Search results for: H₂-optimal model reduction

Authors: Suleman Maliha, Suleman Sidra

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Obstructive sleep apnea (OSA) is a sleep disorder that can result in behavioral and psychomotor impairments in children. The classical treatment modalities for OSA have been continuous positive airway pressure and adenotonsillectomy. However, orthodontic intervention through rapid maxillary expansion (RME) has also been commonly used to manage skeletal transverse maxillary discrepancies. Aim and objectives: The aim of this study is to determine the efficacy of rapid maxillary expansion in paediatric patients with obstructive sleep apnea by assessing pre and post-treatment mean apnea-hypopnea index (AHI) and oxygen saturations. Methodology: Literature was identified through a rigorous search of the Embase, Pubmed, and CINAHL databases. Articles published from 2012 onwards were selected. The inclusion criteria consisted of patients aged 18 years and under with no systemic disease, adenotonsillar surgery, or hypertrophy who are undergoing RME with AHI measurements before and after treatment. In total, six suitable papers were identified. Results: Three studies assessed patients pre and post-RME at 12 months. The first study consisted of 15 patients with an average age of 7.5 years. Following treatment, they found that RME resulted in both higher oxygen saturations (+ 5.3%) and improved AHI (- 4.2 events). The second study assessed 11 patients aged 5–8 years and also noted improvements, with mean AHI reduction from 6.1 to 2.4 and oxygen saturations increasing from 93.1% to 96.8%. The third study reviewed 14 patients aged 6–9 years and similarly found an AHI reduction from 5.7 to 4.4 and an oxygen saturation increase from 89.8% to 95.5%. All modifications noted in these studies were statistically significant. A long-term study reviewed 23 patients aged 6–12 years post-RME treatment on an annual basis for 12 years. They found that the mean AHI reduced from 12.2 to 0.4, with improved oxygen saturations from 78.9% to 95.1%. Another study assessed 19 patients aged 9-12 years at two months into RME and four months post-treatment. Improvements were also noted at both stages, with an overall reduction of the mean AHI from 16.3 to 0.8 and an overall increase in oxygen saturations from 77.9% to 95.4%. The final study assessed 26 children aged 7-11 years on completion of individual treatment and found an AHI reduction from 6.9 to 5.3. However, the oxygen saturation remained stagnant at 96.0%, but this was not clinically significant. Conclusion: Overall, the current evidence suggests that RME is a promising treatment option for paediatric patients with OSA. It can provide efficient and conservative treatment; however, early diagnosis is crucial. As there are various factors that could be contributing to OSA, it is important that each case is treated on its individual merits. Going forward, there is a need for more randomized control trials with larger cohorts being studied. Research into the long-term effects of RME and potential relapse amongst cases would also be useful.

Keywords: orthodontics, sleep apnea, maxillary expansion, review

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Authors: Ormanee Patarathipakorn

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Background: Stress was the major psychological problem that affecting to physical and mental health among undergraduate students. Aim of study was to determine the effective of meditation training program (MTP) for stress reduction measured by biofeedback (BB) machine. Material and Methods: This was quasi-experimental study conducted in Faculty of Dentistry, Thammasat University, Thailand. Study period was between August and December 2023. Participants were the first-year Dentistry students. MTP was concentration meditation (Anapana meditation). Stress measurement was evaluated by using Thai version perceived stress scale (T-PSS-10) was performed at one week before study, 14 and 18 weeks. Stress evaluation by biofeedback machine (skin conductance: SC and skin temperature: ST) were performed at one week before study, 4, 8, 14 and 18 weeks. Data from T-PSS-10 and SC/ST biofeedback were collected and analyzed. Results: A total of 28 subjects were recruited. The mean age of participant was 18.4 years old. Two-thirds (19/28) was female. Stress reduction from MTP was detected since 4 and 8 weeks by STBB and SCBB, respectively. T-PSS 10 scores before MTP, 14 and 18 weeks were 17.7± 5.4, 9.8 ± 3.1 and 8.4 ± 3.1 with statistical significance. Conclusion: Meditation training program could reduce stress and measured by skin conductance and temperature biofeedback.

Keywords: stress, meditation, biofeedback, student

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Authors: B. Van Kessel, P. T. Kockelkorn, T. R. Speelman, T. C. Wierikx, C. Mai Van, T. A. Bogaard

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Saltwater intrusion is a common problem in estuaries around the world, as it could hinder the freshwater supply of coastal zones. This problem is likely to grow due to climate change and sea-level rise. The influence of these factors on the saltwater intrusion was investigated for the Cai River in the Khanh Hoa province in Vietnam. In addition, the Cai River has high seasonal fluctuations in discharge, leading to increased saltwater intrusion during the dry season. Sea level rise, river discharge changes, river mouth widening and a proposed saltwater intrusion prevention dam can have influences on the saltwater intrusion but have not been quantified for the Cai River estuary. This research used both an analytical and numerical model to investigate the effect of the aforementioned factors. The analytical model was based on a model proposed by Savenije and was calibrated using limited in situ data. The numerical model was a 3D hydrodynamic model made using the Delft3D4 software. The analytical model and numerical model agreed with in situ data, mostly for tidally average data. Both models indicated a roughly similar dependence on discharge, also agreeing that this parameter had the most severe influence on the modeled saltwater intrusion. Especially for discharges below 10 m/s3, the saltwater was predicted to reach further than 10 km. In the models, both sea-level rise and river widening mainly resulted in salinity increments up to 3 kg/m3 in the middle part of the river. The predicted sea-level rise in 2070 was simulated to lead to an increase of 0.5 km in saltwater intrusion length. Furthermore, the effect of the saltwater intrusion dam seemed significant in the model used, but only for the highest position of the gate.

Keywords: Cai River, hydraulic models, river discharge, saltwater intrusion, tidal barriers

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

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In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: information theory, prediction model, prosthetic alignment, transtibial prosthesis

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Authors: Enyia James Diwa, Isaiah Thank-God Ebi, Dodeye Ina Igbong

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The privatization of utilities has brought about very strong competition in industries such as petrochemical and gas distribution among others, considering the continuous increase in cost of fuel. This has brought about the intense reason for gas turbine owners and operators to reduce and control performance degradation of the engine in other to minimize cost. The most common and very crucial problem of the gas turbine is the fouling of compressor, which is mostly caused by a reduction in flow capacity, compressor efficiency, and pressure ratio, this, in turn, lead to the engine compressor re-matching and output power and thermal efficiency reduction. The content of this paper encompasses a detailed presentation of the major causes, effects and control mechanism of fouling. The major emphasis is on compressor water washing to enable power augmentation. A modelled gas turbine similar to that of GE LM6000 is modelled for the current study, based on TURBOMATCH which is a Cranfield University software specifically made for gas turbine performance simulation and fouling detection. The compounded and intricate challenges of compressor online water washing of large output gas turbine are carried out. The treatment is applied to axial compressor used in the petrochemical and hydrocarbon industry.

Keywords: gas turbine, fouling, degradation, compressor washing

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Authors: Zhilin Wang, Zhihao Zheng, Linxin Liu

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This study proposes a low-code model-building approach that aims to simplify the development and deployment of artificial intelligence (AI) models. With an intuitive way to drag and drop and connect components, users can easily build complex models and integrate multiple algorithms for training. After the training is completed, the system automatically generates a callable model service API. This method not only lowers the technical threshold of AI development and improves development efficiency but also enhances the flexibility of algorithm integration and simplifies the deployment process of models. The core strength of this method lies in its ease of use and efficiency. Users do not need to have a deep programming background and can complete the design and implementation of complex models with a simple drag-and-drop operation. This feature greatly expands the scope of AI technology, allowing more non-technical people to participate in the development of AI models. At the same time, the method performs well in algorithm integration, supporting many different types of algorithms to work together, which further improves the performance and applicability of the model. In the experimental part, we performed several performance tests on the method. The results show that compared with traditional model construction methods, this method can make more efficient use, save computing resources, and greatly shorten the model training time. In addition, the system-generated model service interface has been optimized for high availability and scalability, which can adapt to the needs of different application scenarios.

Keywords: low-code, model building, artificial intelligence, algorithm integration, model deployment

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Authors: Christopher Deekia Nwimae, Nigel Simms, Liyun Lao

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Erosion in pipe bends caused by particles is a major obstacle in the oil and gas fields and might cause the breakdown of production equipment. This work studied the effects imposed by flow velocity and impact of solid particles diameter in an elbow; erosion rate was verified with experimental data using the computational fluid dynamics (CFD) approach. Two-way coupled Euler-Lagrange and discrete phase model was employed to calculate the air/solid particle flow in an elbow. One erosion model and three-particle rebound models were used to predict the erosion rate on the 90° elbows. The generic erosion model was used in the CFD-based erosion model, and after comparing it with experimental data, results showed agreement with the CFD-based predictions as observed.

Keywords: erosion, prediction, elbow, computational fluid dynamics

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Mohammad Y. Badiee, Saeed Golestani, Mir Saman Pishvaee

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In recent years consumers and governments have been pushing companies to design their activities in such a way as to reduce negative environmental impacts by producing renewable product or threat free disposal policy more and more. It is therefore important to focus more accurate to the optimization of various aspect of total supply chain. Modeling a supply chain can be a challenging process due to the fact that there are a large number of factors that need to be considered in the model. The use of multi-objective optimization can lead to overcome those problems since more information is used when designing the model. Uncertainty is inevitable in real world. Considering uncertainty on parameters in addition to use multi-objectives are ways to give more flexibility to the decision making process since the process can take into account much more constraints and requirements. In this paper we demonstrate a stochastic scenario based robust model to cope with uncertainty in a closed-loop multi-objective supply chain. By applying the proposed model in a real world case, the power of proposed model in handling data uncertainty is shown.

Keywords: supply chain management, closed-loop supply chain, multi-objective optimization, goal programming, uncertainty, robust optimization

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Authors: Selin Guney, Andres Riquelme

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The traditional bio-economic method for fisheries modeling uses some estimate of the growth parameters and the system carrying capacity from a biological model for the population dynamics (usually a logistic population growth model) which is then analyzed as a traditional production function. The stock dynamic is transformed into a revenue function and then compared with the extraction costs to estimate the maximum economic yield. In this paper, the logistic population growth model for the population is combined with a forecast of the abundance and location of the stock by using a generalized additive model approach. The paper focuses on the Chilean hake population. This method allows for the incorporation of climatic variables and the interaction with other marine species, which in turn will increase the reliability of the estimates and generate better extraction paths for different conservation objectives, such as the maximum biological yield or the maximum economic yield.

Keywords: bio-economic, fisheries, GAM, production

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Authors: Santhosh Kumar Sathi

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In India, Growth and spread of cities lead to the reduction of forests and green areas of the urban center with built structures. This is one of the reasons for increasing temperature about 2-5% in an urban environment and consequently also one of the key causes of urban heat island effects. Green roofs are one option that can reduce the negative impact of urban development providing numerous environmental benefits. In this paper, Vijayawada city is taken as case to study as it is experiencing rapid urbanization because of new capital Amaravati. That has resulted in remarkable urban heat island; which once recorded a highest temperature of 49°c. This paper focuses on the change in quality of the local environment with the introduction of green roofs. An in-depth study has to be carried out to understand the distribution of land surface temperature and land use of Vijayawada. Delineation of an area which has the highest temperature has been selected to adopt green roof retrofitting. Latest technologies of green roof retrofitting have to be implemented in the selected region. The results of the study indicate a significant temperature reduction in the local environment of that region, confirming the potential of green roofs as urban heat island mitigation strategy.

Keywords: energy consumption, green roofs, retrofitting, urban heat island

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Authors: Shahrokh Barati

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In this paper, we introduced AIDS disease at first, then proposed dynamic model illustrate its progress, after expression of a short history of nonlinear modeling by polynomial phasing systems, we considered the stability conditions of the systems, which contained a huge amount of researches in order to modeling and control of AIDS in dynamic nonlinear form, in this approach using a frame work of control any polynomial phasing modeling system which have been generalized by part of phasing model of T-S, in order to control the system in better way, the stability conditions were achieved based on polynomial functions, then we focused to design the appropriate controller, firstly we considered the equilibrium points of system and their conditions and in order to examine changes in the parameters, we presented polynomial phase model that was the generalized approach rather than previous Takagi Sugeno models, then with using case we evaluated the equations in both open loop and close loop and with helping the controlling feedback, the close loop equations of system were calculated, to simulate nonlinear model of AIDS disease, we used polynomial phasing controller output that was capable to make the parameters of a nonlinear system to follow a sustainable reference model properly.

Keywords: polynomial fuzzy, AIDS, nonlinear AIDS model, fuzzy control systems

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

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Authors: Babek Erdebilli

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The aim of this study is to develop and describe a new methodology for the Multi-Criteria Decision-Making (MCDM) problem using IFE (Elimination Et Choix Traduisant La Realite (ELECTRE) model. The proposed models enable Decision-Makers (DMs) on the assessment and use Intuitionistic Fuzzy Numbers (IFN). A numerical example is provided to demonstrate and clarify the proposed analysis procedure. Also, an empirical experiment is conducted to validation the effectiveness.

Keywords: multi-criteria decision-making, IFE, DM’s, fuzzy electre model

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Authors: Sangwoo Han, Saeed Khaleghi Rahimian, Ying Liu

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Vehicle electrification is gaining momentum, and many car manufacturers promise to deliver more electric vehicle (EV) models to consumers in the coming years. In controlling the battery pack, the battery management system (BMS) must maintain optimal battery performance while ensuring the safety of a battery pack. Tasks related to battery performance include determining state-of-charge (SOC), state-of-power (SOP), state-of-health (SOH), cell balancing, and battery charging. Safety related functions include making sure cells operate within specified, static and dynamic voltage window and temperature range, derating power, detecting faulty cells, and warning the user if necessary. The BMS often utilizes an RC circuit model to model a Li-ion cell because of its robustness and low computation cost among other benefits. Because an equivalent circuit model such as the RC model is not a physics-based model, it can never be a prognostic model to predict battery state-of-health and avoid any safety risk even before it occurs. A physics-based Li-ion cell model, on the other hand, is more capable at the expense of computation cost. To avoid the high computation cost associated with a full-order model, many researchers have demonstrated the use of a single particle model (SPM) for BMS applications. One drawback associated with the single particle modeling approach is that it forces to use the average current density in the calculation. The SPM would be appropriate for simulating drive cycles where there is insufficient time to develop a significant current distribution within an electrode. However, under a continuous or high-pulse electrical load, the model may fail to predict cell voltage or Li⁺ plating potential. To overcome this issue, a multi-particle reduced-order model is proposed here. The use of multiple particles combined with either linear or nonlinear charge-transfer reaction kinetics enables to capture current density distribution within an electrode under any type of electrical load. To maintain computational complexity like that of an SPM, governing equations are solved sequentially to minimize iterative solving processes. Furthermore, the model is validated against a full-order model implemented in COMSOL Multiphysics.

Keywords: battery management system, physics-based li-ion cell model, reduced-order model, single-particle and multi-particle model

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Authors: W. H. Wan Zakiyatussariroh, A. A. Nasuhar, W. Y. Wan Fairos, Z. A. Nazatul Shahreen

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Forecasting dengue incidence in a population can provide useful information to facilitate the planning of the public health intervention. Many studies on dengue cases in Malaysia were conducted but are limited in modeling the outbreak and forecasting incidence. This article attempts to propose the most appropriate time series model to explain the behavior of dengue incidence in Malaysia for the purpose of forecasting future dengue outbreaks. Several seasonal auto-regressive integrated moving average (SARIMA) models were developed to model Malaysia’s number of dengue incidence on weekly data collected from January 2001 to December 2011. SARIMA (2,1,1)(1,1,1)52 model was found to be the most suitable model for Malaysia’s dengue incidence with the least value of Akaike information criteria (AIC) and Bayesian information criteria (BIC) for in-sample fitting. The models further evaluate out-sample forecast accuracy using four different accuracy measures. The results indicate that SARIMA (2,1,1)(1,1,1)52 performed well for both in-sample fitting and out-sample evaluation.

Keywords: time series modeling, Box-Jenkins, SARIMA, forecasting

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Authors: A. N. Ukwuani, M. G. Abubakar, S. W. Hassan

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Grewia crenata is used locally in the treatment of fractured bones, wound healing and inflammatory conditions. The anti-inflammatory activity of hydromethanolic extract of G. crenata leaves was investigated using egg albumin induced-hind paw oedema model in albino rat. The extract produced a time-dependent inhibition of egg albumin induced-hind paw oedema at 30th minutes up to 150th minutes in all the groups compared to the control. Significant reduction (p<0.05) of hind paw oedema was observed 150 minutes after egg albumin injection. The percentage inhibition produced by the extract at 200 mg/kg (22.1%) was comparable to that produced by 10 mg/kg indomethacin (24.9%) at the 150th minutes of post-egg albumin injection. Preliminary qualitative phytochemical analysis revealed the presence of saponins, steroids, flavonoids, anthraquinones and glycosides. The results obtained in this study suggest that Grewia crenata can be a potential source of anti-inflammatory agent and validates its use in the treatment of inflammatory conditions.

Keywords: Grewia crenata, anti-inflammatory, hind paw, oedema

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Authors: Armineh Nourbakhsh, Sameena Shah, Carolyn Rose

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Layout-aware language models have been used to create multimodal representations for documents that are in image form, achieving relatively high accuracy in document understanding tasks. However, the large number of parameters in the resulting models makes building and using them prohibitive without access to high-performing processing units with large memory capacity. We propose an alternative approach that can create efficient representations without the need for a neural visual backbone. This leads to an 80% reduction in the number of parameters compared to the smallest SOTA model, widely expanding applicability. In addition, our layout embeddings are pre-trained on spatial and visual cues alone and only fused with text embeddings in downstream tasks, which can facilitate applicability to low-resource of multi-lingual domains. Despite using 2.5% of training data, we show competitive performance on two form understanding tasks: semantic labeling and link prediction.

Keywords: layout understanding, form understanding, multimodal document understanding, bias-augmented attention

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Authors: C. S. Paglia, E. Ginercordero, A. Jornet

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Environmental pollution, along with the depletion of natural resources, is among the most serious global challenges in our times. The construction industry is one of the sectors where a relevant reduction of the environmental impact can be achieved. Thus, the cement production will play a key role in sustainability, by reducing the CO₂ emissions and energy consumption and by increasing the durability of the structures. A large number of investigations have been carried out on blended cements, but it exists a lack of information on the Portland cement limestone - silica fume system. Mortar blends are optimized in the mix proportions for the different ingredients, in particular for the dosage of the silica fume. Portland cement and the new binder-based systems are compared with respect to the fresh mortar properties, the mechanical and the durability behaviour of the hardened specimens at 28 and 90 days. The use of this new binder combination exhibits an interesting hydration development with time and maintain the conventional characteristics of Portland cementitious material. On the other hand, it will be necessary to reproduce the Portland Limestone Cement-silica fume system within the concrete. A reduction of the CO₂ production, energy consumption, and a reasonable service life of the concrete structures, including a maintenance free period, will all contribute to a better environment.

Keywords: binder, cement, limestone, silica fume

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Authors: Wen-Bin Liau, Wan-Ting Wang, Chiang-Jen Hsiao, Sheng-Mao Tseng

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In this paper, an interesting and easy method to prepare one-dimensional nanostructured polyaniline/dendritic silver composites is reported. It is well known that the morphology of metal particle is a very important factor to influence the properties of polymer-metal composites. Usually, the dendritic silver is prepared by kinetic control in reduction reaction. It is not a thermodynamically stable structure. It is the goal to reduce silver ion to dendritic silver by polyaniline polymer via kinetic control and form one-dimensional nanostructured polyaniline/dendritic silver composites. The preparation is a two steps sequential reaction. First step, the polyaniline networks composed of nano fibrillar polyaniline are synthesized from aniline monomers aqueous with ammonium persulfate as the initiator at room temperature. In second step, the silver nitrate is added into polyaniline networks dispersed in deionized water. The dendritic silver is formed via reduction by polyaniline networks under the kinetic control. The formation of polyaniline is discussed via transmission electron microscopy (TEM). Nanosheets, nanotubes, nanospheres, nanosticks, and networks are observed via TEM. Then, the mechanism of formation of one-dimensional nanostructured polyaniline/dendritic silver composites is discussed. The formation of dendritic silver is observed by TEM and X-ray diffraction.

Keywords: 1D nanostructured polyaniline, dendritic silver, synthesis

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Authors: Kumaresh Selvakumar, Man Young Kim

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In selective catalytic reduction (SCR) unit, the injection system is provided with unique dosing pump to govern the urea injection phenomenon. The urea based operating liquid from the AdBlue tank links up directly with the dosing pump unit to furnish appropriate high pressure for examining the flow characteristics inside the liquid pump. This work aims in demonstrating the importance of external gear pump to provide pertinent high pressure and respective mass flow rate for each rotation. Numerical simulations are conducted using immersed solid method technique for better understanding of unsteady flow characteristics within the pump. Parametric analyses have been carried out for the gear speed and mass flow rate to find the behavior of pressure fluctuations. In the simulation results, the outlet pressure achieves maximum magnitude with the increase in rotational speed and the fluctuations grow higher.

Keywords: AdBlue tank, external gear pump, immersed solid method, selective catalytic reduction

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Authors: Prasenjit Singha, Sunil Yadav, Soumya Ranjan Mohantry, Ajay Kumar Shukla

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Temperature distribution in the hematite ore mixed with 7.5% coal was predicted by solving a 1-D heat conduction equation using an implicit finite difference approach. In this work, it was considered a square slab of 20 cm x 20 cm, which assumed the coal to be uniformly mixed with hematite ore. It was solved the equations with the use of MATLAB 2018a software. Heat transfer effects in this 1D dimensional slab convective and the radiative boundary conditions are also considered. Temperature distribution obtained inside hematite slab by considering microwave heating time, thermal conductivity, heat capacity, carbon percentage, sample dimensions, and many other factors such as penetration depth, permittivity, and permeability of coal and hematite ore mixtures. The resulting temperature profile can be used as a guiding tool for optimizing the microwave-assisted carbothermal reduction process of hematite slab was extended to other dimensions as well, viz., 1 cm x 1 cm, 5 cm x 5 cm, 10 cm x 10 cm, 20 cm x 20 cm. The model predictions are in good agreement with experimental results.

Keywords: hematite ore, coal, microwave processing, heat transfer, implicit method, temperature distribution

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Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal

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Planning models for fresh products is a very useful tool for improving the net profits. To get an efficient supply chain model, several functions should be considered to get a complete simulation of several operational units. We consider a linear programming model to help farmers to decide if it is convenient to choose what area should be planted for three kinds of export fruits considering their future investment. We consider area, investment, water, productivity minimal unit, and harvest restrictions to develop a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability, and initial investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market. Also, this tool help to support decisions for government and individual farmers.

Keywords: mixed integer problem, fresh fruit production, support decision model, agricultural and biosystems engineering

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Authors: Swati Tomar, Sunil Kumar Gupta

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Anammox is a promising and cost effective alternative to conventional treatment systems that facilitates direct oxidation of ammonium nitrogen under anaerobic conditions with nitrite as an electron acceptor without addition of any external carbon sources. The present study investigates the process kinetics of laboratory scale anammox hybrid reactor (AHR) which combines the dual advantages of attached and suspended growth. The performance & behaviour of AHR was studied under varying hydraulic retention time (HRTs) and nitrogen loading rate (NLRs). The experimental unit consisted of 4 numbers of 5L capacity anammox hybrid reactor inoculated with mixed seed culture containing anoxic and activated sludge. Pseudo steady state (PSS) ammonium and nitrite removal efficiencies of 90.6% and 95.6%, respectively, were achieved during acclimation phase. After establishment of PSS, the performance of AHR was monitored at seven different HRTs of 3.0, 2.5, 2.0, 1.5, 1.0, 0.5 and 0.25 d with increasing NLR from 0.4 to 4.8 kg N/m3d. The results showed that with increase in NLR and decrease in HRT (3.0 to 0.25 d), AHR registered appreciable decline in nitrogen removal efficiency from 92.9% to 67.4 %, respectively. The HRT of 2.0 d was considered optimal to achieve substantial nitrogen removal of 89%, because on further decrease in HRT below 1.5 days, remarkable decline in the values of nitrogen removal efficiency were observed. Analysis of data indicated that attached growth system contributes an additional 15.4 % ammonium removal and reduced the sludge washout rate (additional 29% reduction). This enhanced performance may be attributed to 25% increase in sludge retention time due to the attached growth media. Three kinetic models, namely, first order, Monod and Modified Stover-Kincannon model were applied to assess the substrate removal kinetics of nitrogen removal in AHR. Validation of the models were carried out by comparing experimental set of data with the predicted values obtained from the respective models. For substrate removal kinetics, model validation revealed that Modified Stover-Kincannon is most precise (R2=0.943) and can be suitably applied to predict the kinetics of nitrogen removal in AHR. Lawrence and McCarty model described the kinetics of bacterial growth. The predicted value of yield coefficient and decay constant were in line with the experimentally observed values.

Keywords: anammox, kinetics, modelling, nitrogen removal, sludge wash out rate, AHR

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Authors: Michael Koblischka, Yassine Slimani, Thomas Karwoth, Anjela Koblischka-Veneva, Essia Hannachi

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On bulk FeSe superconductors containing different additions of Ag, a thorough investigation of the microstructures was performed using optical microscopy, SEM and TEM. The electrical resistivity was measured using four-point measurements in the temperature range 2 K ≤ T ≤ 150 K. The data obtained are analyzed in the framework of the excess conductivity approach using the Aslamazov-Larkin (AL) model. The investigated samples comprised of five distinct fluctuation regimes, namely short-wave (SWF), onedimensional (1D), two-dimensional (2D), three-dimensional (3D), and critical (CR) fluctuation regimes. The coherence length along the c-axis at zero-temperature (ξc(0)), the lower and upper critical magnetic fields (Bc1 and Bc2), the critical current density (Jc) and numerous other superconducting parameters were estimated with respect to the Ag content in the samples. The data reveal a reduction of the resistivity and a strong decrease of ξc(0) when doping the 11-samples with silver. The optimum content of the Ag-addition is found at 4 wt.-% Ag, yielding the highest critical current density.

Keywords: iron-based superconductors, FeSe, Ag-addition, excess conductivity, microstructure

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Authors: Sujit K. Basak

Abstract:

The aim of this paper is to analyze the impact of literature review software on researchers. The aim of this study was achieved by analyzing models in terms of perceived usefulness, perceived ease of use, and acceptance level. Collected data was analyzed using WarpPLS 4.0 software. This study used two theoretical frameworks namely Technology Acceptance Model and the Training Needs Assessment Model. The study was experimental and was conducted at a public university in South Africa. The results of the study showed that acceptance level has a high impact on research workload and productivity followed by perceived usefulness and perceived ease of use.

Keywords: technology acceptance model, training needs assessment model, literature review software, research productivity

Procedia PDF Downloads 503

Authors: Yin-Yee Leong, Jack C. Yue, Hsin-Chung Wang

Abstract:

Human longevity has been experiencing its largest increase since the end of World War II, and modeling the mortality rates is therefore often the focus of many studies. Among all mortality models, the Lee–Carter model is the most popular approach since it is fairly easy to use and has good accuracy in predicting mortality rates (e.g., for Japan and the USA). However, empirical studies from several countries have shown that the age parameters of the Lee–Carter model are not constant in time. Many modifications of the Lee–Carter model have been proposed to deal with this problem, including adding an extra cohort effect and adding another period effect. In this study, we propose a spatial modification and use clusters to explain why the age parameters of the Lee–Carter model are not constant. In spatial analysis, clusters are areas with unusually high or low mortality rates than their neighbors, where the “location” of mortality rates is measured by age and time, that is, a 2-dimensional coordinate. We use a popular cluster detection method—Spatial scan statistics, a local statistical test based on the likelihood ratio test to evaluate where there are locations with mortality rates that cannot be described well by the Lee–Carter model. We first use computer simulation to demonstrate that the cluster effect is a possible source causing the problem of the age parameters not being constant. Next, we show that adding the cluster effect can solve the non-constant problem. We also apply the proposed approach to mortality data from Japan, France, the USA, and Taiwan. The empirical results show that our approach has better-fitting results and smaller mean absolute percentage errors than the Lee–Carter model.

Keywords: mortality improvement, Lee–Carter model, spatial statistics, cluster detection

Procedia PDF Downloads 171

Authors: S. P. Akinbogun, P. Oloruntoyin

Abstract:

One of the millennium development goals is the reduction of illiteracy. The state of user friendliness of the educational buildings is expected to play a significant role in the quest, particularly among the physically challenged. This study considers the state of access of educational buildings to disabled on wheel chair and crutches. It draws context from one of the federal universities in Nigeria. The study is basically qualitative; data were collected through structured interview and observation to assess compliance with the prescribed accessibility standard of academic buildings in the Federal University of Technology Akure. The study found that narrow entrances and routes of buildings, raised steps at entrances of the buildings, and ramps were absent. This implies exclusion as it renders most of the buildings inaccessible to wheelchair users. Perhaps, it accounts for low enrolment of wheelchair users in the institution despite many of them in the city. The implication is a challenge in the achievement of the millennium development goal concerning the reduction in the level of illiteracy in the country. The study suggests that government should strictly ensure that public buildings should satisfy or retrofitted to meet disabled access before development approval. This should be followed with the issuance of certificate of compliance upon completion.

Keywords: public building, accessibility, physically challenged, education

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Authors: Munazza A. Mirza, Khawar Khurshid

Abstract:

Individuals are generally associated with different learning styles, which have been explored extensively in recent past. The learning styles refer to the potential of an individual by which s/he can easily comprehend and retain information. Among various learning style models, VARK is the most accepted model which categorizes the learners with respect to their sensory characteristics. Based on the number of preferred learning modes, the learners can be categorized as uni-modal, bi-modal, tri-modal, or quad/multi-modal. Although there is a prevalent belief in the learning styles, however, the model is not being frequently and effectively utilized in the higher education. This research describes the identification model to validate teacher’s didactic practice and student’s performance linkage with the learning styles. The identification model is recommended to check the effective application and evaluation of the various learning styles. The proposed model is a guideline to effectively implement learning styles inventory in order to ensure that it will validate performance linkage with learning styles. If performance is linked with learning styles, this may help eradicate the distrust on learning style theory. For this purpose, a comprehensive study was conducted to compare and understand how VARK inventory model is being used to identify learning preferences and their correlation with learner’s performance. A comparative analysis of the findings of these studies is presented to understand the learning styles of tertiary students in various disciplines. It is concluded with confidence that the learning styles of students cannot be associated with any specific discipline. Furthermore, there is not enough empirical proof to link performance with learning styles.

Keywords: learning style, VARK, sensory preferences, identification model, didactic practices

Procedia PDF Downloads 278