Search results for: input dealers

Authors: Tuti Haryati Jasimin, Hishamuddin Mohd Ali

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Malaysia’s green building development is gaining momentum and green buildings have become a key focus area especially within the commercial sector with the encouragement of government legislation and policy. Due to the emerging awareness among the market players’ views of the benefits associated with the ownership of green buildings in Malaysia, there is a need for valuers to incorporate consideration of sustainability into their assessments of property market value to ensure the green buildings continue to increase in the market. This paper analyses the valuers’ current perception on the valuation practices with regard to the green issues in Malaysia. The study was based on a survey of registered real estate valuers and the experts whose work related to valuation in the Klang Valley area to rate their view regarding the perception on valuation of green building. The findings present evidence that even though Malaysian valuers have limited knowledge of green buildings, they recognize the importance of incorporating the green features in the valuation process. The inclusion of incorporating the green features in valuations in practice was hindered by the inadequacy of sufficient transactional data in the market. Furthermore, valuers experienced difficulty in identifying what are the various input parameters of green building and how to adjust it in order to reflect the benefit of sustainability features correctly in the valuation process. This paper focuses on the present challenges confronted by Malaysian valuers with regards to incorporating the green features in their valuation.

Keywords: green commercial office building, Malaysia, valuers’ perception, valuation, commercial sector

Procedia PDF Downloads 324

Authors: Samuel Oludayo Alamu, Seong Lee

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The need for generating energy from biomass in an efficient way as well as maximizing the yield of total energy from the thermal conversion process has been a major concern for researchers. A holistic approach which involves the combination of First law of thermodynamics (FLT) and the second law of thermodynamics (SLT) is required for conducting an effective assessment of an energy plant since FLT analysis alone fails to identify the quality of the dissipated energy and how much work potential is available. The overall purpose of this study is to investigate the exergy analysis of direct combustion of poultry waste being converted to energy with a handful of environmental assessment of the conversion processes in order to maximize thermal efficiency. The exergy analysis around the shell and tube heat exchanger (STHE) was investigated primarily by varying the operating parameters for different tube shapes and flow direction, and an exergy model was obtained from estimations of the higher heating value and standard entropy of poultry waste from the elemental compositions. The STHE was designed and fabricated by Lee Research Group at Morgan State University. The analysis conducted on theSTHE using the flue gas temperature entering and exiting show that only about one-third of the energy input to the STHE was available to do work with an overall efficiency of 13.8%, while a huge amount was lost to the surrounding. By recirculating the flue gas, the exergy efficiency of the combustion system can be maximized with a greater reduction in the amount of exergy loss.

Keywords: exergy analysis, shell and tube heat exchanger, thermodynamics, combustion system, thermal efficiency

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Authors: Mohammed T. Hayajneh

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Machining of Metal Matrix Composites (MMCs) is very significant process and has been a main problem that draws many researchers to investigate the characteristics of MMCs during different machining process. The poor machining properties of hard particles reinforced MMCs make drilling process a rather interesting task. Unlike drilling of conventional materials, many problems can be seriously encountered during drilling of MMCs, such as tool wear and cutting forces. Cutting tool wear is a very significant concern in industries. Cutting tool wear not only influences the quality of the drilled hole, but also affects the cutting tool life. Prediction the cutting tool life during drilling is essential for optimizing the cutting conditions. However, the relationship between tool life and cutting conditions, tool geometrical factors and workpiece material properties has not yet been established by any machining theory. In this research work, fuzzy subtractive clustering system has been used to model the cutting tool life in drilling of Al₂O₃ particle reinforced aluminum alloy composite to investigate of the effect of cutting conditions on cutting tool life. This investigation can help in controlling and optimizing of cutting conditions when the process parameters are adjusted. The built model for prediction the tool life is identified by using drill diameter, cutting speed, and cutting feed rate as input data. The validity of the model was confirmed by the examinations under various cutting conditions. Experimental results have shown the efficiency of the model to predict cutting tool life.

Keywords: composite, fuzzy, tool life, wear

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Authors: Muhammad Umair, Tanveer Hussain, Khubab Shaker, Yasir Nawab, Muhammad Maqsood, Madeha Jabbar

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The term comfort is defined as 'the absence of unpleasantness or discomfort' or 'a neutral state compared to the more active state'. Comfort mainly is of three types: sensorial (tactile) comfort, psychological comfort and thermo-physiological comfort. Thermophysiological comfort is determined by the air permeability and moisture management properties of the garment. The aim of this study was to investigate the effect of weave structure and picking sequence on the comfort properties of woven fabrics. Six woven fabrics with two different weave structures i.e. 1/1 plain and 3/1 twill and three different picking sequences: (SPI, DPI, 3PI) were taken as input variables whereas air permeability, wetting time, wicking behavior and overall moisture management capability (OMMC) of fabrics were taken as response variables and a comparison is made of the effect of weave structure and picking sequence on the response variables. It was found that fabrics woven in twill weave design and with simultaneous triple pick insertion (3PI) give significantly better air permeability, shorter wetting time and better water spreading rate, as compared to plain woven fabrics and those with double pick insertion (DPI) or single pick insertion (SPI). It could be concluded that the thermophysiological comfort of woven fabrics may be significantly improved simply by selecting a suitable weave design and picking sequence.

Keywords: air permeability, picking sequence, thermophysiological comfort, weave design

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Authors: Ritchie Ann Dionela, Jorilyn Tabuena

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This study measured the economic empowerment of women from small-scale fisheries and agriculture sector of Sebaste, Antique. There were a total of 199 respondents selected using stratified random sampling. The Five Domains of Empowerment (5DE) Index was used in measuring the economic empowerment of study participants. Through this composite index, it was determined how women scored in the five domains of empowerment, namely production, resources, income, leadership, and time. The result of the study shows that women fishers are more economically empowered than women farmers. The two sectors showed high disparity in their scores on input in productive decision; autonomy in production; ownership of assets; control over use of income; group member; speaking in public; workload; and leisure. Group member indicator contributed largely to the disempowered population in both sectors. Although income of women farmers is higher than that of women fishers, the latter are still economically empowered which suggests that economic empowerment is not dependent on income alone. The study recommends that fisheries and agriculture organization for women should be established so that their needs and concerns will be heard and addressed. It is further recommended that government projects focused on enhancing women empowerment should also give importance on other factors such as organization and leisure and not just income to totally promote of women empowerment. Further studies on measuring women’s empowerment using other methods should be pursued to provide more information on women’s well-being.

Keywords: agriculture, composite index, fisheries, women economic empowerment

Procedia PDF Downloads 233

Authors: Mayowa Philips Ibitola, Akinade-Solomon Olorunfemi, Abe Oluwaseun Banji

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Heavy minerals studies were carried out on eighteen sediment samples from Ologe lagoon located at Lagos Barrier complex, with the aim of evaluating the heavy mineral deposits and determining the provenance of the sediments. The samples were subjected to grain analysis techniques in order to collect the finest grain size. Separation of heavy minerals from the samples was done with the aid of bromoform to enable petrographic analyses of the heavy mineral suite, under the polarising microscope. The data obtained from the heavy mineral analysis were used in preparing histograms and pie chart, from which the individual heavy mineral percentage distribution and ZTR index were derived. The percentage composition of the individual heavy mineral analyzed are opaque mineral 63.92%, Zircon 12.43%, Tourmaline 5.79%, Rutile 13.44%, Garnet 1.74% and Staurolite 3.52%. The calculated zircon, tourmaline, rutile index in percentage (ZTR) varied between 76.13 -92.15%, average garnet-zircon index (GZI), average rutile-zircon index (RuZI) and average staurolite-zircon index values in all the stations are 16.18%, 54.33%, 25.11% respectively. The mean ZTR index percentage value is 85.17% indicates that the sediments within the lagoon are mineralogically matured. The high percentage of zircon, rutile, and tourmaline indicates an acid igneous rock source for the sediments. However, the low percentage of staurolite, rutile and garnet occurrence indicates sediment of metamorphic rock source input.

Keywords: lagoon, provenance, heavy mineral, ZTR index

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Authors: Dina Mahmoud Hammouri

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The study investigates the Jordanian EFL learners’ pragmatic competence through their production of the speech acts of responding to requests, making suggestions, making threats and expressing farewells. The sample of the study consists of 130 Jordanian EFL learners and native speakers. 2600 responses were collected through a Discourse Completion Test (DCT). The findings of the study revealed that the tested students showed similarities and differences in performing the strategies of four speech acts. Differences in the students’ performances led to pragmatic failure instances. The pragmatic failure committed by students refers to a lack of linguistic competence (i.e., pragmalinguistic failure), sociocultural differences and pragmatic transfer (i.e., sociopragmatic failure). EFL learners employed many mechanisms to maintain their communicative competence; the analysis of the test on speech acts showed learners’ tendency towards using particular strategies, resorting to modify strategies and relating them to their grammatical competence, prefabrication, performing long forms, buffing and transfer. The results were also suggestive of the learners’ lack of pragmalinguistic and sociopragmatic knowledge. The implications of this study are for language teachers to teach interlanguage pragmatics explicitly in EFL contexts to draw learners’ attention to both pragmalinguistic and sociopragmatic features, pay more attention to these areas and allocate more time and practice to solve learners’ problems in these areas. The implication of this study is also for pedagogical material designers to provide sufficient and well-organized pragmatic input.

Keywords: pragmatic failure, Jordanian EFL learner, sociopragmatic competence, pragmalinguistic competence

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Authors: Tomasz Borowski, Dawid Sołoducha, Rafał Rakoczy, Marian Kordas

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The application of magnetic field is essential for a wide range of technologies or processes (i.e., magnetic hyperthermia, bioprocessing). From the practical point of view, bioprocess control is often limited to the regulation of temperature at constant values favourable to microbial growth. The main aim of this study is to determine the effect of various types of electromagnetic fields (i.e., static or alternating) on the heat transfer in a self-designed magnetically assisted reactor. The experimental set-up is equipped with a measuring instrument which controlled the temperature of the liquid inside the container and supervised the real-time acquisition of all the experimental data coming from the sensors. Temperature signals are also sampled from generator of magnetic field. The obtained temperature profiles were mathematically described and analyzed. The parameters characterizing the response to a step input of a first-order dynamic system were obtained and discussed. For example, the higher values of the time constant means slow signal (in this case, temperature) increase. After the period equal to about five-time constants, the sample temperature nearly reached the asymptotic value. This dynamical analysis allowed us to understand the heating effect under the action of various types of electromagnetic fields. Moreover, the proposed mathematical description can be used to compare the influence of different types of magnetic fields on heat transfer operations.

Keywords: heat transfer, magnetically assisted reactor, dynamical analysis, transient function

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: A. E. Kobryn

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We present both descriptive and predictive modeling of structural properties of blends of PCBM or organic-inorganic hybrid perovskites of the type CH3NH3PbX3 (X=Cl, Br, I) with P3HT, P3BT or squaraine SQ2 dye sensitizer, including adsorption on TiO2 clusters having rutile (110) surface. In our study, we use a methodology that allows computing the microscopic structure of blends on the nanometer scale and getting insight on miscibility of its components at various thermodynamic conditions. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site representation/model (RISM) and uses the universal force field. Input parameters for RISM, such as optimized molecular geometries and charge distribution of interaction sites, are derived with the use of the density functional theory methods. To compare the diffusivity of the PCBM in binary blends with P3HT and P3BT, respectively, the study is complemented with MD simulation. A very good agreement with experiment and the reports of alternative modeling or simulation is observed for PCBM in P3HT system. The performance of P3BT with perovskites, however, seems as expected. The calculated nanoscale morphologies of blends of P3HT, P3BT or SQ2 with perovskites, including adsorption on TiO2, are all new and serve as an instrument in rational design of organic/hybrid photovoltaics. They are used in collaboration with experts who actually make prototypes or devices for practical applications.

Keywords: multiscale theory and modeling, nanoscale morphology, organic-inorganic halide perovskites, three dimensional distribution

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Authors: Aleš Florian, Lenka Ševelová

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Complex sensitivity analysis of stresses in a concrete slab of the real type of rigid pavement made from recycled materials is performed. The computational model of the pavement is designed as a spatial (3D) model, is based on a nonlinear variant of the finite element method that respects the structural nonlinearity, enables to model different arrangements of joints, and the entire model can be loaded by the thermal load. Interaction of adjacent slabs in joints and contact of the slab and the subsequent layer are modeled with the help of special contact elements. Four concrete slabs separated by transverse and longitudinal joints and the additional structural layers and soil to the depth of about 3m are modeled. The thickness of individual layers, physical and mechanical properties of materials, characteristics of joints, and the temperature of the upper and lower surface of slabs are supposed to be random variables. The modern simulation technique Updated Latin Hypercube Sampling with 20 simulations is used. For sensitivity analysis the sensitivity coefficient based on the Spearman rank correlation coefficient is utilized. As a result, the estimates of influence of random variability of individual input variables on the random variability of principal stresses s1 and s3 in 53 points on the upper and lower surface of the concrete slabs are obtained.

Keywords: concrete, FEM, pavement, sensitivity, simulation

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Authors: K. Rusnakova, D. Gerych, M. Stehlik

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Postural stability is a complex process involving actions of biomechanical, motor, sensory and central nervous system components. Numerous joint systems, muscles involved, the complexity of sporting movements and situations require perfect coordination of the body's movement patterns. To adapt to a constantly changing situation in such a dynamic environment as physical performance, optimal input of information from visual, vestibular and somatosensory sensors are needed. Combat soldiers are required to perform physically and mentally demanding tasks in adverse conditions, and poor postural stability has been identified as a risk factor for lower extremity musculoskeletal injury. The aim of this study is to investigate whether some personality traits are related to the performance of static postural stability among soldiers of combat troops. NEO personality inventory (NEO-PI-R) was used to identify personality traits and the Nintendo Wii Balance Board was used to assess static postural stability of soldiers. Postural stability performance was assessed by changes in center of pressure (CoP) and center of gravity (CoG). A posturographic test was performed for 60 s with eyes opened during quiet upright standing. The results showed that facets of neuroticism and conscientiousness personality traits were significantly correlated with measured parameters of CoP and CoG. This study can help for better understanding the relationship between personality traits and static postural stability. The results can be used to optimize the training process at the individual level.

Keywords: neuroticism, conscientiousness, postural stability, combat troops

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Authors: M. Baldini, H. K. Jacobsen, M. Jaccard

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In light of the international efforts to comply with the Paris agreement and emission targets for future energy systems, Denmark and Canada are among the front-runner countries dealing with climate change. The experiences in the energy sector have seen both countries coping with trade-offs between investments in renewable energy technologies and energy efficiency, thus tackling the climate issue from the supply and demand side respectively. On the demand side, the industrial sector is going through a remarkable transformation, with implementation of energy efficiency measures, change of input fuel for end-use processes and forecasted electrification as main features under the spotlight. By looking at Canada and Denmark's experiences as pathfinders on the demand and supply approach to climate change, it is possible to obtain valuable experience that may be applied to other countries aiming at the same goal. This paper presents a comparative study on industrial energy efficiency between Canada and Denmark. The study focuses on technologies and system options, policy design and implementation and modelling methodologies when implementing industrial energy savings in optimization models in comparison to simulation models. The study identifies gaps and junctures in the approach towards climate change actions and, learning from each other, lessen the differences to further foster the adoption of energy efficiency measurements in the industrial sector, aiming at reducing energy consumption and, consequently, CO₂ emissions.

Keywords: industrial energy efficiency, comparative study, CO₂ reduction, energy system modelling

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Mahmoud Zakeri Niri, Saber Moazami, Arman Abdollahipour, Hossein Ghalkhani

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The objective of this research is to hydrological evaluation of four widely-used satellite precipitation products named PERSIANN, TMPA-3B42V7, TMPA-3B42RT, and CMORPH over Zarinehrood basin in Iran. For this aim, at first, daily streamflow of Sarough-cahy river of Zarinehrood basin was simulated using IHACRES rainfall-runoff model with daily rain gauge and temperature as input data from 1988 to 2008. Then, the model was calibrated in two different periods through comparison the simulated discharge with the observed one at hydrometric stations. Moreover, in order to evaluate the performance of satellite precipitation products in streamflow simulation, the calibrated model was validated using daily satellite rainfall estimates from the period of 2003 to 2008. The obtained results indicated that TMPA-3B42V7 with CC of 0.69, RMSE of 5.93 mm/day, MAE of 4.76 mm/day, and RBias of -5.39% performs better simulation of streamflow than those PERSIANN and CMORPH over the study area. It is noteworthy that in Iran, the availability of ground measuring station data is very limited because of the sparse density of hydro-meteorological networks. On the other hand, large spatial and temporal variability of precipitations and lack of a reliable and extensive observing system are the most important challenges to rainfall analysis, flood prediction, and other hydrological applications in this country.

Keywords: hydrological evaluation, IHACRES, satellite precipitation product, streamflow simulation

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Authors: Pingping Zhang, Yanpeng Cai, Jianlong Wang

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Stormwater storage tank (SST) is a popular low impact development technology for reducing stormwater runoff in the construction of sponge city. At present, it is difficult to perform the automatic control of SST for reducing peak flow. In this paper, fuzzy control was introduced into the peak control of SST to improve the efficiency of reducing stormwater runoff. Firstly, the design of SST was investigated. A catchment area and a return period were assumed, a SST model was manufactured, and then the storage capacity of the SST was verified. Secondly, the control parameters of the SST based on reducing stormwater runoff were analyzed, and a schematic diagram of real-time control (RTC) system based on peak control SST was established. Finally, fuzzy control system of a double input (flow and water level) and double output (inlet and outlet valve) was designed. The results showed that 1) under the different return periods (one year, three years, five years), the SST had the effect of delayed peak control and storage by increasing the detention time, 2) rainfall, pipeline flow, the influent time and the water level in the SST could be used as RTC parameters, and 3) the response curves of flow velocity and water level fluctuated very little and reached equilibrium in a short time. The combination of online monitoring and fuzzy control was feasible to control the SST automatically. This paper provides a theoretical reference for reducing stormwater runoff and improving the operation efficiency of SST.

Keywords: stormwater runoff, stormwater storage tank, real-time control, fuzzy control

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Authors: L. Al Ahmad, C. Latrille, D. Hainos, D. Blanc, M. Clausse

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A two-dimensional finite volume axisymmetric model is developed to predict the simultaneous heat and mass transfers during the drying of industrial sludge. The simulations were run using COMSOL-Multiphysics 3.5a. The input parameters of the numerical model were acquired from a preliminary experimental work. Results permit to establish correlations describing the evolution of the various parameters as a function of the drying temperature and the sludge water content. The selection and coupling of the equation are validated based on the drying kinetics acquired experimentally at a temperature range of 45-65 °C and absolute pressure range of 200-1000 mbar. The model, incorporating the heat and mass transfer mechanisms at different operating conditions, shows simulated values of temperature and water content. Simulated results are found concordant with the experimental values, only at the first and last drying stages where sludge shrinkage is insignificant. Simulated and experimental results show that sludge drying is favored at high temperatures and low pressure. As experimentally observed, the drying time is reduced by 68% for drying at 65 °C compared to 45 °C under 1 atm. At 65 °C, a 200-mbar absolute pressure vacuum leads to an additional reduction in drying time estimated by 61%. However, the drying rate is underestimated in the intermediate stage. This rate underestimation could be improved in the model by considering the shrinkage phenomena that occurs during sludge drying.

Keywords: industrial sludge drying, heat transfer, mass transfer, mathematical modelling

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Authors: Rihab Hamdi, Amel Hadri Hamida, Ouafae Bennis, Sakina Zerouali

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This paper features a performance analysis and robustness assessment of a digitally controlled DC-DC three-cell buck converter associated in parallel, operating in continuous conduction mode (CCM), facing feeding parameters variation and loads disturbance. The control strategy relies on the continuous-time with an averaged modeling technique for high-frequency switching converter. The methodology is to modulate the complete design procedure, in regard to the existence of an instantaneous current operating point for designing the digital closed-loop, to the same continuous-time domain. Moreover, the adopted approach is to include a digital voltage control (DVC) technique, taking an account for digital control delays and sampling effects, which aims at improving efficiency and dynamic response and preventing generally undesired phenomena. The results obtained under load change, input change, and reference change clearly demonstrates an excellent dynamic response of the proposed technique, also as provide stability in any operating conditions, the effectiveness is fast with a smooth tracking of the specified output voltage. Simulations studies in MATLAB/Simulink environment are performed to verify the concept.

Keywords: continuous conduction mode, digital control, parallel multi-cells converter, performance analysis, power electronics

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Authors: A. Sivakumar, S. S. Darun Prakash, P. Navaneethakrishnan

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In and around Erode District, it is estimated that more than 1250 chemical and allied textile processing fabric industries are affected, partially closed and shut off for various reasons such as poor management, poor supplier performance, lack of planning for productivity, fluctuation of output, poor investment, waste analysis, labor problems, capital/labor ratio, accumulation of stocks, poor maintenance of resources, deficiencies in the quality of fabric, low capacity utilization, age of plant and equipment, high investment and input but low throughput, poor research and development, lack of energy, workers’ fear of loss of jobs, work force mix and work ethic. The main objective of this work is to analyze the existing conditions in textile fabric sector, validate the break even of Total Productivity (TP), analyze, design and implement fuzzy sets and mathematical programming for improvement of productivity and quality dimensions in the fabric processing industry. It needs to be compatible with the reality of textile and fabric processing industries. The highly risk events from productivity and quality dimension were found by fuzzy systems and results are wrapped up among the textile fabric processing industry.

Keywords: break even point, fuzzy crisp data, fuzzy sets, productivity, productivity cycle, total productive maintenance

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Authors: Ankita Wadhawan, Parteek Kumar, Umesh Kumar

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Gesture is any body movement that expresses some attitude or any sentiment. Gestures as a sign language are used by deaf people for conveying messages which helps in eliminating the communication barrier between deaf people and normal persons. Nowadays, everybody is using mobile phone and computer as a very important gadget in their life. But there are some physically challenged people who are blind/deaf and the use of mobile phone or computer like device is very difficult for them. So, there is an immense need of a system which works on body gesture or sign language as input. In this research, Microsoft Kinect Sensor, SDK V2 and Hidden Markov Toolkit (HTK) are used to recognize the object, motion of object and human body joints through Touch less NUI (Natural User Interface) in real-time. The depth data collected from Microsoft Kinect has been used to recognize gestures of Indian Sign Language (ISL). The recorded clips are analyzed using depth, IR and skeletal data at different angles and positions. The proposed system has an average accuracy of 85%. The developed Touch less NUI provides an interface to recognize gestures and controls the cursor and click operation in computer just by waving hand gesture. This research will help deaf people to make use of mobile phones, computers and socialize among other persons in the society.

Keywords: gesture recognition, Indian sign language, Microsoft Kinect, natural user interface, sign language

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Authors: Yasamiyan Alolaywi

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The sources of second language acquisition vary and depend on a learner’s preferences and choices; however, undoubtedly, the most effective methods provide authentic language input. This current study explores the effectiveness of watching movies as a means of English language acquisition. It explores university students’ views on the impact of this method in improving English language skills. The participants in this study were 74 students (25 males and 49 females) from the Department of English Language and Translation at Qassim University, Saudi Arabia. Data for this research were collected from questionnaires and individual interviews with several selected students. The findings of this study showed that many students watch movies frequently and for various purposes, the most important of which is entertainment. The students also admitted that movies help them acquire a great deal of vocabulary and develop their listening and writing skills. Also, the participants believed that exposure to a target language by native speakers helps enhance language fluency and proficiency. The students learn not only linguistic aspects from films but also other aspects, such as culture, lifestyle, and ways of thinking, in addition to learning other languages such as Spanish. In light of these results, some recommendations are proposed, such as verifying the feasibility of integrating media into a foreign language classroom. While this study covers aspects of the relationship between watching movies and English language acquisition, knowledge gaps remain that need to be filled by further research, such as on incorporating media into the educational process and how movie subtitles can improve learners’ language skills.

Keywords: language acquisition, English movies, EFL learners, perceptions

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Authors: Cheng-Yang Lee, Petrus Tang, Tzu-Hao Chang

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Copy number variations (CNVs) have played an important role in many kinds of human diseases, such as Autism, Schizophrenia and a number of cancers. Many diseases are found in genome coding regions and whole exome sequencing (WES) is a cost-effective and powerful technology in detecting variants that are enriched in exons and have potential applications in clinical setting. Although several algorithms have been developed to detect CNVs using WES and compared with other algorithms for finding the most suitable methods using their own samples, there were not consistent datasets across most of algorithms to evaluate the ability of CNV detection. On the other hand, most of algorithms is using command line interface that may greatly limit the analysis capability of many laboratories. We create a series of simulated WES datasets from UCSC hg19 chromosome 22, and then evaluate the CNV detective ability of 19 algorithms from OMICtools database using our simulated WES datasets. We compute the sensitivity, specificity and accuracy in each algorithm for validation of the exome-derived CNVs. After comparison of 19 algorithms from OMICtools database, we construct a platform to install all of the algorithms in a virtual machine like VirtualBox which can be established conveniently in local computers, and then create a simple script that can be easily to use for detecting CNVs using algorithms selected by users. We also build a table to elaborate on many kinds of events, such as input requirement, CNV detective ability, for all of the algorithms that can provide users a specification to choose optimum algorithms.

Keywords: whole exome sequencing, copy number variations, omictools, pipeline

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Authors: Pooja Verma, Sumana Ghosh

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There is a growing need for alternative way of power generation worldwide. The reason can be attributed to limited resources of fossil fuels, environmental pollution, increasing cost of conventional fuels, and lower efficiency of conversion of energy in existing systems. In this context, one of the potential alternatives for power generation is wave energy. However, it is difficult to estimate the amount of electrical energy generation in an irregular sea condition by experiment and or analytical methods. Therefore in this work, a numerical wave tank is developed using the computational fluid dynamics software Open FOAM. In this software a specific utility known as waves2Foam utility is being used to carry out the simulation work. The computational domain is a tank of dimension: 5m*1.5m*1m with a floating object of dimension: 0.5m*0.2m*0.2m. Regular waves are generated at the inlet of the wave tank according to Stokes second order theory. The main objective of the present study is to validate the numerical model against existing experimental data. It shows a good matching with the existing experimental data of floater displacement. Later the model is exploited to estimate energy extraction due to the movement of such a point absorber in real sea conditions. Scale down the wave properties like wave height, wave length, etc. are used as input parameters. Seasonal variations are also considered.

Keywords: OpenFOAM, numerical wave tank, regular waves, floating object, point absorber

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Authors: C. A. Roberts, S. L. Mascaro, J. R. Gerrish, J. L. Horner

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Management-intensive Grazing (MiG) is a practice that rotates livestock through paddocks in a way that best matches the nutrient requirements of the animal to the yield and quality of the pasture. In the USA, MiG has been taught to livestock producers throughout the state of Missouri in 2- and 3-day workshops called “Missouri Grazing Schools.” The economic impact of these schools was quantified using IMPLAN software. The model included hectares of adoption, animal performance, carrying capacity, and input costs. To date, MiG, as taught in the Missouri Grazing Schools, has been implemented on more than 70,000 hectares in Missouri. The economic impact of these schools is presently $125 million USD per year added to the state economy. This magnitude of impact is the result not only of widespread adoption but also because of increased livestock carrying capacity; in Missouri, a capacity increase of 25 to 30% has been well documented. Additional impacts have been MiG improving forage quality and reducing the cost of feed and fertilizer. The environmental impact of MiG in the state of Missouri is currently being estimated. Environmental impact takes into account the reduction in the application of commercial fertilizers; in MiG systems, nitrogen is supplied by N fixation from legumes, and much of the P and K is recycled naturally by well-distributed manure. The environmental impact also estimates carbon sequestration and methane production; MiG can increase carbon sequestration and reduce methane production in comparison to default grazing practices and feedlot operations in the USA.

Keywords: agricultural education, forage quality, management-intensive grazing, nutrient cycling, stock density, sustainable agriculture

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Authors: Debabrata Das, Preeti Mishra

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A two-phase anaerobic process combining biohydrogen followed by biomethane (biohythane technology) serves as an environment-friendly and economically sustainable approach for the improved valorization of organic wastes. Suitability of the pure cultures like Klebsiela pneumonia, C. freundii, B. coagulan, etc. and mixed acidogenic cultures for the biohydrogen production was already studied. The characteristics of organic wastes play a critical role in biohydrogen production. The choice of an appropriate combination of complementary organic wastes can vastly improve the bioenergy generation besides achieving the significant cost reduction. Suitability and economic viability of using the groundnut deoiled cake (GDOC), mustard deoiled cake (MDOC), distillers’ dried grain with soluble (DDGS) and algal biomass (AB) as a co-substrate were studied for a biohythane production. Results show that maximum gaseous energy of 20.7, 9.3, 16.7 and 15.6 % was recovered using GDOC, MDOC, DDGS and AB in the two stage biohythane production, respectively. Both GDOC and DDGS were found to be better co-substrates as compared to MDOC and AB in terms of hythane production, respectively. The maximum cumulative hydrogen and methane production of 150 and 64 mmol/L were achieved using GDOC. Further, 98 % reduction in substrate input cost (SIC) was achieved using the co-supplementation procedure.

Keywords: Biohythane, algal biomass, distillers’ dried grain with soluble (DDGS), groundnut deoiled cake (GDOC), mustard deoiled cake (MDOC)

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Authors: Ziad M. Hejazi, Hani F. Mokhtar, Mohammed S. Bahabri, Mohammed H. Ghafouri, Ahmed S. Bahaitham

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This paper demonstrates the efforts taken by an Information Technology (IT) organization at Saudi Aramco to establish Operations Guide in a practical manner. Review of related work and literature revealed several important aspects to be considered when implementing the operation guide including Identify supporting IT groups, specify each group roles and responsibilities, formulate the IT operations in terms of processes (input/output), list each process main steps, provide the details of each process main step, develop the RACI (Responsible, Accountable, Consulted, and Informed) chart, highlight the process KPI’s, utilized systems, and forms. Identified aspects were then addressed in the actual implementation via several practices, including developing the operation guide for all IT supported operations, creating a shared folder for the operations guide, and announcing the implementation to all IT staff. The implementation of the mentioned practice was benchmarked, identified as best in class, and adopted by other internal organizations. Moreover, it was evident and appreciated by IT management. The significance of this study stems from the fact that it might be among the first studies in Saudi Arabia that propose a practical guideline to implement IT operations guide by IT organizations. Additional research significance comes from the study being conducted in Saudi Aramco, one of the world’s biggest integrated energy and petrochemical companies.

Keywords: operations guide, process implementation, Saudi Aramco company, information technology, standard of procedure

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

Abstract:

Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: G. Tamilpavai, C. Vishnuppriya

Abstract:

Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCSS) is one of the major challenges in various bioinformatics applications. The computation of the LCSS plays a vital role in biomedicine and also it is an essential task in DNA sequence analysis in genetics. It includes wide range of disease diagnosing steps. The objective of this proposed system is to find the longest common subsequence which presents in a normal and various disease affected human DNA sequence using Self Organizing Map (SOM) and LCSS. The human DNA sequence is collected from National Center for Biotechnology Information (NCBI) database. Initially, the human DNA sequence is separated as k-mer using k-mer separation rule. Mean and median values are calculated from each separated k-mer. These calculated values are fed as input to the Self Organizing Map for the purpose of clustering. Then obtained clusters are given to the Longest Common Sub Sequence (LCSS) algorithm for finding common subsequence which presents in every clusters. It returns nx(n-1)/2 subsequence for each cluster where n is number of k-mer in a specific cluster. Experimental outcomes of this proposed system produce the possible number of longest common subsequence of normal and disease affected DNA data. Thus the proposed system will be a good initiative aid for finding disease causing sequence. Finally, performance analysis is carried out for different DNA sequences. The obtained values show that the retrieval of LCSS is done in a shorter time than the existing system.

Keywords: clustering, k-mers, longest common subsequence, SOM

Procedia PDF Downloads 267

Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

Abstract:

Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

Abstract:

Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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