Search results for: similarity transformation

Authors: Ahmad Fahad Alotaibi, Khalid Hamed Hajri, Humoud Hudiban Rashidi

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Intelligent information technology infrastructure is considered one of the enablers to enhance digital transformation in the gas and oil fields to optimize IT infrastructure reliability by supporting operations and maintenance in a safe and secure method to optimize resources. Smart IT buildings, communication rooms and shelters with intelligent technologies can strengthen the performance and profitability of gas and oil companies by ensuring business continuity. This paper describes the advantages of deploying intelligent IT infrastructure in the oil and gas industry by illustrating its positive impacts on some development aspects, for instance, operations, maintenance, safety, security and resource optimization. Moreover, it highlights the challenges and difficulties of providing smart IT services in a remote area and proposes solutions to overcome such difficulties.

Keywords: intelligent IT infrastructure, remote areas, oil and gas field, digitalization

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Authors: Ritu Vashistha, Jitender Kumar

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Clustering using the K-means algorithm is a very common way to understand and analyze the obtained output data. When a similar object is grouped, this is called the basis of Clustering. There is K number of objects and C number of cluster in to single cluster in which k is always supposed to be less than C having each cluster to be its own centroid but the major problem is how is identify the cluster is correct based on the data. Formulation of the cluster is not a regular task for every tuple of row record or entity but it is done by an iterative process. Each and every record, tuple, entity is checked and examined and similarity dissimilarity is examined. So this iterative process seems to be very lengthy and unable to give optimal output for the cluster and time taken to find the cluster. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses the Euclidian distance formula as well melanosis to find the minimum distance between slots as technically we called clusters and the same approach we have also applied to Ant Colony Optimization(ACO) algorithm, which results in the production of two and multi-dimensional matrix.

Keywords: ant colony optimization, data clustering, centroids, data mining, k-means

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Authors: Moohyun Lee, Soojung Hur, Yongwan Park

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In this paper is proposed a method uses only LIDAR system to classification an obstacle and determine its type by establishing database for classifying obstacles based on LIDAR. The existing LIDAR system, in determining the recognition of obstruction in an autonomous vehicle, has an advantage in terms of accuracy and shorter recognition time. However, it was difficult to determine the type of obstacle and therefore accurate path planning based on the type of obstacle was not possible. In order to overcome this problem, a method of classifying obstacle type based on existing LIDAR and using the width of obstacle materials was proposed. However, width measurement was not sufficient to improve accuracy. In this research, the width data was used to do the first classification; database for LIDAR intensity data by four major obstacle materials on the road were created; comparison is made to the LIDAR intensity data of actual obstacle materials; and determine the obstacle type by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that data declined in quality in comparison to 3D LIDAR and it was possible to classify obstacle materials using 2D LIDAR.

Keywords: obstacle, classification, database, LIDAR, segmentation, intensity

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Authors: Qiang Zhang, Chun Yuan

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Since the PU (Prediction Unit) decision process is the most time consuming part of the emerging HEVC (High Efficient Video Coding) standardin intra and inter frame coding, this paper proposes the fast PU decision algorithm and speed up the algorithm using CUDA (Compute Unified Device Architecture). In intra frame coding, the fast PU decision algorithm uses the texture features to skip intra-frame prediction or terminal the intra-frame prediction for smaller PU size. In inter frame coding of HEVC, the fast PU decision algorithm takes use of the similarity of its own two Nx2N size PU's motion vectors and the hierarchical structure of CU (Coding Unit) partition to skip some modes of PU partition, so as to reduce the motion estimation times. The accelerate algorithm using CUDA is based on the fast PU decision algorithm which uses the GPU to make the motion search and the gradient computation could be parallel computed. The proposed algorithm achieves up to 57% time saving compared to the HM 10.0 with little rate-distortion losses (0.043dB drop and 1.82% bitrate increase on average).

Keywords: HEVC, PU decision, inter prediction, intra prediction, CUDA, parallel

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Authors: Hatim Elhag

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Banking and Financial Service Institutions are possibly the most advanced in terms of technology adoption and use it as a key differentiator. With high levels of business process automation, maturity in the functional portfolio, straight through processing and proven technology outsourcing benefits, Banking sector stand to benefit significantly from Cloud computing capabilities. Additionally, with complex Compliance and Regulatory policies, combined with expansive products and geography coverage, the business impact is even greater. While the benefits are exponential, there are also significant challenges in adopting this model– including Legal, Security, Performance, Reliability, Transformation complexity, Operating control and Governance and most importantly proof for the promised cost benefits. However, new architecture designed should be implemented to align this approach.

Keywords: security, cloud, banking sector, cloud computing

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Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

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Authors: Ellen M. Lukasik

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In coordination and support of the Center for Innovative and Strategic Transformation of Alkane Resources (CISTAR) Research Experience for Teachers (RET) at the University of Texas at Austin and under the guidance and direction of Professor Joan Brennecke, this study examined the addition of silver in an ionic liquid used to separate cyclohexane from cyclohexene. We recreated the liquid-liquid separation experimental results from the literature on cyclohexene, cyclohexane, and [allylmim][Tf2N] to verify our method, then evaluated the separation performance of silver - ionic liquid (IL) mixtures by various characterization techniques. To introduce the concepts of this research in high school education, a lesson plan was developed to instruct students on the principles of liquid-liquid separation.

Keywords: ionic liquids, liquid-liquid separation, hydrocarbon, research experience for teachers

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Authors: Ishan Kumar Garg

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Urban Fringes have been witnessing unforeseeable, haphazard, and ineffective spatial planning systems for many decades. It invades peripheral villages in the zest of the land as an abundant resource. The process, popularly known as "Urban Sprawl", is commonly seen in many fast-growing cities, especially in developing countries like India. The research for this paper reveals significant neglect in rural development policies, which are not recognized as crucial in current town and country planning regulations. This promotes urban-centric development in the fringe areas that are subjected to real-estate speculation. Therefore, being surrounded by arbitrary urban functions, these villages compromise with necessary strategies to retain the rural cultural identities, traditional ways of living, and villages’ interconnections while remaining deprived of urban amenities such as adequate water supply, education, sanitation, etc. Such socio-spatial separation makes us wonder about their right to development. The possibilities of a sustainable and socially inclusive city expansion are also explored through direct consumer–manufacturer media to bring positive socio-financial transformation. The paper aims to identify a rational playground for both the rural and urban population, which creates possibilities for economic and knowledge transactions beyond their local boundaries. This is achieved by empowering the intact community of villages with economic sufficiency and developing skills to pass on to future generations. In the above context, revolving around unregulated urban sprawl, the northeast region of Bareilly city in the Indian state of Uttar Pradesh is also discussed, i.e., currently under the influence of such development pressures. As we see, exclusive developments like residential, hospitality, industries, etc., over the unplanned landscapes are emerging with the development aligned to only urban means, not the rural. The paper ultimately re-envisions urban-rural associations through appropriate design combinations with economic growth. It integrates broken linkages by revising methodologies and encourages local entrepreneurship that taps the possibility of a gradual social transformation. Concurrently, the addition of required urban amenities leads to rural life strengthening and fulfilling aspirations. Since the proposed thesis carries through an inclusive fringe development, the study caters to cities of similar scales and situations that bolster such coexistence.

Keywords: smart growth framework, empowering rural economy, socio spatial separation, urban fringe development, urban sprawl consequences

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Authors: Adil Al-Rammahi

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Image or document encryption is needed through e- government data base. Really in this paper we introduce two matrices images, one is the public, and the second is the secret (original). The analyses of each matrix is achieved using the transformation of singular values decomposition. So each matrix is transformed or analyzed to three matrices say row orthogonal basis, column orthogonal basis, and spectral diagonal basis. Product of the two row basis is calculated. Similarly the product of the two column basis is achieved. Finally we transform or save the files of public, row product and column product. In decryption stage, the original image is deduced by mutual method of the three public files.

Keywords: image cryptography, singular values decomposition

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Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

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Authors: Seda Sahin, Emin Akata

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Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy.

Keywords: medical image analysis, segmentation of bone structures on hip radiographs, marker-controlled watershed segmentation, zernike moment feature descriptor

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Authors: Valentina Alessandra Carvalho do Vale, Elmo Thiago Lins Cöuras Ford, Rudson de Sousa Lima

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Around the planet Earth, access to clean water is a problem whose importance has increased due to population growth and its misuse. Thus, projects that seek to transform water sources improper (salty and brackish) in drinking water sources are current issues. However, this transformation generally requires a high cost of implementation, operation and maintenance. In this context, the aim of this work is the development of a passive solar distiller for brackish water, made from recycled and durable materials such as aluminum, cement, glass and PVC basins. The results reveal factors that influence the performance and viability of the expansion project.

Keywords: solar distiller, passive distiller, distiller with pyramidal roof, ecologically correct

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Authors: M. Y. Malika, Farzana, Abdul Rehman

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The study deals with the steady, 3D MHD boundary layer flow of a non-Newtonian Maxwell fluid flow due to a horizontal surface stretched exponentially in two lateral directions. The temperature at the boundary is assumed to be distributed exponentially and possesses convective boundary conditions. The governing nonlinear system of partial differential equations along with associated boundary conditions is simplified using a suitable transformation and the obtained set of ordinary differential equations is solved through numerical techniques. The effects of important involved parameters associated with fluid flow and heat flux are shown through graphs.

Keywords: boundary layer flow, exponentially stretched surface, Maxwell fluid, numerical solution

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Authors: S. Arun Prakash, V. Malathi, M. S. Mani Rajan

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The analytical bright two soliton solution of the 3-coupled nonlinear Schrödinger equations with variable coefficients in birefringent optical fiber is obtained by Darboux transformation method. To the design of ultra-speed optical devices, Soliton interaction and control in birefringence fiber is investigated. Lax pair is constructed for N coupled NLS system through AKNS method. Using two soliton solution, we demonstrate different interaction behaviors of solitons in birefringent fiber depending on the choice of control parameters. Our results shows that interactions of optical solitons have some specific applications such as construction of logic gates, optical computing, soliton switching, and soliton amplification in wavelength division multiplexing (WDM) system.

Keywords: optical soliton, soliton interaction, soliton switching, WDM

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Authors: Abdul Rahman Hamdan

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The Fourth Industrial Revolution (4IR) has brought unprecedented technological advancements that have disrupted many industries worldwide. In keeping up with the technological advances and rapid disruption by the introduction of many technological advancements brought forth by the 4IR, the use of technology road mapping has emerged as one of the critical tools for organizations to leverage. Technology road mapping can be used by many companies to guide them to become more adaptable and anticipate future transformation and innovation, and avoid being redundant or irrelevant due to the rapid changes in technological advancement. This research paper provides a comprehensive analysis of technology road mapping within the context of the 4IR. The objectives of the paper are to provide companies with practical insights and a strategic framework of technology road mapping for them to navigate the fast-changing nature of the 4IR. This study also contributes to the understanding and practice of technology road mapping in the 4IR and, at the same time, provides organizations with the necessary tools and critical insight to navigate the 4IR transformation by leveraging technology road mapping. Based on the literature review and case studies, the study analyses key principles, methodologies, and best practices in technology road mapping and integrates them with the unique characteristics and challenges of the 4IR. The research paper gives the background of the fourth industrial revolution. It explores the disruptive potential of technologies in the 4IR and the critical need for technology road mapping that consists of strategic planning and foresight to remain competitive and relevant in the 4IR era. It also highlights the importance of technology road mapping as an organisation’s proactive approach to align the organisation’s objectives and resources to their technology and product development in meeting the fast-evolving technological 4IR landscape. The paper also includes the theoretical foundations of technology road mapping and examines various methodological approaches, and identifies external stakeholders in the process, such as external experts, stakeholders, collaborative platforms, and cross-functional teams to ensure an integrated and robust technological roadmap for the organisation. Moreover, this study presents a comprehensive framework for technology road mapping in the 4IR by incorporating key elements and processes such as technology assessment, competitive intelligence, risk analysis, and resource allocation. It provides a framework for implementing technology road mapping from strategic planning, goal setting, and technology scanning to road mapping visualisation, implementation planning, monitoring, and evaluation. In addition, the study also addresses the challenges and limitations related to technology roadmapping in 4IR, including the gap analysis. In conclusion of the study, the study will propose a set of practical recommendations for organizations that intend to leverage technology road mapping as a strategic tool in the 4IR in driving innovation and becoming competitive in the current and future ecosystem.

Keywords: technology management, technology road mapping, technology transfer, technology planning

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Authors: Reem Abou Refaie, Christoph Meinel

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The paper explores the impact of the COVID-19 crisis on the internal as well as external digitalized work practices of public service providers as part of a Public-Private Partnership Model. It focuses on the effect of uncertainty on generating Open Innovation practices. Our inquiry relies on semi-structured interviews (n=14) from a case study of Rwanda’s Public Service Delivery System in the context of research cooperation with IremboGov, the country’s One-Stop-Shop Platform for public services. It presents four propositions on harnessing opportunities for OI in the context of the public sector beyond the pandemic response. Practitioners can find characterizations of OI opportunities and gain insights on fostering OI in Public Sector Organizations.

Keywords: open innovation, digital transformation, public sector, Rwanda

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Authors: Jinseon Song, Yongwan Park

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In this paper, propose method that can user’s position that based on database is built from single camera. Previous positioning calculate distance by arrival-time of signal like GPS (Global Positioning System), RF(Radio Frequency). However, these previous method have weakness because these have large error range according to signal interference. Method for solution estimate position by camera sensor. But, signal camera is difficult to obtain relative position data and stereo camera is difficult to provide real-time position data because of a lot of image data, too. First of all, in this research we build image database at space that able to provide positioning service with single camera. Next, we judge similarity through image matching of database image and transmission image from user. Finally, we decide position of user through position of most similar database image. For verification of propose method, we experiment at real-environment like indoor and outdoor. Propose method is wide positioning range and this method can verify not only position of user but also direction.

Keywords: positioning, distance, camera, features, SURF(Speed-Up Robust Features), database, estimation

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Authors: Irina Khutsishvili

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In the present research, a decision support methodology for the multi-attribute group decision-making (MAGDM) problem is developed, namely for the selection of investment projects. The objective of the investment project selection problem is to choose the best project among the set of projects, seeking investment, or to rank all projects in descending order. The project selection is made considering a set of weighted attributes. To evaluate the attributes in our approach, expert assessments are used. In the proposed methodology, lingual expressions (linguistic terms) given by all experts are used as initial attribute evaluations, since they are the most natural and convenient representation of experts' evaluations. Then lingual evaluations are converted into trapezoidal fuzzy numbers, and the aggregate trapezoidal hesitant fuzzy decision matrix will be built. The case is considered when information on the attribute weights is completely unknown. The attribute weights are identified based on the De Luca and Termini information entropy concept, determined in the context of hesitant fuzzy sets. The decisions are made using the extended Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method under a hesitant fuzzy environment. Hence, a methodology is based on a trapezoidal valued hesitant fuzzy TOPSIS decision-making model with entropy weights. The ranking of alternatives is performed by the proximity of their distances to both the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS). For this purpose, the weighted hesitant Hamming distance is used. An example of investment decision-making is shown that clearly explains the procedure of the proposed methodology.

Keywords: In the present research, a decision support methodology for the multi-attribute group decision-making (MAGDM) problem is developed, namely for the selection of investment projects. The objective of the investment project selection problem is to choose the best project among the set of projects, seeking investment, or to rank all projects in descending order. The project selection is made considering a set of weighted attributes. To evaluate the attributes in our approach, expert assessments are used. In the proposed methodology, lingual expressions (linguistic terms) given by all experts are used as initial attribute evaluations since they are the most natural and convenient representation of experts' evaluations. Then lingual evaluations are converted into trapezoidal fuzzy numbers, and the aggregate trapezoidal hesitant fuzzy decision matrix will be built. The case is considered when information on the attribute weights is completely unknown. The attribute weights are identified based on the De Luca and Termini information entropy concept, determined in the context of hesitant fuzzy sets. The decisions are made using the extended Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method under a hesitant fuzzy environment. Hence, a methodology is based on a trapezoidal valued hesitant fuzzy TOPSIS decision-making model with entropy weights. The ranking of alternatives is performed by the proximity of their distances to both the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS). For this purpose, the weighted hesitant Hamming distance is used. An example of investment decision-making is shown that clearly explains the procedure of the proposed methodology.

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

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Authors: Milica Lilic, Marina Rosales Martínez

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Academic research cannot be oblivious to social problems and needs, so projects that have the capacity for transformation and impact should have the opportunity to go beyond the University circles and bring benefit to society. Apart from patents and R&D research contracts, this opportunity can be achieved through entrepreneurship as one of the most direct tools to turn knowledge into a tangible product. Thus, as an example of good practices, it is intended to analyze the case of an institutional entrepreneurship program carried out at the University of Seville, aimed at researchers interested in assessing the business opportunity of their research and expanding their knowledge on procedures for the commercialization of technologies used at academic projects. The program is based on three pillars: training, teamwork sessions and networking. The training includes aspects such as product-client fit, technical-scientific and economic-financial feasibility of a spin-off, institutional organization and decision making, public and private fundraising, and making the spin-off visible in the business world (social networks, key contacts, corporate image and ethical principles). On the other hand, the teamwork sessions are guided by a mentor and aimed at identifying research results with potential, clarifying financial needs and procedures to obtain the necessary resources for the consolidation of the spin-off. This part of the program is considered to be crucial in order for the participants to convert their academic findings into a business model. Finally, the networking part is oriented to workshops about the digital transformation of a project, the accurate communication of the product or service a spin-off offers to society and the development of transferable skills necessary for managing a business. This blended program results in the final stage where each team, through an elevator pitch format, presents their research turned into a business model to an experienced jury. The awarded teams get a starting capital for their enterprise and enjoy the opportunity of formally consolidating their spin-off company at the University. Studying the results of the program, it has been shown that many researchers have basic or no knowledge of entrepreneurship skills and different ways to turn their research results into a business model with a direct impact on society. Therefore, the described program has been used as an example to highlight the importance of knowledge transfer at the University and the role that this institution should have in providing the tools to promote entrepreneurship within it. Keeping in mind that the University is defined by three main activities (teaching, research and knowledge transfer), it is safe to conclude that the latter, and the entrepreneurship as an expression of it, is crucial in order for the other two to comply with their purpose.

Keywords: good practice, knowledge transfer, a spin-off company, university

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Authors: Prasanna Ramachandran, Kareem Graham, Helena Kiefel, Sunit Jain, Todd DeSantis

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Microbes that reside inside the mammalian GI tract, commonly referred to as the gut microbiome, have been shown to have therapeutic effects in animal models of disease. We hypothesize that specific proteins produced by these microbes are responsible for this activity and may be used directly as therapeutics. To speed up the discovery of these key proteins from the big-data metagenomics, we have applied machine learning techniques. Using amino acid sequences of known epitopes and their corresponding binding partners, protein interaction descriptors (PID) were calculated, making a positive interaction set. A negative interaction dataset was calculated using sequences of proteins known not to interact with these same binding partners. Using Random Forest and positive and negative PID, a machine learning model was trained and used to predict interacting versus non-interacting proteins. Furthermore, the continuous variable, cosine similarity in the interaction descriptors was used to rank bacterial therapeutic candidates. Laboratory binding assays were conducted to test the candidates for their potential as therapeutics. Results from binding assays reveal the accuracy of the machine learning prediction and are subsequently used to further improve the model.

Keywords: protein-interactions, machine-learning, metagenomics, microbiome

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Authors: Mohanad Alhabo, Li Zhang, Naveed Nawaz

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In this paper, a non-cooperative game method is formulated where all players compete to transmit at higher power. Every base station represents a player in the game. The game is solved by obtaining the Nash equilibrium (NE) where the game converges to optimality. The proposed method, named Power Efficient Handover Game Theoretic (PEHO-GT) approach, aims to control the handover in dense small cell networks. Players optimize their payoff by adjusting the transmission power to improve the performance in terms of throughput, handover, power consumption and load balancing. To select the desired transmission power for a player, the payoff function considers the gain of increasing the transmission power. Then, the cell selection takes place by deploying Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A game theoretical method is implemented for heterogeneous networks to validate the improvement obtained. Results reveal that the proposed method gives a throughput improvement while reducing the power consumption and minimizing the frequent handover.

Keywords: energy efficiency, game theory, handover, HetNets, small cells

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Authors: Aditi Chauhan, Neethu S. Mohan

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In the modern era, the advancement and digitalization in technology have taken place during an investigation of crime scene. The rapid enhancement and investigative techniques have changed the mean of identification of suspect. Identification of the person is one of the significant aspects, and personal authentication is the key of security and reliability in society. Since early 90 s, people have relied on comparing handwriting through its class and individual characteristics. But in today’s 21st century we need more reliable means to identify individual through handwriting. An approach employing computer modalities have lately proved itself auspicious enough in exploration of hieroglyphics substantial in investigating the case. Various software’s such as FISH, WRITEON, and PIKASO, CEDAR-FOX SYSTEM identify and verify the associated quantitative measure of the similarity between two samples. The research till date has been confined to identify the authorship of the concerned samples. But prospects associated with the use of computational modalities might help to identify disguised writing, forged handwriting or say altered or modified writing. Considering the applications of such modal, similar work is sure to attract plethora of research in immediate future. It has a promising role in national security too. Documents exchanged among terrorist can also be brought under the radar of surveillance, bringing forth their source of existence.

Keywords: documents, identity, computational system, suspect

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Authors: Ole Michael Spaten

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This article presents similarities and differences between psychotherapy, coaching psychology and coaching, and hence discusses boundaries between these diverse fields of practice. The point of departure will be prevailing arguments and descriptions in the scientific community, and it shows both commonalities and major differences in relation to the application in daily practice. The results (the similarities and differences) are presented and discussed in the light of scientific research and different theoretical perspectives, including both classic and recent scholars. Some of the main differences presented are; the clinical/non-clinical perspective and the educational differences, including the different criteria and demands which professionals working in these three different professions, should undergo to obtain their certification. Further, one of the main similarities is presented: the importance of the relationship between the therapist/coach and the client/coachee. The goal and task oriented focus are also presented as a similarity between the three intervention forms – at least to some extent. Finally, some central concepts from the fields are presented in a table for a proposal of distinctions and interfaces. It is concluded that a comprehensive education in combination with an understanding of the differences and similarities between the three intervention forms is of significant importance for the professional working in either of the fields. Future studies should, however, include additional research on the similarities and differences and how to continue the educational progress in all three disciplines.

Keywords: boundaries, coaching, coaching psychology, interface, psychotherapy

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Authors: Justin Woulfe

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Siloed logistics and supply chain management systems throughout the Department of Defense (DOD) has led to disparate approaches to modeling and simulation (M&S), a lack of understanding of how one system impacts the whole, and issues with “optimal” solutions that are good for one organization but have dramatic negative impacts on another. Many different systems have evolved to try to understand and account for uncertainty and try to reduce the consequences of the unknown. As the DoD undertakes expansive digital transformation initiatives, there is an opportunity to fuse and leverage traditionally disparate data into a centrally hosted source of truth. With a streamlined process incorporating machine learning (ML) and artificial intelligence (AI), advanced M&S will enable informed decisions guiding program success via optimized operational readiness and improved mission success. One of the current challenges is to leverage the terabytes of data generated by monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application analyzing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from an ML and AI application combined with advanced optimization algorithms will improve the mission success and performance of systems, which will improve the overall cost and effectiveness of any program. The Systecon team constructs and employs model-based simulations, cutting across traditional silos of data, aggregating maintenance, and supply data, incorporating sensor information, and applying optimization and simulation methods to an as-maintained digital twin with the ability to aggregate results across a system’s lifecycle and across logical and operational groupings of systems. This coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. A true capability to tie resources (dollars) to weapon system readiness in alignment with the real-world scenarios a warfighter may experience has been an objective yet to be realized to date. By developing and solidifying an organic capability to directly relate dollars to readiness and to inform the digital twin, the decision-maker is now empowered through valuable insight and traceability. This type of educated decision-making provides an advantage over the adversaries who struggle with maintaining system readiness at an affordable cost. The M&S capability developed allows program managers to independently evaluate system design and support decisions by quantifying their impact on operational availability and operations and support cost resulting in the ability to simultaneously optimize readiness and cost. This will allow the stakeholders to make data-driven decisions when trading cost and readiness throughout the life of the program. Finally, sponsors are available to validate product deliverables with efficiency and much higher accuracy than in previous years.

Keywords: artificial intelligence, digital transformation, machine learning, predictive analytics

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Authors: Rana Bazaid, Debajyoti Pati

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Purpose: The aim of this research is to explore and discuss innovative workspaces, and how the design of the space has the potential to facilitate the work process and employees’ satisfaction which can lead to innovative results. Background: The relationship between the workforce and the work environment has a strong potential to enhance human capabilities associated with innovation outcomes. The need for innovation in workplaces can benefit employees’ satisfaction, health, and performance. To understand this complicated relationship, this research explores and comprehends innovative work environments. Methods: A review of 26 peer-reviewed articles, seven books, and 23 companies’ websites was conducted, along with analysis for five case studies on successful types of research and development fields to detect appropriate examples for the study. Results: The analysis of the five case studies showed the similarity-characteristics of innovation work environments among those five fields and observed what is unique about each field that makes them stand out in their industries. Conclusion: Understanding the psychological, cultural, physiological, and social needs of workers, physical workplaces, and issues found in the work environment may help enhance multifaceted innovation and productivity.

Keywords: innovation, productivity , work environment, workers satisfaction

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Authors: Yu Yaochuan

Abstract:

This paper explores the pivotal role of public opinion during the Xinhai Revolution. Examining the dynamics of public sentiment in Chinese society in 1911 shows how information dissemination, ideological propaganda, and public mobilization worked together to drive the revolution to success. The study highlights the indispensable role of revolutionary newspapers, assemblies, and speeches in spreading revolutionary ideas, mobilizing the public, and shaping policy perceptions. By analyzing these historical events, the paper provides a deeper insight into the Xinhai Revolution and offers theoretical and empirical support for understanding the application of public opinion in modern social and political transformations.

Keywords: Xinhai Revolution, public opinion, social mobilization, information dissemination, ideology, political transformation

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Authors: Srinivasacharya Darbhasayanam, Jagadeeshwar Pashikanti

Abstract:

This paper analyzes the Soret and Dufour effects on mixed convection flow, heat and mass transfer from an exponentially stretching surface in a viscous fluid with Hall Effect. The governing partial differential equations are transformed into ordinary differential equations using similarity transformations. The nonlinear coupled ordinary differential equations are reduced to a system of linear differential equations using the successive linearization method and then solved the resulting linear system using the Chebyshev pseudo spectral method. The numerical results for the velocity components, temperature and concentration are presented graphically. The obtained results are compared with the previously published results, and are found to be in excellent agreement. It is observed from the present analysis that the primary and secondary velocities and concentration are found to be increasing, and temperature is decreasing with the increase in the values of the Soret parameter. An increase in the Dufour parameter increases both the primary and secondary velocities and temperature and decreases the concentration.

Keywords: Exponentially stretching sheet, Hall current, Heat and Mass transfer, Soret and Dufour Effects

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Authors: Sapir Handelman

Abstract:

Brief Solution Focused Negotiation is a powerful conflict resolution tool. It can be applied in almost all dimensions of our social life, from politics to family. The initiative invites disputing parties to negotiate practical solutions to their conflict. The negotiation is conducted in a framework of rules, structure, and timeline. The paper presents a model of Brief Solution Focused Negotiation that rests on three pillars: Transformation – turning opposing parties into a negotiating cooperative; Practicality – focusing on practical solutions to a negotiable problem; Discovery – discovering key game changers. This paper introduces these three building blocks. It demonstrates the potential contribution of each one of them to negotiation success. It shows that an effective combination of these three elements has the greatest potential to build, maintain and successfully conclude Brief Solution Focused Negotiation.

Keywords: conflict, negotiation, negotiating cooperative, game changer

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Authors: Lukas Stiburek, Jana Cesnekova, Josef Houstek, Jiri Zeman

Abstract:

Cellular function depends on mitochondrial function and integrity that is therefore maintained by several classes of proteins possessing chaperone and/or proteolytic activities. In this work, we focused on characterization of LACE1 (lactation elevated 1) function in mitochondrial protein homeostasis maintenance. LACE1 is the human homologue of yeast mitochondrial Afg1 ATPase, a member of SEC18-NSF, PAS1, CDC48-VCP, TBP family. Yeast Afg1 was shown to be involved in mitochondrial complex IV biogenesis, and based on its similarity with CDC48 (p97/VCP) it was suggested to facilitate extraction of polytopic membrane proteins. Here we show that LACE1, which is a mitochondrial integral membrane protein, exists as part of three complexes of approx. 140, 400 and 500 kDa and is essential for maintenance of fused mitochondrial reticulum and lamellar cristae morphology. Using affinity purification of LACE1-FLAG expressed in LACE1 knockdown background we show that the protein physically interacts with mitochondrial inner membrane protease YME1L. We further show that human LACE1 exhibits significant pro-apoptotic activity and that the protein is required for normal function of the mitochondrial respiratory chain. Thus, our work establishes LACE1 as a novel factor with the crucial role in mitochondrial homeostasis maintenance.

Keywords: LACE1, mitochondria, apoptosis, protease

Procedia PDF Downloads 313