Search results for: performance comparison

Authors: Kirolos Gerges Yakoub Gerges

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Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

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Authors: S. Lertsakulpasuk, S. Athichanagorn

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As depletion-drive gas reservoirs are abandoned when there is insufficient production rate due to pressure depletion, waterflooding has been proposed to increase the reservoir pressure in order to prolong gas production. Due to high cost, water injection may not be economically feasible. Water dumpflood into gas reservoirs is a new promising approach to increase gas recovery by maintaining reservoir pressure with much cheaper costs than conventional waterflooding. Thus, a simulation study of water dumpflood into multiple nearly abandoned or already abandoned thin-bedded gas reservoirs commonly found in the Gulf of Thailand was conducted to demonstrate the advantage of the proposed method and to determine the most suitable operational parameters for reservoirs having different system parameters. A reservoir simulation model consisting of several thin-layered depletion-drive gas reservoirs and an overlying aquifer was constructed in order to investigate the performance of the proposed method. Two producers were initially used to produce gas from the reservoirs. One of them was later converted to a dumpflood well after gas production rate started to decline due to continuous reduction in reservoir pressure. The dumpflood well was used to flow water from the aquifer to increase pressure of the gas reservoir in order to drive gas towards producer. Two main operational parameters which are wellhead pressure of producer and the time to start water dumpflood were investigated to optimize gas recovery for various systems having different gas reservoir dip angles, well spacings, aquifer sizes, and aquifer depths. This simulation study found that water dumpflood can increase gas recovery up to 12% of OGIP depending on operational conditions and system parameters. For the systems having a large aquifer and large distance between wells, it is best to start water dumpflood when the gas rate is still high since the long distance between the gas producer and dumpflood well helps delay water breakthrough at producer. As long as there is no early water breakthrough, the earlier the energy is supplied to the gas reservoirs, the better the gas recovery. On the other hand, for the systems having a small or moderate aquifer size and short distance between the two wells, performing water dumpflood when the rate is close to the economic rate is better because water is more likely to cause an early breakthrough when the distance is short. Water dumpflood into multiple nearly-depleted or depleted gas reservoirs is a novel study. The idea of using water dumpflood to increase gas recovery has been mentioned in the literature but has never been investigated. This detailed study will help a practicing engineer to understand the benefits of such method and can implement it with minimum cost and risk.

Keywords: dumpflood, increase gas recovery, low-pressure gas reservoir, multiple gas reservoirs

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Authors: Jifeng Zhang , Yongpeng Lei

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Continuous carbon fiber reinforced polyamide 6 (CF/PA6) composites are potential for application in the automotive industry due to their high specific strength and stiffness. However, PA6 resin is sensitive to the moisture in the hydrothermal environment and CF/PA6 composites might undergo several physical and chemical changes, such as plasticization, swelling, and hydrolysis, which induces a reduction of mechanical properties. So far, little research has been reported on the assessment of the effects of hydrothermal aging on the mechanical properties of continuous CF/PA6 composite. This study deals with the effects of hydrothermal aging on moisture absorption and mechanical properties of polyamide 6 (PA6) and polyamide 6 reinforced with continuous carbon fibers composites (CF/PA6) by immersion in distilled water at 30 ℃, 50 ℃, 70 ℃, and 90 ℃. Degradation of mechanical performance has been monitored, depending on the water absorption content and the aging temperature. The experimental results reveal that under the same aging condition, the PA6 resin absorbs more water than the CF/PA6 composite, while the water diffusion coefficient of CF/PA6 composite is higher than that of PA6 resin because of interfacial diffusion channel. In mechanical properties degradation process, an exponential reduction in tensile strength and elastic modulus are observed in PA6 resin as aging temperature and water absorption content increases. The degradation trend of flexural properties of CF/PA6 is the same as that of tensile properties of PA6 resin. Moreover, the water content plays a decisive role in mechanical degradation compared with aging temperature. In contrast, hydrothermal environment has mild effect on the tensile properties of CF/PA6 composites. The elongation at breakage of PA6 resin and CF/PA6 reaches the highest value when their water content reaches 6% and 4%, respectively. Dynamic mechanical analysis (DMA) and scanning electron microscope (SEM) were also used to explain the mechanism of mechanical properties alteration. After exposed to the hydrothermal environment, the Tg (glass transition temperature) of samples decreases dramatically with water content increase. This reduction can be ascribed to the plasticization effect of water. For the unaged specimens, the fibers surface is coated with resin and the main fracture mode is fiber breakage, indicating that a good adhesion between fiber and matrix. However, with absorbed water content increasing, the fracture mode transforms to fiber pullout. Finally, based on Arrhenius methodology, a predictive model with relate to the temperature and water content has been presented to estimate the retention of mechanical properties for PA6 and CF/PA6.

Keywords: continuous carbon fiber reinforced polyamide 6 composite, hydrothermal aging, Arrhenius methodology, interface

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Authors: Marius Sebastian Secula, Mihaela Darie, Gabriela Carja

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Ibuprofen is a non-steroidal anti-inflammatory drug (NSAIDs) and is among the most frequently detected pharmaceuticals in environmental samples and among the most widespread drug in the world. Its concentration in the environment is reported to be between 10 and 160 ng L-1. In order to improve the abatement efficiency of this compound for water source prevention and reclamation, the development of innovative technologies is mandatory. AOPs (advanced oxidation processes) are known as highly efficient towards the oxidation of organic pollutants. Among the promising combined treatments, photo-Fenton processes using layered double hydroxides (LDHs) attracted significant consideration especially due to their composition flexibility, high surface area and tailored redox features. This work presents the self-supported Fe, Mn or Ti on ZnFeAl LDHs obtained by co-precipitation followed by reconstruction method as novel efficient photo-catalysts for Fenton-like catalysis. Fe, Mn or Ti/ZnFeAl LDHs nano-hybrids were tested for the degradation of a model pharmaceutical agent, the anti-inflammatory agent ibuprofen, by photocatalysis and photo-Fenton catalysis, respectively, by means of a lab-scale system consisting of a batch reactor equipped with an UV lamp (17 W). The present study presents comparatively the degradation of Ibuprofen in aqueous solution UV light irradiation using four different types of LDHs. The newly prepared Ti/ZnFeAl 4:1 catalyst results in the best degradation performance. After 60 minutes of light irradiation, the Ibuprofen removal efficiency reaches 95%. The slowest degradation of Ibuprofen solution occurs in case of Fe/ZnFeAl 4:1 LDH, (67% removal efficiency after 60 minutes of process). Evolution of Ibuprofen degradation during the photo Fenton process is also studied using Ti/ZnFeAl 2:1 and 4:1 LDHs in the presence and absence of H2O2. It is found that after 60 min the use of Ti/ZnFeAl 4:1 LDH in presence of 100 mg/L H2O2 leads to the fastest degradation of Ibuprofen molecule. After 120 min, both catalysts Ti/ZnFeAl 4:1 and 2:1 result in the same value of removal efficiency (98%). In the absence of H2O2, Ibuprofen degradation reaches only 73% removal efficiency after 120 min of degradation process. Acknowledgements: This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-0405.

Keywords: layered double hydroxide, advanced oxidation process, micropollutant, heterogeneous Fenton

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Authors: May Fadheel Estephan, Richard Perks

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Context: Cancer is a serious health concern that affects millions of people worldwide. Early detection and treatment are essential for improving patient outcomes. However, current methods for cancer detection have limitations, such as low sensitivity and specificity. Research Aim: The aim of this study was to develop an optical sensor for cancer detection using elastic light scattering spectroscopy (ELSS). ELSS is a noninvasive optical technique that can be used to characterize the size and concentration of particles in a solution. Methodology: An optical probe was fabricated with a 100-μm-diameter core and a 132-μm centre-to-centre separation. The probe was used to measure the ELSS spectra of polystyrene spheres with diameters of 2, 0.8, and 0.413 μm. The spectra were then analysed to determine the size and concentration of the spheres. Findings: The results showed that the optical probe was able to differentiate between the three different sizes of polystyrene spheres. The probe was also able to detect the presence of polystyrene spheres in suspension concentrations as low as 0.01%. Theoretical Importance: The results of this study demonstrate the potential of ELSS for cancer detection. ELSS is a noninvasive technique that can be used to characterize the size and concentration of cells in a tissue sample. This information can be used to identify cancer cells and assess the stage of the disease. Data Collection: The data for this study were collected by measuring the ELSS spectra of polystyrene spheres with different diameters. The spectra were collected using a spectrometer and a computer. Analysis Procedures: The ELSS spectra were analysed using a software program to determine the size and concentration of the spheres. The software program used a mathematical algorithm to fit the spectra to a theoretical model. Question Addressed: The question addressed by this study was whether ELSS could be used to detect cancer cells. The results of the study showed that ELSS could be used to differentiate between different sizes of cells, suggesting that it could be used to detect cancer cells. Conclusion: The findings of this research show the utility of ELSS in the early identification of cancer. ELSS is a noninvasive method for characterizing the number and size of cells in a tissue sample. To determine cancer cells and determine the disease's stage, this information can be employed. Further research is needed to evaluate the clinical performance of ELSS for cancer detection.

Keywords: elastic light scattering spectroscopy, polystyrene spheres in suspension, optical probe, fibre optics

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Authors: Albert Amagyei, Julia Carroll, Amanda R. Amorim Adegboye, Laura Strumidlo, Rosie Kneafsey

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Introduction: Classroom-based learning has persisted as the mainstream model of pre-registration nurse education. This model is often rigid, teacher-centered, and unable to support active learning and the practical learning needs of nursing students. Health Education England (HEE), a public body of the Department of Health and Social Care, hypothesises that blended learning (BL) programmes may address health system and nursing profession challenges, such as nursing shortages and lack of digital expertise, by exploring opportunities for providing predominantly online, remote-access study which may increase nursing student recruitment, offering alternate pathways to nursing other than the traditional classroom route. This study will provide evidence for blended learning strategies adopted in nursing education as well as examine nursing student learning experiences concerning the challenges and opportunities related to using blended learning within nursing education. Objective: This review will explore the challenges and opportunities of BL within pre-registration nurse education from the student's perspective. Methods: The search was completed within five databases. Eligible studies were appraised independently by four reviewers. The JBI-convergent segregated approach for mixed methods review was used to assess and synthesize the data. The study’s protocol has been registered with the International Register of Systematic Reviews with registration number// PROSPERO (CRD42023423532). Results: Twenty-seven (27) studies (21 quantitative and 6 qualitative) were included in the review. The study confirmed that BL positively impacts nursing students' learning outcomes, as demonstrated by the findings of the meta-analysis and meta-synthesis. Conclusion: The review compared BL to traditional learning, simulation, laboratory, and online learning on nursing students’ learning and programme outcomes as well as learning behaviour and experience. The results show that BL could effectively improve nursing students’ knowledge, academic achievement, critical skills, and clinical performance as well as enhance learner satisfaction and programme retention. The review findings outline that students’ background characteristics, BL design, and format significantly impact the success of the BL nursing programme.

Keywords: nursing student, blended learning, pre-registration nurse education, online learning

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Authors: Qian Wang, Haibo Gu

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To drive practical interpretations and applications of various policies in building the transnational education joint-ventures, middle managers learn to navigate through uncertainties and ambiguities. However, the current literature views very little about those middle managers’ experiences, perceptions, and practices. This paper takes the empirical approach and aims to uncover the middle managers’ experiences by conducting interviews, campus visits, and document analysis. Following the qualitative research method approach, the researchers gathered information from a mixture of fourteen foreign and Chinese managers. Their perceptions of the China-foreign cooperation in higher education and their perceived roles have offered important, valuable insights to this group of people’s attitudes and management performances. The diverse cultural and demographic backgrounds contributed to the significance of the study. There are four key findings. One, middle managers’ immediate micro-contexts and individual attitudes are the top two influential factors in managers’ performances. Two, the foreign middle managers showed a stronger sense of self-identity in risk-taking. Three, the Chinese middle managers preferred to see difficulties as part of their assigned responsibilities. Four, middle managers in independent universities demonstrated a stronger sense of belonging and fewer frustrations than middle managers in secondary institutes. The researchers propose that training for managers in a transnational educational setting should consider these discoveries when select fitting topics and content. In particular, middle managers should be better prepared to anticipate their everyday jobs in the micro-environment; hence, information concerning sponsor organizations’ working culture is as essential as knowing the national and local regulations, and socio-culture. Different case studies can help the managers to recognize and celebrate the diversity in transnational education. Situational stories can help them to become aware of the diverse and wide range of work contexts so that they will not feel to be left alone when facing challenges without relevant previous experience or training. Though this research is a case study based in the Chinese transnational higher education setting, the implications could be relevant and comparable to other transnational higher education situations and help to continue expanding the potential applications in this field.

Keywords: educational management, middle manager performance, transnational higher education

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Authors: Buruk Kitachew Wossenyeleh

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Groundwater is the largest freshwater reservoir in the world. Like the other reservoirs of the hydrologic cycle, it is a finite resource. This study focused on the groundwater modeling of the Ter Kamerenbos well field to understand the groundwater flow system and the impact of different scenarios. The study area covers 68.9Km2 in the Brussels Capital Region and is situated in two river catchments, i.e., Zenne River and Woluwe Stream. The aquifer system has three layers, but in the modeling, they are considered as one layer due to their hydrogeological properties. The catchment aquifer system is replenished by direct recharge from rainfall. The groundwater recharge of the catchment is determined using the spatially distributed water balance model called WetSpass, and it varies annually from zero to 340mm. This groundwater recharge is used as the top boundary condition for the groundwater modeling of the study area. During the groundwater modeling using Processing MODFLOW, constant head boundary conditions are used in the north and south boundaries of the study area. For the east and west boundaries of the study area, head-dependent flow boundary conditions are used. The groundwater model is calibrated manually and automatically using observed hydraulic heads in 12 observation wells. The model performance evaluation showed that the root means the square error is 1.89m and that the NSE is 0.98. The head contour map of the simulated hydraulic heads indicates the flow direction in the catchment, mainly from the Woluwe to Zenne catchment. The simulated head in the study area varies from 13m to 78m. The higher hydraulic heads are found in the southwest of the study area, which has the forest as a land-use type. This calibrated model was run for the climate change scenario and well operation scenario. Climate change may cause the groundwater recharge to increase by 43% and decrease by 30% in 2100 from current conditions for the high and low climate change scenario, respectively. The groundwater head varies for a high climate change scenario from 13m to 82m, whereas for a low climate change scenario, it varies from 13m to 76m. If doubling of the pumping discharge assumed, the groundwater head varies from 13m to 76.5m. However, if the shutdown of the pumps is assumed, the head varies in the range of 13m to 79m. It is concluded that the groundwater model is done in a satisfactory way with some limitations, and the model output can be used to understand the aquifer system under steady-state conditions. Finally, some recommendations are made for the future use and improvement of the model.

Keywords: Ter Kamerenbos, groundwater modelling, WetSpass, climate change, well operation

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Authors: Asadullah Sheikh, Kerry Barnett, Paul Ayres

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Despite significant progress in access, equity and public examination success, poor student performance in mathematics in secondary schools has become a major concern in Bangladesh. A substantial body of research has emphasised the important contribution of teaching practices to student achievement. However, this has not been investigated in Bangladesh. Therefore, the study sought to find out the effectiveness of mathematics teaching practices as a means of improving secondary school mathematics in Dhaka Municipality City (DMC) area, Bangladesh. The purpose of this study was twofold, first, to identify the 20 highest performing secondary schools in mathematics in DMC, and second, to investigate the teaching practices of mathematics teachers in these schools. A two-phase mixed method approach was adopted. In the first phase, secondary source data were obtained from the Board of Intermediate and Secondary Education (BISE), Dhaka and value-added measures used to identify the 20 highest performing secondary schools in mathematics. In the second phase, a concurrent mixed method design, where qualitative methods were embedded within a dominant quantitative approach was utilised. A purposive sampling strategy was used to select fifteen teachers from the 20 highest performing secondary schools. The main sources of data were classroom teaching observations, and teacher interviews. The data from teacher observations were analysed with descriptive and nonparametric statistics. The interview data were analysed qualitatively. The main findings showed teachers adopt a direct teaching approach which incorporates orientation, structuring, modelling, practice, questioning and teacher-student interaction that creates an individualistic learning environment. The variation in developmental levels of teaching skill indicate that teachers do not necessarily use the qualitative (i.e., focus, stage, quality and differentiation) aspects of teaching behaviours effectively. This is the first study to investigate teaching behaviours of effective secondary mathematics teachers within Dhaka, Bangladesh. It contributes in an international dimension to the field of educational effectiveness and raise questions about existing constructivist approaches. Further, it contributes to important insights about teaching behaviours that can be used to inform the development of evidence-based policy and practice on quality teaching in Bangladesh.

Keywords: effective teaching, mathematics, secondary schools, student achievement, value-added measures

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Authors: John-Nelson Ekumah, Selorm Yao-Say Solomon Adade, Mingming Zhong, Yufan Sun, Qiufang Liang, Muhammad Safiullah Virk, Xorlali Nunekpeku, Nana Adwoa Nkuma Johnson, Bridget Ama Kwadzokpui, Xiaofeng Ren

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Enhancing starch gel strength and stability is crucial. However, traditional gel property assessment methods are destructive, time-consuming, and resource-intensive. Thus, understanding ethanol treatment effects on kudzu starch gel strength and developing a rapid, nondestructive gel strength assessment method is essential for optimizing the treatment process and ensuring product quality consistency. This study investigated the effects of different ethanol concentrations on the microstructure of kudzu starch gels using a comprehensive microstructural analysis. We also developed a nondestructive method for predicting gel strength and classifying treatment levels using near-infrared (NIR) spectroscopy, and advanced data analytics. Scanning electron microscopy revealed progressive network densification and pore collapse with increasing ethanol concentration, correlating with enhanced mechanical properties. NIR spectroscopy, combined with various variable selection methods (CARS, GA, and UVE) and modeling algorithms (PLS, SVM, and ELM), was employed to develop predictive models for gel strength. The UVE-SVM model demonstrated exceptional performance, with the highest R² values (Rc = 0.9786, Rp = 0.9688) and lowest error rates (RMSEC = 6.1340, RMSEP = 6.0283). Pattern recognition algorithms (PCA, LDA, and KNN) successfully classified gels based on ethanol treatment levels, achieving near-perfect accuracy. This integrated approach provided a multiscale perspective on ethanol-induced starch gel modification, from molecular interactions to macroscopic properties. Our findings demonstrate the potential of NIR spectroscopy, coupled with advanced data analysis, as a powerful tool for rapid, nondestructive quality assessment in starch gel production. This study contributes significantly to the understanding of starch modification processes and opens new avenues for research and industrial applications in food science, pharmaceuticals, and biomaterials.

Keywords: kudzu starch gel, near-infrared spectroscopy, gel strength prediction, support vector machine, pattern recognition algorithms, ethanol treatment

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Authors: Marek Dosbaba

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Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Alicia Yanadira Nava Fuentes, Ilse Cervantes Camacho, Amadeo José Argüelles Cruz, Ana María Balboa Verduzco

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Augmented reality promises being present in future driving, with its immersive technology let to show directions and maps to identify important places indicating with graphic elements when the car driver requires the information. On the other side, driving is considered a multitasking activity and, for some people, a complex activity where different situations commonly occur that require the immediate attention of the car driver to make decisions that contribute to avoid accidents; therefore, the main aim of the project is the instrumentation of a platform with biometric sensors that allows evaluating the performance in driving vehicles with the influence of augmented reality devices to detect the level of attention in drivers, since it is important to know the effect that it produces. In this study, the physiological sensors EPOC X (EEG), ECG06 PRO and EMG Myoware are joined in the driving test platform with a Logitech G29 steering wheel and the simulation software City Car Driving in which the level of traffic can be controlled, as well as the number of pedestrians that exist within the simulation obtaining a driver interaction in real mode and through a MSP430 microcontroller achieves the acquisition of data for storage. The sensors bring a continuous analog signal in time that needs signal conditioning, at this point, a signal amplifier is incorporated due to the acquired signals having a sensitive range of 1.25 mm/mV, also filtering that consists in eliminating the frequency bands of the signal in order to be interpretative and without noise to convert it from an analog signal into a digital signal to analyze the physiological signals of the drivers, these values are stored in a database. Based on this compilation, we work on the extraction of signal features and implement K-NN (k-nearest neighbor) classification methods and decision trees (unsupervised learning) that enable the study of data for the identification of patterns and determine by classification methods different effects of augmented reality on drivers. The expected results of this project include are a test platform instrumented with biometric sensors for data acquisition during driving and a database with the required variables to determine the effect caused by augmented reality on people in simulated driving.

Keywords: augmented reality, driving, physiological signals, test platform

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Authors: S. Gariani, I. Shyha

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Cutting titanium alloys are usually accompanied with low productivity, poor surface quality, short tool life and high machining costs. This is due to the excessive generation of heat at the cutting zone and difficulties in heat dissipation due to relatively low heat conductivity of this metal. The cooling applications in machining processes are crucial as many operations cannot be performed efficiently without cooling. Improving machinability, increasing productivity, enhancing surface integrity and part accuracy are the main advantages of cutting fluids. Conventional fluids such as mineral oil-based, synthetic and semi-synthetic are the most common cutting fluids in the machining industry. Although, these cutting fluids are beneficial in the industries, they pose a great threat to human health and ecosystem. Vegetable oils (VOs) are being investigated as a potential source of environmentally favourable lubricants, due to a combination of biodegradability, good lubricous properties, low toxicity, high flash points, low volatility, high viscosity indices and thermal stability. Fatty acids of vegetable oils are known to provide thick, strong, and durable lubricant films. These strong lubricating films give the vegetable oil base stock a greater capability to absorb pressure and high load carrying capacity. This paper details preliminary experimental results when turning Ti-6Al-4V. The impact of various VO-based cutting fluids, cutting tool materials, working conditions was investigated. The full factorial experimental design was employed involving 24 tests to evaluate the influence of process variables on average surface roughness (Ra), tool wear and chip formation. In general, Ra varied between 0.5 and 1.56 µm and Vasco1000 cutting fluid presented comparable performance with other fluids in terms of surface roughness while uncoated coarse grain WC carbide tool achieved lower flank wear at all cutting speeds. On the other hand, all tools tips were subjected to uniform flank wear during whole cutting trails. Additionally, formed chip thickness ranged between 0.1 and 0.14 mm with a noticeable decrease in chip size when higher cutting speed was used.

Keywords: cutting fluids, turning, Ti-6Al-4V, vegetable oils, working conditions

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Authors: Tejinder Singh Sidhu

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Hot corrosion has been recognized as a severe problem in steam-powered electricity generation plants and industrial waste incinerators as it consumes the material at an unpredictably rapid rate. Consequently, the load-carrying ability of the components reduces quickly, eventually leading to catastrophic failure. The inability to either totally prevent hot corrosion or at least detect it at an early stage has resulted in several accidents, leading to loss of life and/or destruction of infrastructures. A number of countermeasures are currently in use or under investigation to combat hot corrosion, such as using inhibitors, controlling the process parameters, designing a suitable industrial alloy, and depositing protective coatings. However, the protection system to be selected for a particular application must be practical, reliable, and economically viable. Due to the continuously rising cost of the materials as well as increased material requirements, the coating techniques have been given much more importance in recent times. Coatings can add value to products up to 10 times the cost of the coating. Among the different coating techniques, thermal spraying has grown into a well-accepted industrial technology for applying overlay coatings onto the surfaces of engineering components to allow them to function under extreme conditions of wear, erosion-corrosion, high-temperature oxidation, and hot corrosion. In this study, the hot corrosion performances of Ni-20Cr and Cr₃C₂-NiCr coatings developed by High Velocity Oxy-Fuel (HVOF) process have been studied. The coatings were developed on a Fe-based superalloy, and experiments were performed in an actual industrial environment of a coal-fired boiler. The cyclic study was carried out around the platen superheater zone where the temperature was around 1000°C. The study was conducted for 10 cycles, and one cycle was consisting of 100 hours of heating followed by 1 hour of cooling at ambient temperature. Both the coatings deposited on Fe-based superalloy imparted better hot corrosion resistance than the uncoated one. The Ni-20Cr coated superalloy performed better than the Cr₃C₂-NiCr coated in the actual working conditions of the coal fired boiler. It is found that the formation of chromium oxide at the boundaries of Ni-rich splats of the coating blocks the inward permeation of oxygen and other corrosive species to the substrate.

Keywords: hot corrosion, coating, HVOF, oxidation

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Authors: Patricia Jiménez, Rafael Corchuelo, Hassan A. Sleiman

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Web mining is gaining importance at an increasing pace. Currently, there are many complementary research topics under this umbrella. Their common theme is that they all focus on applying knowledge discovery techniques to data that is gathered from the Web. Sometimes, these data are relatively easy to gather, chiefly when it comes from server logs. Unfortunately, there are cases in which the data to be mined is the data that is displayed on a web document. In such cases, it is necessary to apply a pre-processing step to first extract the information of interest from the web documents. Such pre-processing steps are performed using so-called information extractors, which are software components that are typically configured by means of rules that are tailored to extracting the information of interest from a web page and structuring it according to a pre-defined schema. Paramount to getting good mining results is that the technique used to extract the source information is exact, which requires to evaluate and compare the different proposals in the literature from an empirical point of view. According to Google Scholar, about 4 200 papers on information extraction have been published during the last decade. Unfortunately, they were not evaluated within a homogeneous framework, which leads to difficulties to compare them empirically. In this paper, we report on an original information extraction evaluation method. Our contribution is three-fold: a) this is the first attempt to provide an evaluation method for proposals that work on semi-structured documents; the little existing work on this topic focuses on proposals that work on free text, which has little to do with extracting information from semi-structured documents. b) It provides a method that relies on statistically sound tests to support the conclusions drawn; the previous work does not provide clear guidelines or recommend statistically sound tests, but rather a survey that collects many features to take into account as well as related work; c) We provide a novel method to compute the performance measures regarding unsupervised proposals; otherwise they would require the intervention of a user to compute them by using the annotations on the evaluation sets and the information extracted. Our contributions will definitely help researchers in this area make sure that they have advanced the state of the art not only conceptually, but from an empirical point of view; it will also help practitioners make informed decisions on which proposal is the most adequate for a particular problem. This conference is a good forum to discuss on our ideas so that we can spread them to help improve the evaluation of information extraction proposals and gather valuable feedback from other researchers.

Keywords: web information extractors, information extraction evaluation method, Google scholar, web

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Authors: Ahmed Auda, Manjree Agarwala, Giles Hardya, Yonglin Rena

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Botrytis cinerea is a major pest for many plants. It can attack a wide range of plant parts. It can attack buds, flowers, and leaves, stems, and fruit. However, B. cinerea can be mixed with other diseases that cause the same damage. There are many species of botrytis and more than one different strains of each. Botrytis might infect the foliage of nursery stock stored through winter in damp conditions. There are no known resistant plants. Botrytis must have nutrients or food source before it infests the plant. Nutrients leaking from wounded plant parts or dying tissue like old flower petals give the required nutrients. From this food, the fungus becomes more attackers and invades healthy tissue. Dark to light brown rot forms in the ill tissue. High humidity conditions support the growth of this fungus. However, we suppose that selection pressure can act on the morphological and neurophysiologic filter properties of the receiver and on both the biochemical and the physiological regulation of the signal. Communication is implied when signal and receiver evolves toward more and more specific matching, culminating. In other hand, receivers respond to portions of a body odor bouquet which is released to the environment not as an (intentional) signal but as an unavoidable consequence of metabolic activity or tissue damage. Each year Botrytis species can cause considerable economic losses to plant crops. Even with the application of strict quarantine and control measures, these fungi can still find their way into crops and cause the imposition of onerous restrictions on exports. Blueberry fruit mould caused by a fungal infection usually results in major losses during post-harvest storage. Therefore, the management of infection in early stages of disease development is necessary to minimize losses. The overall purpose of this study will develop sensitive, cheap, quick and robust diagnostic techniques for the detection of B. cinerea in blueberry. The specific aim was designed to investigate the performance of volatile organic compounds (VOCs) in the detection and discrimination of blueberry fruits infected by fungal pathogens with an emphasis on Botrytis in the early storage stage of post-harvest.

Keywords: botrytis cinerea, blueberry, GC/MS, VOCs

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Authors: Iman Jabbar

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Within the scope of professionalism and in order to compete with the modern world, teachers, are expected to develop their teaching skills and activities in addition to their professional knowledge. At the college level, the teacher should be able to face classroom challenges through his engagement with the learning situation to understand the students and their needs. In our field of TESOL, the role of the English teacher is no longer restricted to teaching English texts, but rather he should endeavor to enhance the students’ skills such as communication and critical analysis. Within the literature of professionalism, there are certain strategies and tools that an English teacher should adopt to develop his competence and performance. Reflective practice, which is an exploratory process, is one of these strategies. Another strategy contributing to classroom development is motivation. It is crucial in students’ learning as it affects the quality of learning English in the classroom in addition to determining success or failure as well as language achievement. This is a qualitative study grounded on interpretive perspectives of teachers and students regarding the process of professional development. This study aims at (a) understanding how teachers at the college level conceptualize reflective practice and motivation inside EFL classroom, and (b) exploring the methods and strategies that they implement to practice reflection and motivation. This study and is based on two questions: 1. How do EFL teachers perceive and view reflection and motivation in relation to their teaching and professional development? 2. How can reflective practice and motivation be developed into practical strategies and actions in EFL teachers’ professional context? The study is organized into two parts, theoretical and practical. The theoretical part reviews the literature on the concept of reflective practice and motivation in relation to professional development through providing certain definitions, theoretical models, and strategies. The practical part draws on the theoretical one, however; it is the core of the study since it deals with two issues. It involves the research design, methodology, and methods of data collection, sampling, and data analysis. It ends up with an overall discussion of findings and the researcher's reflections on the investigated topic. In terms of significance, the study is intended to contribute to the field of TESOL at the academic level through the selection of the topic and investigating it from theoretical and practical perspectives. Professional development is the path that leads to enhancing the quality of teaching English as a foreign or second language in a way that suits the modern trends of globalization and advanced technology.

Keywords: professional development, motivation, reflection, learning

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Authors: Aspasia Koutsokosta, Stefanos Katsavounis

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This paper reviews the model-based qualitative and quantitative Operations Management research in the context of Construction Supply Chain Management (CSCM). Construction industry has been traditionally blamed for low productivity, cost and time overruns, waste, high fragmentation and adversarial relationships. The construction industry has been slower than other industries to employ the Supply Chain Management (SCM) concept and develop models that support the decision-making and planning. However the last decade there is a distinct shift from a project-based to a supply-based approach of construction management. CSCM comes up as a new promising management tool of construction operations and improves the performance of construction projects in terms of cost, time and quality. Modeling the Construction Supply Chain (CSC) offers the means to reap the benefits of SCM, make informed decisions and gain competitive advantage. Different modeling approaches and methodologies have been applied in the multi-disciplinary and heterogeneous research field of CSCM. The literature review reveals that a considerable percentage of CSC modeling accommodates conceptual or process models which discuss general management frameworks and do not relate to acknowledged soft OR methods. We particularly focus on the model-based quantitative research and categorize the CSCM models depending on their scope, mathematical formulation, structure, objectives, solution approach, software used and decision level. Although over the last few years there has been clearly an increase of research papers on quantitative CSC models, we identify that the relevant literature is very fragmented with limited applications of simulation, mathematical programming and simulation-based optimization. Most applications are project-specific or study only parts of the supply system. Thus, some complex interdependencies within construction are neglected and the implementation of the integrated supply chain management is hindered. We conclude this paper by giving future research directions and emphasizing the need to develop robust mathematical optimization models for the CSC. We stress that CSC modeling needs a multi-dimensional, system-wide and long-term perspective. Finally, prior applications of SCM to other industries have to be taken into account in order to model CSCs, but not without the consequential reform of generic concepts to match the unique characteristics of the construction industry.

Keywords: construction supply chain management, modeling, operations research, optimization, simulation

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Authors: Nyashadzashe Chiraga, Anthony Walker, Glen Bright

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The manufacturing industry is currently experiencing a paradigm shift into the Fourth Industrial Revolution in which customers are increasingly at the epicentre of production. The high degree of production customization and personalization requires a flexible manufacturing system that will rapidly respond to the dynamic and volatile changes driven by the market. They are a gap in technology that allows for the optimal flow of information and optimal manufacturing operations on the shop floor regardless of the rapid changes in the fixture and part demands. Information is the reduction of uncertainty; it gives meaning and context on the state of each cell. The amount of information needed to describe cellular manufacturing systems is investigated by two measures: the structural entropy and the operational entropy. Structural entropy is the expected amount of information needed to describe scheduled states of a manufacturing system. While operational entropy is the amount of information that describes the scheduled states of a manufacturing system, which occur during the actual manufacturing operation. Using Anylogic simulator a typical manufacturing job shop was set-up with a cellular manufacturing configuration. The cellular make-up of the configuration included; a Material handling cell, 3D Printer cell, Assembly cell, manufacturing cell and Quality control cell. The factory shop provides manufactured parts to a number of clients, and there are substantial variations in the part configurations, new part designs are continually being introduced to the system. Based on the normal expected production schedule, the schedule adherence was calculated from the structural entropy and operation entropy of varying the amounts of information communicated in simulated runs. The structural entropy denotes a system that is in control; the necessary real-time information is readily available to the decision maker at any point in time. For contractive analysis, different out of control scenarios were run, in which changes in the manufacturing environment were not effectively communicated resulting in deviations in the original predetermined schedule. The operational entropy was calculated from the actual operations. From the results obtained in the empirical study, it was seen that increasing, the efficiency of a factory communication system increases the degree of adherence of a job to the expected schedule. The performance of downstream production flow fed from the parallel upstream flow of information on the factory state was increased.

Keywords: information entropy, communication in manufacturing, mass customisation, scheduling

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Authors: Bulcha Belay Etana, Benny Malengier, Debelo Oljira, Janarthanan Krishnamoorthy, Lieva Vanlangenhove

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Electromyography (EMG) is a technique used to measure the electrical activity of muscles. EMG can be used to assess muscle function in a variety of settings, including clinical, research, and sports medicine. The aim of this study was to develop a wearable textile sensor for EMG monitoring. The sensor was designed to be soft, stretchable, and washable, making it suitable for long-term use. The sensor was fabricated using a conductive thread material that was embroidered onto a fabric substrate. The sensor was then connected to a microcontroller unit (MCU) and a Wi-Fi-enabled module. The MCU was programmed to acquire the EMG signal and transmit it wirelessly to the Wi-Fi-enabled module. The Wi-Fi-enabled module then sent the signal to a server, where it could be accessed by a computer or smartphone. The sensor was able to successfully acquire and transmit EMG signals from a variety of muscles. The signal quality was comparable to that of commercial EMG sensors. The development of this sensor has the potential to improve the way EMG is used in a variety of settings. The sensor is soft, stretchable, and washable, making it suitable for long-term use. This makes it ideal for use in clinical settings, where patients may need to wear the sensor for extended periods of time. The sensor is also small and lightweight, making it ideal for use in sports medicine and research settings. The data for this study was collected from a group of healthy volunteers. The volunteers were asked to perform a series of muscle contractions while the EMG signal was recorded. The data was then analyzed to assess the performance of the sensor. The EMG signals were analyzed using a variety of methods, including time-domain analysis and frequency-domain analysis. The time-domain analysis was used to extract features such as the root mean square (RMS) and average rectified value (ARV). The frequency-domain analysis was used to extract features such as the power spectrum. The question addressed by this study was whether a wearable textile sensor could be developed that is soft, stretchable, and washable and that can successfully acquire and transmit EMG signals. The results of this study demonstrate that a wearable textile sensor can be developed that meets the requirements of being soft, stretchable, washable, and capable of acquiring and transmitting EMG signals. This sensor has the potential to improve the way EMG is used in a variety of settings.

Keywords: EMG, electrode position, smart wearable, textile sensor, IoT, IoT-integrated textile sensor

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Authors: Sabrina Howard

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'Window Seat' uses public transportation as an entry point for understanding the relationship between public space, politics, and social identity construction. This project argues that by bringing people of different races, classes, and genders in 'contact' with one another, public transit operates as a site of exposure, as people consciously and unconsciously perform social identity within these spaces. These performances offer a form of freedom that we associate with being in urban spaces while simultaneously rendering certain racialized, gendered, and classed bodies vulnerable to violence. Furthermore, due to its exposing function, public transit operates as a site through which we, as urbanites and scholars, can read social injustice and reflect on the work that is necessary to become a truly democratic society. The major questions guiding this research are: How does using public transit as the entry point provide unique insights into the relationship between social identity, politics, and public space? What ideas do Americans hold about public space and how might these ideas reflect a liberal yearning for a more democratic society? To address these research questions, 'Window Seat' critically examines ethnographic data collected on public buses and trains in Los Angeles, California, and online news media. It analyzes these sources through literature in socio-cultural psychology, sociology, and political science. It investigates the 'everyday urban hero' narrative or popular news stories that feature an individual or group of people acting against discriminatory or 'Anti-American' behavior on public buses and trains. 'Window Seat' studies these narratives to assert that by circulating stories of civility in news media, United Statsians construct and maintain ideas of the 'liberal city,' which is characterized by ideals of freedom and democracy. Furthermore, for those involved, these moments create an opportunity to perform the role of the Good Samaritan, an identity that is wrapped up in liberal beliefs in diversity and inclusion. This research expands conversations in urban studies by making a case for the political significance of urban public space. It demonstrates how these sites serve as spaces through which liberal beliefs are circulated and upheld through identity performance.

Keywords: social identity, public space, public transportation, liberalism

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Authors: Rumeysa Gunduz, Dirk Koester, Thomas Schack

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Previous behavioral studies have shown that working memory (WM) and manual actions share limited capacity cognitive resources, which in turn results in execution costs of manual actions in WM. However, to the best of our knowledge, there is no study investigating the neurophysiology of execution costs. The current study aims to fill this research gap investigating the neurophysiology of execution costs of grasping in WM phases (encoding, maintenance, retrieval) considering verbal and visuospatial domains of WM. A WM-grasping dual task paradigm was implemented to examine execution costs. Baseline single task required performing verbal or visuospatial version of a WM task. Dual task required performing the WM task embedded in a high precision grasp to place task. 30 participants were tested in a 2 (single vs. dual task) x 2 (visuo-spatial vs. verbal WM) within subject design. Event related potentials (ERPs) were extracted for each WM phase separately in the single and dual tasks. Memory performance for visuospatial WM, but not for verbal WM, was significantly lower in the dual task compared to the single task. Encoding related ERPs in the single task revealed different ERPs of verbal WM and visuospatial WM at bilateral anterior sites and right posterior site. In the dual task, bilateral anterior difference disappeared due to bilaterally increased anterior negativities for visuospatial WM. Maintenance related ERPs in the dual task revealed different ERPs of verbal WM and visuospatial WM at bilateral posterior sites. There was also anterior negativity for visuospatial WM. Retrieval related ERPs in the single task revealed different ERPs of verbal WM and visuospatial WM at bilateral posterior sites. In the dual task, there was no difference between verbal WM and visuospatial WM. Behavioral and ERP findings suggest that execution of grasping shares cognitive resources only with visuospatial WM, which in turn results in domain specific execution costs. Moreover, ERP findings suggest unique patterns of costs in each WM phase, which supports the idea that each WM phase reflects a separate cognitive process. This study not only contributes to the understanding of cognitive principles of manual action control, but also contributes to the understanding of WM as an entity consisting of separate modalities and cognitive processes.

Keywords: dual task, grasping execution, neurophysiology, working memory domains, working memory phases

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Authors: Abdulmutallib U. Baraya, Aishatu M. Chadi, Zainab A. Aliyu, Agatha Samson

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Learning Resources is the field of study that investigates the process of analyzing, designing, developing, implementing, and evaluating learning materials, learners, and the learning process in order to improve teaching and learning in university-level education essential for empowering students and various sectors of Nigeria’s economy to succeed in a fast-changing global economy. Innovation in the information age of the 21st century is the use of educational technologies in the classroom for instructional delivery, it involves the use of appropriate educational technologies like smart boards, computers, projectors and other projected materials to facilitate learning and improve performance. The study examined learning resources as determinants for improving the teaching and learning process in Abubakar Tafawa Balewa University (ATBU), Bauchi, Bauchi state of Nigeria. Three objectives, three research questions and three null hypotheses guided the study. The study adopted a Survey research design. The population of the study was 880 lecturers. A sample of 260 was obtained using the research advisor table for determining sampling, and 250 from the sample was proportionately selected from the seven faculties. The instrument used for data collection was a structured questionnaire. The instrument was subjected to validation by two experts. The reliability of the instrument stood at 0.81, which is reliable. The researchers, assisted by six research assistants, distributed and collected the questionnaire with a 75% return rate. Data were analyzed using mean and standard deviation to answer the research questions, whereas simple linear regression was used to test the null hypotheses at a 0.05 level of significance. The findings revealed that physical facilities and digital technology tools significantly improved the teaching and learning process. Also, consumables, supplies and equipment do not significantly improve the teaching and learning process in the faculties. It was recommended that lecturers in the various faculties should strengthen and sustain the use of digital technology tools, and there is a need to strive and continue to properly maintain the available physical facilities. Also, the university management should, as a matter of priority, continue to adequately fund and upgrade equipment, consumables and supplies frequently to enhance the effectiveness of the teaching and learning process.

Keywords: education, facilities, learning-resources, technology-tools

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Authors: Agus Hariyadi, Hiroatsu Fukuda

Abstract:

In tropical country like Indonesia, especially in Jakarta, most of the energy consumption on building is for the cooling system, the second one is from lighting electric consumption. One of the passive design strategy that can be done is optimizing the use of natural light from the sun. In this area, natural light is always available almost every day around the year. Natural light have many effect on building. It can reduce the need of electrical lighting but also increase the external load. Another thing that have to be considered in the use of natural light is the visual comfort from occupant inside the room. To optimize the effectiveness of natural light need some modification of façade design. By using external shading device, it can minimize the external load that introduces into the room, especially from direct solar radiation which is the 80 % of the external energy load that introduces into the building. It also can control the distribution of natural light inside the room and minimize glare in the perimeter zone of the room. One of the horizontal blind that can be used for that purpose is Sudare. It is traditional Japanese blind that have been used long time in Japanese traditional house especially in summer. In its original function, Sudare is used to prevent direct solar radiation but still introducing natural ventilation. It has some physical characteristics that can be utilize to optimize the effectiveness of natural light. In this research, different scale of Sudare will be simulated using EnergyPlus and DAYSIM simulation software. EnergyPlus is a whole building energy simulation program to model both energy consumption—for heating, cooling, ventilation, lighting, and plug and process loads—and water use in buildings, while DAYSIM is a validated, RADIANCE-based daylighting analysis software that models the annual amount of daylight in and around buildings. The modelling will be done in Ladybug and Honeybee plugin. These are two open source plugins for Grasshopper and Rhinoceros 3D that help explore and evaluate environmental performance which will directly be connected to EnergyPlus and DAYSIM engines. Using the same model will maintain the consistency of the same geometry used both in EnergyPlus and DAYSIM. The aims of this research is to find the best configuration of façade design which can reduce the external load from the outside of the building to minimize the need of energy for cooling system but maintain the natural light distribution inside the room to maximize the visual comfort for occupant and minimize the need of electrical energy consumption.

Keywords: façade, natural light, blind, energy

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Authors: M. Ghali, M. Elnimr, G. F. Ali, A. M. Eissa, H. Talaat

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CuIn₃Se₅ material is indexed as ordered vacancy compounds having excellent matching properties with CuInGaSe (CIGS) solar absorber layer. For example, the valence band offset of CuIn₃Se₅ with CIGS is nearly 0.3 eV, and the lattice mismatch is less than 1%, besides the absence of discontinuity in their conduction bands. Thus, CuIn₃Se₅ can work as a passivation layer for repelling holes from CIGS/CdS interface and hence to reduce the interface carriers recombination and consequently enhancing the efficiency of CIGS/CdS solar cells. Theoretically, it was reported earlier that an improvement in the efficiency of p-CIGS-based solar cell with a thin ~100 nm of n-CuIn₃Se₅ layer is expected. Recently, a reported experiment demonstrated significant improvement in the efficiency of Molecular Beam Epitaxy (MBE) grown CIGS solar cells from 13.4 to 14.5% via inserting a thin layer of MBE-grown Cu(In,Ga)₃Se₅ layer at the CdS/CIGS interface. It should be mentioned that CuIn₃Se₅ material in either bulk or thin film form, are usually fabricated by high vacuum physical vapor deposition techniques (e.g., three-source co-evaporation, RF sputtering, flash evaporation, and molecular beam epitaxy). In addition, achieving photosensitive films of n-CuIn₃Se₅ material is important for new hybrid organic/inorganic structures, where inorganic photo-absorber layer, with n-type conductivity, can form n–p junction with organic p-type material (e.g., conductive polymers). A detailed study of the physical properties of CuIn₃Se₅ is still necessary for better understanding of device operation and further improvement of solar cells performance. Here, we report on the low-cost synthesis of CuIn₃Se₅ material in nano-scale size, with an average diameter ~10nm, using simple solution-based colloidal chemistry. In contrast to traditionally grown bulk tetragonal CuIn₃Se₅ crystals using high Vacuum-based technology, our colloidal CuIn₃Se₅ nanocrystals show cubic crystal structure with a shape of nanoparticles and band gap ~1.33 eV. Ink-coated thin films prepared from these nanocrystals colloids; display n-type character, 1.26 eV band gap and strong photo-responsive behavior with incident white light. This suggests the potential use of colloidal CuIn₃Se₅ as an active layer in all-solution-processed thin film solar cells.

Keywords: nanocrystals, CuInSe, thin film, optical properties

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Authors: Jelena Nedeljković Trailović, Marko Vasiljević, Jog Raj, Hunor Farkaš, Branko Petrujkić, Stamen Radulović, Gorana Popvić

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The experiment was performed on a total of 99 one-day-old broilers of Cob 500 provenance, which were divided into IX equal groups. Broilers of the E-I group were fed 0.25 mg T-2 toxin/kg feed, E-II and E-III groups 0.25 mg T-2 toxin/kg feed with the addition of 1 kg/t and 3 kg/t of the mycotoxin detoxification agent MDA, respectively. The E-IV group received 1 mg of T-2 toxin/kg of feed, and the broilers of E-V and E-VI groups received 1 mg of T-2 toxin/kg of feed with the addition of 1 kg/t and 3 kg/t of the MDA detoxification preparation, respectively. The E-VII group received commercial feed without toxins and additives, the E-VIII and E-IX groups received feed with 1kg/t and 3kg/t of the MDA detoxification preparation. The trial lasted 42 days. Observing the results obtained on the 42nd day of the experiment, we can conclude that the change in the absolute mass of the spleen occurred in the broilers of the E-IV group (1.66±0.14)g, which was statistically significantly lower compared to the broilers of the E-V and E-VI groups (2.58±0.15 and 2.68±0.23)g. Heart mass was significantly statistically lower in broilers of group E-IV (9.1±0.38)g compared to broilers of group E-V and E-VI (12.23±0.5 and 11.43±0.51)g. It can be concluded that the broilers that received 1 kg/t and 3 kg/t of the detoxification preparation had an absolute mass of organs within physiological limits. Broilers of the E-IV group achieved the lowest BM during the experiment (on the 42nd day of the experiment 1879±52.73)g, they were significantly statistically lower than the BW of broilers of all experimental groups. This trend is observed from the beginning to the end of the experiment. The protective effect of the detoxification preparation can be seen in broilers of the E-V group, that had a significantly statistically higher BM on the 42nd day of the experiment (2225±58.81)g compared to broilers of group E-IV. Broilers of E-VIII group (2452±46.71) g, which received commercial feed with the addition of 1 kg/t MDA preparation, had the highest BMI at the end of the experiment. At the end of the trial on the 42nd day, blood samples were collected from broilers of the experimental groups that received T-2 toxin and MR detoxification preparations in different concentrations. Also, liver and breast musculature samples were collected for testing for the presence and content of T-2 toxin, HT-2 toxin, T-2 tetraol and T-2 triol. Due to very rapid elimination from the blood, no remains of T-2 toxin and its metabolites were detected in the blood of broilers of groups E-I to E-VI. In the breast muscles, T-2 toxin residues below LoQ < 0.2 (μg/kg) were detected in all groups that received T-2 toxin in food, the highest value was recorded in the E-IV group (0.122 μg/kg and the lowest in E -VI group 0.096 μg/kg). No T-2 toxin residues were detected in the liver. Remains of HT-2 were detected in the breast muscles and livers of broilers from E-IV, E-V and E-VI groups, LoQ < 1 (μg/kg); for the breast muscles: 0.054, 0.044 and 0.041 μg/kg, and for the liver: 0.473, 0.231 and 0.185 μg/kg. Summing up all the results, a partial protective effect of the detoxification preparation, added to food in the amount of 1kg/t, can be seen.

Keywords: T-2 toxin, bloiler, MDA, mycotoxuns

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Authors: Arzu K. Harmanci Seren, Burcu Alacam, Serap Altuntas, Ulku Baykal

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Organizational justice has been evaluated as a concept related to rules developed with regards to distributing gains and making decisions of distribution such as duty, goods, service, reward, punishment, fee, organizational position, opportunity or role among those working in that organization, and to social norms on which these rules are based. Studies of organizational justice are crucial for analyzing the organizational life. It is considered that organization justice will be positively influential upon organizational behaviours such as employees’ level of work satisfaction, their performance, and behaviours of organization citizenship, management by values perception, tendency towards cooperation, and towards quitting their jobs. However, when the literature related to health and nurse management is examined, authors could not reach enough findings related to the influence of nurses’ perception of organizational justice upon the perception of management and the intention of quitting in accordance with the values. For that reason, this study has been carried out with the purpose of determining the influence of nurses’ perception of organizational justice upon the perception of management and the intention of quitting in accordance with the values. The study has been carried out with 176 nurses working in a university hospital in Istanbul and a private hospital who accepted to take part in the study, and it is definitive and relation-seeking. Before the data has been collected, ethics committee approval and institutional permissions have been taken, Organizational Justice Scale, Management by Values, Intention to Leave Scale with a questionnaire including 8 questions that aims at defining the personal and professional characteristics of the nurses have been used as a means of data collection. The data collected between 1 May and 20 June 2016 have been evaluated by the researchers in a computer via definitive, relation-seeking and psychometric statistic. As a result of the study, it has been determined that most of the nurses are working in a university hospital (70.5%), that they are 30 and over (49.4%), women (91.5%), single (52.8%) and have a Bachelor’s Degree (48.3%), working in a surgery unit (17.6), have 5 year or less institutional experience (44.9%), 11 year or more professional experience. Cronbach alpha values of the scales used in this study are .94, .95 and .56. Nurses’ average scores of Organizational Justice Scale is M= 3.35±.96, Management by Values Scale is M=3.30±.74, Intention to Leave Scale is M=8.36±3.14. As a result of the analysis carried out in order to determine the influence of nurses’ perception of organizational justice upon the perception of management and the intention of quitting in accordance with the values, it has been pointed out that the Perception of Organizational Justice influenced the perception of Management by Values positively, Intention to Leave negatively.

Keywords: intention to leave, management by values, nursing, organizational justice

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Authors: Hirbod Varasteh, Hamidreza Gohari Darabkhani

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The global warming and its impact on climate change is one of main challenges for current century. Global warming is mainly due to the emission of greenhouse gases (GHG) and carbon dioxide (CO₂) is known to be the major contributor to the GHG emission profile. Whilst the energy sector is the primary source for CO₂ emission, Carbon Capture and Storage (CCS) are believed to be the solution for controlling this emission. Oxyfuel combustion (Oxy-combustion) is one of the major technologies for capturing CO₂ from power plants. For gas turbines, several Oxy-combustion power cycles (Oxyturbine cycles) have been investigated by means of thermodynamic analysis. NetPower cycle is one of the leading oxyturbine power cycles with almost full carbon capture capability from a natural gas fired power plant. In this manuscript, sensitivity analysis of the heat exchanger design in NetPower cycle is completed by means of process modelling. The heat capacity variation and supercritical CO₂ with gaseous admixtures are considered for multi-zone analysis with Aspen Plus software. It is found that the heat exchanger design has a major role to increase the efficiency of NetPower cycle. The pinch-point analysis is done to extract the composite and grand composite curve for the heat exchanger. In this paper, relationship between the cycle efficiency and the minimum approach temperature (∆Tmin) of the heat exchanger has also been evaluated.  Increase in ∆Tmin causes a decrease in the temperature of the recycle flue gases (RFG) and an overall decrease in the required power for the recycled gas compressor. The main challenge in the design of heat exchangers in power plants is a tradeoff between the capital and operational costs. To achieve lower ∆Tmin, larger size of heat exchanger is required. This means a higher capital cost but leading to a better heat recovery and lower operational cost. To achieve this, ∆Tmin is selected from the minimum point in the diagrams of capital and operational costs. This study provides an insight into the NetPower Oxy-combustion cycle’s performance analysis and operational condition based on its heat exchanger design.

Keywords: carbon capture and storage, oxy-combustion, netpower cycle, oxy turbine cycles, zero emission, heat exchanger design, supercritical carbon dioxide, oxy-fuel power plant, pinch point analysis

Procedia PDF Downloads 204