taguchi method, ANOVA

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Authors: Abubakar Maikudi

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Contrastive analysis is the method of analyzing the structure of any two languages with a view to determining the possible differential aspects of their systems irrespective of their genetic affinity or level of development. Contrastive analysis of two languages becomes useful when it is adequately describing the sound structure and grammatical structure of two languages, with comparative statements giving emphasis to the compatible items in the two systems. This research work uses comparative analysis theory to analyze adjective and adjectival phrases in Hausa and Yorùbá languages. The Hausa language belongs to the Chadic family of the Afro-Asiatic phylum, while the Yorùbá language belongs to the Benue-Congo family of the Niger-Congo phylum. The findings of the research clearly demonstrated that there are significant similarities in the adjectival phrase constructions of the two languages, i.e., nominal (Head) and post-nominal (Post-Head) use of the adjective, predicative function of an adjective, use of the reduplicative adjective, use of the comparative and superlative adjective, etc. However, there are dissimilarities in the adjectival phrase of the two languages in gender/number agreement and pre-nominal (Post-Head) use of adjectives.

Keywords: genetic affinity, contrastive analysis, phylum, pre-head, post-head

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Authors: Gilang Maulana Majid

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If a government program remains scientifically uncontested for a long time, it is likely that its effects will be far from expected as there is no concrete evaluation of the steps being taken. This article identifies how such a theory aptly describes the case of the 'tourism ambassador' program in Indonesia. Being set out as one of the tourism promotional means of many regional governments in Indonesia, this program is heavily criticized for being ineffective despite a large number of budgets being spent on an annual basis. Taking the program as a case study, this article applies content analysis, SWOT analysis, and TOPSIS as data analysis methods, with a total of 56 tourism ambassadors invited to become coders, respondents, and/or interviewees in this research. The study reveals the SWOT of the program, recognizes four strategies that can be taken to optimize the program's effects and prioritizes a strategy based on the preferences of the involved tourism ambassadors using TOPSIS. It is found that incorporation of technology such as the creation of an online platform is, among others, the most expected approach to be taken to solve the problems concerning tourism ambassador program. However, based on the costs and benefits of each strategy presented in the current study, each alternative appears to have trade-offs between one and another.

Keywords: Indonesia, optimization strategies, 'Tourism Ambassador' program, SWOT-TOPSIS

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Authors: Munir Ashraf, Shagufta Riaz

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Personal protective equipment (PPE) and bioactive textiles are highly important for the health care of front line hospital workers, patients, and the general population to be safe from highly infectious diseases. This was even more critical in the wake of COVID-19 outbreak. Most of the medical textiles are inactive against various viruses and bacteria, hence there is a need to wash them frequently to avoid the spread of microorganisms. According to survey conducted by the world health organization, more than 500 million people get infected from hospitals, and more than 13 million died due to these hospitals’ acquired deadly diseases. The market available PPE are though effective against the penetration of pathogens and to kill bacteria but, they are not breathable and active against different viruses. Therefore, there was a great need to develop textiles that are not only effective against bacteria, fungi, and viruses but also are comfortable to the medical personnel and patients. In the present study, waterproof breathable, and biologically active textiles were developed using antiviral and antibacterial nanomaterials. These nanomaterials like TiO₂, ZnO, Cu, and Ag were immobilized at the surface of cotton fabric by using different silane coupling agents and electroless deposition that they retained their functionality even after 30 industrial laundering cycles. Afterwards, the treated fabrics were coated with a waterproof breathable film to prevent the permeation of liquid droplets, any particle or microorganisms greater than 80 nm. The developed cotton fabric was highly active against bacteria and viruses. The good durability of nanomaterials at the cotton surface after several industrial washing cycles makes this fabric an ideal candidate for bioactive textiles used in the medical field.

Keywords: antibacterial, antiviral, cotton, durable

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Authors: Vasileios Anastopoulos

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Cyber threat intelligence assists organizations in understanding the threats they face and helps them make educated decisions on preparing their defenses. Sharing of threat intelligence and threat information is increasingly leveraged by organizations and enterprises, and various software solutions are already available, with the open-source malware information sharing platform (MISP) being a popular one. In this work, a methodology for the production of cyber threat intelligence using the threat information stored in MISP is proposed. The methodology leverages the discipline of social network analysis and the diamond model, a model used for intrusion analysis, to produce cyber threat intelligence. The workings are demonstrated with a case study on a production MISP instance of a real organization. The paper concluded with a discussion on the proposed methodology and possible directions for further research.

Keywords: cyber threat intelligence, diamond model, malware information sharing platform, social network analysis

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Authors: Kurt S. Candilas

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This study is a critical analysis of the work of Robert Frost, 'Nothing Gold Can Stay'. It subjects the literary piece into a qualitative analysis using the critical theory of mimesis. In effect, this study is proposed to find out and shed light on the mimetic feature of the poem’s textuality. Generally, it aims to analyze the poem’s deeper meaning in the context of the reality of life from birth to death. For the most part, this critical analysis discerns, investigates, and highlights the features which present the imitation of life in detail and from a deeper view. Based on the result of analysis, it shows that Frost has portrayed the cycle of life from birth to midst life as about proving oneself to others as far as achievements and accomplishments are concerned; secondly, at some point of one’s life, successes and achievements are just one’s perfect signature of living. As Frost discloses his poem, his message of the reality of life from birth to death is clear enough, that nothing is going to last forever.

Keywords: Nothing Gold Can Stay, mimesis, birth, death

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Authors: Jumel Christian P. Nicha, Severino G. Salmo III

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This study investigated the concentration of heavy metals (HM) in mangrove sediments of Lake Uacon, Zambales, Philippines. The relationship among the studied HM (Cr, Ni, Pb, Cu, Cd, Fe) and the mangrove vegetation and sediment characteristics were assessed. Fourteen sampling plots were designated across the lake (10 vegetated and 4 un-vegetated) based on distance from the mining effluents. In each plot, three sediment cores were collected at 20 cm depth. Among the dominant mangrove species recorded were (in order of dominance): Sonneratia alba, Rhizophora stylosa, Avicennia marina, Excoecaria agallocha and Bruguiera gymnorrhiza. Sediment samples were digested with aqua regia, and the HM concentrations were quantified using Atomic Absorption Spectroscopy (AAS). Results showed that HM concentrations were higher in the vegetated plots as compared to the un-vegetated sites. Vegetated sites had high Ni (mean: 881.71 mg/kg) and Cr (mean: 776.36 mg/kg) that exceeded the threshold values (cf. by the United States Environmental Protection Agency; USEPA). Fe, Pb, Cu and Cd had a mean concentration of 2597.92 mg/kg, 40.94 mg/kg, 36.81 mg/kg and 2.22 mg/kg respectively. Vegetation variables were not significantly correlated with HM concentration. However, the HM concentration was significantly correlated with sediment variables particularly pH, redox, particle size, nitrogen, phosphorus, moisture and organic matter contents. The Pollution Load Index (PLI) indicated moderate to high pollution in the lake. Risk assessment and management should be designed in order to mitigate the ecological risk posed by HM. The need of a regular monitoring scheme for lake and mangrove rehabilitation programs and management should be designed.

Keywords: heavy metals, mangrove vegetation, mining, Philippines, sediment

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Authors: Ayse Melissa Ergun

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The lean management concept is a widely used and implemented production improvement solution especially in the automotive sector. However, in the recent years the need for an efficient production system became no longer sufficient for companies. The increasing importance of green production and environmental sustainability pushed companies to modify their manufacturing systems in a more environmentally conscious way. As a result, the recent improvements in the automotive sector has surpassed the lean management directives and currently are in need of a more sustainable and green transformation. At this point a comprehensive approach like Lean-Green (LG) Management, which combines lean management and green applications, gains popularity in the sector. This study conducts a bibliometric analysis between the years 2015-2023 for LG management. This study aims to identify the current standing of the literature. The most researched branches of the concept have been determined by the conducted analysis. Furthermore, this study sheds a light on the future research directions for scholars.

Keywords: LG management, sustainability, lean, green, automotive, bibliometric analysis

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Authors: Abdelrahim Hamadelnil

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Padeye design and analysis plays important roles during the lifting, load out and installation of heavy structures. This paper explains the disadvantages of limiting the effective thickness of the cheek plate to 75% of the main plate thickness. In addition, a sensitivity study about the impact of the out of plane force on the padeye design is discussed. This study also explains the fabrication requirements to ensure that the designed strength is achieved. The objective of this study is to elaborate and discuss the philosophy of padeye design and to propose the suitable effective cheek plate thickness to be considered in the analysis of padeye. A finite element analysis using London University Structure Analysis System (LUSAS), is conducted and compared with the hand calculation. The benefits and advantage of using FE analysis is addressed in this paper. At the end of this paper, a guideline elaborating the philosophy of the design of the padeye is developed and the suitable effective thickness of cheek plate to be considered in the design is recommended. In addition, a comparison between the finite element result and the hand calculation using beam theory is discussed as well.

Keywords: cheek plate, effective thickness, out of plane force, Padeye

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Authors: Hamed Nazeri, Pierre Mertiny, Yongsheng Ma, Kajsa Duke

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The background of the present study is the use of environment-friendly biopolymer and biocomposite materials. Among the recently introduced biopolymers, poly (propylene carbonate) (PPC) has been gaining attention. This study focuses on the size of representative volume elements (RVE) in order to simulate PPC composites reinforced by cellulose nanocrystals (CNCs) as a bio-nanocomposite. Before manufacturing nanocomposites, numerical modeling should be implemented to explore and predict mechanical properties, which may be accomplished by creating and studying a suitable RVE. In other studies, modeling of composites with rod shaped fillers has been reported assuming that fillers are unidirectionally aligned. But, modeling of non-aligned filler dispersions is considerably more difficult. This study investigates the minimum RVE size to enable subsequent FEA modeling. The matrix and nano-fillers were modeled using the finite element software ABAQUS, assuming randomly dispersed fillers with a filler mass fraction of 1.5%. To simulate filler dispersion, a Monte Carlo technique was employed. The numerical simulation was implemented to find composite elastic moduli. After commencing the simulation with a single filler particle, the number of particles was increased to assess the minimum number of filler particles that satisfies the requirements for an RVE, providing the composite elastic modulus in a reliable fashion.

Keywords: biocomposite, Monte Carlo method, nanocomposite, representative volume element

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Authors: Tolulope Aremu

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This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors.

Keywords: production yield optimization, deep learning, tuning of process parameters, LSTM, CNN, precision manufacturing, TensorFlow, Keras, cloud infrastructure, cost saving

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Authors: Sadaf Nawaz, Adnan Ahmed Khan, Asad Mahmood, Chaudhary Farrukh Javed

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Classically, an energy detector is implemented in time domain (TD). However, frequency domain (FD) based energy detector has demonstrated an improved performance. This paper presents a comparison between the two approaches as to analyze their pros and cons. A detailed performance analysis of the classical TD energy-detector and the periodogram based detector is performed. Exact and approximate mathematical expressions for probability of false alarm (Pf) and probability of detection (Pd) are derived for both approaches. The derived expressions naturally lead to an analytical as well as intuitive reasoning for the improved performance of (Pf) and (Pd) in different scenarios. Our analysis suggests the dependence improvement on buffer sizes. Pf is improved in FD, whereas Pd is enhanced in TD based energy detectors. Finally, Monte Carlo simulations results demonstrate the analysis reached by the derived expressions.

Keywords: cognitive radio, energy detector, periodogram, spectrum sensing

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Authors: D. Habibinia, M. R. Feyzi

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Nowadays, specialized software is vastly used to verify the performance of an electric machine prototype by evaluating a model of the system. These models mainly consist of electrical parameters such as inductances and resistances. However, when the operating frequency of the device is above one kHz, the effect of parasitic capacitances grows significantly. In this paper, a software-based procedure is introduced to model these capacitances within the electromagnetic simulation of the device. The case study is a high-frequency high-voltage pulse transformer. The Finite Element Analysis (FEA) software with coupled field analysis is used in this method.

Keywords: finite element analysis, parasitic capacitance, pulse transformer, high frequency

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Authors: Mohit Kumar, Mayur Betharia

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Healthcare domain, with the emergence of Electronic Medical Record (EMR), collects a lot of data which have been attracting Data Mining expert’s interest. In the past, doctors have relied on their intuition while making critical clinical decisions. This paper presents the means to analyze the Medical workflows to get business insights out of huge dumped medical databases. Market Basket Analysis (MBA) which is a special data mining technique, has been widely used in marketing and e-commerce field to discover the association between products bought together by customers. It helps businesses in increasing their sales by analyzing the purchasing behavior of customers and pitching the right customer with the right product. This paper is an attempt to demonstrate Market Basket Analysis applications in healthcare. In particular, it discusses the Market Basket Analysis Algorithm ‘Apriori’ applications within healthcare in major areas such as analyzing the workflow of diagnostic procedures, Up-selling and Cross-selling of Healthcare Systems, designing healthcare systems more user-friendly. In the paper, we have demonstrated the MBA applications using Angiography Systems, but can be extrapolated to other modalities as well.

Keywords: data mining, market basket analysis, healthcare applications, knowledge discovery in healthcare databases, customer relationship management, healthcare systems

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Authors: Montree Bunruangses, Sonath Bhattacharyya, Somchat Sonasang, Preecha Yupapin

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A two-level system is manipulated by a microstrip add-drop circuit configured as an atom like system for wave-particle behavior investigation when its traveling speed along the circuit perimeter is the speed of light. The entangled pair formed by the upper and lower sideband peaks is bound by the angular displacement, which is given by 0≤θ≤π/2. The control signals associated with 3-peak signal frequencies are applied by the external inputs via the microstrip add-drop multiplexer ports, where they are time functions without the space term involved. When a system satisfies the speed of light conditions, the mass term has been changed to energy based on the relativistic limit described by the Lorentz factor and Einstein equation. The different applied frequencies can be utilized to form the 3-phase torques that can be applied for quantum engines. The experiment will use the two-level system circuit and be conducted in the laboratory. The 3-phase torques will be recorded and investigated for quantum engine driving purpose. The obtained results will be compared to the simulation. The optimum amplification of torque can be obtained by the resonant successive filtering operation. Torque will be vanished when the system is balanced at the stopped position, where |Time|=0, which is required to be a system stability condition. It will be discussed for future applications. A larger device may be tested in the future for realistic use. A synchronous and asynchronous driven motor is also discussed for the warp drive use.

Keywords: quantum engine, relativistic motor, 3-phase torque, atomic engine

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Authors: Olivier Delage, Hassan Bencherif, Alain Bourdier

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Signal decomposition approaches represent an important step in time series analysis, providing useful knowledge and insight into the data and underlying dynamics characteristics while also facilitating tasks such as noise removal and feature extraction. As most of observational time series are nonlinear and nonstationary, resulting of several physical processes interaction at different time scales, experimental time series have fluctuations at all time scales and requires the development of specific signal decomposition techniques. Most commonly used techniques are data driven, enabling to obtain well-behaved signal components without making any prior-assumptions on input data. Among the most popular time series decomposition techniques, most cited in the literature, are the empirical mode decomposition and its variants, the empirical wavelet transform and singular spectrum analysis. With increasing popularity and utility of these methods in wide ranging applications, it is imperative to gain a good understanding and insight into the operation of these algorithms. In this work, we describe all of the techniques mentioned above as well as their ability to denoise signals, to capture trends, to identify components corresponding to the physical processes involved in the evolution of the observed system and deduce the dimensionality of the underlying dynamics. Results obtained with all of these methods on experimental total ozone columns and rainfall time series will be discussed and compared

Keywords: denoising, empirical mode decomposition, singular spectrum analysis, time series, underlying dynamics, wavelet analysis

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Authors: Michael Tupý, Jaroslav Císař, Pavel Mokrejš, Dagmar Měřínská, Alice Tesaříková-Svobodová

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The worldwide problem is that the recycled PVB is wildly stored in landfills. However, PVB have very similar chemical properties such as PVC. Moreover, both of them are used in plasticized form. Thus, the thermal properties of plasticized PVC obtained from primary production and the PVB was obtained by recycling of windshields are compared. It is carried out in order to find degradable conditions and decide if blend of PVB/PVC can be processable together. Tested PVC contained 38 % of plasticizer diisononyl phthalate (DINP) and PVB was plasticized with 28 % of triethylene glycol, bis(2-ethylhexanoate) (3GO). Thermal and thermo-oxidative decomposition of both vinyl polymers are compared such as DSC and OOT analysis. The tensile strength analysis is added.

Keywords: polyvinyl chloride, polyvinyl butyral, recycling, reprocessing, thermal analysis, decomposition

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Authors: Ameema Mahroof, Muhammad Saeed

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The purpose of this study was to analyze the papers of computer science conducted by Board of Intermediate and Secondary Education with reference to Bloom’s taxonomy. The present study has two parts. First, the analysis is done on the papers conducted by Board of Intermediate of Secondary Education on the basis of basic rules of item construction especially Bloom’s (1956). And the item analysis is done to improve the psychometric properties of a test. The sample included the question papers of computer science of higher secondary classes (XI-XII) for the years 2011 and 2012. For item analysis, the data was collected from 60 students through convenient sampling. Findings of the study revealed that in the papers by Board of intermediate and secondary education the maximum focus was on knowledge and understanding level and very less focus was on the application, analysis, and synthesis. Furthermore, the item analysis on the question paper reveals that item difficulty of most of the questions did not show a balanced paper, the items were either very difficult while most of the items were too easy (measuring knowledge and understanding abilities). Likewise, most of the items were not truly discriminating the high and low achievers; four items were even negatively discriminating. The researchers also analyzed the items of the paper through software Conquest. These results show that the papers conducted by Board of Intermediate and Secondary Education were not well constructed. It was recommended that paper setters should be trained in developing the question papers that can measure various cognitive abilities of students so that a good paper in computer science should assess all cognitive abilities of students.

Keywords: Bloom’s taxonomy, question paper, item analysis, cognitive domain, computer science

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Authors: Liu Mingxin

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Streets act as the organizers of each image element on the urban spatial route, and the spatial continuity of urban streets is the basis for people to perceive the overall image of the city. This paper takes the walking space of Zhengzhou city as the research object, conducts investigation and analysis through questionnaire interviews, and selects typical walking space for in-depth study. Through the analysis of questionnaire data, the investigation and analysis of the current situation of walking space, and the analysis of pedestrian psychological behavior activities, the paper summarizes the construction suggestions of urban walking space continuity from the three aspects of the composition of walking street, the bottom interface and side interface, and the service facilities of walking space. The walking space is not only the traffic space but also the comfortable experience and the continuity of the space.

Keywords: walking space, spatial continuity, walking psychology, space reconstruction

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Authors: Muhammad Ilham Agus Salim, Mohammad Ali

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In this article, we have elaborated a study over the nature of Islamic financial transaction by comparing between Dinar and IDR currency in Indonesia. We have found the interesting issue among scholars and practitioners in which Dinar would be a single currency ASEAN Economic Community (AEC), then becoming motivation and added value research. The assessment among dinar volatility analysis for three years ago and IDR fluctuation as well as outlook qualitative test regarding dinar are components of analysis that weak Indonesian currency should be altered to be better coinage. The value of dinar more stable than IDR and also eligible as a currency e.g. limited quantities, easy to carry, durable, easy to saved, and has the same quality. On the other hand, the existing of IDR has defeated by inflation. The EViews program explained that Dinar at current level still fluctuate, but in the first different have fixed variant. The result of analysis describing that dinar has potential as the medium exchange, because the material of dinar is relevant and feasible since 14 century until present. Therefore, dinar should be considered to solve Indonesia crisis today.

Keywords: medium of exchange, dinar & IDR currency, volatility analysis, EViews program

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Authors: Jennifer Stephens, Nichole Parker, Kristin Petrovic

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To gain a better understanding of how students enrolled in a Bachelor of Nursing (BN) program are educated, faculty members in the BN program at Athabasca University (AU) in Alberta, Canada, developed a 3-phase comprehensive curriculum review project. Phase one of this review centered around hiring an external curriculum expert to examine and analyze the current curriculum and to propose recommendations focused on identifying gaps as well as building on strengths towards meeting changing health care trends. Phase two incorporated extensive institutional document analysis as well as qualitative and quantitative data collection in reciprocated critical reflection and has yielded insights into valuable processes, challenges, and solutions inherent to the complexities of undertaking curriculum review and analysis. Results of our phase one and two analysis generated a quality improvement (QI) template that could benefit other nursing education programs engaged in curriculum review and analysis. The key processes, lessons, and insights, as well as future project phase three plans, will be presented for iterative discussion and role modelling for other institutions undergoing, or planning, content-based curriculum review and evaluation.

Keywords: curriculum, education, nursing, nursing faculty practice, quality improvement

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Authors: Faisal AlShammari, Abdulmajid Addali

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Monitoring the conditions of rotating machinery as bearing is important in order to improve its stability of works. Acoustic emission (AE) and vibration analysis are some of the most accomplished techniques used for this purpose. Acoustic emission has the ability to detect the initial phase of component degradation. Moreover, it has been observed that the success of vibration analysis does not take place below 100 rpm rotational speed. This because the energy generated below 100 rpm rotational speed is not detectable using conventional vibration. From this pint, this paper has presented a focused review of using acoustic emission techniques for monitoring bearings condition.

Keywords: condition monitoring, stress wave analysis, low-speed bearings, bearing defect diagnosis

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Authors: Shahryar Aftabi, Saeed Fathi, Mohammad H. Aminfar

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It is well known that cemented sand is one of the best approaches for soil stabilization. In some cases, a blend of sand, cement and other pozzolan materials such as zeolite, nano-particles and fiber can be widely (commercially) available and be effectively used in soil stabilization, especially in road construction. In this research, we investigate the effects of CaO which is based on the geotechnical characteristics of zeolite composition with sandy silt soil. Zeolites have low amount of CaO in their structures, that is, varying from 3% to 10%, and by removing the cement paste, we want to investigate the effect of zeolite pozzolan without any activator on soil samples strength. In this research, experiments are concentrated on various weight percentages of zeolite in the soil to examine the effect of the zeolite on drainage shear strength and California Bearing Ratio (CBR) both with and without curing. The study also investigates their liquid limit and plastic limit behavior and makes a comparative result by using Feng's and Wroth-Wood's methods in fall cone (cone penetrometer) device; in the final the SEM images have been presented. The results show that by increasing the percentage of zeolite in without-curing samples, the fine zeolite particles increase some soil's strength, but in the curing-state we can see a relatively higher strength toward without-curing state, since the zeolites have no plastic behavior, the pozzolanic property of zeolites plays a much higher role than cementing properties. Indeed, it is better to combine zeolite particle with activator material such as cement or lime to gain better results.

Keywords: California bearing ratio, CBR, direct shear, fall-cone, sandy silt, SEM, zeolite

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Authors: Fahim Ullah, Muhammad Usman

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Energy consumption and environmental sustainability are hard challenges of 21st century. Energy richness increases environmental pollution while energy poverty hinders economic growth. Considering these two aspects, present study calculates energy deficiency and examines the role of renewable energy to overcome rising energy deficiency and carbon emission for selected countries from 1990 to 2021. For empirical analysis, this study uses methods of moments panel quantile regression analysis and to check the robustness, study used panel quantile robust analysis. Graphical analysis indicated rising global energy deficiency since last three decades where energy consumption is higher than energy production. Empirical results showed that renewable energy is a significant factor for reducing energy deficiency. Secondly, the energy deficiency increases carbon emission level and again renewable energy decreases emissions level. This study recommends that global energy deficiency and rising carbon emissions can be controlled through structural change in the form of energy transition to replace non-renewable resources with renewable resources.

Keywords: energy deficiency, renewable energy, carbon emission, energy trade, PQL analysis

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Authors: Florian Wagner, Christoph Schmidt, Vincent Bagnoud

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Understanding the interaction of ultra-intense laser pulses with overcritical targets is of major interest for many applications such as laser-driven ion acceleration, fast ignition in the frame of inertial confinement fusion or high harmonic generation and the creation of attosecond pulses. One particular aspect of this interaction is the shift of the critical surface, where the laser pulse is stopped and the absorption is at maximum, due to the radiation pressure induced by the laser pulse, also referred to as laser hole boring. We investigate laser-hole boring experimentally by measuring the backscattered spectrum which is doppler-broadened because of the movement of the reflecting surface. Using the high-power, high-energy laser system PHELIX in Darmstadt, we gathered an extensive set of data for different laser intensities ranging from 10^18 W/cm2 to 10^21 W/cm2, two different levels of the nanosecond temporal contrast (10^6 vs. 10^11), elliptical and linear polarization and varying target configurations. In this contribution we discuss how the maximum velocity of the critical surface depends on these parameters. In particular we show that by increasing the temporal contrast the maximum hole boring velocity is decreased by more than a factor of three. Our experimental findings are backed by a basic analytical model based on momentum and mass conservation as well as particle in cell simulations. These results are of particular importance for fast ignition since they contribute to a better understanding of the transport of the ignitor pulse into the overdense region.

Keywords: laser-hole boring, interaction of ultra-intense lasers with overcritical targets, fast ignition, relativistic laser motter interaction

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Authors: Sakshi Piplani, Ajit Kumar

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Valproic acid (VPA) is the first synthetic therapeutic agent used to treat epileptic disorders, which account for affecting nearly 1% world population. Teratogenicity caused by VPA has prompted the search for next generation drug with better efficacy and lower side effects. Recent studies have posed HDAC8 as direct target of VPA that causes the teratogenic effect in foetus. We have employed molecular dynamics (MD) and docking simulations to understand the binding mode of VPA and their analogues onto HDAC8. A total of twenty 3D-structures of human HDAC8 isoforms were selected using BLAST-P search against PDB. Multiple sequence alignment was carried out using ClustalW and PDB-3F07 having least missing and mutated regions was selected for study. The missing residues of loop region were constructed using MODELLER and energy was minimized. A set of 216 structural analogues (>90% identity) of VPA were obtained from Pubchem and ZINC database and their energy was optimized with Chemsketch software using 3-D CHARMM-type force field. Four major neurotransmitters (GABAt, SSADH, α-KGDH, GAD) involved in anticonvulsant activity were docked with VPA and its analogues. Out of 216 analogues, 75 were selected on the basis of lower binding energy and inhibition constant as compared to VPA, thus predicted to have anti-convulsant activity. Selected hHDAC8 structure was then subjected to MD Simulation using licenced version YASARA with AMBER99SB force field. The structure was solvated in rectangular box of TIP3P. The simulation was carried out with periodic boundary conditions and electrostatic interactions and treated with Particle mesh Ewald algorithm. pH of system was set to 7.4, temperature 323K and pressure 1atm respectively. Simulation snapshots were stored every 25ps. The MD simulation was carried out for 20ns and pdb file of HDAC8 structure was saved every 2ns. The structures were analysed using castP and UCSF Chimera and most stabilized structure (20ns) was used for docking study. Molecular docking of 75 selected VPA-analogues with PDB-3F07 was performed using AUTODOCK4.2.6. Lamarckian Genetic Algorithm was used to generate conformations of docked ligand and structure. The docking study revealed that VPA and its analogues have more affinity towards ‘hydrophobic active site channel’, due to its hydrophobic properties and allows VPA and their analogues to take part in van der Waal interactions with TYR24, HIS42, VAL41, TYR20, SER138, TRP137 while TRP137 and SER138 showed hydrogen bonding interaction with VPA-analogues. 14 analogues showed better binding affinity than VPA. ADMET SAR server was used to predict the ADMET properties of selected VPA analogues for predicting their druggability. On the basis of ADMET screening, 09 molecules were selected and are being used for in-vivo evaluation using Danio rerio model.

Keywords: HDAC8, docking, molecular dynamics simulation, valproic acid

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Authors: Danielle Shackley, Yetunde Folajimi

Abstract:

As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

Procedia PDF Downloads 97

Authors: Wang Lin, Li Zhiqiang

Abstract:

The purpose of this paper is to analyze the cooperative learning behavior pattern based on the data of students' movement. The study firstly reviewed the cooperative learning theory and its research status, and briefly introduced the k-means clustering algorithm. Then, it used clustering algorithm and mathematical statistics theory to analyze the activity rhythm of individual student and groups in different functional areas, according to the movement data provided by 10 first-year graduate students. It also focused on the analysis of students' behavior in the learning area and explored the law of cooperative learning behavior. The research result showed that the cooperative learning behavior analysis method based on movement data proposed in this paper is feasible. From the results of data analysis, the characteristics of behavior of students and their cooperative learning behavior patterns could be found.

Keywords: behavior pattern, cooperative learning, data analyze, k-means clustering algorithm

Procedia PDF Downloads 187

Authors: Shin Woo Kim, Eui Ju Lee

Abstract:

The recent development of biofuel production technology facilitates the use of bioethanol and biodiesel on automobile. Bioethanol, especially, can be used as a fuel for gasoline vehicles because the addition of ethanol has been known to increase octane number and reduce soot emissions. However, the wide application of biofuel has been still limited because of lack of detailed combustion properties such as auto-ignition temperature and pollutant emissions such as NOx and soot, which has been concerned mainly on the vehicle fire safety and environmental safety. In this study, the combustion characteristics of gasoline/ethanol fuel were investigated both numerically and experimentally. For auto-ignition temperature and NOx emission, the numerical simulation was performed on the well-stirred reactor (WSR) to simulate the homogeneous gasoline engine and to clarify the effect of ethanol addition in the gasoline fuel. Also, the response surface method (RSM) was introduced as a design of experiment (DOE), which enables the various combustion properties to be predicted and optimized systematically with respect to three independent variables, i.e., ethanol mole fraction, equivalence ratio and residence time. The results of stoichiometric gasoline surrogate show that the auto-ignition temperature increases but NOx yields decrease with increasing ethanol mole fraction. This implies that the bioethanol added gasoline is an eco-friendly fuel on engine running condition. However, unburned hydrocarbon is increased dramatically with increasing ethanol content, which results from the incomplete combustion and hence needs to adjust combustion itself rather than an after-treatment system. RSM results analyzed with three independent variables predict the auto-ignition temperature accurately. However, NOx emission had a big difference between the calculated values and the predicted values using conventional RSM because NOx emission varies very steeply and hence the obtained second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, NOx emission is taken as common logarithms and worked again with RSM. NOx emission predicted through logarithm transformation is in a fairly good agreement with the experimental results. For more tangible understanding of gasoline/ethanol fuel on pollutant emissions, experimental measurements of combustion products were performed in gasoline/ethanol pool fires, which is widely used as a fire source of laboratory scale experiments. Three measurement methods were introduced to clarify the pollutant emissions, i.e., various gas concentrations including NOx, gravimetric soot filter sampling for elements analysis and pyrolysis, thermophoretic soot sampling with transmission electron microscopy (TEM). Soot yield by gravimetric sampling was decreased dramatically as ethanol was added, but NOx emission was almost comparable regardless of ethanol mole fraction. The morphology of the soot particle was investigated to address the degree of soot maturing. The incipient soot such as a liquid like PAHs was observed clearly on the soot of higher ethanol containing gasoline, and the soot might be matured under the undiluted gasoline fuel.

Keywords: gasoline/ethanol fuel, NOx, pool fire, soot, well-stirred reactor (WSR)

Procedia PDF Downloads 212