Search results for: sport services model

Authors: Mizam Dogan, Hande Gökdemir

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In reinforced concrete elements, reinforcement steel bars are placed in concrete both longitudinal and lateral directions. The lateral reinforcement (called as confinement) which is used for confining circular RC elements is in a spiral shape. If the cross section of RC element is rectangular, stirrups should be rectangular too. At very high compressive stresses concrete will reach its limit strain value and therefore concrete outside the lateral reinforcement, which is not confined, will crush and start to spell. At this stage, concrete core of the RC element tries to expand laterally as a reason of high Poisson’s ratio value of concrete. Such a deformation is prevented by the lateral reinforcement which applies lateral passive pressure on concrete. At very high compressive stresses, the strength of reinforced column member rises to four times σ 2. This increase in strength of member is related to the properties of rectangular stirrups. In this paper, effect of stirrup step spacing to column behavior is calculated and presented confined concrete model is proved by numerical solutions.

Keywords: confined concrete, concrete column, stress-strain, stirrup, solid, frame

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Authors: Ahmed I. Shehab, Sabah M. Abdel Basir, M. A. Abdel Khalek, M. H. Soliman, G. Elgemeie

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Spent bleaching earth (SBE) recycling and utilization as an adsorbent to eliminate dyes from aqueous solution was studied. Organic solvents and subsequent thermal treatment were carried out to recover and reactivate the SBE. The effect of pH, temperature, dye’s initial concentration, and contact time on the dye removal using recycled spent bleaching earth (RSBE) was investigated. Recycled SBE showed better removal affinity of cationic than anionic dyes. The maximum removal was achieved at pH 2 and 8 for anionic and cationic dyes, respectively. Kinetic data matched with the pseudo second-order model. The adsorption phenomenon governing this process was identified by the Langmuir and Freundlich isotherms for anionic dye while Freundlich model represented the sorption process for cationic dye. The changes of Gibbs free energy (ΔG°), enthalpy (ΔH°), and entropy (ΔS°) were computed and compared through thermodynamic study for both dyes.

Keywords: Spent bleaching earth, reactivation, regeneration, thermal treatment, dye removal, thermodynamic

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Padma Chutoo, Elena Kulinskaya, Ilyas Bakbergenuly, Nicholas Steel, Dmitri Pchejetski

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Stroke is associated with a significant risk of morbidity and mortality. There is scarcity of research on the long-term survival after first-ever ischaemic stroke (IS) events in England with regards to effects of different medical therapies and comorbidities. The objective of this study was to model the all-cause mortality after an IS diagnosis in the adult population of England. Using a retrospective case-control design, we extracted the electronic medical records of patients born prior to or in year 1960 in England with a first-ever ischaemic stroke diagnosis from January 1986 to January 2017 within the Health and Improvement Network (THIN) database. Participants with a history of ischaemic stroke were matched to 3 controls by sex and age at diagnosis and general practice. The primary outcome was the all-cause mortality. The hazards of the all-cause mortality were estimated using a Weibull-Cox survival model which included both scale and shape effects and a shared random effect of general practice. The model included sex, birth cohort, socio-economic status, comorbidities and medical therapies. 20,250 patients with a history of IS (cases) and 55,519 controls were followed up to 30 years. From 2008 to 2015, the one-year all-cause mortality for the IS patients declined with an absolute change of -0.5%. Preventive treatments to cases increased considerably over time. These included prescriptions of statins and antihypertensives. However, prescriptions for antiplatelet drugs decreased in the routine general practice since 2010. The survival model revealed a survival benefit of antiplatelet treatment to stroke survivors with hazard ratio (HR) of 0.92 (0.90 – 0.94). IS diagnosis had significant interactions with gender and age at entry and hypertension diagnosis. IS diagnosis was associated with high risk of all-cause mortality with HR= 3.39 (3.05-3.72) for cases compared to controls. Hypertension was associated with poor survival with HR = 4.79 (4.49 - 5.09) for hypertensive cases relative to non-hypertensive controls, though the detrimental effect of hypertension has not reached significance for hypertensive controls, HR = 1.19(0.82-1.56). This study of English primary care data showed that between 2008 and 2015, the rates of prescriptions of stroke preventive treatments increased, and a short-term all-cause mortality after IS stroke declined. However, stroke resulted in poor long-term survival. Hypertension, a modifiable risk factor, was found to be associated with poor survival outcomes in IS patients. Antiplatelet drugs were found to be protective to survival. Better efforts are required to reduce the burden of stroke through health service development and primary prevention.

Keywords: general practice, hazard ratio, health improvement network (THIN), ischaemic stroke, multiple imputation, Weibull-Cox model.

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Authors: Ramkumar R., Gaurav Shinde, Pratik Shroff, Sachin Kumar Jain, Nagesh Walke

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Advancements in electric vehicles have significantly reduced the powertrain noise and moving components of vehicles. As a result, in-cab noises have become more noticeable to passengers inside the car. To ensure a comfortable ride for drivers and other passengers, it has become crucial to eliminate undesirable component noises during the development phase. Standard practices are followed to identify the severity of noises based on subjective ratings, but it can be a tedious process to identify the severity of each development sample and make changes to reduce it. Additionally, the severity rating can vary from jury to jury, making it challenging to arrive at a definitive conclusion. To address this, an automotive component was identified to evaluate squeak and rattle noise issue. Physical tests were carried out for random and sine excitation profiles. Aim was to subjectively assess the noise using jury rating method and objectively evaluate the same by measuring the noise. Suitable jury evaluation method was selected for the said activity, and recorded sounds were replayed for jury rating. Objective data sound quality metrics viz., loudness, sharpness, roughness, fluctuation strength and overall Sound Pressure Level (SPL) were measured. Based on this, correlation co-efficients was established to identify the most relevant sound quality metrics that are contributing to particular identified noise issue. Regression analysis was then performed to establish the correlation between subjective and objective data. Mathematical model was prepared using artificial intelligence and machine learning algorithm. The developed model was able to predict the subjective rating with good accuracy.

Keywords: BSR, noise, correlation, regression

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Authors: Adeolu O. Dairo

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Getting the consumer marketing strategy right is a crucial and complex task for firms with a large customer base such as mobile operators in a competitive mobile market. While empirical studies have made efforts to identify key constructs, no geospatial model has been developed to comprehensively assess the viability and interdependency of ground realities regarding the customer, competition, channel and the network quality of mobile operators. With this research, a geo-analytic framework is proposed for strategy formulation and allocation for mobile operators. Firstly, a fuzzy analytic network using a self-organizing feature map clustering technique based on inputs from managers and literature, which depicts the interrelationships amongst ground realities is developed. The model is tested with a mobile operator in the Nigeria mobile market. As a result, a customer-centric geospatial and visualization solution is developed. This provides a consolidated and integrated insight that serves as a transparent, logical and practical guide for strategic, tactical and operational decision making.

Keywords: geospatial, geo-analytics, self-organizing map, customer-centric

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Authors: Ammar Y. Alqahtani

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In order to minimize the negative impact on the environment, it is essential to manage the waste that generated from the premature disposal of end-of-life (EOL) products properly. Consequently, government and international organizations introduced new policies and regulations to minimize the amount of waste being sent to landfills. Moreover, the consumers’ awareness regards environment has forced original equipment manufacturers to consider being more environmentally conscious. Therefore, manufacturers have thought of different ways to deal with waste generated from EOL products viz., remanufacturing, reusing, recycling, or disposing of EOL products. The rate of depletion of virgin natural resources and their dependency on the natural resources can be reduced by manufacturers when EOL products are treated as remanufactured, reused, or recycled, as well as this will cut on the amount of harmful waste sent to landfills. However, disposal of EOL products contributes to the problem and therefore is used as a last option. Number of EOL need to be estimated in order to fulfill the components demand. Then, disassembly process needs to be performed to extract individual components and subassemblies. Smart products, built with sensors embedded and network connectivity to enable the collection and exchange of data, utilize sensors that are implanted into products during production. These sensors are used for remanufacturers to predict an optimal warranty policy and time period that should be offered to customers who purchase remanufactured components and products. Sensor-provided data can help to evaluate the overall condition of a product, as well as the remaining lives of product components, prior to perform a disassembly process. In this paper, a multi-period disassembly-to-order (DTO) model is developed that takes into consideration the different system uncertainties. The DTO model is solved using Nonlinear Programming (NLP) in multiple periods. A DTO system is considered where a variety of EOL products are purchased for disassembly. The model’s main objective is to determine the best combination of EOL products to be purchased from every supplier in each period which maximized the total profit of the system while satisfying the demand. This paper also addressed the impact of sensor embedded products on the cost of warranties. Lastly, this paper presented and analyzed a case study involving various simulation conditions to illustrate the applicability of the model.

Keywords: closed-loop supply chains, environmentally conscious manufacturing, product recovery, reverse logistics

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Authors: Abdelhamid Louliej, Younes Jabrane

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Satellites are becoming indispensable assets to fifth-generation (5G) new radio architecture, complementing wireless and terrestrial communication links. The combination of satellites and 5G architecture allows consumers to access all next-generation services anytime, anywhere, including scenarios, like traveling to remote areas (without coverage). Nevertheless, this solution faces several challenges, such as a significant propagation delay, Doppler frequency shift, and high Peak-to-Average Power Ratio (PAPR), causing signal distortion due to the non-linear saturation of the High-Power Amplifier (HPA). To compensate for HPA non-linearity in 5G satellite transmission, an efficient pre-distorter scheme using Neural Networks (NN) is proposed. To assess the proposed NN pre-distorter, two types of HPA were investigated: Travelling Wave Tube Amplifier (TWTA) and Solid-State Power Amplifier (SSPA). The results show that the NN pre-distorter design presents EVM improvement by 95.26%. NMSE and ACPR were reduced by -43,66 dB and 24.56 dBm, respectively. Moreover, the system suffers no degradation of the Bit Error Rate (BER) for TWTA and SSPA amplifiers.

Keywords: satellites, 5G, neural networks, HPA, TWTA, SSPA, EVM, NMSE, ACPR

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Authors: Arya Pirooz

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The effects of different fuels (DME, RME B100, and SME B100) on barrel engines were studied as a general, single dimensional investigation for characterization of these types of engines. A base computational model was created as reference point to be used as a point of comparison with different cases. The models were computed using the commercial computational fluid dynamics program, Diesel-RK. The base model was created using basic dimensions of the PAMAR-3 engine with inline unit injectors. Four fuel cases were considered. Optimized models were also considered for diesel and DME cases with respect to injection duration, fuel, injection timing, exhaust and intake port opening, CR, angular offset. These factors were optimized for highest BMEP, combined PM and NOx emissions, and highest SFC. Results included mechanical efficiency (eta_m), efficiency and power, emission characteristics, combustion characteristics. DME proved to have the highest performing characteristics in relation to diesel and RME fuels for this type of barrel engine.

Keywords: DME, RME, Diesel-RK, characterization, inline unit injector

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Authors: Ahmad K. Samaila, Basant K. Jha

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This paper presents the effects of suction/injection of transient/steady natural convection flow of reactive viscous fluid in a vertical porous pipe. The mathematical model capturing the time dependent flow of viscous reactive fluid is solved using implicit finite difference method while the corresponding steady state model is solved using regular perturbation technique. Results of analytical and numerical solutions are reported for various parametric conditions to illustrate special features of the solutions. The coefficient of skin friction and rate of heat transfer are obtained and illustrated graphically. The numerical solution is shown to be in excellent agreement with the closed form analytical solution. It is interesting to note that time required to reach steady state is higher in case of injection in comparison to suction.

Keywords: porous pipe, reactive viscous fluid, transient natural-convective flow, analytical solution

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Authors: Joseph Levodo, Ndimbarafine Young Tobin, E. Messina, P. Edouma

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Energy efficiency can contribute significantly towards increasing clean energy access to modern energy services. Many developing countries have largely focused on expanding energy access by increasing supply. This is due to the fact the links between energy efficiency and clean energy access are often unnoticed. Energy efficiency measures offer the promise of reducing energy use and saving money on electricity bills, as well as reducing negative environmental externalities associated with the production of electricity. This paper seeks to address the economic and effectiveness of reducing energy consumption by integrating energy efficiency as a priority to meet energy access examines the barriers to energy efficient in sub-Saharan African countries. The findings from this study reveal that an appropriate policy can promote the development of more energy-efficient buildings, products and strengthen incentives for consumers, businesses, and industrial customers to pursue cost-effective energy-efficiency measures and to make investments that will provide future energy-efficiency improvements.

Keywords: barriers, Sub-Saharan Africa, cost effective, energy savings, clean energy

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Authors: Mircea Pastrav, Horia Constantinescu

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The construction industry is currently trying to develop sustainable reinforced concrete structures. In trying to aid in the effort, the research presented in this paper aims to prove the efficiency of modified special hybrid moment frames composed of discretely jointed precast and post-tensioned concrete members. This aim is due to the fact that current design standards do not cover the spatial design of moment frame structures assembled by post-tensioning with special hybrid joints. This lack of standardization is coupled with the fact that previous experimental programs, available in scientific literature, deal mainly with plane structures and offer little information regarding spatial behavior. A spatial model of a modified hybrid moment frame is experimentally analyzed. The experimental results of a natural scale model test of a corner column-beams sub-structure, cut from an actual multilevel building tested to seismic type loading are presented in order to highlight the behavior of this type of structure. The test is performed under alternative cycles of imposed lateral displacements, up to a storey drift ratio of 0.035. Seismic response of the spatial model is discussed considering the acceptance criteria for reinforced concrete frame structures designed based on experimental tests, as well as some of its major sustainability features. The results obtained show an overall excellent behavior of the system. The joint detailing allows for quick and cheap repairs after an accidental event and a self-balancing behavior of the system that ensures it can be used almost immediately after an accidental event it.

Keywords: modified hybrid joint, seismic type loading response, self-balancing structure, acceptance criteria

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Authors: Maomao Cao

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Objective: To test the performance of ctDNA methylation for the detection of liver cancer. Methods: A total of 1233 individuals have been recruited in 2017. 15 male and 15 female samples (including 10 cases of liver cancer) were randomly selected in the present study. CfDNA was extracted by MagPure Circulating DNA Maxi Kit. The concentration of cfDNA was obtained by Qubit™ dsDNA HS Assay Kit. A pre-constructed predictive model was used to analyze methylation data and to give a predictive score for each cfDNA sample. Individuals with a predictive score greater than or equal to 80 were classified as having liver cancer. CT tests were considered the gold standard. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the diagnosis of liver cancer were calculated. Results: 9 patients were diagnosed with liver cancer according to the prediction model (with high sensitivity and threshold of 80 points), with scores of 99.2, 91.9, 96.6, 92.4, 91.3, 92.5, 96.8, 91.1, and 92.2, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of ctDNA methylation for the diagnosis of liver cancer were 0.70, 0.90, 0.78, and 0.86, respectively. Conclusions: ctDNA methylation could be an acceptable diagnostic modality for the detection of liver cancer.

Keywords: liver cancer, ctDNA methylation, detection, diagnostic performance

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Authors: Huang Yajing

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Multimodal discourse refers to the phenomenon of using various senses such as hearing, vision, and touch to communicate through various means and symbolic resources such as language, images, sounds, and movements. With the development of modern technology and multimedia, language and technology have become inseparable, and foreign language teaching is becoming more and more modal. Teacher-student communication resorts to multiple senses and uses multiple symbol systems to construct and interpret meaning. The classroom is a semiotic space where multimodal discourses are intertwined. College English multi-modal teaching is to rationally utilize traditional teaching methods while mobilizing and coordinating various modern teaching methods to form a joint force to promote teaching and learning. Multimodal teaching makes full and reasonable use of various meaning resources and can maximize the advantages of multimedia and network environments. Based upon the above theories about multimodal discourse and multimedia technology, the present paper will propose a multi-modal teaching model for college English in China.

Keywords: multimodal discourse, multimedia technology, English education, applied linguistics

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Authors: Najib K. Dankadai, Ahmad 'Athif Mohd Faudzi, Khairuddin Osman, Muhammad Rusydi Muhammad Razif, IIi Najaa Aimi Mohd Nordin

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Researches in soft actuators are now growing rapidly because of their adequacy to be applied in sectors like medical, agriculture, biological and welfare. This paper presents system identification (SI) and control of the force generated by a two chambers pneumatic soft actuator (PSA). A force mathematical model for the actuator was identified experimentally using data acquisition card and MATLAB SI toolbox. Two control techniques; a predictive functional control (PFC) and conventional proportional integral and derivative (PID) schemes are proposed and compared based on the identified model for the soft actuator flexible mechanism. Results of this study showed that both of the proposed controllers ensure accurate tracking when the closed loop system was tested with the step, sinusoidal and multi step reference input through MATLAB simulation although the PFC provides a better response than the PID.

Keywords: predictive functional control (PFC), proportional integral and derivative (PID), soft actuator, system identification

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Authors: M. Gulam Kibria, Shourav Ahmed, Kais Zaman

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In system analysis, the information on the uncertain input variables cause uncertainty in the system responses. Different probabilistic approaches for uncertainty representation and propagation in such cases exist in the literature. Different uncertainty representation approaches result in different outputs. Some of the approaches might result in a better estimation of system response than the other approaches. The NASA Langley Multidisciplinary Uncertainty Quantification Challenge (MUQC) has posed challenges about uncertainty quantification. Subproblem A, the uncertainty characterization subproblem, of the challenge posed is addressed in this study. In this subproblem, the challenge is to gather knowledge about unknown model inputs which have inherent aleatory and epistemic uncertainties in them with responses (output) of the given computational model. We use two different methodologies to approach the problem. In the first methodology we use sampling-based uncertainty propagation with first order error analysis. In the other approach we place emphasis on the use of Percentile-Based Optimization (PBO). The NASA Langley MUQC’s subproblem A is developed in such a way that both aleatory and epistemic uncertainties need to be managed. The challenge problem classifies each uncertain parameter as belonging to one the following three types: (i) An aleatory uncertainty modeled as a random variable. It has a fixed functional form and known coefficients. This uncertainty cannot be reduced. (ii) An epistemic uncertainty modeled as a fixed but poorly known physical quantity that lies within a given interval. This uncertainty is reducible. (iii) A parameter might be aleatory but sufficient data might not be available to adequately model it as a single random variable. For example, the parameters of a normal variable, e.g., the mean and standard deviation, might not be precisely known but could be assumed to lie within some intervals. It results in a distributional p-box having the physical parameter with an aleatory uncertainty, but the parameters prescribing its mathematical model are subjected to epistemic uncertainties. Each of the parameters of the random variable is an unknown element of a known interval. This uncertainty is reducible. From the study, it is observed that due to practical limitations or computational expense, the sampling is not exhaustive in sampling-based methodology. That is why the sampling-based methodology has high probability of underestimating the output bounds. Therefore, an optimization-based strategy to convert uncertainty described by interval data into a probabilistic framework is necessary. This is achieved in this study by using PBO.

Keywords: aleatory uncertainty, epistemic uncertainty, first order error analysis, uncertainty quantification, percentile-based optimization

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Authors: Mona Lisa M. Oliveira, Nara A. Policarpo, Ana Luiza B. P. Barros, Carla A. Silva

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Selective catalytic reduction systems for nitrogen oxides reduction by ammonia has been the chosen technology by most of diesel vehicle (i.e. bus and truck) manufacturers in Brazil, as also in Europe. Furthermore, at some conditions, over-stoichiometric ammonia availability is also needed that increases the NH₃ slips even more. Ammonia (NH₃) by this vehicle exhaust aftertreatment system provides a maximum efficiency of NOx removal if a significant amount of NH₃ is stored on its catalyst surface. In the other words, the practice shows that slightly less than 100% of the NOx conversion is usually targeted, so that the aqueous urea solution hydrolyzes to NH₃ via other species formation, under relatively low temperatures. This paper presents a model based on neural networks integrated with a road vehicle simulator that allows to estimate NH₃-slip emission factors for different driving conditions and patterns. The proposed model generates high NH₃slips which are not also limited in Brazil, but more efforts needed to be made to elucidate the contribution of vehicle-emitted NH₃ to the urban atmosphere.

Keywords: ammonia slip, neural-network, vehicles emissions, SCR-NOx

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Authors: Owolabi Kolade Matthew

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In this work, we investigate both the analytical and numerical studies of the dynamical model comprising of three species system. We analyze the linear stability of stationary solutions in the one-dimensional multi-system modeling the interactions of two predators and one prey species. The stability analysis has a lot of implications for understanding the various spatiotemporal and chaotic behaviors of the species in the spatial domain. The analysis results presented have established the possibility of the three interacting species to coexist harmoniously, this feat is achieved by combining the local and global analyzes to determine the global dynamics of the system. In the presence of diffusion, a viable exponential time differencing method is applied to multi-species nonlinear time-dependent partial differential equation to address the points and queries that may naturally arise. The scheme is described in detail, and justified by a number of computational experiments.

Keywords: asymptotically stable, coexistence, exponential time differencing method, global and local stability, predator-prey model, nonlinear, reaction-diffusion system

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Authors: Ashkan Zakaryazad, Ekrem Duman

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A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

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Authors: Andreia Cortes, Carla Pimentel-Rodrigues, Joao Almeida, Myriam Kanoun-Boule, Carla Carvalho, Antonio Tadeu, Armando Silva-Afonso

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The adoption of green infrastructure is nowadays encouraged as an essential measure of urban planning and territorial development whenever it offers a better alternative, or is complementary, to current solutions. Green walls and living facades often provide healthy alternatives to traditional grey infrastructures, offering many benefits for both citizens and cities. Beyond the ability to improve environmental conditions and quality of life, they can augment the energy efficiency of buildings, enhance biodiversity and deliver a range of ecosystem services such as water purification, reduction of the urban heat island effect, improvement of air quality and climate change adaptation. For this communication, a systematic survey of the existing green walls and living facades in Portugal was carried out. Different systems were analyzed and compared in terms of dimensions, constructive solutions, vegetative species, maintenance necessities and environmental aspects.

Keywords: green buildings, green walls, living facades, sustainability construction

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Authors: M. A. Ansari, A. Hussain, A. Uddin

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A side weir is a flow diversion structure provided in the side wall of a channel to divert water from the main channel to a branch channel. The trapezoidal labyrinth weir is a special type of weir in which crest length of the weir is increased to pass higher discharge. Experimental and numerical studies related to the coefficient of discharge of trapezoidal labyrinth weir in an open channel have been presented in the present study. Group Method of Data Handling (GMDH) with the transfer function of quadratic polynomial has been used to predict the coefficient of discharge for the side trapezoidal labyrinth weir. A new model is developed for coefficient of discharge of labyrinth weir by regression method. Generalized models for predicting the coefficient of discharge for labyrinth weir using Group Method of Data Handling (GMDH) network have also been developed. The prediction based on GMDH model is more satisfactory than those given by traditional regression equations.

Keywords: discharge coefficient, group method of data handling, open channel, side labyrinth weir

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Authors: Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson

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Artificial Intelligence (AI) in the Engineering, Procurement, and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Nevertheless, AI has become an attractive technology for organizations looking to automate repetitive tasks to reduce manual work. Meanwhile, the current AI market has started offering various solutions and services. The contribution of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to a specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

Keywords: artificial intelligence, machine learning, deep learning, innovation, engineering, procurement and construction industry, AI in the EPC industry

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Authors: Mahesh Koti

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In this paper, we present a quantum transport study of a quantum dot in steady state in the presence of static gate potential. We consider a quantum dot coupled to the two metallic leads. The quantum dot under study is modeled through Anderson Impurity Model (AIM) with hopping parameter modulated through voltage drop between leads and the central dot region. Based on the Landauer's formula derived from Nonequilibrium Green's Function and Single Electron Theory, the essential ingredients of transport properties are revealed. We show that the results out of two approaches closely agree with each other. We demonstrate that Landauer current response derived from single electron approach converges with non-zero interaction through gate potential whereas Landauer current response derived from Nonequilibrium Green's Function (NEGF) hits a pole.

Keywords: Anderson impurity model (AIM), nonequilibrium Green's function (NEGF), Landauer's formula, single electron analysis

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Authors: Y. H. Wu, Ji-Yuan Lin, Yu-Ming Liou

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This study aims to set up the landslide susceptibility map of County Route 89 at Ren-Ai Township in Nantou County using the Instability Index Method and Logistic regression. Seven susceptibility factors including Slope Angle, Aspect, Elevation, Distance to fold, Distance to River, Distance to Road and Accumulated Rainfall were obtained by GIS based on the Typhoon Toraji landslide area identified by Industrial Technology Research Institute in 2001. To calculate the landslide percentage of each factor and acquire the weight and grade the grid by means of Instability Index Method. In this study, landslide susceptibility can be classified into four grades: high, medium high, medium low and low, in order to determine the advantages and disadvantages of the two models. The precision of this model is verified by classification error matrix and SRC curve. These results suggest that the logistic regression model is a preferred method than instability index in the assessment of landslide susceptibility. It is suitable for the landslide prediction and precaution in this area in the future.

Keywords: instability index method, logistic regression, landslide susceptibility, SRC curve

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Authors: Shin Je Lee, Go Bong Choi, Jeong Cheol Seo, Jong Min Lee, Gibaek Lee

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Water pipe network is installed underground and once equipped; it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using Matlab. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance.

Keywords: fault detection, water pipeline model, fast Fourier transform, discrete wavelet transform

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Authors: B. P. G. Figueira, I. C. Pinto, D. Ferro, F. C. M. Zacharias

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The absenteeism at work constitutes in a temporary absence of labor functions resulting from various reasons, bringing damage to production, increasing costs of care and overburdening other workers, has its principal cause due to illness, often due exposure to several risks in the workplace. This study aims to know, identify and analyze the types and causes of absenteeism, such as the frequency at which it occurs by professional category, for employment contract and days not worked in Emergency Care Public in a city in the interior of São Paulo. We conducted exploratory and descriptive study with a quantitative approach, with nursing professionals, after selection of inclusion criteria was reached a universe of 208 subjects, the data collected are for the years from 2010-2013. Research has shown that the professional category of nursing assistant had 88,11% of total absenteeism, absenteeism lasting 1 day was the with the highest frequency, the women were responsible for 74,80% of absenteeism disease. It was concluded that absenteeism shall be monitored to plan control actions, establishing better political for the management of human resources, because it can be an aggravating factor in the quality of care.

Keywords: absenteeism; nursing; emergency medical services, human resource

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Authors: Sheraz Akhtar

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In recent years, a massive influx of refugees into developing countries has placed significant constraints on the host government’s capacities to provide social services, including education, to all. As a result, the refugee communities often find themselves deprived of their rights to education in these host countries, particularly for those who to live outside camps in urban locations. While previous research has examined the educational experiences of refugees who have resettled in developed nations, there remains a dearth of research on the educational experiences of urban refugees in developing nations. This study examines this issue through a case study of Pakistani Christian refugees living in urban settings in Thailand. Using a combination of observations within community learning centres set up by international non-government organisations (INGOs) working with these communities, and interviews with young Pakistani Christian refugees and their families, the research aims to give greater voice to the Pakistani Christian refugee community living in Thailand, and better understand their educational aspirations.

Keywords: Education, Developing Countries , INGOs, Urban Refugees

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Authors: Saurabh Chanana, Monika Arora

Abstract:

Demand response is getting increased attention these days due to the increase in electricity demand and introduction of renewable resources in the existing power grid. Traditionally demand response programs involve large industrial consumers but with technological advancement, demand response is being implemented for small residential and commercial consumers also. In this paper, demand response program aims to reduce the peak demand as well as overall energy consumption of the residential customers. Air conditioners are the major reason of peak load in residential sector in summer, so a dynamic model of air conditioning load with thermostat action has been considered for applying demand response programs. A programmable communicating thermostat (PCT) is a device that uses real time pricing (RTP) signals to control the thermostat setting. A new model incorporating PCT in air conditioning load has been proposed in this paper. Results show that introduction of PCT in air conditioner is useful in reducing the electricity payments of customers as well as reducing the peak demand.

Keywords: demand response, home energy management, programmable communicating thermostat, thermostatically controlled appliances

Procedia PDF Downloads 591

Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

Procedia PDF Downloads 332

Authors: Chen Niu, Xuesong Zhang, Dejiang Shang, Yongwei Liu

Abstract:

Fishes can secrete high molecular weight fluid on their body skin to enable their rapid movement in the water. In this work, we employ a hybrid method that combines Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) to investigate the effects of different mucus viscosities and injection velocities on fluctuation pressure in the boundary layer and flow-induced structural vibration noise of a flat plate model. To accurately capture the transient flow distribution on the plate surface, we use Large Eddy Simulation (LES) while the mucus inlet is positioned at a sufficient distance from the model to ensure effective coverage. Mucus injection is modeled using the Volume of Fluid (VOF) method for multiphase flow calculations. The results demonstrate that mucus control of pulsating pressure effectively reduces flow-induced structural vibration noise, providing an approach for controlling flow-induced noise in underwater vehicles.

Keywords: mucus, flow control, noise control, flow-induced noise

Procedia PDF Downloads 118