Search results for: traditional techniques

Authors: Sogand Barghi

Abstract:

The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: Basiru Amuneni

Abstract:

Astronomy is one domain with a rise in data. Traditional tools for data management have been employed in the quest for knowledge discovery. However, these traditional tools become limited in the face of big. One means of maximizing knowledge discovery for big data is the use of scientific visualisation. The aim of the work is to explore the possibilities offered by emerging creative technologies of Virtual Reality (VR) systems and game engines to visualize multiwavelength datasets. Game Engines are primarily used for developing video games, however their advanced graphics could be exploited for scientific visualization which provides a means to graphically illustrate scientific data to ease human comprehension. Modern astronomy is now in the era of multiwavelength data where a single galaxy for example, is captured by the telescope several times and at different electromagnetic wavelength to have a more comprehensive picture of the physical characteristics of the galaxy. Visualising this in an immersive environment would be more intuitive and natural for an observer. This work presents a standalone VR application that accesses galaxy FITS files. The application was built using the Unity Game Engine for the graphics underpinning and the OpenXR API for the VR infrastructure. The work used a methodology known as Design Science Research (DSR) which entails the act of ‘using design as a research method or technique’. The key stages of the galaxy modelling pipeline are FITS data preparation, Galaxy Modelling, Unity 3D Visualisation and VR Display. The FITS data format cannot be read by the Unity Game Engine directly. A DLL (CSHARPFITS) which provides a native support for reading and writing FITS files was used. The Galaxy modeller uses an approach that integrates cleaned FITS image pixels into the graphics pipeline of the Unity3d game Engine. The cleaned FITS images are then input to the galaxy modeller pipeline phase, which has a pre-processing script that extracts, pixel, galaxy world position, and colour maps the FITS image pixels. The user can visualise image galaxies in different light bands, control the blend of the image with similar images from different sources or fuse images for a holistic view. The framework will allow users to build tools to realise complex workflows for public outreach and possibly scientific work with increased scalability, near real time interactivity with ease of access. The application is presented in an immersive environment and can use all commercially available headset built on the OpenXR API. The user can select galaxies in the scene, teleport to the galaxy, pan, zoom in/out, and change colour gradients of the galaxy. The findings and design lessons learnt in the implementation of different use cases will contribute to the development and design of game-based visualisation tools in immersive environment by enabling informed decisions to be made.

Keywords: astronomy, visualisation, multiwavelenght dataset, virtual reality

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Authors: C. C .D. Kulathilake, M. Jayatilake, T. Takahashi

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The myocardium is composed of specialized muscle which relies mainly on fatty acid and sugar metabolism and it is widely contribute to the heart functioning. The changes of the cardiac energy-producing system during heart failure have been proved using autoradiography techniques. This study focused on evaluating sugar and fatty acid metabolism in myocardium as cardiac energy getting system using heart-accumulated radiopharmaceuticals. Two sets of autoradiographs of heart cross sections of Lewis male rats were analyzed and the time- accumulation curve obtained with use of the MATLAB image processing software to evaluate fatty acid and sugar metabolic functions.

Keywords: autoradiographs, fatty acid, radiopharmaceuticals, sugar

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Junki Min, Heeyoung Lee, Wonseok Chung

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Traditional marine concrete structures are difficult to construct and may cause environmental pollution. This study presents new concrete bridge system in the marine. The main feature of the proposed bridge is that precast girders and precast coping are applied to facilitate assembly and to improve constructability. In addition, the moment of the girder is reduced by continuation the joint. In this study, a full-scale joint specimen with a span of 7.0 m was fabricated and tested to evaluate the composite behavior of the joint. A finite element model was also developed and compared against the experimental results. All members of the test specimen behaved stably up to the design load. It was found that the precast joint of the proposed bridge showed the composite behavior efficiently before the failure.

Keywords: finite element analysis, full-scale test, coping, joint performance, marine structure, precast

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Authors: Saurabh Rawat, Anushree Sah

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K Maps are generally and ideally, thought to be simplest form for obtaining solution of Boolean equations. Cubical Representation of Boolean equations is an alternate pick to incur a solution, otherwise to be meted out with Truth Tables, Boolean Laws, and different traits of Karnaugh Maps. Largest possible k- cubes that exist for a given function are equivalent to its prime implicants. A technique of minimization of Logic functions is tried to be achieved through cubical methods. The main purpose is to make aware and utilise the advantages of cubical techniques in minimization of Logic functions. All this is done with an aim to achieve minimal cost solution.r

Keywords: K-maps, don’t care conditions, Boolean equations, cubes

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Authors: Quoc Khanh Dang, Thomas Bourdeaud’huy, Khaled Mesghouni, Armand Toguy´eni

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This paper studies a train routing and scheduling problem for busy railway stations. Our objective is to allow trains to be routed in dense areas that are reaching saturation. Unlike traditional methods that allocate all resources to setup a route for a train and until the route is freed, our work focuses on the use of resources as trains progress through the railway node. This technique allows a larger number of trains to be routed simultaneously in a railway node and thus reduces their current saturation. To deal with this problem, this study proposes an abstract model and a mixed-integer linear programming formulation to solve it. The applicability of our method is illustrated on a didactic example.

Keywords: busy railway stations, mixed-integer linear programming, offline railway station management, train platforming, train routing, train scheduling

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Authors: Cassandra Branham, Danielle Farrar

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As writing instructors and writing center administrators at a large research university with a significant population of English language learners (ELLs), we are interested in how peer feedback pedagogy can be effectively translated for writing center purposes, as well as how various modes of peer feedback can enrich the learning experiences of L1 and L2 writers in these spaces. Although peer feedback is widely used in classrooms and centers, instructor, student, and researcher opinions vary in respect to its effectiveness. We argue that peer feedback - traditional and digital, synchronous and asynchronous - is an indispensable element for both classrooms and centers and emphasize that it should occur with both L1 and L2 students to further develop an array of reading and writing skills. We also believe that further understanding of the best practices of peer feedback in such cross-cultural spaces, like the classroom and center, can optimize the benefits of peer feedback. After a critical review of the literature, we implemented an embedded tutoring program in our university’s writing center in collaboration with its First-Year Composition (FYC) program and Language Institute. The embedded tutoring program matches a graduate writing consultant with L1 and L2 writers enrolled in controlled-matriculation composition courses where ELLs make up at least 50% of each class. Furthermore, this program is informed by what we argue to be some best practices of peer feedback for both classroom and center purposes, including expectation-based training through rubrics, modeling effective feedback, hybridizing traditional and digital modes of feedback, recognizing the significance the body in composition (what we call writer embodiment), and maximizing digital technologies to exploit extended cognition. After conducting surveys and follow-up interviews with students, instructors, and writing consultants in the embedded tutoring program, we found that not only did students see an increased value in peer feedback, but also instructors saw an improvement in both writing style and critical thinking skills. Our L2 participants noted improvements in language acquisition while our L1 students recognized a broadening of their worldviews. We believe that both L1 and L2 students developed self-efficacy and agency in their identities as writers because they gained confidence in their abilities to offer feedback, as well as in the legitimacy of feedback they received from peers. We also argue that these best practices situate novice writers as experts, as writers become a valued and integral part of the revision process with their own and their peers’ papers. Finally, the use of iPads in embedded tutoring recovered the importance of the body and its senses in writing; the highly sensory feedback from these multi-modal sessions that offer audio and visual input underscores the significant role both the body and mind play in compositional practices. After beginning with a brief review of the literature that sparked this research, this paper will discuss the embedded tutoring program in detail, report on the results of the pilot program, and will conclude with a discussion of the pedagogical implications that arise from this research for both classroom and center.

Keywords: English language learners, peer feedback, writing center, writing classroom

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Authors: Abdu Zakari, Said Jibrin, Fatope Majekodunmi Oladeji, Mohammed Hassan Shagga, Andrew Sule

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From the Stems of Bark of Echinaceae angustifolia DC three known triterpenes 3a,5,5b,8,8,11a-hexamethyl-1-(prop-1-en-2-yl)icosahydro-1H-cyclopenta[a]chrysene-9-yl acetate (lupeol acetate), 4,4,6a,6b,8a,10,11,14b,octamethyl1,1,2,3,4,4a,5,6,6a,6b,7,8,8a, 9,10, 11,12,12a,14,14a,14b-icosahydropicen-3-yl acetate (derivative of β-amyrin and 9- hydroxy-1-isopropenyl-5a,5b,8,8,11a-pentamethyl-icosahydro-cyclopenta[a]chrysene- 3a-carboxylic acid (betulinic acid), labelled as Ea-7-38, Ea-9-10 and Ea-12-85) were isolated and characterized. All isolates were tested for their cytotoxicities against Artemia salina (brine shrimp larvae). Compound Ea-12-85 exhibited potent cytotoxic activity against the Artemia salina, Ea-7-38, Ea-9-10 were found to be non-toxic in the cytotoxicity test. The result of the study has justified the claim of the traditional medicine practitioners in Girei for the treatment of complicated malaria disease using the stem bark of E. angustifolia DC.

Keywords: cytotoxic, terpenes, Echinaceae angustifolia, brine shrimp, artemia salina

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Authors: Van-Ngoc-Cuong Le, Ngoc-Quy Le

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Approaches to the diagnosis of ectopic solid organs in the abdominopelvic cavity include Accessory liver lobe, Accessory spleens (ectopic splenic tissue), Wandering spleen, Ectopic pancreatic tissue, Ectopic kidney (Pancake kidney), Cryptorchidism (undescended testis, ectopic testis), Ectopic endometriosis. The application of diagnostic imaging techniques, of which magnetic resonance imaging is the most important, includes a clinical case study and reports. Ectopic organs and tumors are easy to confuse. This is a concern, as well as practical challenges encountered and solutions adopted in the fields of Image Analysis.

Keywords: ectopic, accessory, wandering, tumor

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Authors: Jingjing Li, Shifu Wang

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After the irreversible consequences of the traditional renewal mode of ‘function first and then beautification’, such as the constructive destruction, social differentiation, and cultural, ecological imbalance, the renewal of the historical urban area began to pay attention to the excavation of cultural connotation, and entered a new stage from the pursuit of ‘quantity’ growth to the promotion of ‘quality’, expecting to rejuvenate the old city through the intervention of public art. This paper interprets the cases at home and abroad, summarizes the different forms of expression and application strategies of public art in the renewal of historical urban areas, and combs the limitations of the existing practice in Guangzhou through observation. Finally, it puts forward suggestions from three aspects of the system, implementation strategy, and implementation path, respectively, and explores the path of simultaneous rejuvenation of material space and cultural space in historical urban areas under the intervention of public art.

Keywords: public art, historic city, public space, renewal activation

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Authors: Vittorio Caffi, Maria Pignataro, Antonio Cosimo Devito, Marco Pesenti

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This paper deals with the integration of advanced digital design and modelling tools and methodologies, known as Building Information Modelling, into the traditional Academy educational programs for building engineers and architects. Nowadays, the challenge the Academy has to face is to present the new tools and their features to the pupils, making sure they acquire the proper skills in order to leverage the potential they offer also for the other courses embedded in the educational curriculum. The syllabus here presented refers to the “Drawing for building engineering”, “2D and 3D laboratory” and “3D modelling” curricula of the MSc in Building Engineering of the Politecnico di Milano. Such topics, included since the first year in the MSc program, are fundamental to give the students the instruments to master the complexity of an architectural or building engineering project with digital tools, so as to represent it in its various forms.

Keywords: BIM, BIM curricula, computational design, digital modelling

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Authors: Younghoon Kim, Seoung Bum Kim

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One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

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Authors: Sourita Ghosh, Falguni Pati, Suhanya Duraiswamy

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Spheroid, a simple three-dimensional cellular aggregate, allows us to simulate the in-vivo complexity of cellular signaling and interactions in greater detail than traditional 2D cell culture. It can be used as an in-vitro model for drug toxicity testing, tumor modeling and many other such applications specifically for cancer. Our work is focused on the development of an affordable, user-friendly, robust, reproducible, high throughput microfluidic device for water in oil droplet production, which can, in turn, be used for spheroids manufacturing. Here, we have investigated the droplet breakup between two non-Newtonian fluids, viz. silicone oil and decellularized liver matrix, which acts as our extra cellular matrix (ECM) for spheroids formation. We performed some biochemical assays to characterize the liver ECM, as well as rheological studies on our two fluids and observed a critical dependence of capillary number (Ca) on droplet breakup and homogeneous drop formation

Keywords: chip less, droplets, extracellular matrix, liver spheroid

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Authors: Andreea Cervatiuc

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This study focuses on teaching second language writing through a constructivist approach. Unlike traditional approaches to teaching second language writing, which were product-oriented and emphasized surface features of writing, such as spelling and grammar, the constructivist approach to teaching second language writing is process-oriented and fosters discovery of meaning, creativity, collaboration, and writing for an audience. Educators who take a constructivist approach to teaching second language writing create communities of writers in their classrooms, emphasize that the goal of writing is to share ideas with others, and engage their students in collaborative, creative, and authentic writing activities, such as writing conferences, group story writing, finish the story, and chain writing. The constructivist approach to teaching second language writing combines a focus on genres, scaffolding, and treating writing as a process. Through constructivist writing, students co-create knowledge and engage in meaningful dialogue with various texts and their peers. The findings of this study can have implications for applied linguists, teachers, and language learners.

Keywords: constructivist second language, writing genres, process writing, scaffolding

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Authors: Yunwei Tang, Linhai Jing, Hui Li, Qingjie Liu, Xiuxia Li, Qi Yan, Haifeng Ding

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The giant panda (Ailuropoda melanoleuca) is an endangered species, mainly live in central China, where bamboos act as the main food source of wild giant pandas. Knowledge of spatial distribution of bamboos therefore becomes important for identifying the habitat of giant pandas. There have been ongoing studies for mapping bamboos and other tree species using remote sensing. WorldView-2 (WV-2) is the first high resolution commercial satellite with eight Multi-Spectral (MS) bands. Recent studies demonstrated that WV-2 imagery has a high potential in classification of tree species. The advanced classification techniques are important for utilising high spatial resolution imagery. It is generally agreed that object-based image analysis is a more desirable method than pixel-based analysis in processing high spatial resolution remotely sensed data. Classifiers that use spatial information combined with spectral information are known as contextual classifiers. It is suggested that contextual classifiers can achieve greater accuracy than non-contextual classifiers. Thus, spatial correlation can be incorporated into classifiers to improve classification results. The study area is located at Wuyipeng area in Wolong, Sichuan Province. The complex environment makes it difficult for information extraction since bamboos are sparsely distributed, mixed with brushes, and covered by other trees. Extensive fieldworks in Wuyingpeng were carried out twice. The first one was on 11th June, 2014, aiming at sampling feature locations for geometric correction and collecting training samples for classification. The second fieldwork was on 11th September, 2014, for the purposes of testing the classification results. In this study, spectral separability analysis was first performed to select appropriate MS bands for classification. Also, the reflectance analysis provided information for expanding sample points under the circumstance of knowing only a few. Then, a spatially weighted object-based k-nearest neighbour (k-NN) classifier was applied to the selected MS bands to identify seven land cover types (bamboo, conifer, broadleaf, mixed forest, brush, bare land, and shadow), accounting for spatial correlation within classes using geostatistical modelling. The spatially weighted k-NN method was compared with three alternatives: the traditional k-NN classifier, the Support Vector Machine (SVM) method and the Classification and Regression Tree (CART). Through field validation, it was proved that the classification result obtained using the spatially weighted k-NN method has the highest overall classification accuracy (77.61%) and Kappa coefficient (0.729); the producer’s accuracy and user’s accuracy achieve 81.25% and 95.12% for the bamboo class, respectively, also higher than the other methods. Photos of tree crowns were taken at sample locations using a fisheye camera, so the canopy density could be estimated. It is found that it is difficult to identify bamboo in the areas with a large canopy density (over 0.70); it is possible to extract bamboos in the areas with a median canopy density (from 0.2 to 0.7) and in a sparse forest (canopy density is less than 0.2). In summary, this study explores the ability of WV-2 imagery for bamboo extraction in a mountainous region in Sichuan. The study successfully identified the bamboo distribution, providing supporting knowledge for assessing the habitats of giant pandas.

Keywords: bamboo mapping, classification, geostatistics, k-NN, worldview-2

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Authors: Okba Belahssen, Lazhar Torchane, Said Benramache, Abdelouahed Chala

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This paper presents corrosion behavior of the plasma-nitrided 32CrMoV13 steel. Different kinds of samples were tested: non-treated, plasma nitrided samples. The structure of layers was determined by X-ray diffraction, while the morphology was observed by scanning electron microscopy (SEM). The corrosion behavior was evaluated by electrochemical techniques (potentiodynamic curves and electrochemical impedance spectroscopy). The corrosion tests were carried out in acid chloride solution (HCl 1M). Experimental results showed that the nitrides ε-Fe2−3N and γ′-Fe4N present in the white layer are nobler than the substrate but may promote, by galvanic effect, a localized corrosion through open porosity. The better corrosion protection was observed for nitrided sample.

Keywords: plasma-nitrided, 32CrMoV13 steel, corrosion, EIS

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Authors: Wahid Dianbudiyanto, Shou Heng Liu

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Capacitive deionization (CDI) is a prospective desalination technology, which can be operated at low voltage, low temperature and potentially consume low energy for brackish water desalination. To obtain the optimal electrosorption, an electrode should possess high electrical conductivity, large surface area, good wettability, highly mesoporous structure which provide efficient pathways for ion distribution. In this work, a 3D structure graphene was fabricated using hydrothermal method which is assisted with microwave treatments to form 3D rGO (3DG-Mw-Hyd). The prepared samples have excellent specific capacitance (189.2 F / g) and ultrahigh electrosorption capacity (30 mg/g) for the desalination of 500 mg / l NaCl. These results are superior to the electrode which is fabricated only using the hydrothermal method without microwave assistance (3DG-Hyd) and traditional reflux method. Physical characterizations such as SEM, TEM, and XRD have been used to study the property difference of the materials. The preliminary results show that 3DG-Mw-Hyd is one of the promising electrodes for CDI in the practical applications.

Keywords: capacitive deionization, graphene, microwave, hydrothermal, electrosorption

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Authors: Steven Sam, Ximena Schmidt, Hugh Dickinson, Jens Jensen

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The goal of this study is to explore the potential of data science and citizen science approaches to develop an interactive, digital, open infrastructure that pulls together African indigenous agriculture and food systems data from multiple sources, making it accessible and reusable for policy, research and practice in modern food production efforts. The World Bank has recognised that African Indigenous Knowledge (AIK) is innovative and unique among local and subsistent smallholder farmers, and it is central to sustainable food production and enhancing biodiversity and natural resources in many poor, rural societies. AIK refers to tacit knowledge held in different languages, cultures and skills passed down from generation to generation by word of mouth. AIK is a key driver of food production, preservation, and consumption for more than 80% of citizens in Africa, and can therefore assist modern efforts of reducing food insecurity and hunger. However, the documentation and dissemination of AIK remain a big challenge confronting librarians and other information professionals in Africa, and there is a risk of losing AIK owing to urban migration, modernisation, land grabbing, and the emergence of relatively small-scale commercial farming businesses. There is also a clear disconnect between the AIK and scientific knowledge and modern efforts for sustainable food production. The study combines data science and citizen science approaches through active community participation to generate and share AIK for facilitating learning and promoting knowledge that is relevant for policy intervention and sustainable food production through a curated digital platform based on FAIR principles. The study adopts key informant interviews along with participatory photo and video elicitation approach, where farmers are given digital devices (mobile phones) to record and document their every practice involving agriculture, food production, processing, and consumption by traditional means. Data collected are analysed using the UK Science and Technology Facilities Council’s proven methodology of citizen science (Zooniverse) and data science. Outcomes are presented in participatory stakeholder workshops, where the researchers outline plans for creating the platform and developing the knowledge sharing standard framework and copyrights agreement. Overall, the study shows that learning from AIK, by investigating what local communities know and have, can improve understanding of food production and consumption, in particular in times of stress or shocks affecting the food systems and communities. Thus, the platform can be useful for local populations, research, and policy-makers, and it could lead to transformative innovation in the food system, creating a fundamental shift in the way the North supports sustainable, modern food production efforts in Africa.

Keywords: Africa indigenous agriculture knowledge, citizen science, data science, sustainable food production, traditional food system

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Authors: Ali Asmari, Alex Symington, Htaik Than, Austin Caradonna, John Senft

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This paper presents the collaborative effort between ULC Technologies and Con Edison in developing a groundbreaking robotic cable splicing machine. The focus is on the machine's design, which integrates advanced robotics and automation to enhance safety and efficiency in power network maintenance. The paper details the operational steps of the machine, including cable grounding, cutting, and removal of different insulation layers, and discusses its novel technological approach. The significant benefits over traditional methods, such as improved worker safety and reduced outage times, are highlighted based on the field data collected during the validation phase of the project. The paper also explores the future potential and scalability of this technology, emphasizing its role in transforming the landscape of power network maintenance.

Keywords: cable splicing machine, power network maintenance, electric distribution, electric transmission, medium voltage cable

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Authors: Ebrahim Shayesteh, Nikolaos Styliaras, Alin George Raducu, Ozan Sahin, Daniel Pombo VáZquez, Jonas Funkquist, Sotirios Thanopoulos

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One of the important requirements in many code generation projects is defining some of the model parameters tunable. This helps to update the model parameters without performing the code generation again. This paper studies the concept of embedded code generation by MATLAB/Simulink coder targeting the TwinCAT Simulink system. The generated runtime modules are then tested and deployed to the TwinCAT 3 engineering environment. However, defining the parameters tunable in MATLAB/Simulink code generation targeting TwinCAT is not very straightforward. This paper focuses on this subject and reviews some of the techniques tested here to make the parameters tunable in generated runtime modules. Three techniques are proposed for this purpose, including normal tunable parameters, callback functions, and mask subsystems. Moreover, some test Simulink models are developed and used to evaluate the results of proposed approaches. A brief summary of the study results is presented in the following. First of all, the parameters defined tunable and used in defining the values of other Simulink elements (e.g., gain value of a gain block) could be changed after the code generation and this value updating will affect the values of all elements defined based on the values of the tunable parameter. For instance, if parameter K=1 is defined as a tunable parameter in the code generation process and this parameter is used to gain a gain block in Simulink, the gain value for the gain block is equal to 1 in the gain block TwinCAT environment after the code generation. But, the value of K can be changed to a new value (e.g., K=2) in TwinCAT (without doing any new code generation in MATLAB). Then, the gain value of the gain block will change to 2. Secondly, adding a callback function in the form of “pre-load function,” “post-load function,” “start function,” and will not help to make the parameters tunable without performing a new code generation. This means that any MATLAB files should be run before performing the code generation. The parameters defined/calculated in this file will be used as fixed values in the generated code. Thus, adding these files as callback functions to the Simulink model will not make these parameters flexible since the MATLAB files will not be attached to the generated code. Therefore, to change the parameters defined/calculated in these files, the code generation should be done again. However, adding these files as callback functions forces MATLAB to run them before the code generation, and there is no need to define the parameters mentioned in these files separately. Finally, using a tunable parameter in defining/calculating the values of other parameters through the mask is an efficient method to change the value of the latter parameters after the code generation. For instance, if tunable parameter K is used in calculating the value of two other parameters K1 and K2 and, after the code generation, the value of K is updated in TwinCAT environment, the value of parameters K1 and K2 will also be updated (without any new code generation).

Keywords: code generation, MATLAB, tunable parameters, TwinCAT

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Authors: To Quyen Le, Oswald Devisch, Tu Anh Trinh, Els Hannes

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Urban centres worldwide are affected differently by flooding. In Vietnam this impact is increasingly negative caused by a process of rapid urbanisation. Traditional spatial planning and flood mitigation planning are not able to deal with this growing threat. This article therefore proposes to focus on increasing the participation of local communities in flood control and management. It explores, on the basis of a design studio exercise, how lay knowledge on flooding can be integrated within planning processes. The article presents a theoretical basis for the structured criterion for site selection for a flood resilient urban park from the perspective of science, then discloses the tacit and explicit knowledge of the flood-prone area and finally integrates this knowledge into the design strategies for flood resilient urban park design.

Keywords: analytic hierarchy process, AHP, design resilience, flood resilient urban park, knowledge integration

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Authors: Coriolano Salvini

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The increasing utilization of renewable energy sources for electric power production calls for the introduction of energy storage systems to match the electric demand along the time. Although many countries are pursuing as a final goal a “decarbonized” electrical system, in the next decades the traditional fossil fuel fed power plant still will play a relevant role in fulfilling the electric demand. Presently, such plants provide grid ancillary services (frequency control, grid balance, reserve, etc.) by adapting the output power to the grid requirements. An interesting option is represented by the possibility to use traditional plants to improve the grid storage capabilities. The present paper is addressed to small-medium size systems suited for distributed energy storage. The proposed Energy Storage System (ESS) is based on a Compressed Air Energy Storage (CAES) integrated into a Gas-Steam Combined Cycle (GSCC) or a Gas Turbine based CHP plants. The systems can be incorporated in an ex novo built plant or added to an already existing one. To avoid any geological restriction related to the availability of natural compressed air reservoirs, artificial storage is addressed. During the charging phase, electric power is absorbed from the grid by an electric driven intercooled/aftercooled compressor. In the course of the discharge phase, the compressed stored air is sent to a heat transfer device fed by hot gas taken upstream the Heat Recovery Steam Generator (HRSG) and subsequently expanded for power production. To maximize the output power, a staged reheated expansion process is adopted. The specific power production related to the kilogram per second of exhaust gas used to heat the stored air is two/three times larger than that achieved if the gas were used to produce steam in the HRSG. As a result, a relevant power augmentation is attained with respect to normal GSCC plant operations without additional use of fuel. Therefore, the excess of output power can be considered “fuel free” and the storage system can be compared to “pure” ESSs such as electrochemical, pumped hydro or adiabatic CAES. Representative cases featured by different power absorption, production capability, and storage capacity have been taken into consideration. For each case, a technical optimization aimed at maximizing the storage efficiency has been carried out. On the basis of the resulting storage pressure and volume, number of compression and expansion stages, air heater arrangement and process quantities found for each case, a cost estimation of the storage systems has been performed. Storage efficiencies from 0.6 to 0.7 have been assessed. Capital costs in the range of 400-800 €/kW and 500-1000 €/kWh have been estimated. Such figures are similar or lower to those featuring alternative storage technologies.

Keywords: artificial air storage reservoir, compressed air energy storage (CAES), gas steam combined cycle (GSCC), techno-economic analysis

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Authors: H. Kadi-Hanifi, F. Amghar

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Steppes of arid Mediterranean zones are deeply threatened by desertification. To stop or alleviate ecological and economic problems associated with this desertification, management actions have been implemented since the last three decades. The struggle against desertification has become a national priority in many countries. In Algeria, several management techniques have been used to cope with desertification. This study aims at investigating the effect of exclosure on floristic diversity and chemical soil proprieties after four years of implementation. 167 phyto-ecological samples have been studied, 122 inside the exclosure and 45 outside. Results showed that plant diversity, composition, vegetation cover, pastoral value and soil fertility were significantly higher in protected areas.

Keywords: Algeria, arid, desertification, pastoral management, soil fertility

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Authors: Noreen Pulte

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Prior to the 'Back to Sleep' Campaign in 1992, 1 of every 300 infants seen by Advanced Practice Providers had plagiocephaly. Insufficient attention is given to plagiocephaly and brachycephaly diagnoses in practice and pediatric education. In this talk, Nurse Practitioners and Pediatric Providers will be able to: (1) identify red flags associated with head shape abnormalities, (2) learn techniques they can teach parents to prevent head shape abnormalities, and (3) differentiate between plagiocephaly, brachycephaly, and craniosynostosis. The presenter is a Primary Care Pediatric Nurse Practitioner at Ann & Robert H. Lurie Children's Hospital of Chicago and the primary provider for its head shape abnormality clinics. She will help participants translate key information obtained from birth history, review of systems, and developmental history to understand risk factors for head shape abnormalities and progression of deformities. Synostotic and non-synostotic head shapes will be explained to help participants differentiate plagiocephaly and brachycephaly from synostotic head shapes. This knowledge is critical for the prompt referral of infants with craniosynostosis for surgical evaluation and correction. Rapid referral for craniosynostosis can possibly direct the patient to a minimally invasive surgical procedure versus a craniectomy. As for plagiocephaly and brachycephaly, this timely referral can also aid in a physical therapy referral if necessitated, which treats torticollis and aids in improving head shape. A well-timed referral to a head shape clinic can possibly eliminate the need for a helmet and/or minimize the time in a helmet. Practitioners will learn the importance of obtaining head measurements using calipers. The presenter will explain head calculations and how the calculations are interpreted to determine the severity of the head shape abnormalities. Severity defines the treatment plan. Participants will learn when to refer patients to a head shape abnormality clinic and techniques they should teach parents to perform while waiting for the referral appointment. The purpose, mechanics, and logistics of helmet therapy, including optimal time to initiate helmet therapy, recommended helmet wear-time, and tips for helmet therapy compliance, will be described. Case scenarios will be incorporated into the presenter's presentation to support learning. The salient points of the case studies will be explained and discussed. Practitioners will be able to immediately translate the knowledge and skills gained in this presentation into their clinical practice.

Keywords: plagiocephaly, brachycephaly, craniosynostosis, red flags

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Authors: Victoria C. Valdebenito

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Located within the context of accelerating globalization in the 21^st-century knowledge society, this paper focuses on one selected university in Chile at which radical curriculum policy changes have been taking place, diverging from the traditional curriculum in Chile at the undergraduate level as a section of a larger investigation. Using a ‘policy trajectory’ framework, and guided by the interpretivist approach to research, interview transcripts and institutional documents were analyzed in relation to the meso (university administration) and the micro (academics) level. Inside the case study, participants from the university administration and academic levels were selected both via snow-ball technique and purposive selection, thus they had different levels of seniority, with some participating actively in the curriculum reform processes. Guided by an interpretivist approach to research, documents and interview transcripts were analyzed to reveal major themes emerging from the data. A further ‘bigger picture’ analysis guided by critical theory was then undertaken, involving interrogation of underlying ideologies and how political and economic interests influence the cultural production of policy. The case-study university was selected because it represents a traditional and old case of university setting in the country, undergoing curriculum changes based on international trends such as the competency model and the liberal arts. Also, it is representative of a particular socioeconomic sector of the country. Access to the university was gained through email contact. Qualitative research methods were used, namely interviews and analysis of institutional documents. In all, 18 people were interviewed. The number was defined by when the saturation criterion was met. Semi-structured interview schedules were based on the four research questions about influences, policy texts, policy enactment and longer-term outcomes. Triangulation of information was used for the analysis. While there was no intention to generalize the specific findings of the case study, the results of the research were used as a focus for engagement with broader themes, often evident in global higher education policy developments. The research results were organized around major themes in three of the four contexts of the ‘policy trajectory’. Regarding the context of influences and the context of policy text production, themes relate to hegemony exercised by first world countries’ universities in the higher education field, its associated neoliberal ideology, with accountability and the discourse of continuous improvement, the local responses to those pressures, and the value of interdisciplinarity. Finally, regarding the context of policy practices and effects (enactment), themes emerged around the impacts of the curriculum changes on university staff, students, and resistance amongst academics. The research concluded with a few recommendations that potentially provide ‘food for thought’ beyond the localized settings of this study, as well as possibilities for further research.

Keywords: curriculum, global-local dynamics, higher education, policy, sociology of education

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Authors: S. Ghorbani, N. I. Polushin

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In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: cutting condition, surface roughness, decision tree, CART algorithm

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Authors: Gennady P. Ginkul, Sergey Yu. Soloviov

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The article proposes to use so-called "quotation-based algorithm" for simulation of decision making process in distributed expert systems and multi-agent systems. The idea was adopted from the techniques for group decision-making. It is based on the assumption that one expert system to perform its logical inference may use rules from another expert system. The application of the algorithm was demonstrated on the example in which the consolidated decision is the decision that requires minimal quotation.

Keywords: backward chaining inference, distributed expert systems, group decision making, multi-agent systems

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Authors: Pritha Niraj Arya

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Education plays a crucial role in societal evolution by promoting economic expansion and creativity. The varied demands of students in India’s higher education setting signify inclusive and efficient teaching methods. The present study examined how teaching styles, self-efficacy, and personality traits interact among Indian higher education faculty members and how these factors collectively affect pedagogical practices. Specifically, the research explored differences in personality traits -agreeableness, conscientiousness, neuroticism, openness, and extraversion- between teachers with high and low self-efficacy and examined how these traits shape teaching strategies, either student-focused or teacher-focused. Data collection took place for three months, ensuring confidentiality and ethical compliance. 268 faculty members from Indian higher education institutions participated in this comparative study. An online questionnaire was used to gather data in which participants completed three well-established tools: the approaches to teaching inventory, which measures teaching styles; the teacher self-efficacy questionnaire, which measures self-efficacy levels; and the big five inventory, which measures personality traits. The results showed that while teachers with low self-efficacy had higher levels of neuroticism, those with high self-efficacy scored much higher on traits such as agreeableness, conscientiousness, openness, and extraversion. Despite the traditional belief that high self-efficacy is only associated with student-focused teaching, the findings suggest that teachers with high self-efficacy have cognitive flexibility, which enables them to skillfully use both teacher-focused and student-focused approaches to cater to a wide range of classroom needs. Teachers with low self-efficacy, on the other hand, are less flexible and adopt fewer different strategies in their teaching practice. The findings challenge simplistic associations between self-efficacy and teaching strategies, emphasising that high self-efficacy promotes adaptability rather than a fixed preference for specific teaching methods. This adaptability is crucial in India’s diverse educational settings, where teachers must balance standardised curricula with the varied learning needs of students. This study highlights the importance of integrating personality traits and self-efficacy into teacher training programs. By promoting self-efficacy and tailoring professional development to consider individual personality traits, institutions can enhance teachers’ teaching flexibility, hence improving student engagement and learning outcomes. These findings have practical implications for teacher education, suggesting that adopting cognitive flexibility among teachers can improve instructional quality and classroom dynamics. To gain a deeper knowledge of how personality traits and self-efficacy impact teaching practices over time, future research should investigate causal relationships using longitudinal studies. Examining external factors like institutional policies, availability of resources, and cultural settings will help to clarify the dynamics at play. Furthermore, this study emphasises the need to strike a balance between teacher-focused and student-focused approaches to provide a comprehensive education that covers both conceptual understanding and the delivery of key information. This study offers insights into how the Indian educational system is changing and how, to achieve global standards, effective teaching techniques are becoming increasingly important. This study promotes the larger objective of educational excellence by exploring the interaction of internal and external factors impacting teaching styles and providing practical policy and practice recommendations.

Keywords: higher education, personality traits, self-efficacy, teaching styles

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Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

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In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

Procedia PDF Downloads 155