Search results for: Virulence features.

Authors: Hemang Madhusudan Anglay, Akash Gaur

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Value of visual perception in today’s context is vulnerable. Visual Graphic broadly and conveniently expresses culture, language and science of art that satisfactorily is a mould to cast various expressions. It is one of the essential parts of communication design which relatively can be used to approach the above areas of expressions. In between the receptors and interpreters, there is an expanse of comprehension and cliché in relation to the use of Visual Graphics. There are pedagogies, commodification and honest reflections where Visual Graphic is a common area of interest. The traditional receptors amidst the dilemma of this very situation find themselves in the pool of media, medium and interactions. Followed by a very vague interpretation the entire circle of communication becomes a question of comprehension vs cliché. Residing in the same ‘eco-system’ these communities who make pedagogies and multiply its reflections sometimes with honesty and sometimes on commercial values tend to function differently. With the advent of technology, which is a virtual space allows the user to access various forms of content. This diminishes the core characteristics and creates a vacuum even though it satisfies the user. The symbolic interpretation of visual form and structure is transmitted in a culture by the means of contemporary media. Starting from a very individualistic approach, today it is beyond Print & Electronic media. The expected outcome will be a study of Ahmedabad City, situated in the Gujarat State of India. It is identity with respect to socio-cultural as well as economic changes. The methodology will include process to understand the evolution and narratives behind it that will encompass diverse community, its reflection and it will sum up the salient features of communication through combination of visual and graphic that is relevant in Indian context trading its values to global scenario.

Keywords: communication, culture, graphic, visual

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Authors: Francimar Alves, Marcos Castro, Marllus Lustosa

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In the context of the teaching of computer programming, the choice of tool to use is very important in the initiation and continuity of learning a programming language. The literature tools do not always provide usability and pedagogical dynamism clearly and accurately for effective learning. This hypothesis implies fall in productivity and difficulty of learning a particular programming language by students. The integrated development environments (IDEs) Dev-C ++ and Code :: Blocks are widely used in introductory courses for undergraduate courses in Computer Science for learning C and C ++ languages. However, after several years of discontinuity maintaining the source code of Dev-C ++ tool, the continued use of the same in the teaching and learning process of the students of these institutions has led to difficulties, mainly due to the lack of update by the official developers, which resulted in a sequence of problems in using it on educational settings. Much of the users, dissatisfied with the IDE Dev-C ++, migrated to Code :: Blocks platform targeting the more dynamic range in the learning process of the C and C ++ languages. Nevertheless, there is still the need to create a tool that can provide the resources of most IDE's software development literature, however, more interactive, simple, accurate and efficient. This motivation led to the creation of Falcon C ++ tool, IDE that brings with features that turn it into an educational platform, which focuses primarily on increasing student learning index in the early disciplines of programming and algorithms that use the languages ​​C and C ++ . As a working methodology, a field research to prove the truth of the proposed tool was used. The test results and interviews with entry-level students and intermediate in a postsecondary institution gave basis for the composition of this work, demonstrating a positive impact on the use of the tool in teaching programming, showing that the use of Falcon C ++ software is beneficial in the teaching process of the C and C ++ programming languages.

Keywords: ide, education, learning, development, language

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Authors: Steven D. P Moore

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A novel method referred to asKýklos(Ky) dimensional geometry is proposed as an entity specific core geometric dimensional measurement system. Ky geometric measures can constructscaled multi-dimensionalmodels using regular and irregular sets in IRn. This entity specific-derived geometric measurement system shares similar fractal methods in which a ‘fractal transformation operator’ is applied to a set S to produce a union of N copies. The Kýklos’ inputs use 1D geometry as a core measure. One-dimensional inputs include the radius interval of a circle/sphere or the semiminor/semimajor axes intervals of an ellipse or spheroid. These geometric inputs have finite values that can be measured by SI distance units. The outputs for each interval are divided and subdivided 1D subcomponents with a union equal to the interval geometry/length. Setting a limit of subdivision iterations creates a finite value for each 1Dsubcomponent. The uniqueness of this method is captured by allowing the simplest 1D inputs to define entity specific subclass geometric core measurements that can also be used to derive length measures. Current methodologies for celestial based measurement of time, as defined within SI units, fits within this methodology, thus combining spatial and temporal features into geometric core measures. The novel Ky method discussed here offers geometric measures to construct scaled multi-dimensional structures, even models. Ky classes proposed for consideration include celestial even subatomic. The application of this offers incredible possibilities, for example, geometric architecture that can represent scaled celestial models that incorporates planets (spheroids) and celestial motion (elliptical orbits).

Keywords: Kyklos, geometry, measurement, celestial, dimension

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: Vaishali D. Khairnar

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The main aim is to provide lower-latency and highly reliable communication facilities for vehicles in the automobile industry; vehicle-to-everything (V2X) communication basically intends to increase expressway road security and its effectiveness. The Ultra-Reliable Low-Latency Communications (URLLC) algorithm and cellular networks are applied in combination with Mobile Broadband (MBB). This is particularly used in express way safety-based driving applications. Expressway vehicle drivers (humans) will communicate in V2X systems using the sixth-generation (6G) communication systems which have very high-speed mobility features. As a result, we need to determine how to ensure reliable and consistent wireless communication links and improve the quality to increase channel gain, which is becoming a challenge that needs to be addressed. To overcome this challenge, we proposed a unique multi-user scheduling algorithm for ultra-massive multiple-input multiple-output (MIMO) systems using 6G. In wideband wireless network access in case of high traffic and also in medium traffic conditions, moreover offering quality-of-service (QoS) to distinct service groups with synchronized contemporaneous traffic on the highway like the Mumbai-Pune expressway becomes a critical problem. Opportunist MAC (OMAC) is a way of proposing communication across a wireless communication link that can change in space and time and might overcome the above-mentioned challenge. Therefore, a multi-user scheduling algorithm is proposed for MIMO systems using a cross-layered MAC protocol to achieve URLLC and high reliability in V2X communication.

Keywords: ultra-reliable low latency communications, vehicle-to-everything communication, multiple-input multiple-output systems, multi-user scheduling algorithm

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Authors: Khin Thanda Win, Chunying Zhang, Sanghyeob Lee

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Mildew resistance locus o (Mlo), a plant-specific gene family with seven-transmembrane (TM), plays an important role in plant resistance to powdery mildew (PM). PM caused by Podosphaera xanthii is a widespread plant disease and probably represents the major fungal threat for many Cucurbits. The recent Cucurbita maxima genome sequence data provides an opportunity to identify and characterize the MLO gene family in this species. Total twenty genes (designated CmaMLO1 through CmaMLO20) have been identified by using an in silico cloning method with the MLO gene sequences of Cucumis sativus, Cucumis melo, Citrullus lanatus and Cucurbita pepo as probes. These CmaMLOs were evenly distributed on 15 chromosomes of 20 C. maxima chromosomes without any obvious clustering. Multiple sequence alignment showed that the common structural features of MLO gene family, such as TM domains, a calmodulin-binding domain and 30 important amino acid residues for MLO function, were well conserved. Phylogenetic analysis of the CmaMLO genes and other plant species reveals seven different clades (I through VII) and only clade IV is specific to monocots (rice, barley, and wheat). Phylogenetic and structural analyses provided preliminary evidence that five genes belonged to clade V could be the susceptibility genes which may play the importance role in PM resistance. This study is the first comprehensive report on MLO genes in C. maxima to our knowledge. These findings will facilitate the functional analysis of the MLOs related to PM susceptibility and are valuable resources for the development of disease resistance in pumpkin.

Keywords: Mildew resistance locus o (Mlo), powdery mildew, phylogenetic relationship, susceptibility genes

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Authors: Zeynep Tanrıverdi

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The Bosphorus is an international waterway in Istanbul city of Turkey connecting the Sea of Marmara and the Black Sea. The Bosphorus, which has formed an important part of the silhouette of Istanbul throughout history, has also influenced the design of the coastal structures built around it. The waterfront mansions, which are located on both sides of the Bosphorus by the sea, and can be generally of two or three storeys, are called “yalı”. The yalı buildings with their architectural characteristics of the traditional Turkish House are the most grandiose examples of Ottoman residential architecture. However, the classical Ottoman yalı architecture of the 18th century can only be seen in engravings, and today only the modest and smaller yalı examples from the 19th century can be seen because of their disappearance over time. The study aims to reveal the architectural conservation and restoration problems of waterfront mansions and propose solutions for them. Firstly, the development of the waterfront mansion architecture in Bosphorus was evaluated in its historical process. Secondly, the waterfront mansions and their architectural features were explained. Thirdly, the architectural conservation and restoration problems that caused the disappearance of waterfront mansions were discussed. These problems include disruptions in legal regulations and practices about the Bosphorus, dramatic changes in Turkey’s socio-cultural life from the Ottoman Empire to the present, inadequacies in economic resources, negative environmental effects, and errors in restoration works. Finally, solution suggestions were proposed for the problems that threaten the protection of waterfront mansions. In the study, literature on waterfront mansions was reviewed using historical reports, photographs, maps, and drawings in archival documents. It is hoped that this study will contribute the conservation of the “Yalı” waterfront mansions, which occupy a particular role in the cultural heritage of Turkey, and to their transmission with their authentic values to the next generation.

Keywords: bosphorus architecture, conservation, heritage, Istanbul, waterfront mansions (yalı)

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Authors: Shreya Ghosh, Akansha Garg, Chayanika Kala, Ashwani Kumar Thakur

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Background: Granuloma formation is one of the characteristic features of tuberculosis. Besides, chronic inflammation underlying tuberculosis is often indicated by an increase in the concentration of serum amyloid A (SAA) protein. The connection between tuberculosis and SAA-driven secondary amyloidosis is well documented. However, SAA-derived amyloid deposition start sites are not well understood in tuberculosis and other chronic inflammatory conditions. It was hypothesized that granuloma could be a potential site for an amyloid deposition because both SAA protein and proteases that cleave SAA into aggregation-prone fragments are reported to be present in the granuloma. Here the authors have shown the presence of SAA-derived amyloid deposits in the granuloma of tuberculosis patients. Methodology: Over a period of two years, tuberculosis patients were screened, and biopsies were collected from the affected organs of the patients. The gold standard, Congo red dye staining, was used to identify amyloid deposits in the tissue sections of tuberculosis patients containing granulomatous structure. Results: 11 out of 150 FFPE biopsy specimens of tuberculosis patients showed eosinophilic hyaline-rich deposits surrounding granuloma. Upon Congo red staining, these deposits exhibited characteristic apple-green birefringence under polarized light, confirming amyloid deposits. Further, upon immunohistochemical staining with anti-SAA, the amyloid enriched areas showed positive immunoreactivity. Conclusion: In this pilot study, we have shown that granuloma can be a potential site for serum amyloid A-derived amyloid formation in tuberculosis patients. Moreover, the presence of amyloid gave significant cues that granuloma might be a probable amyloid deposition start in tuberculosis patients. This study will set a stage to expand the clinical and fundamental research in the understanding of amyloid formation in granuloma underlying tuberculosis and chronic inflammatory conditions.

Keywords: amyloid, granuloma, periphery, serum amyloid A, tuberculosis

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

Procedia PDF Downloads 158

Authors: C. Salis, D. Zedda, M. F. Wilson

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The IDEA project was conceived for teachers who are interested in enhancing their capacity to effectively implement the use of specific technologies in their teaching practice. Participating teachers are coached and supported as they explore technologies applied to the educational context. They access tools such as the technological platform developed by our team. Among the platform functionalities, teachers access an instructional systems design (ISD) tool (learning designer) that was adapted to the needs of our project. The tool is accessible from computers or mobile devices and used in association with other technologies to create new, meaningful learning environments. The objective of an instructional systems design is to guarantee the quality and effectiveness of education and to enhance learning. This goal involves both teachers who want to become more efficient in transferring knowledge or skills and students as the final recipient of their teaching. The use of Blooms’s taxonomy enables teachers to classify the learning objectives into levels of complexity and specificity, thus making it possible to highlight the kind of knowledge teachers would like their students to reach. The fact that the instructional design features can be visualized through the IDEA platform is a guarantee for those who are looking for specific educational materials to be used in their lessons. Despite the benefits offered, a number of teachers are reluctant to use ISD because the preparatory work of having to thoroughly analyze the teaching/learning objectives, the planning of learning material, assessment activities, etc., is long and felt to be time-consuming. This drawback is minimized using a learning designer, as the tool facilitates to reuse of the didactic contents having a clear view of the processes of analysis, planning, and production of educational or testing materials uploaded on our platform. In this paper, we shall present the feedback of the teachers who used our tool in their didactic.

Keywords: educational benefits, educational quality, educational technology, ISD tool

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Authors: Jingjue Bao, Ye Li, Yujie Qi

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The explosive growth of ride-hailing has reshaped residents' travel behavior and plays a crucial role in urban mobility within the built environment. Contributing to the research of the spatial variation of ride-hailing demand and its relationship to the built environment and socioeconomic factors, this study utilizes multi-source data from Haikou, China, to construct a Multi-scale Geographically Weighted Regression model (MGWR), considering spatial scale heterogeneity. The regression results showed that MGWR model was demonstrated superior interpretability and reliability with an improvement of 3.4% on R2 and from 4853 to 4787 on AIC, compared with Geographically Weighted Regression model (GWR). Furthermore, to precisely identify the surrounding environment of sampling point, DeepLabv3+ model is employed to segment street-level images. Features extracted from these images are incorporated as variables in the regression model, further enhancing its rationality and accuracy by 7.78% improvement on R2 compared with the MGWR model only considered region-level variables. By integrating multi-scale geospatial data and utilizing advanced computer vision techniques, this study provides a comprehensive understanding of the spatial dynamics between ride-hailing demand and the urban built environment. The insights gained from this research are expected to contribute significantly to urban transportation planning and policy making, as well as ride-hailing platforms, facilitating the development of more efficient and effective mobility solutions in modern cities.

Keywords: travel behavior, ride-hailing, spatial relationship, built environment, street-level image

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Authors: M. Eskandarighadi, C. R. McGann

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It is observed from past experiences of earthquakes that local site conditions can significantly affect the strong ground motion characteristics experience at the site. One-dimensional seismic site response analysis is the most common approach for investigating site response. This approach assumes that soil is homogeneous and infinitely extended in the horizontal direction. Therefore, tying side boundaries together is one way to model this behavior, as the wave passage is assumed to be only vertical. However, 1D analysis cannot capture the 2D nature of wave propagation, soil heterogeneity, and 2D soil profile with features such as inclined layer boundaries. In contrast, 2D seismic site response modeling can consider all of the mentioned factors to better understand local site effects on strong ground motions. 2D wave propagation and considering that the soil profile on the two sides of the model may not be identical clarifies the importance of a boundary condition on each side that can minimize the unwanted reflections from the edges of the model and input appropriate loading conditions. Ideally, the model size should be sufficiently large to minimize the wave reflection, however, due to computational limitations, increasing the model size is impractical in some cases. Another approach is to employ free-field boundary conditions that take into account the free-field motion that would exist far from the model domain and apply this to the sides of the model. This research focuses on implementing free-field boundary conditions in OpenSees for 2D site response analysisComparisons are made between 1D models and 2D models with various boundary conditions, and details and limitations of the developed free-field boundary modeling approach are discussed.

Keywords: boundary condition, free-field, opensees, site response analysis, wave propagation

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Authors: Soheila Sadeghi

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Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: E. Tarantino, G. Disciglio, G. Gatta, L. Frabboni, A. Libutti, A. Tarantino

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Worldwide, about two-thirds of industrial and domestic wastewater effluent is discharged without treatment, which can cause contamination and eutrophication of the water. In particular, for Mediterranean countries, irrigation with treated wastewater would mitigate the water stress and support the agricultural sector. Changing global weather patterns will make the situation worse, due to increased susceptibility to drought, which can cause major environmental, social, and economic problems. The study was carried out in open field in an intensive agricultural area of the Apulian region in Southern Italy where freshwater resources are often scarce. As well as providing a water resource, irrigation with treated wastewater represents a significant source of nutrients for soil–plant systems. However, the use of wastewater might have further effects on soil. This study thus investigated the long-term impact of irrigation with reclaimed agro-industrial wastewater on the chemical characteristics of the soil. Two crops (processing tomato and broccoli) were cultivated in succession in Stornarella (Foggia) over four years from 2012 to 2016 using two types of irrigation water: groundwater and tertiary treated agro-industrial wastewater that had undergone an activated sludge process, sedimentation filtration, and UV radiation. Chemical analyses were performed on the irrigation waters and soil samples. The treated wastewater was characterised by high levels of several chemical parameters including TSS, EC, COD, BOD₅, Na⁺, Ca²⁺, Mg²⁺, NH₄-N, PO₄-P, K⁺, SAR and CaCO₃, as compared with the groundwater. However, despite these higher levels, the mean content of several chemical parameters in the soil did not show relevant differences between the irrigation treatments, in terms of the chemical features of the soil.

Keywords: agro-industrial wastewater, broccoli, long-term re-use, tomato

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: Ipek Gunay, Efe B. Orman, Metin Ozer, Bekir Salih, Ali R. Ozkaya

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Phthalocyanines with macrocyclic ring containing at least three heteroatoms have nine or more membered structures. Metal-free phthalocyanines react with metal salts to obtain chelate complexes. This is one of the most important features of metal-free phthalocyanine as ligand structure. Although phthalocyanines have very similar properties with porphyrins, they have some advantages such as lower cost, easy to prepare, and chemical and thermal stability. It’s known that Pc compounds have shown one-electron metal-and/or ligand-based reversible or quasi-reversible reduction and oxidation processes. The redox properties of phthalocyanines are critically related to the desirable properties of these compounds in their technological applications. Thus, Pc complexes have also been receiving increasing interest in the area of fuel cells due to their high electrocatalytic activity in dioxygen reduction and fuel cell applications. In this study, novel phthalocyanine complexes coordinated with Fe(II) and Co (II) to be used as catalyst were synthesized. Aiming this goal, a new nitrile ligand was synthesized starting from 4-hydroxy-3,5-dimethoxy benzyl alcohol and 4-nitrophthalonitrile in the presence of K2CO3 as catalyst. After the isolation of the new type of nitrile and metal complexes, the characterization of mentioned compounds was achieved by IR, H-NMR and UV-vis methods. In addition, the electrochemical behaviour of Pc complexes was identified by cyclic voltammetry, square wave voltammetry and in situ spectroelectrochemical measurements. Furthermore, the catalytic performances of Pc complexes for oxygen reduction were tested by dynamic voltammetry measurements, carried out by the combined system of rotating ring-disk electrode and potentiostat, in a medium similar to fuel-cell working conditions.

Keywords: phthalocyanine, electrocatalysis, electrochemistry, in-situ spectroelectrochemistry

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Authors: Irene M. Arora

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Hypothesis: Attention Deficit Hyperactivity Disorder is the result of an underdeveloped prefrontal cortex which is the primary cause for the signs and symptoms seen as defining features of ADHD. Methods: Through ‘PubMed’, ‘Wiley’ and ‘Google Scholar’ studies published between 2011-2018 were evaluated, determining if a dysfunctional prefrontal cortex caused the characteristic symptoms associated with ADHD. The search terms "prefrontal cortex", "Attention-Deficit/Hyperactivity Disorder", "cognitive control", "frontostriatal tract" among others, were used to maximize the assortment of relevant studies. Excluded papers were systematic reviews, meta-analyses and publications published before 2010 to ensure clinical relevance. Results: Nine publications were analyzed in this review, all of which were non-randomized matched control studies. Three studies found a decrease in the functional integrity of the frontostriatal tract fibers in conjunction with four studies finding impaired frontal cortex stimulation. Prefrontal dysfunction, specifically medial and orbitofrontal areas, displayed abnormal functionality of reward processing in ADHD patients when compared to their normal counterparts. A total of 807 subjects were studied in this review, yielding that a little over half (54%) presented with remission of symptoms in adulthood. Conclusion: While the prefrontal cortex shows the highest consistency of impaired activity and thinner volumes in patients with ADHD, this is a heterogenous disorder implicating its pathophysiology to the dysfunction of other neural structures as well. However, remission of ADHD symptomatology in adulthood was found to be attributable to increased prefrontal functional connectivity and integration, suggesting a key role for the prefrontal cortex in the development of ADHD.

Keywords: prefrontal cortex, ADHD, inattentive, impulsivity, reward processing

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Authors: Saeed Vahedikamal, Ian Hepburn

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Microwave Kinetic Inductance Detectors (MKIDs) are considered as one of the most promising photon detectors of the future in many Astronomical applications such as exoplanet detections. The MKID advantages stem from their single photon sensitivity (ranging from UV to optical and near infrared), photon energy resolution and high temporal capability (~microseconds). There has been substantial progress in the development of these detectors and MKIDs with Megapixel arrays is now possible. The unique capability of recording an incident photon and its energy (or wavelength) while also registering its time of arrival to within a microsecond enables an array of MKIDs to produce a four-dimensional data block of x, y, z and t comprising x, y spatial, z axis per pixel spectral and t axis per pixel which is temporal. This offers the possibility that the spectrum and brightness variation for any detected piece of space debris as a function of time might offer a unique identifier or fingerprint. Such a fingerprint signal from any object identified in multiple detections by different observers has the potential to determine the orbital features of the object and be used for their tracking. Modelling performed so far shows that with a 20 cm telescope located at an Astronomical observatory (e.g. La Palma, Canary Islands) we could detect sub cm objects at GEO. By considering a Lambertian sphere with a 10 % reflectivity (albedo of the Moon) we anticipate the following for a GEO object: 10 cm object imaged in a 1 second image capture; 1.2 cm object for a 70 second image integration or 0.65 cm object for a 4 minute image integration. We present details of our modelling and the potential instrument for a dedicated GEO surveillance system.

Keywords: space debris, orbital debris, detection system, observation, microwave kinetic inductance detectors, MKID

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Authors: Rithika Sriram, Kiran M. Bhojwani

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Introduction : Tuberculosis of the head and neck has proved to be a diagnostic challenge for otorhinolarynologists around the world. These lesions are often misdiagnosed as cancer. So in order to contribute to a better understanding of these lesions, we have conducted our study among patients affected by TB in the head and neck region with the objective of assessing the various manifestations, presentations, diagnostic techniques, risk factors such as smoking and alcohol consumption, coexisting illnesses and treatment modalities. Materials and Methods: This was a retrospective study conducted over a three year period (2012-2014) in 2 hospitals affliated to Kasturba Medical College in Mangalore, South India. A semi structured proforma was used to capture information from the medical records pertaining to the various objectives of the study such as clinical features and history of smoking. Data was analysed using SPSS version 16.0 and results obtained were depicted as percentages. Chi square test was used to find association between the variables and p<0.05 was considered statistically significant. Results: 104 patients were found to have TB of the head and neck and among them,the most common manifestation was found to be Tubercular Lymphadenitis (86.53%), followed by laryngeal TB (4.8%), submandibular gland TB (3.8%), deep neck space abscess(3.8%) and adenotonsillar TB. FNAC was found to be the gold standard for the diagnosis of TB disease of the lymph node.26% of the patients had coexisting HIV infection and 16.3% of the patients had associated pulmonary TB. More than 20% of the patients were smokers. Most patients were treated using ATT. Conclusion: Tuberculosis affecting regions of head and neck is no longer uncommon. Sufficient knowledge and appropriate diagnostic means is required while dealing with these lesions and must be included in the differential diagnosis of pathological lesions of head and neck.

Keywords: FNAC, Mangalore, smoking, tuberculosis

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Authors: Jana Stepanovska, Roman Matejka, Jozef Rosina, Marta Vandrovcova, Lucie Bacakova

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The aim of this study was to develop a system for mechanical and also electrical stimulation controlling in vitro osteogenesis under conditions more similar to the in vivo bone microenvironment than traditional static cultivation, which would achieve good adhesion, growth and other specific behaviors of osteogenic cells in cultures. An engineered culture system for mechanical stimulation of the mesenchymal stem cells on the charged surface was designed. The bioreactor allows efficient mechanical loading inducing an electrical response and perfusion of the culture chamber with seeded cells. The mesenchymal stem cells were seeded to specific charged materials, like polarized hydroxyapatite (Hap) or other materials with piezoelectric and ferroelectric features, to create electrical potentials for stimulating of the cells. The material of the matrix was TiNb alloy designed for these purposes, and it was covered by BaTiO3 film, like a kind of piezoelectric material. The process of mechanical stimulation inducing electrical response is controlled by measuring electrical potential in the chamber. It was performed a series of experiments, where the cells were seeded, perfused and stimulated up to 48 hours under different conditions, especially pressure and perfusion. The analysis of the proteins expression was done, which demonstrated the effective mechanical and electrical stimulation. The experiments demonstrated effective stimulation of the cells in comparison with the static culture. This work was supported by the Ministry of Health, grant No. 15-29153A and the Grant Agency of the Czech Republic grant No. GA15-01558S.

Keywords: charged surface, dynamic cultivation, electrical stimulation, ferroelectric layers, mechanical stimulation, piezoelectric layers

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Authors: Hosam A. Shawky, Amr A. Abdel Fatah, Moustafa M. S. Abo ElFad, Abdel Hameed M. El-Aassar

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Water desalination projects based on reverse osmosis technology are being introduced in Egypt to combat drinking water shortage in remote areas. Reverse osmosis (RO) desalination is a pressure driven process. This paper focuses on the design of an integrated brackish water and seawater RO desalination and solar Photovoltaic (PV) technology. A small Mobile PV driven RO desalination plant prototype without batteries is designed and tested. Solar-driven reverse osmosis desalination can potentially break the dependence of conventional desalination on fossil fuels, reduce operational costs, and improve environmental sustainability. Moreover, the innovative features incorporated in the newly designed PV-RO plant prototype are focusing on improving the cost effectiveness of producing drinkable water in remote areas. This is achieved by maximizing energy yield through an integrated automatic single axis PV tracking system with programmed tilting angle adjustment. An autonomous cleaning system for PV modules is adopted for maximizing energy generation efficiency. RO plant components are selected so as to produce 4-5 m3/day of potable water. A basic criterion in the design of this PV-RO prototype is to produce a minimum amount of fresh water by running the plant during peak sun hours. Mobility of the system will provide potable water to isolated villages and population as well as ability to provide good drinking water to different number of people from any source that is not drinkable.

Keywords: design, reverse osmosis, photovoltaic, energy, desalination, Egypt

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Authors: Svitlana Yermak, Olubukola Aluko

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The purpose of this study was a literature review on the topic of social entrepreneurship, a review of new trends and best practices, the study of existing inclusive business models and their interaction with the principles of the circular economy for possible implementation in the practice of Ukraine in war and post-war times in conditions of scarce resources. Thus, three research questions were identified and substantiated: to determine the characteristics of social entrepreneurship, consider the features in Ukraine and the UK; highlight the criteria for inclusion in social entrepreneurship and its legal support; explore examples of existing inclusive circular business models to illustrate how the two concepts may be combined. A detailed review of the literature selected from the Scopus and Web of Science databases was carried out. The study revealed signs of social entrepreneurship, the main of which are doing business and making a profit, as well as the social orientation of the business, which is prescribed in the constituent documents of the enterprise immediately upon its creation. Considered are the characteristics of social entrepreneurship in the UK and Ukraine. It has been established that in the UK, social entrepreneurship is clearly regulated by the state; there are special legislative norms and support programs, in contrast to Ukraine, where these processes are only partially regulated. The study identified the main criteria for inclusion in inclusive circular business models: economic (sustainability and efficiency, job creation and economic growth, promotion of local development), social (accessibility, equity and fairness, inclusion and participation), and resources in their interconnection. It is substantiated that the resource criterion is especially important for this type of business model. It provides for the efficient and sustainable use of resources, as well as the cyclical nature of resources. And it was concluded that the principles of the circular economy not only do not contradict but, on the contrary, complement and expand the inclusive business models on which social entrepreneurship is based.

Keywords: social entrepreneurship, inclusive business models, circular economy, inclusion criteria

Procedia PDF Downloads 102

Authors: Carla Nardelli, Jérome Holtzmann, Céline Baeyens, Catherine Bortolon

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Background. Emotion regulation (ER) is a process associated with difficulties in mental health. Given its transdiagnostic features, its improvement could facilitate the recovery of various psychological issues. A limit of current studies is the lack of knowledge regarding whether available interventionsimprove ER flexibility (i.e., the ability to implement ER strategies in line with contextual demands), even though this capacity has been associated with better mental health and well-being. Therefore, the aim of the study is to test the efficacy of a 9-weeks ER group program (the Affect Regulation Training-ART), using the most appropriate measures (i.e., experience sampling method) in a student population. Plus, the goal of the study is to explore the potential mediative role of ER flexibility on mental health improvement. Method. This Randomized Controlled Trial will comparethe ER program group to an active control group (a relaxation program) in 100 participants. To test the mediative role of ER flexibility on mental health, daily measures will be used before, during, and after the interventions to evaluate the extent to which participants are flexible in their ER. Expected outcomes. Using multilevel analyses, we expect an improvement in anxious-depressive symptomatology for both groups. However, we expect the ART group to improve specifically on ER flexibility ability and the last to be a mediative variable on mental health. Conclusion. This study will enhance knowledge on interventions for students and the impact of interventions on ER flexibility. Also, this research will improve knowledge on ecological measures for assessing the effect of interventions. Overall, this project represents new opportunities to improve ER skills to improve mental health in undergraduate students.

Keywords: emotion regulation flexibility, experience sampling method, psychological intervention, emotion regulation skills

Procedia PDF Downloads 137

Authors: Choumous Mannoubi, Dahlia Kairy, Brigitte Vachon

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The use of technology allows clinicians to provide an individualized approach in a cost-effective manner and to reach a broader client base more easily. Such interventions can be effective in ensuring self-management and follow-up of people with diabetes, reducing the risk of complications by improving accessibility to care services, and better adherence to health recommendations. Consideration of users' opinions and fears to inform the design and implementation stages of these telehealth services seems to be essential to improve their acceptance and usability. The objective of this study is to describe the telepractice of nutritionists supporting the therapeutic management of diabetic patients and document the functional requirements of nutritionists for the design of a tele-nutrition platform. To best identify the requirements and constraints of nutritionists, we conducted individual semi-structured interviews with 10 nutritionists who offered tele-nutrition services. Using a qualitative design with a descriptive approach based on the Nutrition Care Process Model (mNCP) framework, we explored in depth the state of nutritionists' telepractice in public and private health care settings, as well as their requirements for teleconsultation. Qualitative analyses revealed that nutritionists primarily used telephone calls during the COVID 19 pandemic to provide teleconsultations. Nutritionists identified the following important features for the design of a tele-nutrition platform: it should support interprofessional collaboration, allow for the development and monitoring of a care plan, integrate with the existing IT environment, be easy to use, accommodate different levels of patient literacy, and allow for easy sharing of educational materials to support nutrition education.

Keywords: telehealth, nutrition, diabetes, telenutrition, teleconsultation, telemonitoring

Procedia PDF Downloads 136

Authors: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

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The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

Procedia PDF Downloads 363

Authors: Faseluka Olamide Tope

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Giving quality education to students with special needs requires that all necessary resources should be harnessed and digital devices has become important part of resources used as instructional materials in educating students with special needs. Teachers who will make use of these technologies are considered as a part of the most important elements in any educational programme and the effective usage of these technologies largely depends on them. Out of numerous determinants of the effective use of these digital devices, this study examines teachers’ attitude and knowledge as predictors of effective use of digital technology for education of special needs student in Oyo state, Nigeria. The descriptive survey research design of the expo-facto type was adopted for the study, using simple random sampling technique. The study was carried out among sixty (60) participants. Two research questions and two research hypotheses were formulated and used. The data collected through the research instruments for the study were analysedusing frequency, percentage, mean and standard deviation, Pearson, Product, Moment Correlation (PPMC) and Multiple Regression Analysis. The study revealed a significant relationship between teachers attitude (50, < 0.05) and effective use of digital technologies for special needs students. Furthermore, there was a significant contribution F (F=4.289; R=0.876 and R2 =0.758) in the joint contribution of the independent variable  (teacher’s attitude and teacher’s knowledge) and dependent variable (effective use of digital technologies) while teachers knowledge have the highest contribution(b=7.926, t=4.376), the study therefore revealed that teachers attitude and knowledge are potent factors that predicts the effective usage of digital technologies for the education of special needs student. The study recommended that due to the ever-changing nature of technology which comes with new features, teachers should be equipped with appropriate knowledge in order to effectively make use of them and teachers should also develop right attitude toward the use of digital technologies

Keywords: teachers’ knowledge, teachers’ attitude, digital devices, special needs students

Procedia PDF Downloads 51

Authors: Henrique Fernandes, Sofia Catalucci, Richard Leach, Kapil Sugand

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Background: Holograms are 3D images formed by the interference of light beams from a laser or other coherent light source. Pepper’s ghost is a form of hologram conceptualised in the 18th century. This Holographic visualisation with metrology measuring techniques by displaying measurements taken in real-time in holographic form can assist in research and education. New structural designs such as the Plexiglass Stand and the Hologram Box can optimise the holographic experience. Method: The equipment used included: (i) Zeiss’s ATOS Core 300 optical coordinate measuring instrument that scanned real-world objects; (ii) Cloud Compare, open-source software used for point cloud processing; and (iii) Hologram Box, designed and manufactured during this research to provide the blackout environment needed to display 3D point clouds in real-time measurements in holographic format, in addition to a portability aspect to holograms. The equipment was tailored to realise the goal of displaying measurements in an innovative technique and to improve on conventional methods. Three test scans were completed before doing a holographic conversion. Results: The outcome was a precise recreation of the original object in the holographic form presented with dense point clouds and surface density features in a colour map. Conclusion: This work establishes a way to visualise data in a point cloud system. To our understanding, this is a work that has never been attempted. This achievement provides an advancement in holographic visualisation. The Hologram Box could be used as a feedback tool for measurement quality control and verification in future smart factories.

Keywords: holography, 3D scans, hologram box, metrology, point cloud

Procedia PDF Downloads 89

Authors: C. Malça, P. Gonçalves, N. Alves, A. Mateus

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Regardless of the manufacturing process used, subtractive or additive, material, purpose and application, produced components are conventionally solid mass with more or less complex shape depending on the production technology selected. Aspects such as reducing the weight of components, associated with the low volume of material required and the almost non-existent material waste, speed and flexibility of production and, primarily, a high mechanical strength combined with high structural performance, are competitive advantages in any industrial sector, from automotive, molds, aviation, aerospace, construction, pharmaceuticals, medicine and more recently in human tissue engineering. Such features, properties and functionalities are attained in metal components produced using the additive technique of Rapid Prototyping from metal powders commonly known as Selective Laser Melting (SLM), with optimized internal topologies and varying densities. In order to produce components with high strength and high structural and functional performance, regardless of the type of application, three different internal topologies were developed and analyzed using numerical computational tools. The developed topologies were numerically submitted to mechanical compression and four point bending testing. Finite Element Analysis results demonstrate how different internal topologies can contribute to improve mechanical properties, even with a high degree of porosity relatively to fully dense components. Results are very promising not only from the point of view of mechanical resistance, but especially through the achievement of considerable variation in density without loss of structural and functional high performance.

Keywords: additive manufacturing, internal topologies, porosity, rapid prototyping, selective laser melting

Procedia PDF Downloads 332

Authors: Yen-Mei Lee

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Mobile learning (m-learning) has been applied in the education field not only because it is an alternative to web-based learning but also it possesses the ‘anytime, anywhere’ learning features. However, most studies focus on the technology-related issue, such as usability and functionality instead of addressing m-learning from the motivational perspective. Accordingly, the main purpose of the current paper is to integrate critical factors from different motivational theories and related findings to have a better understand the catalysts of an individual’s learning motivation toward m-learning. The main research question for this study is stated as follows: based on different motivational perspectives, what factors of applying mobile devices as medium can facilitate people’s learning motivations? Self-Determination Theory (SDT), Uses and Gratification Theory (UGT), Malone and Lepper’s taxonomy of intrinsic motivation theory, and different types of motivation concepts were discussed in the current paper. In line with the review of relevant studies, three motivating factors with five essential elements are proposed. The first key factor is autonomy. Learning on one’s own path and applying personalized format are two critical elements involved in the factor of autonomy. The second key factor is to apply a build-in instant feedback system during m-learning. The third factor is creating an interaction system, including communication and collaboration spaces. These three factors can enhance people’s learning motivations when applying mobile devices as medium toward learning. To sum up, in the currently proposed paper, with different motivational perspectives to discuss the m-learning is different from previous studies which are simply focused on the technical or functional design. Supported by different motivation theories, researchers can clearly understand how the mobile devices influence people’s leaning motivation. Moreover, instructional designers and educators can base on the proposed factors to build up their unique and efficient m-learning environments.

Keywords: autonomy, learning motivation, mobile learning (m-learning), motivational perspective

Procedia PDF Downloads 182

Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

Procedia PDF Downloads 92