Search results for: engine performance analysis

Authors: Ulriikka Savela-Huovinen, Hanni Muukkonen, Auli Toom

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The development of workplace as a learning environment has been emphasized in research field of workplace learning. The prior literature on sensory performance emphasized the individual’s competences as assessor, while the competences in the collaborative interactional and knowledge creation practices as workplace learning method are not often mentioned. In the present study aims to find out what kinds of competences and contexts are central when assessor conducts food sensory evaluation in authentic professional context. The aim was to answer the following questions: first, what kinds of competences does sensory evaluation require according to assessors? And second, what kinds of contexts for sensory evaluation do assessors report? Altogether thirteen assessors from three Finnish food companies were interviewed by using semi-structural thematic interviews to map practices and development intentions as well as to explicate already established practices. The qualitative data were analyzed by following the principles of abductive and inductive content analysis. Analysis phases were combined and their results were considered together as a cross-analysis. When evaluated independently required competences were perception, knowledge of specific domains and methods and cognitive skills e.g. memory. Altogether, 42% of analysis units described individual evaluation contexts, 53% of analysis units described collaborative interactional contexts, and 5% of analysis units described collaborative knowledge creation contexts. Related to collaboration, analysis reviewed learning, sharing and reviewing both external and in-house consumer feedback, developing methods to moderate small-panel evaluation and developing product vocabulary collectively between the assessors. Knowledge creation contexts individualized from daily practices especially in cases product defects were sought and discussed. The study findings contribute to the explanation that sensory assessors learn extensively from one another in the collaborative interactional and knowledge creation context. Assessors learning and abilities to work collaboratively in the interactional and knowledge creation contexts need to be ensured in the development of the expertise.

Keywords: assessor, collaboration, competences, contexts, learning and practices, sensory evaluation

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Authors: Ng'etich K. Stephen

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Abstract—Globally analytics in basketball has advanced tremendously in the last decade. Organizations are leveraging the insights to improve team and player performance and, in the long run, generate revenue out of it. Due to limited basketball game-related statistics in African competitions, teams are unaware of how they compete with other continental basketball teams. The purpose of this study is to evaluate the regional difference in basketball game-related statistics between African teams that played in the newly formed league, the basketball African league and the European league. The basketball African league, a competition created through the partnership between NBA and FIBA, offers a good starting point since it has valuable basketball metrics to analyze. This study sought to use multivariate linear discriminant analysis to identify the game-related statistics that discriminate the teams in Euro league and the basketball African league.

Keywords: basketball africa league, basketball, euroleague, fiba, africa

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Authors: Nafsi K. Ashraf, C. V. Vipin, V. Anantha Subramanian

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This paper reports the design of a waterjet propulsion system for an amphibious vehicle based on circulation distribution over the camber line for the sections of the impeller and stator. In contrast with the conventional waterjet design, the inlet duct is straight for water entry parallel and in line with the nozzle exit. The extended nozzle after the stator bowl makes the flow more axial further improving thrust delivery. Waterjet works on the principle of volume flow rate through the system and unlike the propeller, it is an internal flow system. The major difference between the propeller and the waterjet occurs at the flow passing the actuator. Though a ducted propeller could constitute the equivalent of waterjet propulsion, in a realistic situation, the nozzle area for the Waterjet would be proportionately larger to the inlet area and propeller disc area. Moreover, the flow rate through impeller disk is controlled by nozzle area. For these reasons the waterjet design is based on pump systems rather than propellers and therefore it is important to bring out the characteristics of the flow from this point of view. The analysis is carried out using computational fluid dynamics. Design of waterjet propulsion is carried out adapting the axial flow pump design and performance analysis was done with three-dimensional computational fluid dynamics (CFD) code. With the varying environmental conditions as well as with the necessity of high discharge and low head along with the space confinement for the given amphibious vehicle, an axial pump design is suitable. The major problem of inlet velocity distribution is the large variation of velocity in the circumferential direction which gives rise to heavy blade loading that varies with time. The cavitation criteria have also been taken into account as per the hydrodynamic pump design. Generally, waterjet propulsion system can be parted into the inlet, the pump, the nozzle and the steering device. The pump further comprises an impeller and a stator. Analytical and numerical approaches such as RANSE solver has been undertaken to understand the performance of designed waterjet propulsion system. Unlike in case of propellers the analysis was based on head flow curve with efficiency and power curves. The modeling of the impeller is performed using rigid body motion approach. The realizable k-ϵ model has been used for turbulence modeling. The appropriate boundary conditions are applied for the domain, domain size and grid dependence studies are carried out.

Keywords: amphibious vehicle, CFD, impeller design, waterjet propulsion

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Authors: Md. Maksudur Rahman Khan, Chee Wai Woon, Huei Ruey Ong, Vignes Rasiah, Chin Kui Cheng, Kar Min Chan, E. Baranitharan

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The present work represents a preparation of non-precious iron-based electrocatalyst (FeN) for ORR in air-cathode microbial fuel cell by pyrolysis treatment. Iron oxalate which recovered from the industrial wastewater and Phenanthroline (Phen) were used as the iron and nitrogen precursors, respectively in preparing FeN catalyst. The performance of as prepared catalyst (FeN) was investigated in a single chambered air cathode MFC in which anaerobic sludge was used as inoculum and palm oil mill effluent as substrate. The maximum open circuit potential (OCV) and the highest power density recorded were 0.543 V and 4.9 mW/m2, respectively. Physical characterization of FeN was elucidated by using Brunauner Emmett Teller (BET), X-Ray Diffraction (XRD) analysis and Field Emission Scanning Electron Microscopy (FESEM) while the electrochemical properties were characterized by cyclic voltammetry (CV) analysis. The presence of biofilm on anode surface was examined using FESEM and confirmed using Infrared Spectroscopy and Thermogravimetric Analysis. The findings of this study demonstrated that FeN is electrochemically active and further modification is needed to increase the ORR catalytic activity.

Keywords: iron based catalyst, microbial fuel cells, oxygen reduction reaction, palm oil mill effluent

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Authors: A. Al-Rashidi, A. El-Hamalawi

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In this paper, a performance study was conducted to investigate single and dual-axis PV systems to generate electricity in five different sites in Kuwait. Relevant data were obtained by using two sources for validation purposes. A commercial software, PVsyst, was used to analyse the data, such as metrological data and other input parameters, and compute the performance parameters such as capacity factor (CF) and final yield (YF). The results indicated that single and dual-axis PV systems would be very beneficial to electricity generation in Kuwait as an alternative source to conventional power plants, especially with the increased demand over time. The ranges were also found to be competitive in comparison to leading countries using similar systems. A significant increase in CF and YF values around 24% and 28.8% was achieved related to the use of single and dual systems, respectively.

Keywords: single-axis and dual-axis photovoltaic systems, capacity factor, final yield, Kuwait

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Authors: M. Ben Slimene, M.-S. Ouali

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Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: artificial Intelligence, clustering, culvert, regression model, slow degradation

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Authors: Deborah O. Ajumobi, Michael Kyobe

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Studies show that women entrepreneurs are constrained and faced with challenges that inhibit the growth and performance of their businesses. However, with their human competencies, mobile technology and the appropriate business strategy, women-led SMEs can steer their businesses to better performance. While the need for SMEs to align these three elements has been suggested, there is limited knowledge on how SMEs can achieve this and no studies to the authors’ knowledge have examined this in women-led SMEs. This study therefore seeks to fill this gap by investigating how Women-led SMEs can best align these three elements to enhance business performance. In light of this, extensive literature review and theoretical work on the phenomenon has been conducted. Given the existence of the interplay between these three elements, we argue that the perspective of alignment as gestalts is most appropriate in determining the best way women-Led SMEs may align these aspects.

Keywords: women-led SMEs, human Competencies, mobile technology, business strategy, alignment

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Authors: Olajumoke Temilola Fatimat, Fasasi Fausat Kikelomo, Ishola Ganiyat Folasayo, Omayeka Mary

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Psychoactive substances are chemical substances that affect the normal functioning of the brain and cause changes in behavior, mood, and consciousness. Psychoactive substance abuse constitutes one of the most important risk–taking behavior among adolescents and young adults in secondary schools. The study, therefore, assessed the factors influencing the use of psychoactive substances among senior secondary students in Ibadan South–West Local Government Area, Oyo State. A descriptive non-experimental design was adopted; purposive and simple random sampling techniques were used to select 330 respondents, while questionnaires were used for data collection. The descriptive statistics of frequency count, percentages, inferential statistics of chi-square, and analysis of variance were used for the analysis. The results revealed that the majority of the respondents had heard of the term substance abuse before 226 (75.3%); it was also revealed that the majority of the respondents had good knowledge of psychoactive substances, 67.8%. There was no significant relationship between age and knowledge of psychoactive substances among senior secondary students, with a p-value of 0.199. The outcome of this study indicates that drug abuse is increasing day by day among secondary school students and may have greatly contributed to poor performance in examinations as well as undermining academic ability and performance among students. It was recommended that efforts should be made by the school authorities of the secondary schools in Ibadan South–West Local Government Area, Oyo State, and in Oyo State generally in collaboration with health personnel to educate adolescents on psychoactive substance abuse. This is to ensure that adolescents are adequately educated and updated on knowledge of psychoactive substance abuse.

Keywords: factors, influence, psychoactive substance, secondary school

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Authors: Olivia A. Kjorlien, Maryam Asghari, Farshid Alizadeh-Shabdiz

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The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case.

Keywords: accuracy, false positives, mosaic augmentation, object detection, YOLOV4, YOLOV4-Tiny

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Authors: Mohammad Soliman, Mohamed A. Abou-Shouk

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Knowledge management focuses on the development, storage, retrieval, and dissemination of information and expertise. It has become an important tool to improve performance in tourism enterprises. This includes improving decision-making, developing customer services, and increasing sales and profits. Knowledge management adoption depends on human, organizational and technological factors. This study aims to explore the concept of knowledge management in travel agents in Egypt. It explores the requirements of adoption and its impact on performance in these agencies. The study targets Category A travel agents in Egypt. The population of the study encompasses Category A travel agents having online presence. An online questionnaire is used to collect data from managers of travel agents. This study is useful for travel agents who are in urgent need to restructure their intermediary role and support their survival in the global travel market. The study sheds light on the requirements of adoption and the expected impact on performance. This could help travel agents identify their situation and the determine the extent to which they are ready to adopt knowledge management. This study is contributing to knowledge by providing insights from the tourism sector in a developing country where the concept of knowledge management is still in its infancy stages.

Keywords: knowledge management, knowledge management adoption, performance, travel agents

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Authors: Zhi Hao Chow, Cian Mulligan, Jack Walsh, Antonio Garzon Vico, Dimitar Krastev

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Drug development is resource-intensive, time-consuming, and increasingly expensive with each developmental stage. The success rates of drug development are also relatively low, and the resources committed are wasted with each failed candidate. As such, a reliable method of predicting the success of drug development is in demand. The hypothesis was that some examples of failed drug candidates are pushed through developmental pipelines based on false confidence and may possess common linguistic features identifiable through sentiment analysis. Here, the concept of using text analysis to discover such features in research publications and investor reports as predictors of success was explored. R studios were used to perform text mining and lexicon-based sentiment analysis to identify affective phrases and determine their frequency in each document, then using SPSS to determine the relationship between our defined variables and the accuracy of predicting outcomes. A total of 161 publications were collected and categorised into 4 groups: (i) Cancer treatment, (ii) Neurodegenerative disease treatment, (iii) Vaccines, and (iv) Others (containing all other drugs that do not fit into the 3 categories). Text analysis was then performed on each document using 2 separate datasets (BING and AFINN) in R within the category of drugs to determine the frequency of positive or negative phrases in each document. A relative positivity and negativity value were then calculated by dividing the frequency of phrases with the word count of each document. Regression analysis was then performed with SPSS statistical software on each dataset (values from using BING or AFINN dataset during text analysis) using a random selection of 61 documents to construct a model. The remaining documents were then used to determine the predictive power of the models. Model constructed from BING predicts the outcome of drug performance in clinical trials with an overall percentage of 65.3%. AFINN model had a lower accuracy at predicting outcomes compared to the BING model at 62.5% but was not effective at predicting the failure of drugs in clinical trials. Overall, the study did not show significant efficacy of the model at predicting outcomes of drugs in development. Many improvements may need to be made to later iterations of the model to sufficiently increase the accuracy.

Keywords: data analysis, drug development, sentiment analysis, text-mining

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Authors: Dinesh Munjurulimana, Anil Tiwari, Tingwen Li, Carlos Pereira, Sreekanth Pannala, John Waters

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With the rapid transition to electric vehicles (EVs) across the globe, car manufacturers are in need of integrated and lightweight solutions for the battery packs of these vehicles. An integral part of a battery pack is the battery tray, which constitutes a significant portion of the pack’s overall weight. Based on the functional requirements, cost targets, and packaging space available, a range of materials –from metals, composites, and plastics– are often used to develop these battery trays. This paper considers the design and development of integrated thermoplastic-intensive battery trays, using the available packaging space from a representative EV battery pack. Presented as a proposed alternative are multiple concepts to integrate several connected systems such as cooling plates and underbody impact protection parts of a multi-piece incumbent battery pack. The resulting digital prototype was evaluated for several mechanical performance measures such as mechanical shock, drop, crush resistance, modal analysis, and torsional stiffness. The performance of this alternative design is then compared with the incumbent solution. In addition, insights are gleaned into how these novel approaches can be optimized to meet or exceed the performance of incumbent designs. Preliminary manufacturing feasibility of the optimal solution using injection molding and other commonly used manufacturing methods for thermoplastics is briefly explained. Then numerical and analytical evaluations are performed to show a representative Pareto front of cost vs. volume of the production parts. The proposed solution is observed to offer weight savings of up to 40% on a component level and part elimination of up to two systems in the battery pack of a typical battery EV while offering the potential to meet the required performance measures highlighted above. These conceptual solutions are also observed to potentially offer secondary benefits such as improved thermal and electrical isolations and be able to achieve complex geometrical features, thus demonstrating the ability to use the complete packaging space available in the vehicle platform considered. The detailed study presented in this paper serves as a valuable reference for researches across the globe working on the development of EV battery packs – especially those with an interest in the potential of employing alternate solutions as part of a mixed-material system to help capture untapped opportunities to optimize performance and meet critical application requirements.

Keywords: thermoplastics, lightweighting, part integration, electric vehicle battery packs

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Authors: Shrikaant Kulkarni, Akshata Naik Nimbalkar

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In this worki a method protocol has been developed for the synthesis of innovative Co3O4 material by using a method of chemical synthesis followed by calcination. The effect of calcination temperature on the morphology, structure and catalytic performance on material in question is investigated by using characterization tools like scanning electron microscopy (SEM), X-ray diffraction (XRD) spectroscopy and electrochemical techniques. The SEM images reveal that the morphology of the Co3O4 material undergoes a change from the rod to a beadlike shape on calcination at temperature of 700 °C. The XRD image shows that although the morphology of synthesized Co3O4 material exhibits a cubic phase but it differs in crystallinity depending upon morphology. Similarly spherical beadlike Co3O4 material has exhibited better activity than its rodlike counterpart which is reflected from electrochemical findings. Further, its performance in terms of bifunctional nature and hlods a lot much of promise as a excellent electrode material in the next generation batteries and fuel cells.

Keywords: bifunctional, next generation material, Co3O4, XRD

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Authors: Chen Bo, Wen Zengping

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Based on the results of previous studies, we focus on the research of real ground motion selection and scaling method for structural performance-based seismic evaluation using nonlinear dynamic analysis. The input of earthquake ground motion should be determined appropriately to make them compatible with the site-specific hazard level considered. Thus, an optimized selection and scaling method are established including the use of not only Monte Carlo simulation method to create the stochastic simulation spectrum considering the multivariate lognormal distribution of target spectrum, but also a spectral shape parameter. Its applications in structural fragility analysis are demonstrated through case studies. Compared to the previous scheme with no consideration of the uncertainty of target spectrum, the method shown here can make sure that the selected records are in good agreement with the median value, standard deviation and spectral correction of the target spectrum, and greatly reveal the uncertainty feature of site-specific hazard level. Meanwhile, it can help improve computational efficiency and matching accuracy. Given the important infection of target spectrum’s uncertainty on structural seismic fragility analysis, this work can provide the reasonable and reliable basis for structural seismic evaluation under scenario earthquake environment.

Keywords: ground motion selection, scaling method, seismic fragility analysis, spectral shape

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Authors: A. Bouabid, B. Bielenberg, S. Ainane, N. Pasha

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In this paper, a team of faculty members of the Petroleum Institute in Abu Dhabi, UAE representing six different courses across General Engineering (ENGR), Communication (COMM), and Design (STPS) worked together to establish a clear developmental progression of learning outcomes and performance indicators for targeted knowledge, areas of competency, and skills for the first three semesters of the Bachelor of Sciences in Engineering curriculum. The sequences of courses studied in this project were ENGR/COMM, COMM/STPS, and ENGR/STPS. For each course’s nine areas of knowledge, competency, and skills, the research team reviewed the existing learning outcomes and related performance indicators with a focus on identifying linkages across disciplines as well as within the courses of a discipline. The team reviewed existing performance indicators for developmental progression from semester to semester for same discipline related courses (vertical alignment) and for different discipline courses within the same semester (horizontal alignment). The results of this work have led to recommendations for modifications of the initial indicators when incoherence was identified, and/or for new indicators based on best practices (identified through literature searches) when gaps were identified. It also led to recommendations for modifications of the level of emphasis within each course to ensure developmental progression. The exercise has led to a revised Sequence Performance Indicator Mapping for the knowledge, skills, and competencies across the six core courses.

Keywords: curriculum alignment, horizontal and vertical progression, performance indicators, skill level

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Authors: Milica Z. Karadzic, Lidija R. Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Z. Kovacevic, Anamarija I. Mandic, Katarina Penov-Gasi, Andrea R. Nikolic, Aleksandar M. Okljesa

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In this study, a set of 29 newly synthesized steroid derivatives were investigated using reversed-phase high-performance liquid chromatography (RP-HPLC) as a first step in preselection of drug candidates. This analysis presents an experimental determination of chromatographic lipophilicity, and it was conducted to obtain physicochemical characterization of these molecules. As the most widely used bonded phases in RP-HPLC, octadecyl (C18) and octyl (C8) were used. Binary mixtures of water and acetonitrile or methanol were used as mobile phases. Obtained results were expressed as retention factor values logk and they were correlated with logP values. The results showed that both columns provide good estimations of the chromatographic lipophilicity of the molecules included in this study. This analysis was conducted in order to characterize newly synthesized steroid derivatives for further investigation regarding their antiproliferative and antimicrobial activity. This article is based upon work from COST Action (CM1306), supported by COST (European Cooperation in Science and Technology).

Keywords: antiproliferative activity, chromatographic lipophilicity, liquid chromatography, steroids

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Authors: Harishkumar Kamath, Chandrakant R. Kini, N. Yagnesh Sharma

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Gas turbines are highly effective engineered prime movers for converting energy from thermal form (combustion stage) to mechanical form – are widely used for propulsion and power generation systems. One method of increasing both the power output and thermal efficiency is to increase the temperature of the gas entering the turbine. In the advanced gas turbines of today, the turbine inlet temperature can be as high as 1500°C; however, this temperature exceeds the melting temperature of the metal blade. With modern gas turbines operating at extremely high temperatures, it is necessary to implement various cooling methods, so the turbine blades and vanes endure in the path of the hot gases. Merely passing coolant air through the blade does not provide adequate cooling; therefore, it is necessary to implement techniques that will further enhance the heat transfer from the blade walls. It is seen that by incorporating helical grooved passages into the leading edge built on turbulence and higher flow rates through the passages, the blade can be cooled effectively. It seen from the analysis helical grooved passages with diameter 5 mm, helical pitch of 50 mm and 8 starts results in better cooling of turbine blade and gives the best thermal performance.

Keywords: blade cooling, helical grooves, leading edge, numerical analysis

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Authors: Luana N. Cardozo, Debora A. S. Coutinho, Fabiola Lagher, Bruno J. G. Silva, Ivonilce Venture, Mainara Tesser, Graciela Venera

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Ostrich oil is used as food in Brazil, and it has been the subject of scientific research because it contains essential fatty acids (Omega 3, 6, 7, and 9), which provide benefits to human health. Alkylglycerols are lipid ethers consisted of a saturated or unsaturated hydrocarbon chain joined by ether-type bonding to one of the glycerol hydroxyls. It is known that supplementation with alkylglycerols can act significantly on the functioning of immune system cells, both in pathological situations and in homeostasis. Objective: Analyze the presence of alkylglycerols in ostrich oil. Methods: The ostrich oil was bought from an industry that manufactures the product for sale as food, located in Mirante da Serra, northern Brazil. The samples were sent for analysis to the chemistry department of the Federal University of Paraná, where they were analyzed by the gas chromatography method. Results: The analysis of the ostrich oil presented alkylglycerols in area 514505154. Comparison, it is possible to observe that shark liver oil contains the area 26190196, and the difference between both is highly significant. Conclusion: The importance of alkylglycerol supplementation for the immune system is known. The analysis of the results made it possible to verify the presence of alkylglycerols in the ostrich oil, which is five times higher than in the shark liver oil, that would be the largest source food, but was surpassed by the ostrich oil until the present time. The present study emphasizes that ostrich oil can be considered a food source of alkylglycerols and may play a promising role in the immune system because it contains such substance, but further studies are needed to prove its performance in the body.

Keywords: ostrich oil, nutritional composition, alkylglycerols, food

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Princess Stephanie P. Llanos, Rinlee Butch M. Cervera

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Lithium iron phosphate (LiFePO4) is a potential cathode material for lithium-ion batteries due to its promising characteristics. In this study, carbon-coated nanograined LiFePO4 is synthesized via wet chemistry method at a low temperature of 400 °C and investigated its performance as a cathode in Lithium battery. The X-ray diffraction pattern of the synthesized samples can be indexed to an orthorhombic LiFePO4 structure. Agglomerated particles that range from 200 nm to 300 nm are observed from scanning electron microscopy images. Transmission electron microscopy images confirm the crystalline structure of LiFePO4 and coating of amorphous carbon layer. Elemental mapping using Energy dispersive spectroscopy analysis revealed the homogeneous dispersion of Fe, P, O, and C elements. On the other hand, the electrochemical performances of the synthesized cathodes were investigated using cyclic voltammetry, galvanostatic charge/discharge tests with different C-rates, and cycling performances. Galvanostatic charge and discharge measurements revealed that the sample sintered at 400 °C for 3 hours with carbon coating demonstrated the highest capacity among the samples which reaches up to 160 mAhg⁻¹ at 0.1C rate.

Keywords: cathode, charge-discharge, electrochemical, lithium batteries

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Authors: Muhammad Shehu Garba

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The financial statements of the firms between the periods of 2013 and 2022 were collected using the secondary method of data collection, and the study aims to investigate the effect of ownership structure on the financial performance of listed industrial goods companies in Nigeria. 10 firms were used as the study's sample size. The study used panel data variables of the study. The ownership structure is measured with managerial ownership, institutional ownership and foreign ownership, while financial performance is measured with return on asset and return on equity; the study made use of control variables leverage and firm size. The result shows a multivariate relationship that exists between variables of the study, which shows ROA has a positive correlation with ROE (0.4053), MO (0.2001), and FS (0.3048). It has a negative correlation with FO (-0.1933), IO (-0.0919), and LEV (-0.3367). ROE has a positive correlation with ROA (0.4053), MO (0.2001), and FS (0.2640). It has a negative correlation with FO (-0.1864), IO (-0.1847), and LEV (-0.0319). It is recommended that firms should focus on increasing their ROA. Firms should also consider increasing their MO, as this can help to align the interests of managers and shareholders. Firms should also be aware of the potential impact of FO and IO on their ROA.

Keywords: firm size, ownership structure, financial performance, leaverage

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Authors: Ghita Benayad

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Asset allocation, which affects the long-term profitability of portfolios by distributing assets to fulfill a range of investment objectives, is the cornerstone of investment management in the dynamic and complicated world of financial markets. This paper offers a technique for optimizing strategic asset allocation with the goal of improving portfolio performance by addressing the inherent complexity and uncertainty of the market through the use of Principal Component Analysis (PCA) in a multi-objective modeling framework. The study's first section starts with a critical evaluation of conventional asset allocation techniques, highlighting how poorly they are able to capture the intricate relationships between assets and the volatile nature of the market. In order to overcome these challenges, the project suggests a PCA-driven methodology that isolates important characteristics influencing asset returns by decreasing the dimensionality of the investment universe. This decrease provides a stronger basis for asset allocation decisions by facilitating a clearer understanding of market structures and behaviors. Using a multi-objective optimization model, the project builds on this foundation by taking into account a number of performance metrics at once, including risk minimization, return maximization, and the accomplishment of predetermined investment goals like regulatory compliance or sustainability standards. This model provides a more comprehensive understanding of investor preferences and portfolio performance in comparison to conventional single-objective optimization techniques. While applying the PCA-driven multi-objective optimization model to historical market data, aiming to construct portfolios better under different market situations. As compared to portfolios produced from conventional asset allocation methodologies, the results show that portfolios optimized using the proposed method display improved risk-adjusted returns, more resilience to market downturns, and better alignment with specified investment objectives. The study also looks at the implications of this PCA technique for portfolio management, including the prospect that it might give investors a more advanced framework for navigating financial markets. The findings suggest that by combining PCA with multi-objective optimization, investors may obtain a more strategic and informed asset allocation that is responsive to both market conditions and individual investment preferences. In conclusion, this capstone project improves the field of financial engineering by creating a sophisticated asset allocation optimization model that integrates PCA with multi-objective optimization. In addition to raising concerns about the condition of asset allocation today, the proposed method of portfolio management opens up new avenues for research and application in the area of investment techniques.

Keywords: asset allocation, portfolio optimization, principle component analysis, multi-objective modelling, financial market

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Authors: Jiexuan Lin, Huiyi Chen

Abstract:

The development of L2 pronunciation competence remains understudied in the literature and it is not clear what may influence learners’ development of L2 pronunciation. The present study was an attempt to find out which of the two common factors in L2 acquisition, i.e., foreign language anxiety or self-perceived L2 proficiency, can better predict Chinese EFL learners’ pronunciation performance. 78 first-year English majors, who had received a three-month pronunciation training course, were asked to 1) fill out a questionnaire on foreign language classroom anxiety, 2) self-report their L2 proficiency in general, in speaking and in pronunciation, and 3) complete an oral and a written test on their L2 pronunciation (the score of the oral part indicates participants’ pronunciation proficiency in oral production, and the score of the written part indexes participants’ ability in applying pronunciation knowledge in comprehension.) Results showed that the pronunciation scores were negatively correlated with the anxiety scores, and were positively correlated with the self-perceived pronunciation proficiency. But only the written scores in the L2 pronunciation test, not the oral scores, were positively correlated with the L2 self-perceived general proficiency. Neither the oral nor the written scores in the L2 pronunciation test had a significant correlation with the self-perceived speaking proficiency. Given the fairly strong correlations, the anxiety scores and the self-perceived pronunciation proficiency were put in regression models to predict L2 pronunciation performance. The anxiety factor alone accounted for 13.9% of the variance and the self-perceived pronunciation proficiency alone explained 12.1% of the variance. But when both anxiety scores and self-perceived pronunciation proficiency were put in a stepwise regression model, only the anxiety scores had a significant and unique contribution to the L2 pronunciation performance (4.8%). Taken together, the results suggested that the learners’ anxiety level could better predict their L2 pronunciation performance, compared with the self-perceived proficiency levels. The obtained data have the following pedagogical implications. 1) Given the fairly strong correlation between anxiety and L2 pronunciation performance, the instructors who are interested in predicting learners’ L2 pronunciation proficiency may measure their anxiety level, instead of their proficiency, as the predicting variable. 2) The correlation of oral scores (in the pronunciation test) with pronunciation proficiency, rather than with speaking proficiency, indicates that a) learners after receiving some amounts of training are to some extent able to evaluate their own pronunciation ability, implying the feasibility of incorporating self-evaluation and peer comments in course instruction; b) the ‘proficiency’ measure used to predict pronunciation performance should be used with caution. The proficiency of specific skills seemingly highly related to pronunciation (i.e., speaking in this case) may not be taken for granted as an effective predictor for pronunciation performance. 3) The correlation between the written scores with general L2 proficiency is interesting.

Keywords: anxiety, Chinese EFL learners, L2 pronunciation, self-perceived L2 proficiency

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Authors: Hamed Hossein Afshari, Shahrzad Jalali, Amir Hossein Ghods, Bijan Raahemi

Abstract:

This paper introduces an automated clustering solution that applies to WiFi/Bluetooth sensing data and is later used for traffic management applications. The paper initially summarizes a number of clustering approaches and thereafter shows their performance for noise removal. In this context, clustering is used to recognize WiFi and Bluetooth MAC addresses that belong to passengers traveling by a public urban transit bus. The main objective is to build an intelligent system that automatically filters out MAC addresses that belong to persons located outside the bus for different routes in the city of Ottawa. The proposed intelligent system alleviates the need for defining restrictive thresholds that however reduces the accuracy as well as the range of applicability of the solution for different routes. This paper moreover discusses the performance benefits of the presented clustering approaches in terms of the accuracy, time and space complexity, and the ease of use. Note that results of clustering can further be used for the purpose of the origin-destination estimation of individual passengers, predicting the traffic load, and intelligent management of urban bus schedules.

Keywords: WiFi-Bluetooth sensing, cluster analysis, artificial intelligence, traffic management

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Authors: Marcelo Franzkowiak Stahlschmidt

Abstract:

This paper discusses the experiments carried out based on the wire drawing process analysis and later annealing on lead furnace on a galvanizing line. Using Design of Experiments methodology, the aim of this work is to understand the occurrence of lead entrainment originating from the annealed wires in order to decrease this problem. Wire samples were collected from wire drawing machines and galvanizing line and submitted to surface roughness analysis and its implications on lead drag out based on wire speed, wire diameter, lead bath temperature, thermal capacity of the lead kettle, wire surface condition, wire roughness and wire superficial cleanliness. Proposals to decrease lead drag out were made in order to increase wire drawing machines and galvanizing line performance.

Keywords: wire drawing process, galvanizing, heat treatment, lead

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Authors: Harsha B. R., Anil Kumar K. S.

Abstract:

Climate and soils are the two most dynamic entities among the factors affecting growth and grapes productivity. Studying of prevailing climate over the years in a region provides sufficient information related to management practices to be carried out in vineyards. Evaluating the suitability of vineyard soils under different climatic conditions serves as the yardstick to analyse the performance of grapevines. This study was formulated to study the climate and evaluate the site-suitability of soils in vineyards of southern Karnataka, which has registered its superiority in the quality production of wine. Ten soil profiles were excavated for suitability evaluation of soils, and six taluks were studied for climatic analysis. In almost all the regions studied, recharge starts at the end of the May or June months, peaking in either September or October months. Soil Starts drying from mid of December months in the taluks studied. Bangalore North (Rajanukunte) soils were highly suited for grapes cultivation with no or slight limitations. Bangalore North (GKVK Farm) was moderately suited with slight to moderate limitations of slope and available nitrogen content. Moderate suitability was observed in the rest of the profiles studied in Eastern dry zone soils with the slight to moderate limitations of either organic carbon or available nitrogen or both in the Eastern dry zone. Magadi (Southern dry zone) soils were moderately suitable with slight to moderate limitations of graveliness, available nitrogen, organic carbon, and exchangeable sodium percentage. Sustainable performance of vineyards in terms of yield can be achieved in these taluks by managing the constraints existing in soils.

Keywords: climatic analysis, dry zone, water recharge, growing period, suitability, sustainability

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Authors: D. M. Bassuney, W. A. Ibrahim, Medhat A. E. Moustafa

Abstract:

Appropriate start-up of an anaerobic baffled reactor (ABR) is considered to be the most delicate and important issue in the anaerobic process, and depends on several factors such as wastewater composition, reactor configuration, inoculum and operating conditions. In this work, the start-up performance of an ABR with working volume of 30 liters, fed continuously with synthetic food industrial wastewater along with semi-batch study to measure the methangenic activity by specific methanogenic activity (SMA) test were carried out at various organic loading rates (OLRs) to determine the best OLR used to start up the reactor. The comparison was based on COD removal efficiencies, start-up time, pH stability and methane production. An OLR of 1.8 Kg COD/m3d (5400 gCOD/m3 and 3 days HRT) showed best overall performance with COD removal efficiency of 94.44% after four days from the feeding and methane production of 3802 ml/L with an overall SMA of 0.36 gCH4-COD/gVS.d

Keywords: anaerobic baffled reactor, anaerobic reactor start-up, food industrial wastewater, specific methanogenic activity

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Authors: S. Ahmad, M. Rizwan, B. Ayub, S. Mehmood, P. Akhtar

Abstract:

This experiment was conducted to investigate the effect of supplementation of pure herbal blend on growth performance of boilers. One hundred and twenty birds were randomly distributed into 4 experimental units of 3 replicates (10 birds/replicate) as: negative control (basal diet), positive control (Lincomycin at the rate of 5g/bag), pure herbal blend at the rate of 150g/bag and pure herbal blend at the rate of 300g/bag. The data regarding weekly feed intake, body weight gain and feed conversion ratio were recorded, and fecal samples were collected at the end of starter and finisher phase for nutrient digestibility trial. The results of feed intake showed significant (P < 0.05) results in 1st (305g), 2nd (696.88g), 3rd (1046.9g) and 4th (1173.2g) week and feed conversion ratio indicated significant (P < 0.05) variations in 1st (2.54) and 4th (2.28) week of age. Also, both starter and finisher phase indicated significant (P < 0.05) differences among all treatment groups in feed intake (2023.4g) and (2302.6g) respectively. The statistical analysis indicated significant (P < 0.05) results in crop filling percentage (86.6%) after 2 hours of first feed supplementation. In case of nutrient digestibility trial, results showed significant (P < 0.05) values of crude protein and crude fat in starter phase as 69.65% and 56.62% respectively, and 69.57% and 48.55% respectively, in finisher phase. Based on overall results, it was concluded that the dietary inclusion of pure herbal blend containing neem tree leaves powder, garlic powder, ginger powder and turmeric powder increase the production performance of broilers.

Keywords: neem tree leave, garlic, ginger, herbal blend, broiler

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Authors: Lalit Ahuja, Nancy Das, Yashas Shetty

Abstract:

LED lighting has been increasingly adopted for vehicles in both domestic and foreign automotive markets. Although this miniaturized technology gives the best light output, low energy consumption, and cost-efficient solutions for driving, the same is the need of the hour. In this paper, we present a methodology for driving the highest class two-wheeler headlamp with regulator and rectifier (RR) output. Unlike usual LED headlamps, which are driven by a battery, regulator, and rectifier (RR) driven, a low-cost and highly efficient LED Driver Module (LDM) is proposed. The positive half of magneto output is regulated and used to charge batteries used for various peripherals. While conventionally, the negative half was used for operating bulb-based exterior lamps. But with advancements in LED-based headlamps, which are driven by a battery, this negative half pulse remained unused in most of the vehicles. Our system uses negative half-wave rectified DC output from RR to provide constant light output at all RPMs of the vehicle. With the negative rectified DC output of RR, we have the advantage of pulsating DC input which periodically goes to zero, thus helping us to generate a constant DC output equivalent to the required LED load, and with a change in RPM, additional active thermal bypass circuit help us to maintain the efficiency and thermal rise. The methodology uses the negative half wave output of the RR along with a linear constant current driver with significantly higher efficiency. Although RR output has varied frequency and duty cycles at different engine RPMs, the driver is designed such that it provides constant current to LEDs with minimal ripple. In LED Headlamps, a DC-DC switching regulator is usually used, which is usually bulky. But with linear regulators, we’re eliminating bulky components and improving the form factor. Hence, this is both cost-efficient and compact. Presently, output ripple-free amplitude drivers with fewer components and less complexity are limited to lower-power LED Lamps. The focus of current high-efficiency research is often on high LED power applications. This paper presents a method of driving LED load at both High Beam and Low Beam using the negative half wave rectified pulsating DC from RR with minimum components, maintaining high efficiency within the thermal limitations. Linear regulators are significantly inefficient, with efficiencies typically about 40% and reaching as low as 14%. This leads to poor thermal performance. Although they don’t require complex and bulky circuitry, powering high-power devices is difficult to realise with the same. But with the input being negative half wave rectified pulsating DC, this efficiency can be improved as this helps us to generate constant DC output equivalent to LED load minimising the voltage drop on the linear regulator. Hence, losses are significantly reduced, and efficiency as high as 75% is achieved. With a change in RPM, DC voltage increases, which can be managed by active thermal bypass circuitry, thus resulting in better thermal performance. Hence, the use of bulky and expensive heat sinks can be avoided. Hence, the methodology to utilize the unused negative pulsating DC output of RR to optimize the utilization of RR output power and provide a cost-efficient solution as compared to costly DC-DC drivers.

Keywords: class D LED headlamp, regulator and rectifier, pulsating DC, low cost and highly efficient, LED driver module

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Authors: Varsha Hemant Patil, Swati A. Bhavsar, Abolee H. Patil

Abstract:

Image quality assessment plays an important role in image evaluation. This paper aims to present an investigation of classic techniques in use for image quality assessment, especially for super-resolution imaging. Researchers have contributed a lot towards the development of super-resolution imaging techniques. However, not much attention is paid to the development of metrics for testing the performance of developed techniques. In this paper, the study report of existing image quality measures is given. The paper classifies reviewed approaches according to functionality and suitability for super-resolution imaging. Probable modifications and improvements of these to suit super-resolution imaging are presented. The prime goal of the paper is to provide a comprehensive reference source for researchers working towards super-resolution imaging and suggest a better framework for measuring the performance of super-resolution imaging techniques.

Keywords: interpolation, MSE, PSNR, SSIM, super resolution

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