Search results for: cloud radio access network

Authors: Dana R. Thomson, Catherine Linard, Sabine Vanhuysse, Jessica E. Steele, Michal Shimoni, Jose Siri, Waleska Caiaffa, Megumi Rosenberg, Eleonore Wolff, Tais Grippa, Stefanos Georganos, Helen Elsey

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The Sustainable Development Goals (SDGs) and Urban Health Equity Assessment and Response Tool (Urban HEART) identify dozens of key indicators to help local decision-makers prioritize and track inequalities in health outcomes. However, presentations and discussions at the International Conference on Urban Health (ICUH) 2017 suggested that additional indicators are needed to make decisions and policies. A local decision-maker may realize that malaria or road accidents are a top priority. However, s/he needs additional health determinant indicators, for example about standing water or traffic, to address the priority and reduce inequalities. Health determinants reflect the physical and social environments that influence health outcomes often at community- and societal-levels and include such indicators as access to quality health facilities, access to safe parks, traffic density, location of slum areas, air pollution, social exclusion, and social networks. Indicator identification and disaggregation are necessarily constrained by available datasets – typically collected about households and individuals in surveys, censuses, and administrative records. Continued advancements in earth observation, data storage, computing and mobile technologies mean that new sources of health determinants indicators derived from 'big data' are becoming available at fine geographic scale. Big data includes high-resolution satellite imagery and aggregated, anonymized mobile phone data. While big data are themselves not representative of the population (e.g., satellite images depict the physical environment), they can provide information about population density, wealth, mobility, and social environments with tremendous detail and accuracy when combined with population-representative survey, census, administrative and health system data. The aim of this paper is to (1) flag to data scientists important indicators needed by health decision-makers at the city and sub-city scale - ideally free and publicly available, and (2) summarize for local decision-makers new datasets that can be generated from big data, with layperson descriptions of difficulties in generating them. We include SDGs and Urban HEART indicators, as well as indicators mentioned by decision-makers attending ICUH 2017.

Keywords: health determinant, health outcome, mobile phone, remote sensing, satellite imagery, SDG, urban HEART

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Authors: Sarita Magu, Deepak Singh

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Study Design: This prospective study was conducted at the department of Radio Diagnosis, at Pt B.D. Sharma PGIMS, Rohtak in 57 patients of spine injury on radiographs or radiographically normal patients with neurological deficits presenting within 72 hours of injury. Aims: Evaluation of the role of Magnetic Resonance Imaging (MRI) in Spinal Trauma Patients and to compare MRI findings with clinical profile and neurological status of the patient and to correlate the MRI findings with neurological recovery of the patient and predict the outcome. Material and Methods: Neurological status of patients was assessed at the time of admission and discharge in all the patients and at long term interval of six months to one year in 27 patients as per American spine injury association classification (ASIA). On MRI cord injury was categorized into cord hemorrhage, cord contusion, cord edema only, and normal cord. Quantitative assessment of injury on MRI was done using mean canal compromise (MCC), mean spinal cord compression (MSCC) and lesion length. Neurological status at admission and neurological recovery at discharge and long term follow up was compared with various qualitative cord findings and quantitative parameters on MRI. Results: Cord edema and normal cord was associated with favorable neurological outcome. Cord contusion show lesser neurological recovery as compared to cord edema. Cord hemorrhage was associated with worst neurological status at admission and poor neurological recovery. Mean MCC, MSCC, and lesion length values were higher in patients presenting with ASIA A grade injury and showed decreasing trends towards ASIA E grade injury. Patients showing neurological recovery over the period of hospital stay and long term follow up had lower mean MCC, MSCC, and lesion length as compared to patients showing no neurological recovery. The data was statistically significant with p value <.05. Conclusion: Cord hemorrhage and higher MCC, MSCC and lesion length has poor prognostic value in spine injury patients.

Keywords: spine injury, cord hemorrhage, cord contusion, MCC, MSCC, lesion length, ASIA grading

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Authors: Nurudeen Ayeni, Kasali Bello, Ovi Abayeh

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Study of the effect of dye–resin complexation on the degree of dye absorption were carried out using Procion Blue MX-R to dye cotton fabric in the presence hexamethylol melamine (MR 6) and its phosphate derivative (MPR 4) for resination. The highest degree of dye exhaustion was obtained at 400 C for 1 hour with the resinated fabric showing more affinity for the dye than the ordinary fiber. Improved fastness properties was recorded which show a relatively higher stability of dye–resin–cellulose network formed.

Keywords: cotton fabric, reactive dye, dyeing, resination

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Authors: R. Ghasemi, M. R. Rahimi Khoygani

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This paper proposes the designing direct adaptive neural controller to apply for a class of a nonlinear pendulum dynamic system. The radial basis function (RBF) neural adaptive controller is robust in presence of external and internal uncertainties. Both the effectiveness of the controller and robustness against disturbances are importance of this paper. The simulation results show the promising performance of the proposed controller.

Keywords: adaptive neural controller, nonlinear dynamical, neural network, RBF, driven pendulum, position control

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Authors: Sascha Kraus, Wolfgang Hora, Philipp Stieg, Thomas Niemand, Ferdinand Thies, Matthias Filser

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In most countries, small and medium-sized enterprises (SME) are the backbone of the economy due to their impact on job creation, innovation and wealth creation. Moreover, the ongoing globalization makes it inevitable – even for SME that traditionally focused on their domestic markets – to internationalize their business activities to realize further growth and survive in international markets. Thus, internationalization has become one of the most common growth strategies for SME and has received increasing scholarly attention over the last two decades. One the downside internationalization can be also regarded as the most complex strategy that a firm can undertake. Particularly for family firms, that are often characterized by limited financial capital, a risk-averse nature and limited growth aspirations, it could be argued that family firms are more likely to face greater challenges when taking the pathway to internationalization. Especially the triangulation of family, ownership, and management (so-called ‘familiness’) manifests in a unique behavior and decision-making process which is often characterized by the importance given to noneconomic goals and distinguishes a family firm from other businesses. Taking this into account, the concept of socio-emotional wealth (SEW) has been evolved to describe the behavior of family firms. In order to investigate how different internal and external firm characteristics shape internationalization success of family firms, we drew on a sample consisting of 297 small and medium-sized family firms from Germany, Austria, Switzerland, and Liechtenstein. Thus, we include SEW as essential family firm characteristic and added the two major intra-organizational characteristics, entrepreneurial orientation (EO), absorptive capacity (AC) as well as collaboration intensity (CI) and relational knowledge (RK) as two major external network characteristics. Based on previous research we assume that these characteristics are important to explain internationalization success of family firm SME. Regarding the data analysis, we applied a Fuzzy Set Qualitative Comparative Analysis (fsQCA), an approach that allows identifying configurations of firm characteristics, specifically used to study complex causal relationships where traditional regression techniques reach their limits. Results indicate that several combinations of these family firm characteristics can lead to international success, with no permanently required key characteristic. Instead, there are many roads to walk down for family firms to achieve internationalization success. Consequently, our data states that family owned SME are heterogeneous and internationalization is a complex and dynamic process. Results further show that network related characteristics occur in all sets, thus represent an essential element in the internationalization process of family owned SME. The contribution of our study is twofold, as we investigate different forms of international expansion for family firms and how to improve them. First, we are able to broaden the understanding of the intersection between family firm and SME internationalization with respect to major intra-organizational and network-related variables. Second, from a practical perspective, we offer family firm owners a basis for setting up internal capabilities to achieve international success.

Keywords: entrepreneurial orientation, family firm, fsQCA, internationalization, socio-emotional wealth

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Authors: Carole Salis, Marie F. Wilson, Fabrizio Murgia, Cristian Lai, Franco Atzori, Giulia M. Orrù

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ANDASA is a knowledge management platform for the capitalization of knowledge and cultural assets for the artistic and cultural sectors. It was built based on the priorities expressed by the participating artists. Through mapping artistic activities and specificities, it enables to highlight various aspects of the artistic research and production. Such instrument will contribute to create networks and partnerships, as it enables to evidentiate who does what, in what field, using which methodology. The platform is accessible to network participants and to the general public.

Keywords: cultural promotion, knowledge representation, cultural maping, ICT

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Authors: Bruno RamaëL, GwenaëL Page, Catherine Knopf-Lenoir, Olivier Baledent, Anne-Virginie Salsac

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No routine technique currently exists for clinicians to measure the mechanical properties of vascular walls non-invasively. Most of the data available in the literature come from traction or dilatation tests conducted ex vivo on native blood vessels. The objective of the study is to develop a non-invasive characterization technique based on Magnetic Resonance Imaging (MRI) measurements of the deformation of vascular walls under pulsating blood flow conditions. The goal is to determine the mechanical properties of the vessels by inverse analysis, coupling imaging measurements and numerical simulations of the fluid-structure interactions. The hyperelastic properties are identified using Solidworks and Ansys workbench (ANSYS Inc.) solving an optimization technique. The vessel of interest targeted in the study is the common carotid artery. In vivo MRI measurements of the vessel anatomy and inlet velocity profiles was acquired along the facial vascular network on a cohort of 30 healthy volunteers: - The time-evolution of the blood vessel contours and, thus, of the cross-section surface area was measured by 3D imaging angiography sequences of phase-contrast MRI. - The blood flow velocity was measured using a 2D CINE MRI phase contrast (PC-MRI) method. Reference arterial pressure waveforms were simultaneously measured in the brachial artery using a sphygmomanometer. The three-dimensional (3D) geometry of the arterial network was reconstructed by first creating an STL file from the raw MRI data using the open source imaging software ITK-SNAP. The resulting geometry was then transformed with Solidworks into volumes that are compatible with Ansys softwares. Tetrahedral meshes of the wall and fluid domains were built using the ANSYS Meshing software, with a near-wall mesh refinement method in the case of the fluid domain to improve the accuracy of the fluid flow calculations. Ansys Structural was used for the numerical simulation of the vessel deformation and Ansys CFX for the simulation of the blood flow. The fluid structure interaction simulations showed that the systolic and diastolic blood pressures of the common carotid artery could be taken as reference pressures to identify the mechanical properties of the different arteries of the network. The coefficients of the hyperelastic law were identified using Ansys Design model for the common carotid. Under large deformations, a stiffness of 800 kPa is measured, which is of the same order of magnitude as the Young modulus of collagen fibers. Areas of maximum deformations were highlighted near bifurcations. This study is a first step towards patient-specific characterization of the mechanical properties of the facial vessels. The method is currently applied on patients suffering from facial vascular malformations and on patients scheduled for facial reconstruction. Information on the blood flow velocity as well as on the vessel anatomy and deformability will be key to improve surgical planning in the case of such vascular pathologies.

Keywords: identification, mechanical properties, arterial walls, MRI measurements, numerical simulations

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Authors: Palwinder Bhatia

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As all know new media is a broad term in media studies that emerged in the latter part of the 20th century which refers to on-demand access to content any time, anywhere, on any digital device, as well as interactive user feedback, creative participation and community formation around the media content. The role of new media in advertisement is impeccable these days. It becomes the cheap and best way of advertising. Another important promise of new media is the democratization of the creation, publishing, distribution and consumption of media content. New media brings a revolution in about every field. It makes bridge between customer and companies. World make a global village with the only help of new media. Advertising helps in shaping the consumer behavior and effect on consumer psychology, sociology, social anthropology and economics. People do comments and like the particular brands on the networking sites which create mesmerism impact on the behavior of customer. Recent study did by Times of India shows that 64% of Facebook users have liked a brand on Facebook.

Keywords: film, visual, culture, media, advertisement

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Authors: Joy Cao, Min Zhou

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Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

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Authors: S. Malebary, W. Xu

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Antennas are important components that enable transmitting and receiving signals in mid-air (wireless). The radiation pattern of omni-directional (i.e., dipole) antennas, reflects the variation of power radiated by an antenna as a function of direction when transmitting. As the performance of the antenna is the same in transmitting and receiving, it also reflects the sensitivity of the antenna in different directions when receiving. The main observation when dealing with omni-directional antennas, regardless the application, is they equally radiate power in all directions in reference to Equivalent Isotropically Radiated Power (EIRP). Disseminating radio frequency signals in an omni-directional manner form a doughnut-shape-field with a cone in the middle of the elevation plane (when mounted vertically). In this paper, we investigate the existence of this physical phenomena namely silence cone zone (the zone where radiated power is nulled). First, we overview antenna types and properties that have the major impact on the shape of the electromagnetic field. Then we model various off the shelf dipoles in Matlab based on antennas’ features (dimensions, gain, operating frequency, … etc.) and compare the resulting radiation patterns. After that, we validate the existence of the null zone in Omni-directional antennas by conducting experiments and generating waveforms (using USRP1 and USRP2) at various frequencies using different types of antennas and gains in indoor/outdoor. We capture the generated waveforms around antennas' null zone in the reactive, near, and far field with a spectrum analyzer mounted on a drone, using various off the shelf antennas. We analyze the captured signals in RF-Explorer and plot the impact on received power and signal amplitude inside and around the null zone. Finally, it is concluded from evaluation and measurements the existence of null zones in Omni-directional antennas which we plan on extending this work in the near future to investigate the usability of the null zone for various applications such as localization and path finding.

Keywords: antennas, amplitude, field regions, frequency, FSPL, omni-directional, radiation pattern, RSSI, silence zone cone

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Authors: R. Hellmuth

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The method of factory planning has changed a lot, especially when it is about planning the factory building itself. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity and Ambiguity) lead to more frequent restructuring measures within a factory. A digital factory model is the planning basis for rebuilding measures and becomes an indispensable tool. Furthermore, digital building models are increasingly being used in factories to support facility management and manufacturing processes. The main research question of this paper is, therefore: What kind of digital factory model is suitable for the different areas of application during the operation of a factory? First, different types of digital factory models are investigated, and their properties and usabilities for use cases are analysed. Within the scope of investigation are point cloud models, building information models, photogrammetry models, and these enriched with sensor data are examined. It is investigated which digital models allow a simple integration of sensor data and where the differences are. Subsequently, possible application areas of digital factory models are determined by means of a survey and the respective digital factory models are assigned to the application areas. Finally, an application case from maintenance is selected and implemented with the help of the appropriate digital factory model. It is shown how a completely digitalized maintenance process can be supported by a digital factory model by providing information. Among other purposes, the digital factory model is used for indoor navigation, information provision, and display of sensor data. In summary, the paper shows a structuring of digital factory models that concentrates on the geometric representation of a factory building and its technical facilities. A practical application case is shown and implemented. Thus, the systematic selection of digital factory models with the corresponding application cases is evaluated.

Keywords: building information modeling, digital factory model, factory planning, maintenance

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Authors: Narges Hamidipour

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VOD, which stands for "video on demand", is one kind of watching movies and series on web platforms that, by using them, individuals can access lots of video content by paying abonnement. The first platform in Iran was funded in 2014, and in the last 10 years, it has become the main part of the movie and series industry. There are 374 VOD platforms in Iran, but just three of them are in the mainstream. However, in these years, they have been developed and famed in different ways. This article focuses on the reasons for this development in the past years. For the framework, "digital economy", "media industries," and "political economy" have been used with the interview method. In this research, some experts in SATRA (regulatory organization of inclusive audio and video media in Iran), owners or managers of VODs and some others who directly have been in the system conveyed their opinions. By the way, some documents and analysis statistics are invoked to reach complete results.

Keywords: digital economy, political economy, VOD, interview, iran

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Authors: A. Anastasiou, K. S. Tingay

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Background: Discovering suitable datasets is an important part of health research, particularly for projects working with clinical data from patients organized in cohorts (cohort data), but with the proliferation of so many national and international initiatives, it is becoming increasingly difficult for research teams to locate real world datasets that are most relevant to their project objectives. We present a method for identifying healthcare institutes in the European Union (EU) which may hold haemato-oncology (HO) data. A key enabler of this research was the bibInsight platform, a scientometric data management and analysis system developed by the authors at Swansea University. Method: A PubMed search was conducted using HO clinical terms taken from previous work. The resulting XML file was processed using the bibInsight platform, linking affiliations to the Global Research Identifier Database (GRID). GRID is an international, standardized list of institutions, including the city and country in which the institution exists, as well as a category of the main business type, e.g., Academic, Healthcare, Government, Company. Countries were limited to the 28 current EU members, and institute type to 'Healthcare'. An article was considered valid if at least one author was affiliated with an EU-based healthcare institute. Results: The PubMed search produced 21,310 articles, consisting of 9,885 distinct affiliations with correspondence in GRID. Of these articles, 760 were from EU countries, and 390 of these were healthcare institutes. One affiliation was excluded as being a veterinary hospital. Two EU countries did not have any publications in our analysis dataset. The results were analysed by country and by individual healthcare institute. Networks both within the EU and internationally show institutional collaborations, which may suggest a willingness to share data for research purposes. Geographical mapping can ensure that data has broad population coverage. Collaborations with industry or government may exclude healthcare institutes that may have embargos or additional costs associated with data access. Conclusions: Data reuse is becoming increasingly important both for ensuring the validity of results, and economy of available resources. The ability to identify potential, specific data sources from over twenty thousand articles in less than an hour could assist in improving knowledge of, and access to, data sources. As our method has not yet specified if these healthcare institutes are holding data, or merely publishing on that topic, future work will involve text mining of data-specific concordant terms to identify numbers of participants, demographics, study methodologies, and sub-topics of interest.

Keywords: data reuse, data discovery, data linkage, journal articles, text mining

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Computational linguistics is a growing field dealing with such issues of data collection for technological development. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Computational analysis on such linguistic data is used to find patterns of misogyny. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: computational analysis, gendered grammar, misogynistic language, neural networks

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Authors: Sharda Kumari, Saiman Shetty

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Autonomous trucking is just one of the numerous significant shifts impacting fleet management services. The Society of Automotive Engineers (SAE) has defined six levels of vehicle automation that have been adopted internationally, including by the United States Department of Transportation. On public highways in the United States, organizations are testing driverless vehicles with at least Level 4 automation which indicates that a human is present in the vehicle and can disable automation, which is usually done while the trucks are not engaged in highway driving. However, completely driverless vehicles are presently being tested in the state of California. While autonomous trucking can increase safety, decrease trucking costs, provide solutions to trucker shortages, and improve efficiencies, logistics, too, requires advancements to keep up with trucking innovations. Given that artificial intelligence, machine learning, and automated procedures enable people to do their duties in other sectors with fewer resources, CRM (Customer Relationship Management) can be applied to the autonomous trucking business to provide the same level of efficiency. In a society witnessing significant digital disruptions, fleet management is likewise being transformed by technology. Utilizing strategic alliances to enhance core services is an effective technique for capitalizing on innovations and delivering enhanced services. Utilizing analytics on CRM systems improves cost control of fuel strategy, fleet maintenance, driver behavior, route planning, road safety compliance, and capacity utilization. Integration of autonomous trucks with automated fleet management, yard/terminal management, and customer service is possible, thus having significant power to redraw the lines between the public and private spheres in autonomous trucking logistics.

Keywords: autonomous vehicles, customer relationship management, customer experience, autonomous trucking, digital transformation

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Authors: Laura N. Bolivar T.

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The tourism importance for regional development is mainly highlighted by the collaborative, cooperating and competitive relationships of the involved agents. The fostering of associativity processes, in particular, the cluster approach emphasizes the beneficial outcomes from the concentration of enterprises, where innovation and entrepreneurship flourish and shape the dynamics for tourism empowerment. Considering the department of Huila, it is located in the south-west of Colombia and holds the biggest coffee production in the country, although it barely contributes to the national GDP. Hence, its economic development strategy is looking for more dynamism and Huila could be consolidated as a leading destination for cultural, ecological and heritage tourism, if at least the public policy making processes for the tourism management of La Tatacoa Desert, San Agustin Park and Bambuco’s National Festival, were implemented in a more efficient manner. In this order of ideas, this study attempts to address the potential restrictions and beneficial factors for the consolidation of the tourism sector of Huila-Colombia as a cluster and how could it impact its regional development. Therefore, a set of theoretical frameworks such as the Tourism Routes Approach, the Tourism Breeding Environment, the Community-based Tourism Method, among others, but also a collection of international experiences describing tourism clustering processes and most outstanding problematics, is analyzed to draw up learning points, structure of proceedings and success-driven factors to be contrasted with the local characteristics in Huila, as the region under study. This characterization involves primary and secondary information collection methods and comprises the South American and Colombian context together with the identification of involved actors and their roles, main interactions among them, major tourism products and their infrastructure, the visitors’ perspective on the situation and a recap of the related needs and benefits regarding the host community. Considering the umbrella concepts, the theoretical and the empirical approaches, and their comparison with the local specificities of the tourism sector in Huila, an array of shortcomings is analytically constructed and a series of guidelines are proposed as a way to overcome them and simultaneously, raise economic development and positively impact Huila’s well-being. This non-exhaustive bundle of guidelines is focused on fostering cooperating linkages in the actors’ network, dealing with Information and Communication Technologies’ innovations, reinforcing the supporting infrastructure, promoting the destinations considering the less known places as well, designing an information system enabling the tourism network to assess the situation based on reliable data, increasing competitiveness, developing participative public policy-making processes and empowering the host community about the touristic richness. According to this, cluster dynamics would drive the tourism sector to meet articulation and joint effort, then involved agents and local particularities would be adequately assisted to cope with the current changing environment of globalization and competition.

Keywords: innovative strategy, local development, network of tourism actors, tourism cluster

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Authors: Chitraporn Boonthanom, Pailin Sisookho

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The Sexual Literacy Training Teachers Program (SLTTP) based on sexual health problems in Thai students includes sexual development, sexual hygiene, high-risk behaviors, Sexual Transmitter Infections and HIV/AIDS, unwanted pregnancy, unsafe abortion, and sexual abuse that increase among Thai students. The training aimed at providing teachers with the sexuality knowledge, sexuality information access skills, communication skill to effectively teach in their class. 28 teachers were participated from schools in Bangkok metropolitan and Nakorn Patom province. The result show the mean score of the pre-test was 8.46 (SD=1.77) and the post-test was 10.13 (SD=2.34). The post-training results were found to be higher than pre–training results but not significant. As teachers are increasing the sexual literacy is very important for Thai students.

Keywords: sexual literacy, teachers, training, sexuality education

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Authors: Kanduru Lokesh, Venkitasamy Kesavan

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The unique structural features of spiro-oxindoles with diverse biological activities have made them privileged structures in new drug discovery. The key structural characteristic of these compounds is the spiro ring fused at the C-3 position of the oxindole core with varied heterocyclic motifs. Structural diversification of heterocyclic scaffolds to synthesize new chemical entities as pharmaceuticals and agrochemicals is one of the important goals of synthetic organic chemists. Nitrogen and oxygen containing heterocycles are by far the most widely occurring privileged structures in medicinal chemistry. The structural complexity and distinct three-dimensional arrangement of functional groups of these privileged structures are generally responsible for their specificity against biological targets. Structurally diverse compound libraries have proved to be valuable assets for drug discovery against challenging biological targets. Thus, identifying a new combination of substituents at C-3 position on oxindole moiety is of great importance in drug discovery to improve the efficiency and efficacy of the drugs. The development of suitable methodology for the synthesis of spiro-oxindole compounds has attracted much interest often in response to the significant biological activity displayed by the both natural and synthetic compounds. So creating structural diversity of oxindole scaffolds is need of the decade and formidable challenge. A general way to improve synthetic efficiency and also to access diversified molecules is through the annulation reactions. Annulation reactions allow the formation of complex compounds starting from simple substrates in a single transformation consisting of several steps in an ecologically and economically favorable way. These observations motivated us to develop the annulation reaction protocol to enable the synthesis of a new class of spiro-oxindole motifs which in turn would enable the enhancement of molecular diversity. As part of our enduring interest in the development of novel, efficient synthetic strategies to enable the synthesis of biologically important oxindole fused spirocarbocyclic systems, We have developed an efficient methodology for the construction of highly functionalized spirocarbocyclic oxindoles through [4+2] annulation of phthalides via Hauser annulation. functionalized spirocarbocyclic oxindoles was accomplished for the first time in the literature using Hauser annulation strategy. The reaction between methyleneindolinones and arylsulfonylphthalides catalyzed by cesium carbonate led to the access of new class of biologically important spiro[indoline-3,2'-naphthalene] derivatives in very good yields. The synthetic utility of the annulated product was further demonstrated by fluorination Using NFSI as a fluorinating agent to furnish corresponding fluorinated product.

Keywords: Hauser-Kraus annulation, spiro carbocyclic oxindoles, oxindole-ester, fluoridation

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Authors: Husen Ahmed Tura

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There are strong links between secure land rights and food security in Africa. However, as land is owned by governments, land users do not have adequate legislative protection. This article explores normative and implementation gaps in relation to small-scale farmers’ land rights under the Ethiopia’s law. It finds that the law facilitates eviction of small-scale farmers and indigenous peoples from their land without adequate alternative means of livelihood. It argues that as access to land and other natural resources is strongly linked to the right to adequate food, Ethiopia should reform its land laws in the light of its legal obligations under international human rights law to respect, protect and fulfill the right to adequate food and ensure freedom from hunger.

Keywords: smallholder, secure land rights , food security, right to food, land grabbing, forced evictions

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Authors: Forouzan Salehi Fergeni

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Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

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Authors: Suleiman Garba, Haruna Ishaku

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Information and communication technology (ICT) includes any communication device encompassing: radio, television, cellular phones, computer, satellite systems and so on, as well as the various services and applications associated with them. The significance of ICT cannot be over-emphasized in education. The teaching and learning processes have integrated with the application of ICTs for effectiveness and enhancement of academic performance among the students. Today, as the educational sector is faced with series of changes and reforms, it was noted that the problem of information technology illiteracy was a serious one among the schools’ teachers in the country as it cuts across primary, secondary schools and tertiary institutions. This study investigated the impact of ICT on the academic performance of senior secondary schools students in Gwagwalada Area Council of Federal Capital Territory (FCT), Abuja. A sample of 120 SSS III students was involved in the study. They were selected by using simple random sampling technique. A questionnaire was developed and validated through expert judgement and reliability co-efficient of 0.81 was obtained. It was used to gather relevant data from the respondents. Findings revealed that there was positive impact of ICT on academic performance of senior secondary schools students. The findings indicated the causes of poor academic performance among the students as lack of qualified teachers to teach in schools, peer group influence, and bullying. Significantly, the findings revealed that ICT had a positive impact on students’ academic performance. The null hypotheses were tested using t-test at 0.05 level of significance. It was discovered that there was significant difference between male and female secondary schools’ students' impact of ICT on academic performance in Gwagawalada Area Council of FCT-Abuja. Based on these findings, some recommendations were made which include: adequate funds should be provided towards procurement of ICT resources, relevant textbooks to enhance students’ active participation in learning processes and students should be provided with internet accessibility at inexpensive rate so as to create a platform for accessing useful information in the pursuit of academic excellence.

Keywords: academic performance, impact, information communication technology, schools, students

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Gaurav Kumar Sinha

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In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

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Authors: Roberto Feltrero, Sara Osuna-Acedo

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Responsible Research and Innovation have to accomplish one fundamental aim: everybody has to participate in the benefits of innovation, but also innovation has to be democratic; that is to say, everybody may have the possibility to participate in the decisions in the innovation process. Particularly, a democratic and inclusive model of social participation and innovation includes persons with disabilities and people at risk of discrimination. Innovations on Artificial Intelligence for social development have to accomplish the same dual goal: improving equality for accessing fields of public interest like education, training and public services, as well as improving civic and democratic participation in the process of developing such innovations for all. This research aims to develop innovations, policies and policy recommendations to apply and disseminate such artificial intelligence and social model for making educational and administrative processes more accessible. First, designing a citizen participation process to engage citizens in the designing and use of artificial intelligence tools for public services. This will result in improving trust in democratic institutions contributing to enhancing the transparency, effectiveness, accountability and legitimacy of public policy-making and allowing people to participate in the development of ethical standards for the use of such technologies. Second, improving educational tools for lifelong learning with AI models to improve accountability and educational data management. Dissemination, education and social participation will be integrated, measured and evaluated in innovative educational processes to make accessible all the educational technologies and content developed on AI about responsible and social innovation. A particular case will be presented regarding access for all to educational tools and public services. This accessibility requires cognitive adaptability because, many times, legal or administrative language is very complex. Not only for people with cognitive disabilities but also for old people or citizens at risk of educational or social discrimination. Artificial Intelligence natural language processing technologies can provide tools to translate legal, administrative, or educational texts to a more simple language that can be accessible to everybody. Despite technological advances in language processing and machine learning, this becomes a huge project if we really want to respect ethical and legal consequences because that kinds of consequences can only be achieved with civil and democratic engagement in two realms: 1) to democratically select texts that need and can be translated and 2) to involved citizens, experts and nonexperts, to produce and validate real examples of legal texts with cognitive adaptations to feed artificial intelligence algorithms for learning how to translate those texts to a more simple and accessible language, adapted to any kind of population.

Keywords: responsible research and innovation, AI social innovations, cognitive accessibility, public participation

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Authors: Miria Gambardella

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The influence of solidarity demonstrations towards the Zapatista National Liberation Army has been constantly present over the years, both locally and internationally, guaranteeing visibility to the cause, shaping the movement’s choices, and influencing its hopes of impact worldwide. Most of the coffee produced by the autonomous cooperatives from Chiapas is exported, therefore making coffee trade the main income from international solidarity networks. The question arises about the implications of the relations established between the communities in resistance in Southeastern Mexico and international solidarity movements, specifically on the strategies adopted to conciliate army's demands for autonomy and economic asymmetries between Zapatista cooperatives producing coffee and European collectives who hold purchasing power. In order to deepen the inquiry on those topics, a year-long multi-site investigation was carried out. The first six months of fieldwork were based in Barcelona, where Zapatista coffee was first traded in Spain and where one of the historical and most important European solidarity groups can be found. The last six months of fieldwork were carried out directly in Chiapas, in contact with coffee producers, Zapatista political authorities, international activists as well as vendors, and the rest of the network implicated in coffee production, roasting, and sale. The investigation was based on qualitative research methods, including participatory observation, focus groups, and semi-structured interviews. The analysis did not only focus on retracing the steps of the market chain as if it could be considered a linear and unilateral process, but it rather aimed at exploring actors’ reciprocal perceptions, roles, and dynamics of power. Demonstrations of solidarity and the money circulation they imply aim at changing the system in place and building alternatives, among other things, on the economic level. This work analyzes the formulation of discourse and the organization of solidarity activities that aim at building opportunities for action within a highly politicized economic sphere to which access must be regularly legitimized. The meaning conveyed by coffee is constructed on a symbolic level by the attribution of moral criteria to transactions. The latter participate in the construction of imaginaries that circulate through solidarity movements with the Zapatista rebellion. Commercial exchanges linked to solidarity networks turned out to represent much more than monetary transactions. The social, cultural, and political spheres are invested by ethics, which penetrates all aspects of militant action. It is at this level that the boundaries of different collective actors connect, contaminating each other: merely following the money flow would have been limiting in order to account for a reality within which imaginary is one of the main currencies. The notions of “trust”, “dignity” and “reciprocity” are repeatedly mobilized to negotiate discontinuous and multidirectional flows in the attempt to balance and justify commercial relations in a politicized context that characterizes its own identity through demonizing “market economy” and its dehumanizing powers.

Keywords: coffee trade, economic anthropology, international cooperation, Zapatista National Liberation Army

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Authors: Diego De Almeida Pereira, Diana Borchenko

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Artificial neural networks are used for many applications as they are able to learn complex nonlinear relationships between input and output data. As the number of neurons and layers in a neural network increases, it is possible to represent more complex behaviors. The present study proposes that artificial neural networks are a valuable tool for architecture and engineering professionals concerned with understanding how buildings influence human and social well-being based on theories of environmental psychology.

Keywords: environmental psychology, architecture, neural networks, human and social well-being

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Authors: Fiyinfoluwa Giwa, Nicholas Ngepah

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This paper investigates the relationship between the Fourth Industrial Revolution and food security in South Africa. The Ordinary Least Square was adopted from 2012 Q1 to 2021 Q4. The study used artificial intelligence investment and the food production index as the measure for the fourth industrial revolution and food security, respectively. Findings reveal a significant and positive coefficient of 0.2887, signifying a robust statistical relationship between AI adoption and the food production index. As a policy recommendation, this paper recommends the introduction of incentives for farmers and agricultural enterprises to adopt AI technologies -and the expansion of digital connectivity and access to technology in rural areas.

Keywords: Fourth Industrial Revolution, food security, artificial intelligence investment, food production index, ordinary least square

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Authors: Enrique Barra, Aldo Gordillo, Juan Quemada

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This paper presents the Virtual Science Hub platform. It is an open source platform that combines a social network, an e-learning authoring tool, a video conference service and a learning object repository for science teaching enrichment. These four main functionalities fit very well together. The platform was released in April 2012 and since then it has not stopped growing. Finally we present the results of the surveys conducted and the statistics gathered to validate this approach.

Keywords: e-learning, platform, authoring tool, science teaching, educational sciences

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Authors: Ademola Ibukunolu Atanda

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Research undertakings are meant to proffer solutions to issues and challenges in society. This justifies the need for research in ivory towers. Multinational and non-governmental organisations, as well as foundations, commit financial resources to support research endeavours. In recent times, the direction and dimension of research undertaking encourage collaborations, whereby experts from different disciplines or specializations would bring their expertise in addressing any identified problem, whether in humanities or sciences. However, the extent to which collaborative research undertakings are perceived and embraced by academic staff would determine the impact collaborative research would have on society. To this end, this study investigated academic staff’s perception and willingness to be involved in collaborative research for the purpose of proffering solutions to societal problems. The study adopted a descriptive research design. The population comprised academic staff in southern Nigeria. The sample was drawn through a convenient sampling technique. The data were collected using a questionnaire titled “Perception and Willingness to Participate in Collaborative Research Questionnaire (PWPCRQ)’ using Google Forms. Data collected were analyzed using descriptive statistics of simple percentages, mean and charts. The findings showed that Academic Staff’s readiness to participate in collaborative research is to a great extent (89%) and they participate in collaborative research very often (51%). The Academic Staff was involved more in collaboration research among their colleagues within their universities (1.98) than participation in inter-disciplines collaboration (1.47) with their colleagues outside Nigeria. Collaborative research was perceived to impact on development (2.5). Collaborative research offers the following benefits to members’ aggregation of views, the building of an extensive network of contacts, enhancement of sharing of skills, facilitation of tackling complex problems, increased visibility of research network and citations and promotion of funding opportunities. The study concluded that Academic staff in universities in the South-West of Nigeria participate in collaborative research but with their colleagues within Nigeria rather than outside the country. Based on the findings, it was recommended that the management of universities in South-West Nigeria should encourage collaborative research with some incentives.

Keywords: collaboration, research, development, participation

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Authors: A. Kavita Murugkar, B. Anurag Kashyap

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With the Rights for Persons with Disabilities Act (RPWD Act) 2016, the Indian government has made it mandatory for all establishments, including Heritage Sites, to be accessible for People with Disabilities. However, recent access audit surveys done under the Accessible India Campaign by Ministry of Culture indicate that there are very few accessibility measures provided in the Heritage sites for people with disabilities. Though there are some measures for the mobility impaired, surveys brought out that there are almost no provisions for people with vision impairment (PwVI) in heritage sites thus depriving them of a reasonable physical & intellectual access that facilitates an enjoyable experience and enriching interpretation of the Heritage Site. There is a growing need to develop multisensory interpretative tools that can help the PwVI in perceiving heritage sites in the absence of vision. The purpose of this research was to examine the usability of an audio-tactile model as a haptic and sound-based strategy for augmenting the perception and experience of PwVI in a heritage site. The first phase of the project was a multi-stage phenomenological experimental study with visually impaired users to investigate the design parameters for developing an audio-tactile model for PwVI. The findings from this phase included user preferences related to the physical design of the model such as the size, scale, materials, details, etc., and the information that it will carry such as braille, audio output, tactile text, etc. This was followed by the second phase in which a working prototype of an audio-tactile model is designed and developed for a heritage site based on the findings from the first phase of the study. A nationally listed heritage site from the author’s city was selected for making the model. The model was lastly tested by visually impaired users for final refinements and validation. The prototype developed empowers People with Vision Impairment to navigate independently in heritage sites. Such a model if installed in every heritage site, can serve as a technological guide for the Person with Vision Impairment, giving information of the architecture, details, planning & scale of the buildings, the entrances, location of important features, lifts, staircases, and available, accessible facilities. The model was constructed using 3D modeling and digital printing technology. Though designed for the Indian context, this assistive technology for the blind can be explored for wider applications across the globe. Such an accessible solution can change the otherwise “incomplete’’ perception of the disabled visitor, in this case, a visually impaired visitor and augment the quality of their experience in heritage sites.

Keywords: accessibility, architectural perception, audio tactile model , inclusive heritage, multi-sensory perception, visual impairment, visitor experience

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