Search results for: siamese networks

Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: Bianca Price, Jennifer Stokes

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Academic and social support systems are critical for students in enabling education; these support systems have the potential to enhance the student experience whilst also serving a vital role for student retention. In the context of international moves toward widening university participation, Australia has developed enabling programs designed to support underrepresented students to access to higher education. The purpose of this study is to examine the effectiveness of a mentor program based within an enabling course. This study evaluates how the mentor program supports new students to develop social networks, improve retention, and increase satisfaction with the student experience. Guided by Social Learning Theory (SLT), this study highlights the benefits that can be achieved when students engage in peer-to-peer based mentoring for both social and learning support. Whilst traditional peer mentoring programs are heavily based on face-to-face contact, the present study explores the difference between mentors who provide face-to-face mentoring, in comparison with mentoring that takes place through the virtual space, specifically via a virtual community in the shape of a Facebook group. This paper explores the differences between these two methods of mentoring within an enabling program. The first method involves traditional face-to-face mentoring that is provided by alumni students who willingly return to the learning community to provide social support and guidance for new students. The second method requires alumni mentor students to voluntarily join a Facebook group that is specifically designed for enabling students. Using this virtual space, alumni students provide advice, support and social commentary on how to be successful within an enabling program. Whilst vastly different methods, both of these mentoring approaches provide students with the support tools needed to enhance their student experience and improve transition into University. To evaluate the impact of each mode, this study uses mixed methods including a focus group with mentors, in-depth interviews, as well as engaging in netnography of the Facebook group ‘Wall’. Netnography is an innovative qualitative research method used to interpret information that is available online to better understand and identify the needs and influences that affect the users of the online space. Through examining the data, this research will reflect upon best practice for engaging students in enabling programs. Findings support the applicability of having both face-to-face and online mentoring available for students to assist enabling students to make a positive transition into University undergraduate studies.

Keywords: enabling education, mentoring, netnography, social learning theory

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Authors: André Pasti

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Historically, the communication sector in Argentina is characterized by intense monopolization and geographical concentration in the city of Buenos Aires. In 2000, the four major media conglomerates in operation – Clarín, Telefónica, America and Hadad – controlled 84% of the national media market. By 2009, new policies were implemented as a result of civil society organizations demands. Legally, a new regulatory framework was approved: the law 26,522 of Audiovisual Communications Services. Supposedly, these policies intend to create new conditions for the development of the audiovisual economy in the territory of Argentina. The regionalization of audiovisual production and the democratization of channels and access to media were among the priorities. This paper analyses the main changes and continuities in the organization of the spatial circuit of the audiovisual industry in Argentina provoked by these new policies. These new policies aim at increasing the diversity of audiovisual producers and promoting regional audiovisual industries. For this purpose, a national program for the development of audiovisual centers within the country was created. This program fostered a federalized production network, based on nine audiovisual regions and 40 nodes. Each node has created technical, financial and organizational conditions to gather different actors in audiovisual production – such as SMEs, social movements and local associations. The expansion of access to technical networks was also a concern of other policies, such as ‘Argentina connected’, whose objective was to expand access to broadband Internet. The Open Digital Television network also received considerable investments. Furthermore, measures have been carried out in order to impose limits on the concentration of ownership as well as to eliminate the oligopolies and to ensure more competition in the sector. These actions intended to force a divide of the media conglomerates into smaller groups. Nevertheless, the corporations that compose these conglomerates resist strongly, making full use of their economic and judiciary power. Indeed, the absence of effective impact of such measures can be testified by the fact that the audiovisual industry remains strongly concentrated in Argentina. Overall, these new policies were designed properly to decentralize audiovisual production and expand the regional diversity of the audiovisual industry. However, the effective transformation of the organization of the audiovisual circuit in the territory faced several resistances. This can be explained firstly and foremost by the ideological and economic power of the media conglomerates. In the second place, there is an inherited inertia from the unequal distribution of the objects needed for the audiovisual production and consumption. Lastly, the resistance also relies on financial needs and in the excessive dependence of the state for the promotion of regional audiovisual production.

Keywords: Argentina, audiovisual industry, communication policies, geographic concentration, regionalization, spatial circuit

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Subham Ghosh, Arnab Nandi

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Background: The proliferation of Wireless Body Area Networks (WBAN) and Internet of Things (IoT) applications demonstrates the potential for continuous monitoring of physical changes in the body. These technologies are vital for health monitoring tasks, such as identifying coughing and breathing activities, which are necessary for disease diagnosis and management. Monitoring activities such as coughing and deep breathing can provide valuable insights into a variety of medical issues. Wearable radio-based antenna sensors, which are lightweight and easy to incorporate into clothing or portable goods, provide continuous monitoring. This mobility gives it a substantial advantage over stationary environmental sensors like as cameras and radar, which are constrained to certain places. Furthermore, using compressive techniques provides benefits such as reduced data transmission speeds and memory needs. These wearable sensors offer more advanced and diverse health monitoring capabilities. Methodology: This study analyzes the feasibility of using a semi-flexible antenna operating at 2.4 GHz (ISM band) and positioned around the neck and near the mouth to identify three activities: coughing, deep breathing, and idleness. Vector network analyzer (VNA) is used to collect time-varying complex reflection coefficient data from perturbed antenna nearfield. The reflection coefficient (S11) conveys nuanced information caused by simultaneous variations in the nearfield radiation of three activities across time. The signatures are sparsely represented with gaussian windowed Gabor spectrograms. The Gabor spectrogram is used as a sparse representation approach, which reassigns the ridges of the spectrogram images to improve their resolution and focus on essential components. The antenna is biocompatible in terms of specific absorption rate (SAR). The sparsely represented Gabor spectrogram pictures are fed into a lightweight deep learning (DL) model for feature extraction and classification. Two antenna locations are investigated in order to determine the most effective localization for three different activities. Findings: Cross-validation techniques were used on data from both locations. Due to the complex form of the recorded S11, separate analyzes and assessments were performed on the magnitude, phase, and their combination. The combination of magnitude and phase fared better than the separate analyses. Various sliding window sizes, ranging from 1 to 5 seconds, were tested to find the best window for activity classification. It was discovered that a neck-mounted design was effective at detecting the three unique behaviors.

Keywords: activity recognition, antenna, deep-learning, time-frequency

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Authors: Shankaran Sitarama

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New Product Development involves team members coming together and working in teams to come up with innovative solutions to problems, resulting in new products. Thus, a core attribute of a successful NPD team is their creativity and innovation. They need to be creative as a group, generating a breadth of ideas and innovative solutions that solve or address the problem they are targeting and meet the user’s needs. They also need to be very efficient in their teamwork as they work through the various stages of the development of these ideas, resulting in a POC (proof-of-concept) implementation or a prototype of the product. There are two distinctive traits that the teams need to have, one is ideational creativity, and the other is effective and efficient teamworking. There are multiple types of tensions that each of these traits cause in the teams, and these tensions reflect in the team dynamics. Ideational conflicts arising out of debates and deliberations increase the collective knowledge and affect the team creativity positively. However, the same trait of challenging each other’s viewpoints might lead the team members to be disruptive, resulting in interpersonal tensions, which in turn lead to less than efficient teamwork. Teams that foster and effectively manage these creative tensions are successful, and teams that are not able to manage these tensions show poor team performance. In this paper, it explore these tensions as they result in the team communication social network and propose a Creative Tension Balance index along the lines of Degree of Balance in social networks that has the potential to highlight the successful (and unsuccessful) NPD teams. Team communication reflects the team dynamics among team members and is the data set for analysis. The emails between the members of the NPD teams are processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. This social network is subjected to traditional social network analysis methods to arrive at some established metrics and structural balance analysis metrics. Traditional structural balance is extended to include team interaction pattern metrics to arrive at a creative tension balance metric that effectively captures the creative tensions and tension balance in teams. This CTB (Creative Tension Balance) metric truly captures the signatures of successful and unsuccessful (dissonant) NPD teams. The dataset for this research study includes 23 NPD teams spread out over multiple semesters and computes this CTB metric and uses it to identify the most successful and unsuccessful teams by classifying these teams into low, high and medium performing teams. The results are correlated to the team reflections (for team dynamics and interaction patterns), the team self-evaluation feedback surveys (for teamwork metrics) and team performance through a comprehensive team grade (for high and low performing team signatures).

Keywords: team dynamics, social network analysis, new product development teamwork, structural balance, NPD teams

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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: Adam Gushgari

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The SARS-CoV-2 pandemic has catalyzed the rapid adoption of wastewater-based epidemiology (WBE) methodologies both domestically and internationally. To support the rapid scale-up of pandemic-response wastewater surveillance systems, multiple federal agencies (i.e. US CDC), non-government organizations (i.e. Water Environment Federation), and private charities (i.e. Bill and Melinda Gates Foundation) have funded over $220 million USD supporting development and expanding equitable access of surveillance methods. Funds were primarily distributed directly to municipalities under the CARES Act (90.6%), followed by academic projects (7.6%), and initiatives developed by private companies (1.8%). In addition to federal funding for wastewater monitoring primarily conducted at wastewater treatment plants, state/local governments and private companies have leveraged wastewater sampling to obtain health and lifestyle data on student, prison inmate, and employee populations. We explore the viable paths for expansion of the WBE m1ethodology across a variety of analytical methods; the development of WBE-specific samplers and real-time wastewater sensors; and their application to various governments and private sector industries. Considerable investment in, and public acceptance of WBE suggests the methodology will be applied to other future notifiable diseases and health risks. Early research suggests that WBE methods can be applied to a host of additional “biological insults” including communicable diseases and pathogens, such as influenza, Cryptosporidium, Giardia, mycotoxin exposure, hepatitis, dengue, West Nile, Zika, and yellow fever. Interest in chemical insults is also likely, providing community health and lifestyle data on narcotics consumption, use of pharmaceutical and personal care products (PPCP), PFAS and hazardous chemical exposure, and microplastic exposure. Successful application of WBE to monitor analytes correlated with carcinogen exposure, community stress prevalence, and dietary indicators has also been shown. Additionally, technology developments of in situ wastewater sensors, WBE-specific wastewater samplers, and integration of artificial intelligence will drastically change the landscape of WBE through the development of “smart sewer” networks. The rapid expansion of the WBE field is creating significant business opportunities for professionals across the scientific, engineering, and technology industries ultimately focused on community health improvement.

Keywords: wastewater surveillance, wastewater-based epidemiology, smart cities, public health, pandemic management, substance abuse

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Authors: Julio Aguirre, Yohnny Campana, Elmer Guerrero, Daniel De La Torre Ugarte

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A well-developed transportation network is a necessary condition for a country to derive full benefits from good trade and macroeconomic policies. Road infrastructure plays a key role in the economic development of rural areas of developing countries; where agriculture is the main economic activity. The ability to move agricultural production from the place of production to the market, and then to the place of consumption, greatly influence the economic value of farming activities, and of the resources involved in the production process, i.e., labor and land. Consequently, investment in transportation networks contributes to enhance or overcome the natural advantages or disadvantages that topography and location have imposed over the agricultural sector. This is of particular importance when dealing with countries, like Peru, with a great topographic diversity. The objective of this research is to estimate the impacts of road infrastructure on the performance of the agricultural sector. Specific variables of interest are changes in travel time, shifts of production for self-consumption to production for the market, changes in farmers income, and impacts on the diversification of the agricultural sector. In the study, a cross-section model with instrumental variables is the central methodological instrument. The data is obtained from agricultural and transport geo-referenced databases, and the instrumental variable specification utilized is based on the Kruskal algorithm. The results show that the expansion of road connectivity reduced farmers' travel time by an average of 3.1 hours and the proportion of output sold in the market increases by up to 40 percentage points. The increase in connectivity has an unexpected increase in the districts index of diversification of agricultural production. The results are robust to the inclusion of year and region fixed-effects, and to control for geography (i.e., slope and altitude), population variables, and mining activity. Other results are also very eloquent. For example, a clear positive impact can be seen in access to local markets, but this does not necessarily correlate with an increase in the production of the sector. This can be explained by the fact that agricultural development not only requires provision of roads but additional complementary infrastructure and investments intended to provide the necessary conditions so that producers can offer quality products (improved management practices, timely maintenance of irrigation infrastructure, transparent management of water rights, among other factors). Therefore, complementary public goods are needed to enhance the effects of roads on the welfare of the population, beyond enabling them to increase their access to markets.

Keywords: agriculture devolepment, market access, road connectivity, regional development

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Authors: Neyzielle Ronnicque Cadiz

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The Philippine is a hotbed of disasters and is a locus of natural hazards. With an average of 20 typhoons entering the Philippine Area of Responsibility (PAR) each year, seven to eight (7-8) of which makes landfall. The country rather inevitably suffers from climate-related calamities. With this vulnerability to natural hazards, the relevant hazard-related issues that come along with the potential threat and occurrence of a disaster oftentimes garners lesser media attention than when a disaster actually occurred. Post-disaster news and events flood the content of news networks primarily focusing on, but not limited to, the efforts of the national government in resolving post-disaster displacement, and all the more on the community leaders’ incompetence in disaster mitigation-- even though the University of the Philippines’ NOAH Center work hand in hand with different stakeholders for disaster mitigation communication efforts. Disaster risk communication is actually a perennial dilemma. There are so many efforts to reach the grassroots level but emergency and disaster preparedness messages inevitably fall short.. The Philippines is very vulnerable to hazards risk and disasters but social media posts and communication efforts mostly go unnoticed, if not argued upon. This study illustrates the outcomes of a research focusing on the print, broadcast, and social media’s role on disaster communication involving the natural catastrophic events that took place in the Philippines from 2009 to present. Considering the country’s state of development, this study looks on the rapid and reliable communication between the government, and the relief/rescue workers in the affected regions; and how the media portrays these efforts effectively. Learning from the disasters that have occurred in the Philippines over the past decade, effective communication can ensure that any efforts to prepare and respond to disasters can make a significant difference. It can potentially either break or save lives. Recognizing the role of communications is not only in improving the coordination of vital services for post disaster; organizations gave priority in reexamining disaster preparedness mechanisms through the Communication with Communities (CwC) programs. This study, however, looks at the CwC efforts of the Philippine media platforms. CwC, if properly utilized by the media, is an essential tool in ensuring accountability and transparency which require effective exchange of information between disasters and survivors and responders. However, in this study, it shows that the perennial dilemma of the Philippine media is that the Disaster Risk Reduction and Management (DRRM) efforts of the country lie in the clouded judgment of political aims. This kind of habit is a multiplier of the country’s risk and insecurity. Sometimes the efforts in urging the public to take action seem useless because the challenge lies on how to achieve social, economic, and political unity using the tri-media platform.

Keywords: Philippines at risk, pre/post disaster communication, tri-media platform, UP NOAH

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Authors: Boriana Karamanova, Svetlana Veleva, Luybomir Soserov, Ana Arenillas, Francesco Lufrano, Antonia Stoyanova

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Supercapacitors have received a lot of research attention and are promising energy storage devices due to their high power and long cycle life. In order to developed an advanced device with significant capacity for storing charge and cheap carbon materials, efforts must focus not only on improving synthesis by controlling the morphology and pore size but also on improving electrode-electrolyte compatibility of the resulting systems. The present study examines the relationship between the surface chemistry of two activated carbon xerogels, the electrolyte type, and the electrochemical properties of supercapacitors. Activated carbon xerogels were prepared by varying the initial pH of the resorcinol-formaldehyde aqueous solution. The materials produced are physicochemical characterized by DTA/TGA, porous characterization, and SEM analysis. The carbon xerogel based electrodes were prepared by spreading over glass plate a slurry containing the carbon gel, graphite, and poly vinylidene difluoride (PVDF) binder. The layer formed was dried consecutively at different temperatures and then detached by water. After, the layer was dried again to improve its mechanical stability. The developed electrode materials and the Aquivion® E87-05S membrane (Solvay Specialty Polymers), socked in Na2SO4 as a polymer electrolyte, were used to assembly the solid-state supercapacitor. Symmetric supercapacitor cells composed by same electrodes and 1 M KOH electrolytes are also assembled and tested for comparison. The supercapacitor performances are verified by different electrochemical methods - cyclic voltammetry, galvanostatic charge/discharge measurements, electrochemical impedance spectroscopy, and long-term durability tests in neutral and alkaline electrolytes. Specific capacitances, energy, and power density, energy efficiencies, and durability were compared into studied supercapacitors. Ex-situ physicochemical analyses on the synthesized materials have also been performed, which provide information about chemical and structural changes in the electrode morphology during charge / discharge durability tests. They are discussed on the basis of electrode-electrolyte interaction. The obtained correlations could be of significance in order to design sustainable solid-state supercapacitors with high power and energy density. Acknowledgement: This research is funded by the Ministry of Education and Science of Bulgaria under the National Program "European Scientific Networks" (Agreement D01-286 / 07.10.2020, D01-78/30.03.2021). Authors gratefully acknowledge.

Keywords: carbon xerogel, electrochemical tests, neutral and alkaline electrolytes, supercapacitors

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Authors: Yasmeen Cheema, Parvinder Singh

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The evolution of Islamic State (acronym IS) has an ultimate goal of restoring the caliphate. IS threat to the global security is main concern of international community but has also raised a factual concern for India about the regular radicalization of IS ideology among Indian youth. The incident of joining Arif Ejaz Majeed, an Indian as ‘jihadist’ in IS has set strident alarm in law & enforcement agencies. On 07.03.2017, many people were injured in an Improvised Explosive Device (IED) blast on-board of Bhopal Ujjain Express. One perpetrator of this incident was killed in encounter with police. But, the biggest shock is that the conspiracy was pre-planned and the assailants who carried out the blast were influenced by the ideology perpetrated by the Islamic State. This is the first time name of IS has cropped up in a terror attack in India. It is a red indicator of violent presence of IS in India, which is spreading through social media. The IS have the capacity to influence the younger Muslim generation in India through its brutal and aggressive propaganda videos, social media apps and hatred speeches. It is a well known fact that India is on the radar of IS, as well on its ‘Caliphate Map’. IS uses Twitter, Facebook and other social media platforms constantly. Islamic State has used enticing videos, graphics, and articles on social media and try to influence persons from India & globally that their jihad is worthy. According to arrested perpetrator of IS in different cases in India, the most of Indian youths are victims to the daydreams which are fondly shown by IS. The dreams that the Muslim empire as it was before 1920 can come back with all its power and also that the Caliph and its caliphate can be re-established are shown by the IS. Indian Muslim Youth gets attracted towards these euphemistic ideologies. Islamic State has used social media for disseminating its poisonous ideology, recruitment, operational activities and for future direction of attacks. IS through social media inspired its recruits & lone wolfs to continue to rely on local networks to identify targets and access weaponry and explosives. Recently, a pro-IS media group on its Telegram platform shows Taj Mahal as the target and suggested mode of attack as a Vehicle Born Improvised Explosive Attack (VBIED). Islamic State definitely has the potential to destroy the Indian national security & peace, if timely steps are not taken. No doubt, IS has used social media as a critical mechanism for recruitment, planning and executing of terror attacks. This paper will therefore examine the specific characteristics of social media that have made it such a successful weapon for Islamic State. The rise of IS in India should be viewed as a national crisis and handled at the central level with efficient use of modern technology.

Keywords: ideology, India, Islamic State, national security, recruitment, social media, terror attack

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Authors: Qimin Shi, Yi Sun, Constantinus Politis, Shoufeng Yang

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In-situ preparation of particle-reinforced aluminium matrix composites (PRAMCs) by laser powder bed fusion (LPBF) additive manufacturing is a promising strategy to strengthen traditional Al-based alloys. The laser-driven thermite reaction can be a practical mechanism to in-situ synthesize PRAMCs. However, introducing oxygen elements through adding Fe₂O₃ makes the powder mixture highly sensitive to form porosity and Al₂O₃ film during LPBF, bringing challenges to producing dense Al-based materials. Therefore, this work develops a processing strategy combined with consecutive high-energy laser melting scanning and low-energy laser remelting scanning to prepare PRAMCs from a Fe₂O₃/AlSi12 powder mixture. The powder mixture consists of 5 wt% Fe₂O₃ and the remainder AlSi12 powder. The addition of 5 wt% Fe₂O₃ aims to achieve balanced strength and ductility. A high relative density (98.2 ± 0.55 %) was successfully obtained by optimizing laser melting (Emelting) and laser remelting surface energy density (Eremelting) to Emelting = 35 J/mm² and Eremelting = 5 J/mm². Results further reveal the necessity of increasing Emelting, to improve metal liquid’s spreading/wetting by breaking up the Al₂O₃ films surrounding the molten pools; however, the high-energy laser melting produced much porosity, including H₂₋, O₂₋ and keyhole-induced pores. The subsequent low-energy laser remelting could close the resulting internal pores, backfill open gaps and smoothen solidified surfaces. As a result, the material was densified by repeating laser melting and laser remelting layer by layer. Although with two-times laser scanning, the microstructure still shows fine cellular Si networks with Al grains inside (grain size of about 370 nm) and in-situ nano-precipitates (Al₂O₃, Si, and Al-Fe(-Si) intermetallics). Finally, the fine microstructure, nano-structured dispersion strengthening, and high-level densification strengthened the in-situ PRAMCs, reaching yield strength of 426 ± 4 MPa and tensile strength of 473 ± 6 MPa. Furthermore, the results can expect to provide valuable information to process other powder mixtures with severe porosity/oxide-film formation potential, considering the evidenced contribution of laser melting/remelting strategy to densify material and obtain good mechanical properties during LPBF.

Keywords: densification, laser powder bed fusion, metal matrix composites, microstructures, mechanical properties

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Authors: Klaudia Pluta, Katarzyna Bialik-Wąs, Dagmara Malina, Mateusz Barczewski

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Hydrogel networks, due to their unique properties, are highly attractive materials for wound dressing. The three-dimensional structure of hydrogels provides tissues with optimal moisture, which supports the wound healing process. Moreover, a characteristic feature of hydrogels is their absorption properties which allow for the absorption of wound exudates. For the fabrication of biomedical hydrogels, a combination of natural polymers ensuring biocompatibility and synthetic ones that provide adequate mechanical strength are often used. Sodium alginate (SA) is one of the polymers widely used in wound dressing materials because it exhibits excellent biocompatibility and biodegradability. However, due to poor strength properties, often alginate-based hydrogel materials are enhanced by the addition of another polymer such as poly(vinyl alcohol) (PVA). This paper is concentrated on the preparation methods of sodium alginate/polyvinyl alcohol hydrogel system incorporating Aloe vera extract and glycerin for wound healing material with particular focus on the role of their composition on structure, thermal properties, and stability. Briefly, the hydrogel preparation is based on the chemical cross-linking method using poly(ethylene glycol) diacrylate (PEGDA, Mn = 700 g/mol) as a crosslinking agent and ammonium persulfate as an initiator. In vitro degradation tests of SA/PVA/AV hydrogels were carried out in Phosphate-Buffered Saline (pH – 7.4) as well as in distilled water. Hydrogel samples were firstly cut into half-gram pieces (in triplicate) and immersed in immersion fluid. Then, all specimens were incubated at 37°C and then the pH and conductivity values were measurements at time intervals. The post-incubation fluids were analyzed using SEC/GPC to check the content of oligomers. The separation was carried out at 35°C on a poly(hydroxy methacrylate) column (dimensions 300 x 8 mm). 0.1M NaCl solution, whose flow rate was 0.65 ml/min, was used as the mobile phase. Three injections with a volume of 50 µl were made for each sample. The thermogravimetric data of the prepared hydrogels were collected using a Netzsch TG 209 F1 Libra apparatus. The samples with masses of about 10 mg were weighed separately in Al2O3 crucibles and then were heated from 30°C to 900°C with a scanning rate of 10 °C∙min−1 under a nitrogen atmosphere. Based on the conducted research, a fast and simple method was developed to produce potential wound dressing material containing sodium alginate, poly(vinyl alcohol) and Aloe vera extract. As a result, transparent and flexible SA/PVA/AV hydrogels were obtained. The degradation experiments indicated that most of the samples immersed in PBS as well as in distilled water were not degraded throughout the whole incubation time.

Keywords: hydrogels, wound dressings, sodium alginate, poly(vinyl alcohol)

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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: Eva Degraeuwe, Margo Vandenheede, Nicholas Rennie, Jolien Braem, Miryam Serry, Frederik Berrevoet, Piet Pattyn, Wouter Willaert, InciSioN Belgium Consortium

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Background: Worldwide, five billion people lack access to safe and affordable surgical care. An added 1.27 million surgeons, anesthesiologists, and obstetricians (SAO) are needed by 2030 to meet the target of 20 per 100,000 population and to reach the goal of the Lancet Commission on Global Surgery. A well-informed future generation exposed early on to the current challenges in global surgery (GS) is necessary to ensure a sustainable future. Methods: InciSioN, the International Student Surgical Network, is a non-profit organization by and for students, residents, and fellows in over 80 countries. InciSioN Belgium, one of the prominent national working groups, has made a vast progression and collaborated with other networks to fill the educational gap, stimulate advocacy efforts and increase interactions with the international network. This report describes a roadmap to achieve sustainable development and education within GS, with the example of InciSioN Belgium. Results: Since the establishment of the organization’s branch in 2019, it has hosted an educational workshop for first-year residents in surgery, engaging over 2500 participants, and established a recurring directing board of 15 members. In the year 2020-2021, InciSioN Ghent has organized three workshops combining educational and interactive sessions for future prime advocates and surgical candidates. InciSioN Belgium has set up a strong formal coalition with the Belgian Medical Students’ Association (BeMSA), with its own standing committee, reaching over 3000+ medical students annually. In 2021-2022, InciSioN Belgium broadened to a multidisciplinary approach, including dentistry and nursing students and graduates within workshops and research projects, leading to a member and exposure increase of 450%. This roadmap sets strategic goals and mechanisms for the GS community to achieve nationwide sustained improvements in the research and education of GS focused on future SAOs, in order to achieve the GS sustainable development goals. In the coming year, expansion is directed to a formal integration of GS into the medical curriculum and increased international advocacy whilst inspiring SAOs to integrate into GS in Belgium. Conclusion: The development and implementation of durable change for GS are necessary. The student organization InciSioN Belgium is growing and hopes to close the colossal gap in GS and inspire the growth of other branches while sharing the know-how of a student organization.

Keywords: advocacy, education, global surgery, InciSioN, student network

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Authors: Anirudh Lahiri

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Neuromorphic computing, inspired by the intricate operations of biological neural networks, offers a revolutionary approach to overcoming the limitations of traditional computing architectures. This research proposes the integration of spintronics with neuromorphic systems, aiming to enhance computational performance, scalability, and energy efficiency. Traditional computing systems, based on the Von Neumann architecture, struggle with scalability and efficiency due to the segregation of memory and processing functions. In contrast, the human brain exemplifies high efficiency and adaptability, processing vast amounts of information with minimal energy consumption. This project explores the use of spintronics, which utilizes the electron's spin rather than its charge, to create more energy-efficient computing systems. Spintronic devices, such as magnetic tunnel junctions (MTJs) manipulated through spin-transfer torque (STT) and spin-orbit torque (SOT), offer a promising pathway to reducing power consumption and enhancing the speed of data processing. The integration of these devices within a neuromorphic framework aims to replicate the efficiency and adaptability of biological systems. The research is structured into three phases: an exhaustive literature review to build a theoretical foundation, laboratory experiments to test and optimize the theoretical models, and iterative refinements based on experimental results to finalize the system. The initial phase focuses on understanding the current state of neuromorphic and spintronic technologies. The second phase involves practical experimentation with spintronic devices and the development of neuromorphic systems that mimic synaptic plasticity and other biological processes. The final phase focuses on refining the systems based on feedback from the testing phase and preparing the findings for publication. The expected contributions of this research are twofold. Firstly, it aims to significantly reduce the energy consumption of computational systems while maintaining or increasing processing speed, addressing a critical need in the field of computing. Secondly, it seeks to enhance the learning capabilities of neuromorphic systems, allowing them to adapt more dynamically to changing environmental inputs, thus better mimicking the human brain's functionality. The integration of spintronics with neuromorphic computing could revolutionize how computational systems are designed, making them more efficient, faster, and more adaptable. This research aligns with the ongoing pursuit of energy-efficient and scalable computing solutions, marking a significant step forward in the field of computational technology.

Keywords: material science, biological engineering, mechanical engineering, neuromorphic computing, spintronics, energy efficiency, computational scalability, synaptic plasticity.

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Authors: Ahmad S. Darwish, Tarek Lemaoui, Hanifa Taher, Inas M. AlNashef, Fawzi Banat

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This research reports a systematic top-down approach for designing neoteric hydrophobic solvents –particularly, deep eutectic solvents (DES) and ionic liquids (IL)– as furfural extractants from aqueous media for the application of sustainable biomass conversion. The first stage of the framework entailed screening 32 neoteric solvents to determine their efficacy against toluene as the application’s conventional benchmark for comparison. The selection criteria for the best solvents encompassed not only their efficiency in extracting furfural but also low viscosity and minimal toxicity levels. Additionally, for the DESs, their natural origins, availability, and biodegradability were also taken into account. From the screening pool, two neoteric solvents were selected: thymol:decanoic acid 1:1 (Thy:DecA) and trihexyltetradecyl phosphonium bis(trifluoromethylsulfonyl) imide [P₁₄,₆,₆,₆][NTf₂]. These solvents outperformed the toluene benchmark, achieving efficiencies of 94.1% and 97.1% respectively, compared to toluene’s 81.2%, while also possessing the desired properties. These solvents were then characterized thoroughly in terms of their physical properties, thermal properties, critical properties, and cross-contamination solubilities. The selected neoteric solvents were then extensively tested under various operating conditions, and an exceptional stable performance was exhibited, maintaining high efficiency across a broad range of temperatures (15–100 °C), pH levels (1–13), and furfural concentrations (0.1–2.0 wt%) with a remarkable equilibrium time of only 2 minutes, and most notably, demonstrated high efficiencies even at low solvent-to-feed ratios. The durability of the neoteric solvents was also validated to be stable over multiple extraction-regeneration cycles, with limited leachability to the aqueous phase (≈0.1%). Moreover, the extraction performance of the solvents was then modeled through machine learning, specifically multiple non-linear regression (MNLR) and artificial neural networks (ANN). The models demonstrated high accuracy, indicated by their low absolute average relative deviations with values of 2.74% and 2.28% for Thy:DecA and [P₁₄,₆,₆,₆][NTf₂], respectively, using MNLR, and 0.10% for Thy:DecA and 0.41% for [P₁₄,₆,₆,₆][NTf₂] using ANN, highlighting the significantly enhanced predictive accuracy of the ANN. The neoteric solvents presented herein offer noteworthy advantages over traditional organic solvents, including their high efficiency in both extraction and regeneration processes, their stability and minimal leachability, making them particularly suitable for applications involving aqueous media. Moreover, these solvents are more environmentally friendly, incorporating renewable and sustainable components like thymol and decanoic acid. This exceptional efficacy of the newly developed neoteric solvents signifies a significant advancement, providing a green and sustainable alternative for furfural production from biowaste.

Keywords: sustainable biomass conversion, furfural extraction, ionic liquids, deep eutectic solvents

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Authors: S. M. Giripunje, Mohit Kumar

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Acoustic energy that exists in our everyday life and environment have been overlooked as a green energy that can be extracted, generated, and consumed without any significant negative impact to the environment. The harvested energy can be used to enable new technology like wireless sensor networks. Technological developments in the realization of truly autonomous MEMS devices and energy storage systems have made acoustic energy harvesting (AEH) an increasingly viable technology. AEH is the process of converting high and continuous acoustic waves from the environment into electrical energy by using an acoustic transducer or resonator. AEH is not popular as other types of energy harvesting methods since sound waves have lower energy density and such energy can only be harvested in very noisy environment. However, the energy requirements for certain applications are also correspondingly low and also there is a necessity to observe the noise to reduce noise pollution. So the ability to reclaim acoustic energy and store it in a usable electrical form enables a novel means of supplying power to relatively low power devices. A quarter-wavelength straight-tube acoustic resonator as an acoustic energy harvester is introduced with polyvinylidene fluoride (PVDF) and PVDF doped with ZnO nanoparticles, piezoelectric cantilever beams placed inside the resonator. When the resonator is excited by an incident acoustic wave at its first acoustic eigen frequency, an amplified acoustic resonant standing wave is developed inside the resonator. The acoustic pressure gradient of the amplified standing wave then drives the vibration motion of the PVDF piezoelectric beams, generating electricity due to the direct piezoelectric effect. In order to maximize the amount of the harvested energy, each PVDF and PVDF-ZnO piezoelectric beam has been designed to have the same structural eigen frequency as the acoustic eigen frequency of the resonator. With a single PVDF beam placed inside the resonator, the harvested voltage and power become the maximum near the resonator tube open inlet where the largest acoustic pressure gradient vibrates the PVDF beam. As the beam is moved to the resonator tube closed end, the voltage and power gradually decrease due to the decreased acoustic pressure gradient. Multiple piezoelectric beams PVDF and PVDF-ZnO have been placed inside the resonator with two different configurations: the aligned and zigzag configurations. With the zigzag configuration which has the more open path for acoustic air particle motions, the significant increases in the harvested voltage and power have been observed. Due to the interruption of acoustic air particle motion caused by the beams, it is found that placing PVDF beams near the closed tube end is not beneficial. The total output voltage of the piezoelectric beams increases linearly as the incident sound pressure increases. This study therefore reveals that the proposed technique used to harvest sound wave energy has great potential of converting free energy into useful energy.

Keywords: acoustic energy, acoustic resonator, energy harvester, eigenfrequency, polyvinylidene fluoride (PVDF)

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Authors: Rasha Waheeb, Bjorn Andersen, Rafa Shakir

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Purpose The purpose of this study is to avoid delays that occur in emergency reconstruction projects especially in post disaster circumstances whether if they were natural or manmade due to their particular national and humanitarian importance. We presented a theoretical and practical concepts for projects management in the field of construction industry that deal with a range of global and local trails. This study aimed to identify the factors of effective delay in construction projects in Iraq that affect the time and the specific quality cost, and find the best solutions to address delays and solve the problem by setting parameters to restore balance in this study. 30 projects were selected in different areas of construction were selected as a sample for this study. Design/methodology/approach This study discusses the reconstruction strategies and delay in time and cost caused by different delay factors in some selected projects in Iraq (Baghdad as a case study).A case study approach was adopted, with thirty construction projects selected from the Baghdad region, of different types and sizes. Project participants from the case projects provided data about the projects through a data collection instrument distributed through a survey. Mixed approach and methods were applied in this study. Mathematical data analysis was used to construct models to predict delay in time and cost of projects before they started. The artificial neural networks analysis was selected as a mathematical approach. These models were mainly to help decision makers in construction project to find solutions to these delays before they cause any inefficiency in the project being implemented and to strike the obstacles thoroughly to develop this industry in Iraq. This approach was practiced using the data collected through survey and questionnaire data collection as information form. Findings The most important delay factors identified leading to schedule overruns were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. Some of these are quite in line with findings from similar studies in other countries/regions, but some are unique to the Iraqi project sample, such as security issues and low-price bid selection. Originality/value we selected ANN’s analysis first because ANN’s was rarely used in project management , and never been used in Iraq to finding solutions for problems in construction industry. Also, this methodology can be used in complicated problems when there is no interpretation or solution for a problem. In some cases statistical analysis was conducted and in some cases the problem is not following a linear equation or there was a weak correlation, thus we suggested using the ANN’s because it is used for nonlinear problems to find the relationship between input and output data and that was really supportive.

Keywords: construction projects, delay factors, emergency reconstruction, innovation ANN, post disasters, project management

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Authors: Naziema Begum Jappie

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Leadership and entrepreneurship in higher education have become critical components in navigating the evolving landscape of academia in the 21st century. This abstract explores the multifaceted relationship between leadership and entrepreneurship within the realm of higher education, emphasizing their roles in fostering innovation and sustainability. Higher education institutions, often characterized as slow-moving and resistant to change, are facing unprecedented challenges. Globalization, rapid technological advancements, changing student demographics, and financial constraints necessitate a reimagining of traditional models. Leadership in higher education must embrace entrepreneurial thinking to effectively address these challenges. Entrepreneurship in higher education involves cultivating a culture of innovation, risk-taking, and adaptability. Visionary leaders who promote entrepreneurship within their institutions empower faculty and staff to think creatively, seek new opportunities, and engage with external partners. These entrepreneurial efforts lead to the development of novel programs, research initiatives, and sustainable revenue streams. Innovation in curriculum and pedagogy is a central aspect of leadership and entrepreneurship in higher education. Forward-thinking leaders encourage faculty to experiment with teaching methods and technology, fostering a dynamic learning environment that prepares students for an ever-changing job market. Entrepreneurial leadership also facilitates the creation of interdisciplinary programs that address emerging fields and societal challenges. Collaboration is key to entrepreneurship in higher education. Leaders must establish partnerships with industry, government, and non-profit organizations to enhance research opportunities, secure funding, and provide real-world experiences for students. Entrepreneurial leaders leverage their institutions' resources to build networks that extend beyond campus boundaries, strengthening their positions in the global knowledge economy. Financial sustainability is a pressing concern for higher education institutions. Entrepreneurial leadership involves diversifying revenue streams through innovative fundraising campaigns, partnerships, and alternative educational models. Leaders who embrace entrepreneurship are better equipped to navigate budget constraints and ensure the long-term viability of their institutions. In conclusion, leadership and entrepreneurship are intertwined elements essential to the continued relevance and success of higher education institutions. Visionary leaders who champion entrepreneurship foster innovation, enhance the student experience, and secure the financial future of their institutions. As academia continues to evolve, leadership and entrepreneurship will remain indispensable tools in shaping the future of higher education. This abstract underscores the importance of these concepts and their potential to drive positive change within the higher education landscape.

Keywords: entrepreneurship, higher education, innovation, leadership

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Authors: Pardis Moslemzadeh Tehrani

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Due to the emergence of blockchain technology and the benefits of cryptocurrencies, intelligent or smart contracts are gaining traction. Artificial intelligence (AI) is transforming our lives, and it is being embraced by a wide range of sectors. Smart contracts, which are at the heart of blockchains, incorporate AI characteristics. Such contracts are referred to as "smart" contracts because of the underlying technology that allows contracting parties to agree on terms expressed in computer code that defines machine-readable instructions for computers to follow under specific situations. The transmission happens automatically if the conditions are met. Initially utilised for financial transactions, blockchain applications have since expanded to include the financial, insurance, and medical sectors, as well as supply networks. Raw material acquisition by suppliers, design, and fabrication by manufacturers, delivery of final products to consumers, and even post-sales logistics assistance are all part of supply chains. Many issues are linked with managing supply chains from the planning and coordination stages, which can be implemented in a smart contract in a blockchain due to their complexity. Manufacturing delays and limited third-party amounts of product components have raised concerns about the integrity and accountability of supply chains for food and pharmaceutical items. Other concerns include regulatory compliance in multiple jurisdictions and transportation circumstances (for instance, many products must be kept in temperature-controlled environments to ensure their effectiveness). Products are handled by several providers before reaching customers in modern economic systems. Information is sent between suppliers, shippers, distributors, and retailers at every stage of the production and distribution process. Information travels more effectively when individuals are eliminated from the equation. The usage of blockchain technology could be a viable solution to these coordination issues. In blockchains, smart contracts allow for the rapid transmission of production data, logistical data, inventory levels, and sales data. This research investigates the legal and technical advantages and disadvantages of AI-blockchain technology in the supply chain business. It aims to uncover the applicable legal problems and barriers to the use of AI-blockchain technology to supply chains, particularly in the food industry. It also discusses the essential legal and technological issues and impediments to supply chain implementation for stakeholders, as well as methods for overcoming them before releasing the technology to clients. Because there has been little research done on this topic, it is difficult for industrial stakeholders to grasp how blockchain technology could be used in their respective operations. As a result, the focus of this research will be on building advanced and complex contractual terms in supply chain smart contracts on blockchains to cover all unforeseen supply chain challenges.

Keywords: blockchain, supply chain, IoT, smart contract

Procedia PDF Downloads 126

Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Juan Peña Aguilar, Lilia Villasana, Rodrigo Valencia, Alberto Pastrana, Martin Vivanco, Juan Peña C

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Each year a growing number of companies entering Mexico in search of the domestic market share. These activities, including stores, telephone long distance and local raw materials and energy, and particularly the financial sector, have managed to significantly increase its weight in the flows of FDI in Mexico , however, you should consider whether these trends FDI are positive for the Mexican economy and these activities increase Mexican exports in the medium term , and its share in GDP , gross fixed capital formation and employment. In general stresses that these activities, by far, have been unable to significantly generate linkages with the rest of the economy, a process that has not favored with competitiveness policies and activities aimed at these neutral or horizontal. Since the nineties foreign direct investment (FDI) has shown a remarkable dynamism, both internationally and in Latin America and in Mexico. Only in Mexico the first recipient of FDI in importance in Latin America during 1990-1995 and was displaced by Brazil since FDI increased from levels below 1 % of GDP during the eighties to around 3 % of GDP during the nineties. Its impact has been significant not only from a macroeconomic perspective , it has also allowed the generation of a new industrial production structure and organization, parallel to a significant modernization of a segment of the economy. The case of Mexico also is particularly interesting and relevant because the destination of FDI until 1993 had focused on the purchase of state assets during privatization process. This paper aims to present FDI flows in Mexico and analyze the different business strategies that have been touched and encouraged by the FDI. On the one hand, looking briefly discuss regulatory issues and source and recipient of FDI sectors. Furthermore, the paper presents in more detail the impacts and changes that generated the FDI contribution of FDI in the Mexican economy , besides the macroeconomic context and later legislative changes that resulted in the current regulations is examined around FDI in Mexico, including aspects of the Free Trade Agreement (NAFTA). It is worth noting that foreign investment can not only be considered from the perspective of the receiving economic units. Instead, these flows also reflect the strategic interests of transnational corporations (TNCs) and other companies seeking access to markets and increased competitiveness of their production networks and global distribution, among other reasons. Similarly it is important to note that foreign investment in its various forms is critically dependent on historical and temporal aspects. Thus, the same functionality can vary significantly depending on the specific characteristics of both receptor units as sources of FDI, including macroeconomic, institutional, industrial organization, and social aspects, among others.

Keywords: foreign direct investment (FDI), competitiveness, neoliberal regime, globalization, gross domestic product (GDP), NAFTA, macroeconomic

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Authors: Vivek Hokam, Vishrut Landge

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An ideal maintenance program for an industrial road network is one that would maintain all sections at a sufficiently high level of functional and structural conditions. However, due to various constraints such as budget, manpower and equipment, it is not possible to carry out maintenance on all the needy industrial road sections within a given planning period. A rational and systematic priority scheme needs to be employed to select and schedule industrial road sections for maintenance. Priority analysis is a multi-criteria process that determines the best ranking list of sections for maintenance based on several factors. In priority setting, difficult decisions are required to be made for selection of sections for maintenance. It is more important to repair a section with poor functional conditions which includes uncomfortable ride etc. or poor structural conditions i.e. sections those are in danger of becoming structurally unsound. It would seem therefore that any rational priority setting approach must consider the relative importance of functional and structural condition of the section. The maintenance priority index and pavement performance models tend to focus mainly on the pavement condition, traffic criteria etc. There is a need to develop the model which is suitably used with respect to limited budget provisions for maintenance of pavement. Linear programming is one of the most popular and widely used quantitative techniques. A linear programming model provides an efficient method for determining an optimal decision chosen from a large number of possible decisions. The optimum decision is one that meets a specified objective of management, subject to various constraints and restrictions. The objective is mainly minimization of maintenance cost of roads in industrial area. In order to determine the objective function for analysis of distress model it is necessary to fix the realistic data into a formulation. Each type of repair is to be quantified in a number of stretches by considering 1000 m as one stretch. A stretch considered under study is having 3750 m length. The quantity has to be put into an objective function for maximizing the number of repairs in a stretch related to quantity. The distress observed in this stretch are potholes, surface cracks, rutting and ravelling. The distress data is measured manually by observing each distress level on a stretch of 1000 m. The maintenance and rehabilitation measured that are followed currently are based on subjective judgments. Hence, there is a need to adopt a scientific approach in order to effectively use the limited resources. It is also necessary to determine the pavement performance and deterioration prediction relationship with more accurate and economic benefits of road networks with respect to vehicle operating cost. The infrastructure of road network should have best results expected from available funds. In this paper objective function for distress model is determined by linear programming and deterioration model considering overloading is discussed.

Keywords: budget, maintenance, deterioration, priority

Procedia PDF Downloads 207

Authors: Juan Sanmartín Bastida

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When a country experiences a period of transition from authoritarianism to democracy, involving an earlier process of political liberalization and a later process of democratization, a cycle of protest usually outbreaks, as there is a reciprocal influence between that kind of political change and the frequency and scale of social protest events. That is what happened in Spain during the first years of its transition to democracy from the Francoist authoritarian regime, roughly between November 1975 and December 1978. Thus, the object of this study is to show and explain how that cycle of protest started, developed, and finished in relation to such a political change, and offer specific information about the main features of all protest cycles: the social movements that arose during that period, the number of protest events by month, the forms of collective action that were utilized, the groups of challengers that engaged in contentious politics, the reaction of the authorities to the action and claims of those groups, etc. The study of this cycle of protest, using the primary sources and analytical tools that characterize the model of research of protest cycles, will make a contribution to the field of contentious politics and its phenomenon of cycles of contention, and more broadly to the political and social history of contemporary Spain. The cycle of protest and the process of political liberalization of the authoritarian regime began around the same time, but the first concluded long before the process of democratization was completed in 1982. The ascending phase of the cycle and therefore the process of liberalization started with the death of Francisco Franco and the proclamation of Juan Carlos I as King of Spain in November 1975; the peak of the cycle was around the first months of 1977; the descending phase started after the first general election of June 1977; and the level of protest stabilized in the last months of 1978, a year that finished with a referendum in which the Spanish people approved the current democratic constitution. It was then when we can consider that the cycle of protest came to an end. The primary sources are the news of protest events and social movements in the three main Spanish newspapers at the time, other written or audiovisual documents, and in-depth interviews; and the analytical tools are the political opportunities that encourage social protest, the available repertoire of contention, the organizations and networks that brought together people with the same claims and allowed them to engage in contentious politics, and the interpretative frames that justify, dignify and motivates their collective action. These are the main four factors that explain the beginning, development and ending of the cycle of protest, and therefore the accompanying social movements and events of collective action. Among those four factors, the political opportunities -their opening, exploitation, and closure-proved to be most decisive.

Keywords: contentious politics, cycles of protest, political opportunities, social movements, Spanish transition to democracy

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Authors: M. Verde, G. Falzarano

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The paper examines the connection between corporate social responsibility (CSR), capability approach and poverty reduction; in particular, the local employment development (LED) by way of CSR initiatives. The joint action of LED/CSR results in a win-win situation, not only for the enterprises but also for all the stakeholders involved; in this regard, subsidiarity and coordination between national and regional/local authorities are central to a socially-oriented market economy. In the first section, the CSR is analysed on the basis of its social function in the fight against poverty, as a 'capabilities deprivation'. In the central part, the attention is focused on the relationship between CSR and LED; ergo, on the role of the enterprises in fostering capabilities development (the employment). Besides, all the potential solutions are presented, stressing the possible combinations, in the last part. The benchmark is the enterprise as an economic and a social institution: the business should not be combined with profit merely, paying more attention to its sustainable impact and social contribution. In which way could it be possible? The answer is the CSR. The impact of CSR on poverty reduction is still little explored. The companies help to reduce poverty through economic contribution, human rights and social inclusion; hence, the business becomes an 'agent of development' in order to fight against 'inequality'. The starting point is the pyramid of social responsibility, where ethic and philanthropic responsibilities involve programmes and actions aimed at personal development of the individuals, improving human standard of living in all forms, including poverty, when people do not have a choice between different 'life options', ranging from level of education to employment. At this point, CSR comes into play and works on two dimensions: poverty reduction and poverty prevention, by means of a series of initiatives: first of all, job creation and precarious work reduction. Empowerment of the local actors, financial support and combination of top down and bottom up initiatives are some of CSR areas of activity. Several positive effects occur on individual levels of educations, access to capital, individual health status, empowerment of youth and woman, access to social networks and it was observed that these effects depend on the type of CSR strategy. Indeed, CSR programmes should take into account fundamental criteria, such as the transparency, the information about benefits, a coordination unit among institutions and more clear guidelines. In this way, the advantages to the corporate reputation and to the community translate into a better job matching on the labour market, inter alia. It is important to underline that the success depends on the specific measures of the areas in question, by adapting them to the local needs, in light of general principles and index; therefore, the concrete commitment of the all stakeholders involved is decisive in order to achieve the goals. The enterprise would represent a concrete contribution for the pursuit of sustainable development and for the dissemination of a social and well being awareness.

Keywords: capability approach, local employment development, poverty, social inclusion

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Authors: Nastaran Khoshsabk

Abstract:

The evolution of communication through the Internet has influenced shaping and reshaping the self-presentation of social media users. Online communities both connect people and give voice to the voiceless allowing them to present themselves nationally and globally. People all around the world are experiencing censorship in different aspects of their life. Censorship can be externally imposed because of the political situations, or it can be self-imposed. Social media users choose the content they want to share and decide about the online audiences with whom they want to share this content. Most social media networks, such as Facebook, enable their users to be selective about the shared content and its availability to other people. However, sometimes instead of targeting a specific audience, users self-censor themselves or decide not to share various forms of information. These decisions are of particular importance in countries such as Iran where Internet is not the arena of free self-presentation and people are encouraged to stay away from political participation in the country and acting against the Islamic values. Facebook and some other social media tools are blocked in countries such as Iran. This project investigates the importance of social media in the life of Iranians to explore how they present themselves and construct their digital selves. The notion of cultural identity is applied in this research to explore the educational and informative role of social media in the identity formation and cultural representation of Facebook users. This study explores the self-censorship of Iranian adult Facebook users through their online self-representation and communication on the Internet. The data in this qualitative multiple case study have been collected through individual synchronous online interviews with the researcher’s Facebook friends and through the analysis of the participants’ Facebook profiles and activities over a period of six months. The data is analysed with an emphasis on the identity formation of participants through the recognition of the underlying themes. The exploration of online interviews is on the basis of participants’ personal accounts of self-censorship and cultural understanding through using social media. The driven codes and themes have been categorised considering censorship and place of culture on representation of self. Participants were asked to explain their views about censorship and conservatism through using social media. They reported their thoughts about deciding which content to share on Facebook and which to self-censor and their reasons behind these decisions. The codes and themes have been categorised considering censorship and its role in representation of idealised self. The ‘actual self’ showed to be hidden by an individual for different reasons such as its influence on their social status, academic achievements and job opportunities. It is hoped that this research will have implications for education contexts in countries that are experiencing social media filtering by offering an increased understanding of the importance of online communities; which can provide an educational environment to talk and learn about social taboos and constructing adults’ identity in virtual environment and through cultural self-presentation.

Keywords: cultural identity, identity formation, online communities, self-censorship

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Authors: Alexandru-Ion Marinescu

Abstract:

There exist a plethora of methods in the scientific literature which tackle the well-established task of credit score evaluation. In its most abstract form, a credit scoring algorithm takes as input several credit applicant properties, such as age, marital status, employment status, loan duration, etc. and must output a binary response variable (i.e. “GOOD” or “BAD”) stating whether the client is susceptible to payment return delays. Data imbalance is a common occurrence among financial institution databases, with the majority being classified as “GOOD” clients (clients that respect the loan return calendar) alongside a small percentage of “BAD” clients. But it is the “BAD” clients we are interested in since accurately predicting their behavior is crucial in preventing unwanted loss for loan providers. We add to this whole context the constraint that the algorithm must yield an actual, tractable mathematical formula, which is friendlier towards financial analysts. To this end, we have turned to genetic algorithms and genetic programming, aiming to evolve actual mathematical expressions using specially tailored mutation and crossover operators. As far as data representation is concerned, we employ a very flexible mechanism – LINQ expression trees, readily available in the C# programming language, enabling us to construct executable pieces of code at runtime. As the title implies, they model trees, with intermediate nodes being operators (addition, subtraction, multiplication, division) or mathematical functions (sin, cos, abs, round, etc.) and leaf nodes storing either constants or variables. There is a one-to-one correspondence between the client properties and the formula variables. The mutation and crossover operators work on a flattened version of the tree, obtained via a pre-order traversal. A consequence of our chosen technique is that we can identify and discard client properties which do not take part in the final score evaluation, effectively acting as a dimensionality reduction scheme. We compare ourselves with state of the art approaches, such as support vector machines, Bayesian networks, and extreme learning machines, to name a few. The data sets we benchmark against amount to a total of 8, of which we mention the well-known Australian credit and German credit data sets, and the performance indicators are the following: percentage correctly classified, area under curve, partial Gini index, H-measure, Brier score and Kolmogorov-Smirnov statistic, respectively. Finally, we obtain encouraging results, which, although placing us in the lower half of the hierarchy, drive us to further refine the algorithm.

Keywords: expression trees, financial credit scoring, genetic algorithm, genetic programming, symbolic evolution

Procedia PDF Downloads 117

Authors: Sergey Nikitenko, Vladimir Klishin, Yury Malakhov, Elena Goosen

Abstract:

Transition to renewable energy sources (solar, wind, bioenergy, etc.) and launching of alternative energy generation has weakened the role of coal as a source of energy. The Paris Agreement and assumption of obligations by many nations to orderly reduce CO₂ emissions by means of technological modernization and climate change adaptation has abridged coal demand yet more. This paper aims to assess current resilience of the coal industry to stress and to define prospects for coal production optimization using high technologies pursuant to global challenges and requirements of energy transition. Our research is based on the resilience concept adapted to the coal industry. It is proposed to divide the coal sector into segments depending on the prevailing value chains (VC). Four representative models of VC are identified in the coal sector. The most promising lines of upgrading VC in the coal industry include: •Elongation of VC owing to introduction of clean technologies of coal conversion and utilization; •Creation of parallel VC by means of waste management; •Branching of VC (conversion of a company’s VC into a production network). The upgrade effectiveness is governed in many ways by applicability of advanced coal processing technologies, usability of waste, expandability of production, entrance to non-rival markets and localization of new segments of VC in receiving regions. It is also important that upgrade of VC by means of formation of agile high-tech inter-industry production networks within the framework of operating surface and underground mines can reduce social, economic and ecological risks associated with closure of coal mines. Such promising route of VC upgrade is application of methanotrophic bacteria to produce protein to be used as feed-stuff in fish, poultry and cattle breeding, or in production of ferments, lipoids, sterols, antioxidants, pigments and polysaccharides. Closed mines can use recovered methane as a clean energy source. There exist methods of methane utilization from uncontrollable sources, including preliminary treatment and recovery of methane from air-and-methane mixture, or decomposition of methane to hydrogen and acetylene. Separated hydrogen is used in hydrogen fuel cells to generate power to feed the process of methane utilization and to supply external consumers. Despite the recent paradigm of carbon-free energy generation, it is possible to preserve the coal mining industry using the differentiated approach to upgrade of value chains based on flexible technologies with regard to specificity of mining companies.

Keywords: resilience, resilience concept, resilience indicator, resilience in the Russian coal industry, value chains

Procedia PDF Downloads 107