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Authors: Abro Zain-Ul-Aabdin, Naheed Baloch, Khuhro Niaz Hussain, Waseem Akbar, Noor Abid Saeed

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The Melon Fruit Fly Bactrocera cucurbitae (Coq.) belongs to family: Tephritidae order: Diptera and is distributed throughout the vegetable growing areas of Pakistan. The B. cucurbitae is injurious pest of more than 125 species of the vegetables throughout the world. In the present studies we investigated the population of this important pest in cucurbit crops and influence of abiotic parameters such as: temperature, relative humidity and rainfall. The study was carried out at two different locations of District, Hyderabad. The locations were Jeay Shah and Dehli farm where three cucurbit vegetable crops, such as bottle gourd (Lagenaria siceraria), bitter gourd (Momordica charantia) and ridge gourd (Luffa acutangula) were grown. The traps were baited with Cue-lure and deployed at three meter height in the all locations from 01.01.2015 and up to 30.06.2015. Results revealed that overall significantly higher (P < 0.05) population was recorded on L.acutangula, M.charantia and L.siceraria (130.64, 127.21, and 122.91), respectively. However, significantly higher (P < 0.05) population was observed on L. acutangula (339.4±22.59) during the 4th week of May 2015 followed by M. charantia (334.6±22.76) L. siceraria (333.2±20.13). Whereas; lowest population was recorded on L. siceraria (5.8±1.39) followed by L. acutangula and M. charantia (6.8±0.80g, 8.0±1.30) respectively during the 4th week of January. The population of B. cucurbitae was significantly correlated with the temperature while negatively correlated with relative humidity. Meanwhile in the parasitism preference experiment pupal parasitoid Dirhinus giffardii showed significantly higher (P<0.05) parasitization when the pupae of B.cucurbitae were reared on Cucumber (Cucumis sativus) (24.8±0.48) and also female were yielded from pupae reared on C.sativus under no choice experiment. Similarly higher parasitization and female were recovered when pupae were supplied C. sativus under free choice experiment. Results of the present investigation would be useful in developing a sustainable pest management strategy in the vegetable agro-ecosystem.

Keywords: Dirhinus giffardii, Bactrocera cucurbitae Cucumis sativus, diptera, free choice, parasitization

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Authors: Alicja Sułek, Grażyna Cacak-Pietrzak, Marta Wyzińska, Anna Nieróbca

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In 2013/2014 and 2014/2015, a field experiment was conducted in two locations: Osiny and Wielichowo (Poland). The two-factor experiment was based on the method of randomized subblocks, in three replications. The first factor (A) was dose of nitrogen fertilization (two levels). The second factor (B) was nine winter wheat cultivars. It was found that winter wheat cultivars exhibited different reactions to higher nitrogen fertilization depending on the years and localities. Only KWS Dacanto cultivar under all growing conditions showed a significant increase in grain yield after the application of a higher level of nitrogen fertilization. The increase in nitrogen fertilization influenced the increase in gluten proteins content in wheat grain, but these changes were statistically significant only in the first year of the study. The quality of gluten does not depend on nitrogen fertilization. The quality of wheat grain depends on cultivars.

Keywords: fertilization, grain quality, winter wheat, yield

Procedia PDF Downloads 185

Authors: Ana L. Gonzalez-Celis, Edgardo Ruiz-Carrillo, Karina Reyes-Jarquin, Margarita Chavez-Becerra

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Since recent decades, the aging has been viewed from a more positive perspective, where is not only about losses and damage, but also about being on a stage where you can enjoy life and live with well-being and quality of life. The challenge to feel better is to find those resources that seniors have. For that reason, psychological well-being has shown interest in the study of the affect and life satisfaction (hedonic well-being), while from a more recent tradition, focus on the development of capabilities and the personal growth, considering both as the main indicators of the quality of life. A resource that can be used in the later age is generativity, which refers to the ability of older people to develop and grow through activities that contribute with the improvement of the context in which they live and participate. In this way the generative interest is understood as a favourable attitude that contribute to the common benefit while strengthening and enriching the social institutions, to ensure continuity between generations and social development. On the other hand, generative behavior, differentiating from generative interest, is the expression of that attitude reflected in activities that make a social contribution and a benefit for generations to come. Hence the purpose of the research was to test if there is an association between the generative behaviour type and the psychological well-being with their dimensions. For this reason 188 Mexican adults from 60 to 94 years old (M = 69.78), 67% women, 33% men, completed two instruments: The Ryff’s Well-Being Scales to measure psychological well-being with 39 items with two dimensions (Hedonic and Eudaimonic well-being), and the Loyola’s Generative Behaviors Scale, grouped in five categories: Knowledge transmitted to the next generation, things to be remember, creativity, be productive, contribution to the community, and responsibility of other people. In addition, the socio-demographic data sheet was tested, and self-reported health status. The results indicated that the psychological well-being and its dimensions were significantly associated with the presence of generative behavior, where the level of well-being was higher when the frequency of some generative behaviour excelled; finding that the behavior with greater psychological well-being (M = 81.04, SD = 8.18) was "things to be remembered"; while with greater hedonic well-being (M = 73.39, SD = 12.19) was the behavior "responsibility of other people"; and with greater Eudaimonic well-being (M = 84.61, SD = 6.63), was the behavior "things to be remembered”. The most important findings highlight the importance of generative behaviors in adulthood, finding empirical evidence that the generativity in the last stage of life is associated with well-being. However, by finding differences in the types of generative behaviors at the level of well-being, is proposed the idea that generativity is not situated as an isolated construct, but needs other contextualized and related constructs that can simultaneously operate at different levels, taking into account the relationship between the environment and the individual, encompassing both the social and psychological dimension.

Keywords: eudaimonic well-being, generativity, hedonic well-being, Mexican elders, psychological well-being

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Ekaterina Posokhova, Boris Gavrilov

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Priority national projects have become an essential phenomenon in the Russian Federation. Both regional and local government institutions and a significant part of the society have been involved in their implementation. The scale and multispectricity of the national projects give a reason to believe that their concept is beyond the scope of the individual state programs. The national project “environment” contains federal projects on waste management, water, and air quality, ecotourism development, and biodiversity conservation highlights the importance of the preservation and restoration of Volga River and Lake Baikal ecosystems. This study assesses the national projects according to their relativeness with the current SDGs (i.e., SGD 14 and 15), evaluates the methodology of the projects. The paper considers the peculiarities of the national projects as strategic management tools as well as the possibility of amending the project objective indicators. Conclusion on the effectiveness of NP in terms of achieving SDGs is provided.

Keywords: management, SDP, russia, conservation, law

Procedia PDF Downloads 127

Authors: Hanane Boutaj, Abdelilah Meddich, Said Wahbi, Zainab El Alaoui-Talibi, Allal Douira, Abdelkarim Filali-Maltouf, Cherkaoui El Modafar

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The present work aims to investigate the effect of arbuscular mycorrhizal fungi (AMF) in improving the olive tree resistance to Verticillium wilt caused by Verticillium dahliae. Inoculated plants with a mycorrhizal autochthonous consortium 'Rhizolive consortium' and pure strain 'Glomus irregulare' were infected after three months with V. dahliae. The improving of olive tree resistance was determined through disease severity, incidence, and defoliation. On the other hand, the defense mechanisms of olive plants were evaluated through lignin content, phenylalanine ammonia lyase (PAL) activity, and polyphenol content. The results revealed that both AMF significantly (p < 0.05) reduced disease development and the rate of defoliation in infected olive plants. Moreover, the contents of lignin were boosted after mycorrhizal inoculation in both the roots and the stems of olive plants, which remained significantly (p < 0.001) higher after the 90th days of V. dahliae inoculation. PAL activity was increased after V. dahliae inoculation in the stems of 'Rhizolive consortium' treatment that were 17 times higher than those in the roots of olive plants. The polyphenol content in the stems was about twice higher than those in the roots. The reduction of disease severity was accompanied by increased levels of lignin content, PAL activity, and polyphenol content, particularly in the stems of olive plants, indicating the strengthening of the olive plant immune system against V. dahliae.

Keywords: olive tree, Mycorrhizal autochthonous consortium, Glomus irregulare, Verticillium dahliae, defense mechanisms

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Authors: Chingwen Yeh, Yu Ru Chen

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This paper aims to explore the growth and development of children in the creative dance class of Cloud Gate Dance School in Taichung Taiwan. Professor Chingwen Yeh’s qualitative research method was applied in this study. First of all, application of Dalcroze Eurhythmic teaching materials such as music, teaching aids, speaking language through classroom situation was collected and exam. Second, the in-class observation on the participation of the young children's learning situation was recorded both by words and on video screen as the research data. Finally, data analysis was categorized into the following aspects: children's body movement coordination, children’s mind concentration and imagination and children’s verbal expression. Through the in-depth interviews with the in-class teachers, parents of participating children and other in class observers were conducted from time to time; this research found the children's body rhythm, language skills, and social learning growth were improved in certain degree through the creative dance training. These authors hope the study can contribute as the further research reference on the related topic.

Keywords: Cloud Gate Dance School, creative dance, Dalcroze, Eurhythmic

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Authors: Jian Huang, Weixin Qian, Hongfang Ma, Haitao Zhang, Weiyong Ying

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Cobalt catalysts were supported on extruded silica carrier and different-type (SiO₂, γ-Al₂O₃) commercial supports with different shapes and sizes to produce heavy hydrocarbons for Fischer-Tropsch synthesis. The catalysts were characterized by N₂ physisorption and H₂-TPR. The catalytic performance of the catalysts was tested in a fixed bed reactor. The results of Fischer-Tropsch synthesis performance showed that the cobalt catalyst supported on spherical silica supports displayed a higher activity and a higher selectivity to C₅⁺ products, due to the fact that the active components were only distributed in the surface layer of spherical carrier, and the influence of gas diffusion restriction on catalytic performance was weakened. Therefore, it can be concluded that the eggshell cobalt catalyst was superior to precious metals modified catalysts in the synthesis of heavy hydrocarbons.

Keywords: fischer-tropsch synthesis, cobalt catalyst, support shape, heavy hydrocarbons

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Authors: Suegnet Smit

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Prior to 1994, special education in South Africa was marginalized and fragmented. Moving away from a Medical model approach to special education, the Government, after 1994, promoted an Inclusive approach, as a means to transform education in general, and special education in particular. This transformation, however, is moving at too a slow pace for learners with barriers to learning and development to benefit fully from their education. The goal of the Department of Basic Education is to minimize, remove, and prevent barriers to learning and development in the educational setting, by attending to the unique needs of the individual learner. However, the implementation of Inclusive education is problematic, and general education remains poor. This paper highlights the historical development of special education in South Africa, underpinned by a bio-ecological perspective. Problematic areas within the systemic levels of the education system are highlighted in order to indicate how the interactive processes within the systemic levels affect special needs learners on the personal dimension of the bio-ecological approach. As part of the methodology, thorough document analysis was conducted on information collected from a large body of research literature, which included academic articles, reports, policies, and policy reviews. Through a qualitative analysis, data were grouped and categorized according to the bio-ecological model systems, which revealed various successes and challenges within the education system. The challenges inhibit change, growth, and development for the child, who experience barriers to learning. From these findings, it is established that special education in South Africa has been, and still is, on a bumpy road. Sadly, the transformation process of change, envisaged by implementing Inclusive education, is still yet a dream, not fully realized. Special education seems to be stuck at what is, and the education system has not moved forward significantly enough to reach what special education should and could be. The gap that exists between a vision of Inclusive quality education for all, and the current reality, is still too wide. Problems encountered in all the education system levels, causes a funnel-effect downward to learners with special educational needs, with negative effects for the development of these learners.

Keywords: bio-ecological perspective, education systems, inclusive education, special education

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Authors: Michael A. Sprayberry, Vincent C. Paquit

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Process parameter optimization in metal powder bed electron beam melting (MPBEBM) is crucial to ensure the technology's repeatability, control, and industry-continued adoption. Despite continued efforts to address the challenges via the traditional design of experiments and process mapping techniques, there needs to be more successful in an on-the-fly optimization framework that can be adapted to MPBEBM systems. Additionally, data-intensive physics-based modeling and simulation methods are difficult to support by a metal AM alloy or system due to cost restrictions. To mitigate the challenge of resource-intensive experiments and models, this paper introduces a Multi-Objective Reinforcement Learning (MORL) methodology defined as an optimization problem for MPBEBM. An off-policy MORL framework based on policy gradient is proposed to discover optimal sets of beam power (P) – beam velocity (v) combinations to maintain a steady-state melt pool depth and phase transformation. For this, an experimentally validated Eagar-Tsai melt pool model is used to simulate the MPBEBM environment, where the beam acts as the agent across the P – v space to maximize returns for the uncertain powder bed environment producing a melt pool and phase transformation closer to the optimum. The culmination of the training process yields a set of process parameters {power, speed, hatch spacing, layer depth, and preheat} where the state (P,v) with the highest returns corresponds to a refined process parameter mapping. The resultant objects and mapping of returns to the P-v space show convergence with experimental observations. The framework, therefore, provides a model-free multi-objective approach to discovery without the need for trial-and-error experiments.

Keywords: additive manufacturing, metal powder bed fusion, reinforcement learning, process parameter optimization

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Authors: Saba Latif

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The study is about exploring the Impact of Classroom Management Techniques on students’ Well-being at the secondary level. The objectives of the study are to identify the classroom management practices of teachers and their impact on students’ achievement. All secondary schools of Lahore city are the population of study. The researcher randomly selected ten schools, and from these schools, two hundred students participated in this study. Data has been collected by using Classroom Management and Students’ Wellbeing questionnaire. Frequency analysis has been applied. The major findings of the study are calculated as follows: The teacher’s instructional activities affect classroom management. The secondary school students' seating arrangement can influence the learning-teaching process. Secondary school students strongly disagree with the statement that the large size of the class affects the teacher’s classroom management. The learning environment of the class helps students participate in question-and-answer sessions. All the activities of the teachers are in accordance with practices in the class. The discipline of the classroom helps the students to learn more. The role of the teacher is to guide, and it enhances the performance of the teacher. The teacher takes time on disciplinary rules and regulations of the classroom. The teacher appreciates them when they complete the given task. The teacher appreciates teamwork in the class.

Keywords: classroom management, strategies, wellbeing, practices

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Ambreen Aslam, Tahira Aziz Mughal, Skye R. Thomas-Hall, Peer M. Schenk

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Biodiesel is an alternative to petroleum-derived fuel mainly composed of fatty acid from oleaginous microalgae feedstock. Microalgae produced fatty acid methyl esters (FAMEs) as they can store high levels of lipids without competing for food productivity. After lipid extraction and esterification, fatty acid profile from algae feedstock possessed the abundance of fatty acids with carbon chain length specifically C16 and C18. The qualitative analysis of FAME was done by cultivating mix microalgae consortia under three different CO₂ concentrations (1%, 3%, and 5.5%) from a coal fired flue gas. FAME content (280.3 µg/mL) and productivity (18.69 µg/mL/D) was higher under 1% CO₂ (flue gas) as compare to other treatments. Whereas, Mixed C. (F) supplemented with 5.5% CO₂ (50% flue gas) had higher SFA (36.28%) and UFA (63.72%) which improve the oxidative stability of biodiesel. Subsequently, low Iodine value (136.3 gI₂/100g) and higher Cetane number (52) of Mixed C.+P (F) were found to be in accordance with European (EN 14214) standard under 5.5% CO₂ along with 50mM phosphate buffer. Experimental results revealed that sufficient phosphate reduced FAME productivity but significantly enhance biodiesel quality. This research aimed to develop an integrated approach of utilizing flue gas (as CO₂ source) for significant improvement in biodiesel quality under surplus phosphorus. CO₂ sequestration from industrial flue gas not only reduce greenhouse gases (GHG) emissions but also ensure sustainability and eco-friendliness of the biodiesel production process through microalgae.

Keywords: biodiesel analysis, carbon dioxide, coal fired flue gas, FAME productivity, fatty acid profile, fuel properties, lipid content, mixed culture microalgae

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Authors: Khashane Stephen Malatji, Makwalete Johanna Malatji

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Student evaluation of teaching (SET) is the common way of assessing teaching quality at universities and tracing the professional growth of lecturers. The aim of this study was to investigate the role played by student evaluation in the teaching and learning agenda at one South African University. The researchers used a qualitative approach and a case study research design. With regards to data collection, document analysis was used. Evaluation reports were reviewed to monitor the growth of lecturers who were evaluated during the academic years 2020 and 2021 in one faculty. The results of the study reveal that student evaluation remains the most relevant tool to inform the teaching agenda at a university. Lecturers who were evaluated were found to grow academically. All lecturers evaluated during 2020 have shown great improvement when evaluated repeatedly during 2021. Therefore, it can be concluded that student evaluation helps to improve the pedagogical and professional proficiency of lecturers. The study therefore, recommends that lecturers conduct an evaluation for each module they teach every semester or annually in case of year modules. The study also recommends that lecturers attend to all areas that draw negative comments from students in order to improve.

Keywords: students’ voices, teaching agenda, evaluation, feedback, responses

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Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Narjis Zehra

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After automobile revolution 1.0, we have automobile revolution 2.0 standing at the horizon, Autonomous Vehicles (AVs). While the technology is developing into more adaptable form, the conversations around its impact on our cities have already started on multiple scales, from academic institutions and community town halls, to the offices of mayors. In order to explore more the AVs impact on Urban transformation, we first inquire if cities can be redesigned or rebuilt. Secondly, we discuss expectation management for the public and policy in terms of what people think/believe AV technology will deliver, and what the current technological evidence suggests the technology and its adoption will look like. Thirdly, based on these discussions, we take Pittsburgh, PA, as a case study to extrapolate what other cities might need to do in order to prepare themselves for the upcoming technological revolution, that may impact more than just the research institutes. Finally, we conclude by suggesting a political way forward to embed urban design with AV technology for equitable cities of tomorrow.

Keywords: urban design, autonomous vehicles, transformation, policy

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Authors: Andrea Szabó Nagy, János Szabó, Zsófia Csanádi, József Erdős

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The main objective of this study was to assess the seasonal variation of atmospheric polycyclic aromatic hydrocarbon (PAH) concentrations associated with PM10 in an urban site of Győr, Hungary. A total of 112 PM10 aerosol samples were collected in the years of 2012 and 2013 and analyzed for PAHs by gas chromatography method. The total PAH concentrations (sum of the concentrations of 19 individual PAH compounds) ranged from 0.19 to 70.16 ng/m3 with the mean value of 12.29 ng/m3. Higher concentrations of both total PAHs and benzo[a]pyrene (BaP) were detected in samples collected in the heating seasons. Using BaP-equivalent potency index on the carcinogenic PAH concentration data, the local population appears to be exposed to significantly higher cancer risk in the heating seasons. However, the comparison of the BaP and total PAH concentrations observed for Győr with other cities it was found that the PAH levels in Győr generally corresponded to the EU average.

Keywords: air quality, benzo[a]pyrene, PAHs, polycyclic aromatic hydrocarbons

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Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

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Authors: Musa Auwal Mamman, S. G. Isa

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This study was carried out in order to investigate the effectiveness of GeoGebra software in teaching and learning of linear and quadratic equations amongst senior secondary school students in Fagge Local Government Area, Kano State–Nigeria. Five research items were raised in objectives, research questions and hypotheses respectively. A random sampling method was used in selecting 398 students from a population of 2098 of SS2 students. The experimental group was taught using the GeoGebra software while the control group was taught using the conventional teaching method. The instrument used for the study was the mathematics performance test (MPT) which was administered at the beginning and at the end of the study. The results of the study revealed that students taught with GeoGebra software (experimental group) performed better than students taught with traditional teaching method. The t- test was used to analyze the data obtained from the study.

Keywords: GeoGebra Software, mathematics performance, random sampling, mathematics teaching

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Authors: Huanhuan Zhao, Bryony J. James

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The relationship between the non-fat solid ratio and bloom formation in untempered chocolate was investigated using two types of chocolate: model chocolate made of varying cocoa powder ratios (46, 49.5 and 53%) and cocoa butter, and commercial Lindt chocolate with varying cocoa content (70, 85 and 90%). X-ray diffraction and colour measurement techniques were used to examine the polymorphism of cocoa butter and the surface whiteness index (WI), respectively. The polymorphic transformation of cocoa butter was highly correlated with the changes of WI during 30 days of storage since it led to the redistribution of fat within the chocolate matrix and resulted in a bloomed surface. The change in WI indicated a similar bloom rate in the chocolates, but the model chocolates with a higher cocoa powder ratio had more pronounced total bloom. This is due to a higher ratio of non-fat solid particles on the surface resulting in microscopic changes in morphology. The ratio of non-fat solids is an important factor in determining the extent of bloom but not the bloom rate.

Keywords: untempered chocolate, microstructure of bloom, polymorphic transformation, surface whiteness

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Authors: Agni Stylianou-Georgiou, Eliza Pitri

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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: creative problem-based play, educational games, flexible thinking, tertiary education

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Authors: Madhavi-Priya Singh, Liam Lowe, Farouk Arnaout, Ludmilla Pillay, Giordan Perez, Luke Mischker, Steve Costa

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An Emergency Department consultant identified the failure of medical students to complete the task of clerking a patient in its entirety. As six medical students on our first clinical placement, we recognised our own failure and endeavoured to examine why this failure was consistent among all medical students that had been given this task, despite our best motivations as adult learner. Our aim is to understand and investigate the elements which impeded our ability to learn and perform as medical students in the clinical environment, with reference to the prescribed task. We also aim to generate a discussion around the delivery of medical education with potential solutions to these barriers. Six medical students gathered together to have a comprehensive reflective discussion to identify possible factors leading to the failure of the task. First, we thoroughly analysed the delivery of the instructions with reference to the literature to identify potential flaws. We then examined personal, social, ethical, and cultural factors which may have impacted our ability to complete the task in its entirety. Through collation of our shared experiences, with support from discussion in the field of medical education and ethics, we identified two major areas that impacted our ability to complete the set task. First, we experienced an ethical conflict where we believed the inconvenience and potential harm inflicted on patients did not justify the positive impact the patient interaction would have on our medical learning. Second, we identified a lack of confidence stemming from multiple factors, including the conflict between preclinical and clinical learning, perceptions of perfectionism in the culture of medicine, and the influence of upward social comparison. After discussions, we found that the various factors we identified exacerbated the fears and doubts we already had about our own abilities and that of the medical education system. This doubt led us to avoid completing certain aspects of the tasks that were prescribed and further reinforced our vulnerability and perceived incompetence. Exploration of philosophical theories identified the importance of the role of doubt in education. We propose the need for further discussion around incorporating both pedagogic and andragogic teaching styles in clinical medical education and the acceptance of doubt as a driver of our learning. Doubt will continue to permeate our thoughts and actions no matter what. The moral or psychological distress that arises from this is the key motivating factor for our avoidance of tasks. If we accept this doubt and education embraces this doubt, it will no longer linger in the shadows as a negative and restrictive emotion but fuel a brighter dialogue and positive learning experience, ultimately assisting us in achieving our full potential.

Keywords: medical education, clinical education, andragogy, pedagogy

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Authors: Zachary Blanks, Solomon Sonya

Abstract:

Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

Procedia PDF Downloads 274

Authors: Valeri Arghutashvili, Mari Gogochuri

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Tourism industry development is one of the key priorities in Georgia, as it has positive influence on economic activities. Its contribution is very important for the different regions, as well as for the national economy. Benefits of the tourism industry include new jobs, service development, and increasing tax revenues, etc. The main aim of this research is to review and analyze the potential of the Georgian tourism industry with its long-term strategy and current challenges. To plan activities in a long-term development, it is required to evaluate several factors on the regional and on the national level. Factors include activities, transportation, services, lodging facilities, infrastructure and institutions. The major research contributions are practical estimates about regional tourism development which plays an important role in the integration process with global markets.

Keywords: regional tourism, tourism industry, tourism in Georgia, tourism benefits

Procedia PDF Downloads 819

Authors: Marlon J. Remedios, Maria T. Morell, Jesse D. Cano

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Diffusion and accessibility of historical collections is one of the main objectives of the institutions that aim to safeguard archival documents (General Archives). Several countries have Web applications that try to make accessible and public the large number of documents that they guard. Each of these sites has a set of features in order to facilitate access, navigability, and search for information. Different sources of information include Recommender Systems as a way of customizing content. This paper aims at describing a process for the production of archival documents relevant to the user. To comply with this, the characteristics ruling archival description, elements and main techniques that establishes the design of Recommender Systems, a set of rules to follow, and how these rules operate and the way in which take advantage of the domain knowledge are discussed. Finally, relevant issues are discussed in the design of the proposed tests and the results obtained are shown.

Keywords: archival document, recommender system, procedure, information management

Procedia PDF Downloads 504

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 99

Authors: B. Khatri, K. Lappe, M. Habedank, T. Müller, C. Megnin, T. Hanemann

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We present a process flow for the development of ceramic-filled polymer composite filaments compatible with the fused deposition modeling (FDM) 3D printing process. Thermoplastic-ceramic composites were developed using acrylonitrile butadiene styrene (ABS) and 10- and 20 vol.% barium titanate (BaTiO3) powder (corresponding to 39.47- and 58.23 wt.% respectively) and characterized for their flow properties. To make them compatible with the existing FDM process, the composites were extruded into filaments. These composite filaments were subsequently structured into tensile stress specimens using a commercially available FDM 3D printer and characterized for their mechanical properties. Rheometric characterization of the material composites revealed non-Newtonian behavior with the viscosity logarithmically decreasing over increasing shear rates, as well as higher viscosities for samples with higher BaTiO3 filler content for a given shear rate (with the ABS+20vol.% BaTiO3 composite being over 50% more viscous compared to pure ABS at a shear rate of 1x〖10〗^3 s^(-1)). Mechanical characterization of the tensile stress specimens exhibited increasingly brittle behavior as well as a linearly decreasing ultimate tensile strength of the material composites with increasing volumetric ratio of BaTiO3 (from σ_max=32.4MPa for pure ABS to σ_max=21.3MPa for ABS+20vol.% BaTiO3). Further studies being undertaken include the development of composites with higher filler concentrations, sintering of the printed composites to yield pure dielectric structures and the determination of the dielectric characteristics of the composites.

Keywords: ceramic composites, fused deposition modeling, material characterization, rapid prototyping

Procedia PDF Downloads 322

Authors: Petar Dobrev

Abstract:

There is no agreed-upon definition of firm quality. While profitability and stock performance often qualify as popular proxies of quality, in this project, we aim to identify quality without relying on a firm’s financial statements or stock returns as selection criteria. Instead, we use firm-level data on management practices across small to medium-sized U.S. manufacturing firms from the World Management Survey (WMS) to measure firm quality. Each firm in the WMS dataset is assigned a mean management score from 0 to 5, with higher scores identifying better-managed firms. This management score serves as our proxy for firm quality and is the sole criteria we use to separate firms into portfolios comprised of high-quality and low-quality firms. We define high-quality (low-quality) firms as those firms with a management score of one standard deviation above (below) the mean. To study whether this proxy for firm quality can identify better-performing firms, we link this data to Compustat and The Center for Research in Security Prices (CRSP) to obtain firm-level data on financial performance and monthly stock returns, respectively. We find that from 1999 to 2019 (our sample data period), firms in the high-quality portfolio are consistently more profitable — higher operating profitability and return on equity compared to low-quality firms. In addition, high-quality firms also exhibit a lower risk of bankruptcy — a higher Altman Z-score. Next, we test whether the stocks of the firms in the high-quality portfolio earn superior risk-adjusted excess returns. We regress the monthly excess returns on each portfolio on the Fama-French 3-factor, 4-factor, and 5-factor models, the betting-against-beta factor, and the quality-minus-junk factor. We find no statistically significant differences in excess returns between both portfolios, suggesting that stocks of high-quality (well managed) firms do not earn superior risk-adjusted returns compared to low-quality (poorly managed) firms. In short, our proxy for firm quality, the WMS management score, can identify firms with superior financial performance (higher profitability and reduced risk of bankruptcy). However, our management proxy cannot identify stocks that earn superior risk-adjusted returns, suggesting no statistically significant relationship between managerial quality and stock performance.

Keywords: excess stock returns, management, profitability, quality

Procedia PDF Downloads 79

Authors: Mohammed Abaoli, Omer Kara

Abstract:

The current research has investigated the soil quality status under dryland vegetation of Yabello district, Southern Ethiopia in which we should identify the nature and extent of salinity problem of the area for further research bases. About 48 soil samples were taken from 0-30, 31-60, 61-90 and 91-120 cm soil depths by opening 12 representative soil profile pits at 1.5 m depth. Soil color, texture, bulk density, Soil Organic Carbon (SOC), Cation Exchange Capacity (CEC), Na, K, Mg, Ca, CaCO₃, gypsum (CaSO₄), pH, Sodium Adsorption Ratio (SAR), Exchangeable Sodium Percentage (ESP) were analyzed. The dominant soil texture was silty-clay-loam.  Bulk density varied from 1.1 to 1.31 g/cm³. High SOC content was observed in 0-30 cm. The soil pH ranged from 7.1 to 8.6. The electrical conductivity shows indirect relationship with soil depth while CaCO₃ and CaSO₄ concentrations were observed in a direct relationship with depth. About 41% are non-saline, 38.31% saline, 15.23% saline-sodic and 5.46% sodic soils. Na concentration in saline soils was greater than Ca and Mg in all the soil depths. Ca and Mg contents were higher above 60 cm soil depth in non-saline soils. The concentrations of SO₂^-4 and HCO^-3 were observed to be higher at the most lower depth than upper. SAR value tends to be higher at lower depths in saline and saline-sodic soils, but decreases at lower depth of the non-saline soils. The distribution of ESP above 60 cm depth was in an increasing order in saline and saline-sodic soils. The result of the research has shown the direction to which extent of salinity we should consider for the Commiphora plant species we want to grow on the area. 

Keywords: commiphora species, dryland vegetation, ecological significance, soil quality, salinity problem

Procedia PDF Downloads 182