Search results for: the connectivity of innovative network

Authors: Yeliz Sarı Nayim, B. Niyami Nayim

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Biotopes such as forest areas rich in biodiversity, wetlands, hedgerows and woodlands play important ecological roles in agricultural landscapes. Of these semi-natural areas and features, hedgerows are the most common landscape elements. Their most significant features are that they serve as a barrier between the agricultural lands, serve as shelter, add aesthetical value to the landscape and contribute significantly to the wildlife and biodiversity. Hedgerows surrounding agricultural landscapes also provide an important habitat for pollinators which are important for agricultural production. This study looks into the identification of hedgerows in agricultural lands in the Mugada rural area within Bartın province, Turkey. From field data and-and satellite images, it is clear that in this area, especially around rural settlements, large forest areas have been cleared for settlement and agriculture. A network of hedgerows is also apparent, which might potentially play an important role in the otherwise open agricultural landscape. We found that these hedgerows serve as an ecological and biological corridor, linking forest ecosystems. Forest patches of different sizes and creating a habitat network across the landscape. Some examples of this will be presented. The overall conclusion from the study is that ecologically, biologically and aesthetically important hedge biotopes should be maintained in the long term in agricultural landscapes such as this. Some suggestions are given for how they could be managed sustainably into the future.

Keywords: agricultural biotopes, Hedgerows, landscape ecology, Turkey

Procedia PDF Downloads 295

Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

Procedia PDF Downloads 128

Authors: Thilini M. Yatanwala

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CCTV (Closed-Circuit-Television) Surveillance System is being used in public places over decades and a large variety of data is being produced every moment. However, most of the CCTV data is stored in isolation without having integrity. As a result, identification of the behavior of suspicious people along with their location has become strenuous. This research was conducted to acquire more accurate and reliable timely information from the CCTV video records. The implemented system can identify human objects in public places based on outfit colors. Inter-process communication technologies were used to implement the CCTV camera network to track people in the premises. The research was conducted in three stages and in the first stage human objects were filtered from other movable objects available in public places. In the second stage people were uniquely identified based on their outfit colors and in the third stage an individual was continuously tracked in the CCTV network. A face detection algorithm was implemented using cascade classifier based on the training model to detect human objects. HAAR feature based two-dimensional convolution operator was introduced to identify features of the human face such as region of eyes, region of nose and bridge of the nose based on darkness and lightness of facial area. In the second stage outfit colors of human objects were analyzed by dividing the area into upper left, upper right, lower left, lower right of the body. Mean color, mod color and standard deviation of each area were extracted as crucial factors to uniquely identify human object using histogram based approach. Color based measurements were written in to XML files and separate directories were maintained to store XML files related to each camera according to time stamp. As the third stage of the approach, inter-process communication techniques were used to implement an acknowledgement based CCTV camera network to continuously track individuals in a network of cameras. Real time analysis of XML files generated in each camera can determine the path of individual to monitor full activity sequence. Higher efficiency was achieved by sending and receiving acknowledgments only among adjacent cameras. Suspicious incidents such as a person staying in a sensitive area for a longer period or a person disappeared from the camera coverage can be detected in this approach. The system was tested for 150 people with the accuracy level of 82%. However, this approach was unable to produce expected results in the presence of group of people wearing similar type of outfits. This approach can be applied to any existing camera network without changing the physical arrangement of CCTV cameras. The study of human identification and suspicious incident detection using outfit color analysis can achieve higher level of accuracy and the project will be continued by integrating motion and gait feature analysis techniques to derive more information from CCTV videos.

Keywords: CCTV surveillance, human detection and identification, image processing, inter-process communication, security, suspicious detection

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Authors: Ahmad Lutfi, Nikolas Dhega

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The Malala deposit in northwest Sulawesi is the only known porphyry molybdenum and the only source for rhenium, occurrence in Indonesia. The neural network method produces results that correspond very closely to those of the knowledge-based fuzzy logic method and weights of evidence method. This method required data of solid geology, regional faults, airborne magnetic, gamma-ray survey data and GIS data. This interpretation of the network output fits with the intuitive notion that a prospective area has characteristics that closely resemble areas known to contain mineral deposits. Contrasts with the weights of evidence and fuzzy logic methods, where, for a given grid location, each input-parameter value automatically results in an increase in the prospective estimated. Malala District indicated molybdenum anomalies in stream sediments from in excess of 15 km2 were obtained, including the Takudan Fault as most prominent structure with striking 40̊ to 60̊ over a distance of about 30 km and in most places weakly at anomaly B, developed over an area of 4 km2, with a ‘shell’ up to 50 m thick at the intrusive contact with minor mineralization occurring in the Tinombo Formation. Series of NW trending, steeply dipping fracture zones, named the East Zone has an estimated resource of 100 Mt at 0.14% MoS2 and minimum target of 150 Mt 0.25%. The Malala porphyries occur as stocks and dykes with predominantly granitic, with fluorine-poor class of molybdenum deposits and belongs to the plutonic sub-type. Unidirectional solidification textures consisting of subparallel, crenulated layers of quartz that area separated by layers of intrusive material textures. The deuteric nature of the molybdenum mineralization and the dominance of carbonate alteration.The nature of the Stage I with alteration barren quartz K‐feldspar; and Stage II with alteration quartz‐K‐feldspar‐apatite-molybdenite veins combined with the presence of disseminated molybdenite with primary biotite in the host intrusive.

Keywords: molybdenite, Malala, porphyries, anomaly B

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Authors: Nan Deng, Zhenqiu Liu

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Thousands of patients with metastatic tumors were diagnosed with cancers of unknown primary sites each year. The inability to identify the primary cancer site may lead to inappropriate treatment and unexpected prognosis. Nowadays, a large amount of genomics and transcriptomics cancer data has been generated by next-generation sequencing (NGS) technologies, and The Cancer Genome Atlas (TCGA) database has accrued thousands of human cancer tumors and healthy controls, which provides an abundance of resource to differentiate cancer types. Meanwhile, deep convolutional neural networks (CNNs) have shown high accuracy on classification among a large number of image object categories. Here, we utilize 25 cancer primary tumors and 3 normal tissues from TCGA and convert their RNA-Seq gene expression profiling to color images; train, validate and test a CNN classifier directly from these images. The performance result shows that our CNN classifier can archive >80% test accuracy on most of the tumors and normal tissues. Since the gene expression pattern of distant metastases is similar to their primary tumors, the CNN classifier may provide a potential computational strategy on identifying the unknown primary origin of metastatic cancer in order to plan appropriate treatment for patients.

Keywords: bioinformatics, cancer, convolutional neural network, deep leaning, gene expression pattern

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Authors: A. Assad, T. Zayed

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Water networks (WNs) are responsible of providing adequate amounts of safe, high quality, water to the public. As other critical infrastructure systems, WNs are subjected to deterioration which increases the number of breaks and leaks and lower water quality. In Canada, 35% of water assets require critical attention and there is a significant gap between the needed and the implemented investments. Thus, the need for efficient rehabilitation programs is becoming more urgent given the paradigm of aging infrastructure and tight budget. The first step towards developing such programs is to formulate a Performance Index that reflects the current condition of water assets along with its criticality. While numerous studies in the literature have focused on various aspects of condition assessment and reliability, limited efforts have investigated the criticality of such components. Critical water mains are those whose failure cause significant economic, environmental or social impacts on a community. Inclusion of criticality in computing the performance index will serve as a prioritizing tool for the optimum allocating of the available resources and budget. In this study, several social, economic, and environmental factors that dictate the criticality of a water pipelines have been elicited from analyzing the literature. Expert opinions were sought to provide pairwise comparisons of the importance of such factors. Subsequently, Fuzzy Logic along with Analytical Network Process (ANP) was utilized to calculate the weights of several criteria factors. Multi Attribute Utility Theories (MAUT) was then employed to integrate the aforementioned weights with the attribute values of several pipelines in Montreal WN. The result is a criticality index, 0-1, that quantifies the severity of the consequence of failure of each pipeline. A novel contribution of this approach is that it accounts for both the interdependency between criteria factors as well as the inherited uncertainties in calculating the criticality. The practical value of the current study is represented by the automated tool, Excel-MATLAB, which can be used by the utility managers and decision makers in planning for future maintenance and rehabilitation activities where high-level efficiency in use of materials and time resources is required.

Keywords: water networks, criticality assessment, asset management, fuzzy analytical network process

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Authors: Nadia Samantha Zuñiga-Peña, Norberto Hernández-Romero, Omar Aguilar-Mejia, Salatiel García-Nava

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Using unmanned aerial vehicles (UAVs) for load transport has gained significant importance in various sectors due to their ability to improve efficiency, reduce costs, and access hard-to-reach areas. Although UAVs offer numerous advantages for load transport, several complications and challenges must be addressed to exploit their potential fully. Complexity relays on UAVs are underactuated, non-linear systems with a high degree of coupling between their variables and are subject to forces with uncertainty. One of the biggest challenges is modeling and controlling the system formed by UAVs carrying a load. In order to solve the controller problem, in this work, a hybridization of Neural Network and Hunger Games Search (HGS) metaheuristic algorithm is developed and implemented to find the parameters of the Super Twisting Sliding Mode Controller for the 8 degrees of freedom model of UAV with payload. The optimized controller successfully tracks the UAV through the three-dimensional desired path, demonstrating the effectiveness of the proposed solution. A comparison of performance shows the superiority of the neural network HGS (NNHGS) over the HGS algorithm, minimizing the tracking error by 57.5 %.

Keywords: neural networks, hunger games search, super twisting sliding mode controller, UAVs.

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Authors: Hao Yan, Xiaobing Zhang

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The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

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Authors: Christine Winberg, James Garraway

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Many key university documents, such as vision and mission statements and strategic plans, are aspirational and future-oriented. There is a wide range of future-oriented methods that are used in planning applications, ranging from mathematical modelling to expert opinions. Many of these methods have limitations, and planners using these tools might, for example, make the technical-rational assumption that their plans will unfold in a logical and inevitable fashion, thus underestimating the many complex forces that are at play in planning for an unknown future. This is the issue that this study addresses. The overall project aim was to assist a new university of technology in developing appropriate responses to its social responsibility, graduate employability and research missions in its strategic plan. The specific research question guiding the research activities and approach was: how might the use of innovative future-oriented planning tools enable or constrain a strategic planning process? The research objective was to engage collaborating groups in the use of an innovative tool to develop and assess future scenarios, for the purpose of developing deeper understandings of possible futures and their challenges. The scenario planning tool chosen was ‘fictive scripting’, an analytical technique derived from Technology Forecasting and Innovation Studies. Fictive scripts are future projections that also take into account the present shape of the world and current developments. The process thus began with a critical diagnosis of the present, highlighting its tensions and frictions. The collaborative groups then developed fictive scripts, each group producing a future scenario that foregrounded different institutional missions, their implications and possible consequences. The scripts were analyzed with a view to identifying their potential contribution to the university’s strategic planning exercise. The unfolding fictive scripts revealed a number of insights in terms of unexpected benefits, unexpected challenges, and unexpected consequences. These insights were not evident in previous strategic planning exercises. The contribution that this study offers is to show how better choices can be made and potential pitfalls avoided through a systematic foresight exercise. When universities develop strategic planning documents, they are looking into the future. In this paper it is argued that the use of appropriate tools for future-oriented exercises, can help planners to understand more fully what achieving desired outcomes might entail, what challenges might be encountered, and what unexpected consequences might ensue.

Keywords: fictive scripts, scenarios, strategic planning, technological forecasting

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Authors: Jesus Carretero, Mario Vasile, Guillermo Izquierdo, Javier Garcia-Blas

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Currently, there is a so called ‘identity crisis’ in cybersecurity caused by the substantial security, privacy and usability shortcomings encountered in existing systems for identity management. Federated Identity Management (FIM) could be solution for this crisis, as it is a method that facilitates management of identity processes and policies among collaborating entities without enforcing a global consistency, that is difficult to achieve when there are ID legacy systems. To cope with this problem, the Connecting Europe Facility (CEF) initiative proposed in 2014 a federated solution in anticipation of the adoption of the Regulation (EU) N°910/2014, the so-called eIDAS Regulation. At present, a network of eIDAS Nodes is being deployed at European level to allow that every citizen recognized by a member state is to be recognized within the trust network at European level, enabling the consumption of services in other member states that, until now were not allowed, or whose concession was tedious. This is a very ambitious approach, since it tends to enable cross-border authentication of Member States citizens without the need to unify the authentication method (eID Scheme) of the member state in question. However, this federation is currently managed by member states and it is initially applied only to citizens and public organizations. The goal of this paper is to present the results of a European Project, named eID@Cloud, that focuses on the integration of eID in 5 cloud platforms belonging to authentication service providers of different EU Member States to act as Service Providers (SP) for private entities. We propose an initiative based on a private eID Scheme both for natural and legal persons. The methodology followed in the eID@Cloud project is that each Identity Provider (IdP) is subscribed to an eIDAS Node Connector, requesting for authentication, that is subscribed to an eIDAS Node Proxy Service, issuing authentication assertions. To cope with high loads, load balancing is supported in the eIDAS Node. The eID@Cloud project is still going on, but we already have some important outcomes. First, we have deployed the federation identity nodes and tested it from the security and performance point of view. The pilot prototype has shown the feasibility of deploying this kind of systems, ensuring good performance due to the replication of the eIDAS nodes and the load balance mechanism. Second, our solution avoids the propagation of identity data out of the native domain of the user or entity being identified, which avoids problems well known in cybersecurity due to network interception, man in the middle attack, etc. Last, but not least, this system allows to connect any country or collectivity easily, providing incremental development of the network and avoiding difficult political negotiations to agree on a single authentication format (which would be a major stopper).

Keywords: cybersecurity, identity federation, trust, user authentication

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Authors: Rumeysa Terzioglu

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Social entrepreneurship is not only a new concept but also an engaging factor of development that utilizes opportunities in economic and social areas for women. Social entrepreneurs have experience in determining and solving social problems with community participation. Social entrepreneurship is a consequence of individual social and economic initiatives contributing to women’s social and economic development against poverty. Women’s empowerment is an essential point for development. Türkiye has been developing an alternative empowerment model for women affected by the national development plan. Social entrepreneurship is an alternative model of social and economic empowerment of women’s status in Türkiye.

Keywords: social entrepreneurship, women, women empowerment, development

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Authors: Mohanad Alhabo, Li Zhang, Naveed Nawaz

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Small cell deployment in 5G networks is a promising technology to enhance capacity and coverage. However, unplanned deployment may cause high interference levels and high number of unnecessary handovers, which in turn will result in an increase in the signalling overhead. To guarantee service continuity, minimize unnecessary handovers, and reduce signalling overhead in heterogeneous networks, it is essential to properly model the handover decision problem. In this paper, we model the handover decision according to Multiple Attribute Decision Making (MADM) method, specifically Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this paper, we propose a hybrid TOPSIS method to control the handover in heterogeneous network. The proposed method adopts a hybrid weighting, which is a combination of entropy and standard deviation. A hybrid weighting control parameter is introduced to balance the impact of the standard deviation and entropy weighting on the network selection process and the overall performance. Our proposed method shows better performance, in terms of the number of frequent handovers and the mean user throughput, compared to the existing methods.

Keywords: handover, HetNets, interference, MADM, small cells, TOPSIS, weight

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Authors: Mohamamd Shahjahan

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Background: Cybercrime is common phenomenon at present both developed and developing countries. Young generation, especially adolescents now engaged internet frequently and they commit cybercrime frequently in Bangladesh. Objective: In this regard, the present study on the pattern of cybercrime among youngers of Bangladesh has been conducted. Methods and tools: This study was a cross-sectional study, descriptive in nature. Non-probability accidental sampling technique has been applied to select the sample because of the nonfinite population and the sample size was 167. A printed semi-structured questionnaire was used to collect data. Results: The study shows that adolescents mainly do hacking (94.6%), pornography (88.6%), software piracy (85 %), cyber theft (82.6%), credit card fraud (81.4%), cyber defamation (75.6%), sweet heart swindling (social network) (65.9%) etc. as cybercrime. According to findings the major causes of cybercrime among the respondents in Bangladesh were- weak laws (88.0%), defective socialization (81.4%), peer group influence (80.2%), easy accessibility to internet (74.3%), corruption (62.9%), unemployment (58.7%), and poverty (24.6%) etc. It is evident from the study that 91.0% respondents used password cracker as the techniques of cyber criminality. About 76.6%, 72.5%, 71.9%, 68.3% and 60.5% respondents’ technique was key loggers, network sniffer, exploiting, vulnerability scanner and port scanner consecutively. Conclusion: The study concluded that pattern of cybercrimes is frequently changing and increasing dramatically. Finally, it is recommending that the private public partnership and execution of existing laws can be controlling this crime.

Keywords: cybercrime, adolescents, pattern, internet

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Authors: Bhim Kumar Dahal

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Transportation network development in the developing country is in rapid pace. The majority of the network belongs to railway and expressway which passes through diverse topography, landform and geological conditions despite the avoidance principle during route selection. Construction of such networks demand many low to high embankment which required improvement in the foundation soil. This paper is mainly focused on the various advanced ground improvement techniques used to improve the soft soil, modelling approach and its predictability for embankments construction. The ground improvement techniques can be broadly classified in to three groups i.e. densification group, drainage and consolidation group and reinforcement group which are discussed with some case studies.  Various methods were used in modelling of the embankments from simple 1-dimensional to complex 3-dimensional model using variety of constitutive models. However, the reliability of the predictions is not found systematically improved with the level of sophistication.  And sometimes the predictions are deviated more than 60% to the monitored value besides using same level of erudition. This deviation is found mainly due to the selection of constitutive model, assumptions made during different stages, deviation in the selection of model parameters and simplification during physical modelling of the ground condition. This deviation can be reduced by using optimization process, optimization tools and sensitivity analysis of the model parameters which will guide to select the appropriate model parameters.

Keywords: cement, improvement, physical properties, strength

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Authors: B.Shukir, H.Woo, P.Barzo, D.Kis

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Preoperative brain mapping in tumors involving the speech areas has an important role to reduce surgical risks. Functional magnetic resonance imaging (fMRI) is the gold standard method to localize cortical speech areas preoperatively, but its availability in clinical routine is difficult. Diffusion MRI based probabilistic tractography is available in head MRI. It’s used to segment cortical subregions by their structural connectivity. In our study, we used probabilistic tractography to localize the frontal and temporal cortical speech areas. 15 patients with left frontal tumor were enrolled to our study. Speech fMRI and diffusion MRI acquired preoperatively. The standard automated anatomical labelling atlas 3 (AAL3) cortical atlas used to define 76 left frontal and 118 left temporal potential speech areas. 4 types of tractography were run according to the structural connection of these regions to the left arcuate fascicle (FA) to localize those cortical areas which have speech functions: 1, frontal through FA; 2, frontal with FA; 3, temporal to FA; 4, temporal with FA connections were determined. Thresholds of 1%, 5%, 10% and 15% applied. At each level, the number of affected frontal and temporal regions by fMRI and tractography were defined, the sensitivity and specificity were calculated. At the level of 1% threshold showed the best results. Sensitivity was 61,631,4% and 67,1523,12%, specificity was 87,210,4% and 75,611,37% for frontal and temporal regions, respectively. From our study, we conclude that probabilistic tractography is a reliable preoperative technique to localize cortical speech areas. However, its results are not feasible that the neurosurgeon rely on during the operation.

Keywords: brain mapping, brain tumor, fMRI, probabilistic tractography

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Authors: Kedar J. Mahadeshwar

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Concept introduction: This paper talks about how an innovative cloud based analytics enabled solution that could address a major industry challenge that is approaching all of us globally faster than what one would think. The world of supply chain for drugs and devices is changing today at a rapid speed. In the US, the Drug Supply Chain Security Act (DSCSA) is a new law for Tracing, Verification and Serialization phasing in starting Jan 1, 2015 for manufacturers, repackagers, wholesalers and pharmacies / clinics. Similarly we are seeing pressures building up in Europe, China and many countries that would require an absolute traceability of every drug and device end to end. Companies (both manufacturers and distributors) can use this opportunity not only to be compliant but to differentiate themselves over competition. And moreover a country such as UAE can be the leader in coming up with a global solution that brings innovation in this industry. Problem definition and timing: The problem of counterfeit drug market, recognized by FDA, causes billions of dollars loss every year. Even in UAE, the concerns over prevalence of counterfeit drugs, which enter through ports such as Dubai remains a big concern, as per UAE pharma and healthcare report, Q1 2015. Distribution of drugs and devices involves multiple processes and systems that do not talk to each other. Consumer confidence is at risk due to this lack of traceability and any leading provider is at risk of losing its reputation. Globally there is an increasing pressure by government and regulatory bodies to trace serial numbers and lot numbers of every drug and medical devices throughout a supply chain. Though many of large corporations use some form of ERP (enterprise resource planning) software, it is far from having a capability to trace a lot and serial number beyond the enterprise and making this information easily available real time. Solution: The solution here talks about a service provider that allows all subscribers to take advantage of this service. The solution allows a service provider regardless of its physical location, to host this cloud based traceability and analytics solution of millions of distribution transactions that capture lots of each drug and device. The solution platform will capture a movement of every medical device and drug end to end from its manufacturer to a hospital or a doctor through a series of distributor or retail network. The platform also provides advanced analytics solution to do some intelligent reporting online. Why Dubai? Opportunity exists with huge investment done in Dubai healthcare city also with using technology and infrastructure to attract more FDI to provide such a service. UAE and countries similar will be facing this pressure from regulators globally in near future. But more interestingly, Dubai can attract such innovators/companies to run and host such a cloud based solution and become a hub of such traceability globally.

Keywords: cloud, pharmaceutical, supply chain, tracking

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Authors: João Costa, Francisco Albuquerque, Ricardo J. Santos, Madalena M. Dias, José Carlos B. Lopes, Marcelo Costa

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Nowadays, it is well recognized that carbon dioxide emissions, together with other greenhouse gases, are responsible for the dramatic climate changes that have been occurring over the past decades. Gas hydrates are currently seen as a promising and disruptive set of materials that can be used as a basis for developing new technologies for CO₂ capture and storage. Its potential as a clean and safe pathway for CCS is tremendous since it requires only water and gas to be mixed under favorable temperatures and mild high pressures. However, the hydrates formation process is highly exothermic; it releases about 2 MJ per kilogram of CO₂, and it only occurs in a narrow window of operational temperatures (0 - 10 °C) and pressures (15 to 40 bar). Efficient continuous hydrate production at a specific temperature range necessitates high heat transfer rates in mixing processes. Past technologies often struggled to meet this requirement, resulting in low productivity or extended mixing/contact times due to inadequate heat transfer rates, which consistently posed a limitation. Consequently, there is a need for more effective continuous hydrate production technologies in industrial applications. In this work, a network mixing continuous production technology has been shown to be viable for producing CO₂ hydrates. The structured mixer used throughout this work consists of a network of unit cells comprising mixing chambers interconnected by transport channels. These mixing features result in enhanced heat and mass transfer rates and high interfacial surface area. The mixer capacity emerges from the fact that, under proper hydrodynamic conditions, the flow inside the mixing chambers becomes fully chaotic and self-sustained oscillatory flow, inducing intense local laminar mixing. The device presents specific heat transfer rates ranging from 107 to 108 W⋅m⁻³⋅K⁻¹. A laboratory scale pilot installation was built using a device capable of continuously capturing 1 kg⋅h⁻¹ of CO₂, in an aqueous slurry of up to 20% in mass. The strong mixing intensity has proven to be sufficient to enhance dissolution and initiate hydrate crystallization without the need for external seeding mechanisms and to achieve, at the device outlet, conversions of 99% in CO₂. CO₂ dissolution experiments revealed that the overall liquid mass transfer coefficient is orders of magnitude larger than in similar devices with the same purpose, ranging from 1 000 to 12 000 h⁻¹. The present technology has shown itself to be capable of continuously producing CO₂ hydrates. Furthermore, the modular characteristics of the technology, where scalability is straightforward, underline the potential development of a modular hydrate-based CO₂ capture process for large-scale applications.

Keywords: network, mixing, hydrates, continuous process, carbon dioxide

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Authors: Salisu Abdu Bagga

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This paper attempts to discuss contemporary issues in teacher education and address challenges therein within the context of the Nigeria society. Teacher education is an educational programme aimed at producing the right crop of people (teachers) who will teach at various levels of schooling i.e. primary, secondary and tertiary. The programme targets to inculcate desirable knowledge, skills, attitudes, values and competencies in teachers with the prime motive of keeping them fully abreast with contemporary challenges such as overcrowded classrooms, inadequate instructional materials, ineffective teaching methodology in the teaching industry in Nigeria. Nigeria needs competent, skilful, knowledgeable and innovative classroom teachers for better teaching and learning.

Keywords: teacher education, contemporary issues, competencies, higher education

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Authors: Amir Davood Elmi

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From the earliest light microscope to the most innovative X-ray imaging techniques, all of them have refined and improved our knowledge about the organization and composition of living tissues. The old techniques are time consuming and ultimately destructive to the tissues under the examination. In recent few decades, thanks to the boost of technology, non-destructive visualization techniques, such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), selective plane illumination microscopy (SPIM), and optical projection tomography (OPT), have come to the forefront. Among these techniques, CT is excellent for mineralized tissues such as bone or dentine. In addition, CT it is faster than other aforementioned techniques and the sample remains intact. In this article, applications, advantages, and limitations of micro-CT is discussed, in addition to some information about micro-CT of soft tissue.

Keywords: Micro-CT, hard tissue, bone, attenuation coefficient, rapid prototyping

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Authors: Reza Dinarvand, Mahdi Sadeghian, Somaye Sadeghian

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Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.

Keywords: artificial neural network, clayey soil, imperialist competition algorithm, lateral bearing capacity, short pile

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Authors: Rubina Shakya, Srijana Dangol, Dil Islam Mansur

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The exposure of constant stress stimuli in our daily lives is causing deterioration of neural connectivity in the brain. Interestingly, the improvement in larger-scale neural communication has been argued to rely on brain rhythms, which might be sensitive to binaural beats of particular frequency bands. The theoretical idea behind neural entrainment is that the rhythmic oscillatory activity within and between different brain regions can enhance cognitive function and mood state. So, we aimed to investigate whether the binaural beats of 40 Hz could enhance the cognition and the mood stability of the medical students at Kathmandu University of age 18-25 years old, which possibly, in the long run, might help to enhance their work productivity. The participants were asked to focus on the auditory stimuli of binaural beats with 200 Hz on the right side and 240 Hz on the left side of the headset for 15 minutes, every alternative day of three consecutive weeks. The Stroop’s test and the Brunel Mood Scale (BRUMS) were applied to assess the cognitive function and the mood state, respectively. The binaural beats significantly decreased the reaction time for the incoherent component of Stroop’s test in both male and female participants. For the mood state, scores of all positive emotions except ‘Calmness’ were significantly increased in the case of males. Whereas, scores of all positive emotions except ‘Vigor’ were significantly increased in the case of females. The results suggested that the meditation aided by binaural beats of 40 Hz helps in improving cognition and mood states to some extent.

Keywords: binaural beats, cognitive function, gamma neural oscillation, mood states

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Authors: Maier Dorin, Maier Andreea

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Today, any organization - regardless of the specific activity - must be prepared to face continuous radical changes, innovation thus becoming a condition of survival in a globalized market. Not all managers have an overall view on the real size of necessary innovation potential. Unfortunately there is still no common (and correct) understanding of the term of innovation among managers. Moreover, not all managers are aware of the need for innovation. This article highlights and analyzes a series of models of innovation processes and their evolution. The models analyzed encompass both the strategic level and the operational one within an organization, indicating performance innovation on each landing. As the literature review shows, there are no easy answers to the innovation process as there are no shortcuts to great results. Successful companies do not have a silver innovative bullet - they do not get results by making one or few things better than others, they make everything better.

Keywords: innovation, innovation process, business success, models of innovation

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Authors: D. K. Sreekantha, T. M. Girish, R. H. Fattepur

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In this article, we have given an overview of medical expert systems, which can be used for the developed of physicians in making decisions such as appropriate, prognostic, and therapeutic decisions which help to organize, store, and gives appropriate medical knowledge needed by physicians and practitioners during medical operations or further treatment. If they support the studies by using these systems, advanced tools in medicine will be developed in the future. New trends in the methodology of development of medical expert systems have also been discussed in this paper. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to gain expertise in Medical Science for treating patients. This paper aims to survey the Soft Computing techniques in treating patient’s problems used throughout the world.

Keywords: expert system, fuzzy logic, knowledge base, soft computing, epilepsy

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Authors: Elisa Esposito, Johannes C. Jansen, Loredana Dellamuzia, Ugo Moretti, Lidietta Giorno

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The reduction of CO₂ emission into the atmosphere as a result of human activity is one of the most important environmental challenges to face in the next decennia. Emission of CO₂, related to the use of fossil fuels, is believed to be one of the main causes of global warming and climate change. In this scenario, the production of biomethane from organic waste, as a renewable energy source, is one of the most promising strategies to reduce fossil fuel consumption and greenhouse gas emission. Unfortunately, biogas upgrading still produces the greenhouse gas CO₂ as a waste product. Therefore, this work presents a case study on biogas upgrading, aimed at the simultaneous purification of methane and CO₂ via different steps, including CO₂/methane separation by polymeric membranes. The original objective of the project was the biogas upgrading to distribution grid quality methane, but the innovative aspect of this case study is the further purification of the captured CO₂, transforming it from a useless by-product to a pure gas with food-grade quality, suitable for commercial application in the food and beverage industry. The study was performed on a pilot plant constructed by Tecno Project Industriale Srl (TPI) Italy. This is a model of one of the largest biogas production and purification plants. The full-scale anaerobic digestion plant (Montello Spa, North Italy), has a digestive capacity of 400.000 ton of biomass/year and can treat 6.250 m3/hour of biogas from FORSU (organic fraction of solid urban waste). The entire upgrading process consists of a number of purifications steps: 1. Dehydration of the raw biogas by condensation. 2. Removal of trace impurities such as H₂S via absorption. 3.Separation of CO₂ and methane via a membrane separation process. 4. Removal of trace impurities from CO₂. The gas separation with polymeric membranes guarantees complete simultaneous removal of microorganisms. The chemical purity of the different process streams was analysed by a certified laboratory and was compared with the guidelines of the European Industrial Gases Association and the International Society of Beverage Technologists (EIGA/ISBT) for CO₂ used in the food industry. The microbiological purity was compared with the limit values defined in the European Collaborative Action. With a purity of 96-99 vol%, the purified methane respects the legal requirements for the household network. At the same time, the CO₂ reaches a purity of > 98.1% before, and 99.9% after the final distillation process. According to the EIGA/ISBT guidelines, the CO₂ proves to be chemically and microbiologically sufficiently pure to be suitable for food-grade applications.

Keywords: biogas, CO₂ separation, CO2 utilization, CO₂ food grade

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Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

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Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Molly Gerrish

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This work explores the physical, emotional, social, and cognitive risks and benefits related to nature-based teaching and highlights the importance of promoting a sustainable workforce within early childhood programs. Assessing and managing risks can help programs reimagine their approach to teaching, learning, recruitment, family connectivity, and staff motivation. The importance of staff sustainability and motivation/engagement related to social justice and the environment will be discussed. We will explore ways to manage fears and limitations faced by early childhood programs regarding nature experiences and risky play in a variety of locations using a lens of place-based learning. We will also examine the alignment of sustainability and social-emotional development, mental health supports, social awareness, and risk assessment. The work will discuss the varied perceptions of risk in diverse areas and the impact on the early childhood workforce. Motivational theory and compassion resiliency are hallmarks of both recruiting and retaining high-quality early childhood educators; the work will discuss how to balance programmatic constraints and healthy motivation for students and teachers while empowering individuals to advocate for their mental health and well-being. Finally, the work will highlight the positive impact of nature-based teaching practices and the overall benefit to young children and their educators.

Keywords: child’s rights, inclusion, nature-based education, risk assessment

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Authors: Naghmeh Moradpoor Sheykhkanloo

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Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: neural networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection

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Authors: Nigar Kantarci Carsibasi

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Coarse-grained elastic network models, namely Gaussian network model (GNM) and Anisotropic network model (ANM), are utilized in order to investigate the fluctuation dynamics of Murine Double Minute 2 (MDM2), which is the native inhibitor of p53. Conformational dynamics of MDM2 are elucidated in unbound, p53 bound, and non-peptide small molecule inhibitor bound forms. With this, it is aimed to gain insights about the alterations brought to global dynamics of MDM2 by native peptide inhibitor p53, and two small molecule inhibitors (HDM201 and NVP-CGM097) that are undergoing clinical stages in cancer studies. MDM2 undergoes significant conformational changes upon inhibitor binding, carrying pieces of evidence of induced-fit mechanism. Small molecule inhibitors examined in this work exhibit similar fluctuation dynamics and characteristic mode shapes with p53 when complexed with MDM2, which would shed light on the design of novel small molecule inhibitors for cancer therapy. The results showed that residues Phe 19, Trp 23, Leu 26 reside in the minima of slowest modes of p53, pointing to the accepted three-finger binding model. Pro 27 displays the most significant hinge present in p53 and comes out to be another functionally important residue. Three distinct regions are identified in MDM2, for which significant conformational changes are observed upon binding. Regions I (residues 50-77) and III (residues 90-105) correspond to the binding interface of MDM2, including (α2, L2, and α4), which are stabilized during complex formation. Region II (residues 77-90) exhibits a large amplitude motion, being highly flexible, both in the absence and presence of p53 or other inhibitors. MDM2 exhibits a scattered profile in the fastest modes of motion, while binding of p53 and inhibitors puts restraints on MDM2 domains, clearly distinguishing the kinetically hot regions. Mode shape analysis revealed that the α4 domain controls the size of the cleft by keeping the cleft narrow in unbound MDM2; and open in the bound states for proper penetration and binding of p53 and inhibitors, which points to the induced-fit mechanism of p53 binding. P53 interacts with α2 and α4 in a synchronized manner. Collective modes are shifted upon inhibitor binding, i.e., second mode characteristic motion in MDM2-p53 complex is observed in the first mode of apo MDM2; however, apo and bound MDM2 exhibits similar features in the softest modes pointing to pre-existing modes facilitating the ligand binding. Although much higher amplitude motions are attained in the presence of non-peptide small molecule inhibitor molecules as compared to p53, they demonstrate close similarity. Hence, NVP-CGM097 and HDM201 succeed in mimicking the p53 behavior well. Elucidating how drug candidates alter the MDM2 global and conformational dynamics would shed light on the rational design of novel anticancer drugs.

Keywords: cancer, drug design, elastic network model, MDM2

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Authors: Meital Amzalag, Sharon Hardof-Jaffe

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In the last few decades, children and youth have been immersed in digital technologies. Indeed, recent studies explored the implication of technology use in their leisure and learning activities. Educators face an essential need to utilize technology and implement them into the curriculum. To do that, educators need to understand how young people use digital technology. This study aims to explore K12 students' digital lives from their point of view, to reveal their digital activities, age and gender differences with respect to digital activities, and to present the students' attitudes towards technologies in learning. The study approach is quantitative and includes354 students ages 6-16 from three schools in Israel. The online questionnaire was based on self-reports and consists of four parts: Digital activities: leisure time activities (such as social networks, gaming types), search activities (information types and platforms), and digital application use (e.g., calendar, notes); Digital skills (requisite digital platform skills such as evaluation and creativity); Social and emotional aspects of digital use (conducting digital activities alone and with friends, feelings, and emotions during digital use such as happiness, bullying); Digital attitudes towards digital integration in learning. An academic ethics board approved the study. The main findings reveal the most popular K12digital activities: Navigating social network sites, watching TV, playing mobile games, seeking information on the internet, and playing computer games. In addition, the findings reveal age differences in digital activities, such as significant differences in the use of social network sites. Moreover, the finding raises gender differences as girls use more social network sites and boys use more digital games, which are characterized by high complexity and challenges. Additionally, we found positive attitudes towards technology integration in school. Students perceive technology as enhancing creativity, promoting active learning, encouraging self-learning, and helping students with learning difficulties. The presentation will provide an up-to-date, accurate picture of the use of various digital technologies by k12 students. In addition, it will discuss the learning potentials of such use and how to implement digital technologies in the curriculum. Acknowledgments: This study is a part of a broader study about K-12 digital life in Israel and is supported by Mofet-the Israel Institute for Teachers'Development.

Keywords: technology and learning, K-12, digital life, gender differences

Procedia PDF Downloads 116