Search results for: multi-layer perception neural networks

Authors: Paola Clerici Maestosi

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Positive Energy District (PED) is a rather recent concept whose aim is to contribute to the main objectives of the Energy Union strategy. It is based on an integrated multi-sectoral approach in response to Europe's most complex challenges. PED integrates energy efficiency, renewable energy production, and energy flexibility in an integrated, multi-sectoral approach at the city level. The core idea behind Positive Energy Districts (PEDs) is to establish an urban area that can generate more energy than it consumes. Additionally, it should be flexible enough to adapt to changes in the energy market. This is crucial because a PED's goal is not just to achieve an annual surplus of net energy but also to help reduce the impact on the interconnected centralized energy networks. It achieves this by providing options to increase on-site load matching and self-consumption, employing technologies for short- and long-term energy storage, and offering energy flexibility through smart control. Thus, it seems that PEDs can encompass all types of buildings in the city environment. Given this which is the added value of having green buildings being constitutive part of PEDS? The paper will present a systematic literature review identifying the role of green building in Positive Energy District to provide answer to following questions: (RQ1) the state of the art of PEDs implementation; (RQ2) penetration of green building in Positive Energy District selected case studies. Methodological approach is based on a broad holistic study of bibliographic sources according to Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) further data will be analysed, mapped and text mining through VOSviewer. Main contribution of research is a cognitive framework on Positive Energy District in Europe and a selection of case studies where green building supported the transition to PED. The inclusion of green buildings within Positive Energy Districts (PEDs) adds significant value for several reasons. Firstly, green buildings are designed and constructed with a focus on environmental sustainability, incorporating energy-efficient technologies, materials, and design principles. As integral components of PEDs, these structures contribute directly to the district's overall ability to generate more energy than it consumes. Secondly, green buildings typically incorporate renewable energy sources, such as solar panels or wind turbines, further boosting the district's capacity for energy generation. This aligns with the PED objective of achieving a surplus of net energy. Moreover, green buildings often feature advanced systems for on-site energy management, load-matching, and self-consumption. This enhances the PED's capability to respond to variations in the energy market, making the district more agile and flexible in optimizing energy use. Additionally, the environmental considerations embedded in green buildings align with the broader sustainability goals of PEDs. By reducing the ecological footprint of individual structures, PEDs with green buildings contribute to minimizing the overall impact on centralized energy networks and promote a more sustainable urban environment. In summary, the incorporation of green buildings within PEDs not only aligns with the district's energy objectives but also enhances environmental sustainability, energy efficiency, and the overall resilience of the urban environment.

Keywords: positive energy district, renewables energy production, energy flexibility, energy efficiency

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Authors: Samina Yasmin, Anzar Khaliq, Zareen Tabassum

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Acute power shortage in Pakistan requires an urgent attention to take preliminary steps to spread energy awareness at all levels. One such initiative is taken at Habib University (HU), Pakistan, through renewable energy course, one of the core offerings, where students are trained to investigate various aspects of renewable energy concepts. The course is offered to all freshmen enrolled at HU regardless of their academic backgrounds and degree programs. A four-credit modular course includes both theory and laboratory elements. Hands-on laboratories play an important role in science classes, particularly to enhance the motivation and deep understanding of energy science. A set of selected hands-on activities included in course introduced students to explore the latest developments in the field of renewable energy such as dye-sensitized solar cells, gas chromatography, global warming, climate change, fuel cell energy and power of biomass etc. These projects not only helped HU freshmen to build on energy fundamentals but also provided them greater confidence in investigating, questioning and experimenting with renewable energy related conceptions. A feedback survey arranged during and end of term revealed the effectiveness of the hands-on laboratory to enhance the common understanding of real world problems related to energy such as awareness of energy saving, the level of concern about global climate change, environmental pollution and science of energy behind the energy usage.

Keywords: biochemical approaches, energy curriculum, hands-on laboratory, renewable energy

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Authors: Laila Habiballah, Ahmad Tubaishat

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Background: with the development of information and communication technology, using the internet as a source to obtain health information is increasing. Nursing students as future health care providers should have the skills of locating, evaluating and using online health information. This will enable them to help their patients and families to make informed decisions. Aim: this study has a two-fold aim. The first is to assess the eHealth literacy among nursing students in Jordan. The second aim is to explore the factors that have an effect on the eHealth literacy. Methods: this is a descriptive cross-sectional survey that conducted in two universities in Jordan; public and private one. A number of 541 students from both universities were completed the eHEALS scale, which is an instrument designed to measure the eHealth literacy. Some additional personal and demographical variable were collected to explore its effect on eHealth literacy. Results: Students have a high perceived level of e-Health literacy (M=3.62, SD=0.58). They are aware of the available online health resources, know how to search, locate, and use these resources. But, they do not have the skills to evaluate these resources and cannot differentiate between the high and low-quality resources. The results showed as well that type of university, type of students' admission, academic level, students' skills of using the internet, and the perception of usefulness and importance of internet have an effect on the eHealth literacy. While the age, gender, GPA, and the frequency of using the internet was no significant factors. Conclusion: This study represents a baseline reference for the eHealth literacy in Jordan. Students have some skills of eHealth literacy and other skills need to be improved. Nursing educators and administrators should integrate and incorporate the skills of eHealth literacy in the curriculum.

Keywords: eHealth, literacy, nursing, students, Jordan

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Authors: Amós García Montaño

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In a democracy, a public policy must be developed within the regulatory framework and considering citizen participation in its planning, design, execution, and evaluation stages, necessary factors to have both legal support and sufficient legitimacy for its operation. However, the complexity and magnitude of certain public problems results in difficulties for the generation of consensus among society members, leading to unstable and unsuccessful scenarios for the exercise of the right to citizen participation and the generation of effective and efficient public policies. This is the case of public policies against corruption, an issue that in Mexico is difficult to define and generates conflicting opinions. To provide a possible solution to this delicate reality, this paper analyzes the principle of reasonableness as a tool for identifying the basic elements that guarantee a fundamental level of the exercise of the right to citizen participation in the fight against corruption, adopting elements of human rights indicator methodologies. In this sense, the relevance of having a legal framework that establishes obligations to incorporate proactive and transversal citizen participation in the matter is observed. It is also noted the need to monitor the operation of various citizen participation mechanisms in the decision-making processes of the institutions involved in the fight and prevention of corruption, which lead to an increase in the improvement of the perception of the citizen role as a relevant actor in this field. It is concluded that the principle of reasonableness is presented as a very useful tool for the identification of basic elements that facilitate the fulfillment of human rights commitments in the field of public policies.

Keywords: anticorruption, public participation, public policies, reasonableness

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Authors: Jens Ehm

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Manufacturing in supply chains requires an efficient organisation of production and transport processes in order to guarantee the supply of all partners within the chain with the material that is needed for the reliable fulfilment of tasks. If one partner is not able to supply products for a certain period, these products might be missing as the working material for the customer to perform the next manufacturing step, potentially as supply for further manufacturing steps. This way, local disruptions can influence the whole supply chain. In order to avoid material shortages, an efficient scheduling of tasks is necessary. However, the occurrence of unexpected disruptions cannot be eliminated, so that a modification of the schedule should be arranged as fast as possible. This paper discusses the challenges for the implementation of real-time scheduling and control methods and presents a graph-based approach that enables the integrated scheduling of production and transport processes for multiple supply chain partners and offers the potential for quick adaptations to parts of the initial schedule.

Keywords: production, logistics, integrated scheduling, real-time scheduling

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Authors: Proud Arunrangsiwed, Sarinya Kongtieng

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Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: meta-regression analysis, social networking sites, academic Performances, multitasking, motivation

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Authors: Alexander Scott English, Yilin Ma, Xiaoying Liu

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The widespread use of artificial intelligence in everyday life has triggered a fervent discussion covering a wide range of areas. However, to date, research on the influence of gender in various segments and factors from a social science perspective is still limited. This study aims to explore whether there are gender differences in human trust in AI for its application in basic everyday life and correlates with human perceived similarity, perceived emotions (including competence and warmth), and attractiveness. We conducted a study involving 321 participants using a two-subject experimental design with a two-factor (masculinized vs. feminized voice of the AI) multiplied by a two-factor (pitch level of the AI's voice) between-subject experimental design. Four contexts were created for the study and randomly assigned. The results of the study showed significant gender differences in perceived similarity, trust, and perceived emotion of the AIs, with females rating them significantly higher than males. Trust was higher in relation to AIs presenting the same gender (e.g., human female to female AI, human male to male AI). Mediation modeling tests indicated that emotion perception and similarity played a sufficiently mediating role in trust. Notably, although trust in AIs was strongly correlated with human gender, there was no significant effect on the gender of the AI. In addition, the study discusses the effects of subjects' age, job search experience, and job type on the findings.

Keywords: artificial intelligence, gender differences, human-robot trust, mediation modeling

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Authors: Varda Gil, Ella Shoval, Tussia Mira

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Teacher mentoring is a complex practical profession whose unique characteristic is the teacher-mentors' commitment to helping teachers link theory with teaching practice in the process of decision-making and in their reflections on teaching. The aim of this research is to examine the way practicing teacher-mentors participating in a teacher mentoring course made the connection between theory and practice. The researchers analyzed 20 final papers submitted by participants in a course to train teacher mentors. The participants were all veteran high-school teachers. The course comprised 112 in-class hours in addition to mentoring novices in the field. The course covered the following topics: The teacher-mentors' perception of their role; formative and summative evaluation of the novices; tutoring strategies and tools; types of learners; and ways of communicating and dealing with novice teachers' resistance to counseling. The course participants were required to write a 4-5 page reflective summary of their field mentoring practice. In addition, they were required to link theories explicitly learned in the course to their practice in the field. A qualitative analysis of the papers led to the creation of the taxonomy of the link between theory and practice relating to four topics: The kinds of links made between theory and practice, the quality of these links, the links made between private teaching theories and official teaching theory, and the qualities of these links. This taxonomy may prove to be a useful tool in the teacher-mentor training processes.

Keywords: taxonomy, teacher-mentors, theory, practice, teacher-mentor training

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Authors: Joyraj Chakraborty, Geoffrey Ottoy, Jean-Pierre Goemaere, Lieven De Strycker

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Sensor swarms can be a cost-effectieve and more user-friendly alternative for location based service systems in different application like health-care. To increase the lifetime of such swarm networks, the energy consumption should be scaled to the required localization accuracy. In this paper we have investigated some parameter for energy model that couples localization accuracy to energy-related sensor parameters such as signal length,Bandwidth and sample frequency. The goal is to use the model for the localization of undetermined environmental sounds, by means of wireless acoustic sensors. we first give an overview of TDOA-based localization together with the primary sources of TDOA error (including reverberation effects, Noise). Then we show that in localization, the signal sample rate can be under the Nyquist frequency, provided that enough frequency components remain present in the undersampled signal. The resulting localization error is comparable with that of similar localization systems.

Keywords: sensor swarms, localization, wireless sensor swarms, scalable energy

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Authors: Ganratchakan Ninlawan, Witthaya Mekhum

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The objective of this research was to investigate motivation needs in working of the employees in Rayong Province at Panakom Co., Ltd. The sample group included 59 operational employees of Panakom Co., Ltd divided into 2 main parts to complete the questionnaires. Part 1 dealt with personal information of the staff in the form of checklist questions. Part 2 was about the motivation needs in working in the form of 5 rating scales. The data were analyzed to find frequency, percentage, arithmetic mean, and SD with the results as follows. They reported their working conditions in the moderate level while the first aspect was on the regulation of the Working Support Department. The second rated aspect was on the modern facilities and office stationeries followed by the working environment such as lighting, temperature, sound system, and atmosphere. The last aspect was on the provision of enough working equipment. On the part of work stability and work progress, they rated this aspect at the moderate level with the details below. The first aspect was their satisfaction in work rotation followed by the encouragement in joining training and seminar to increase working knowledge. The third aspect was their perception on the progress of their careers. The last aspect was on the chance to get promoted in special cases. On the payment and fringe benefit, they rated this part in the moderate level with the highest aspect being on the provided fringe benefit such as health care. The second aspect was on the suitable salary compared to their knowledge and proficiency. The third aspect was on the satisfaction on the activities and seminars provided by the company. Finally, the last aspect was on the sufficient salary when compared with the current cost of living.

Keywords: motivation needs, working, employees, Rayong Province

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Authors: Emily Rivera, Toni DiDona

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The Big Five personality traits is a hierarchical classification of personality traits that applies factor analysis to a personality survey data in order to describe human personality using five broad dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness (Fetvadjiev & Van de Vijer, 2015). Research shows that personality constructs underline individual differences in processing conflict and interpersonal relations. (Graziano et al., 1996). This research explores the understudied correlation between the Big Five personality traits and perceived interpersonal conflict in the workplace. It revises social psychological literature on Big Five personality traits within a social context and discusses organizational development journal articles on the perceived efficacy of conflict tactics and approach to interpersonal relationships. The study also presents research undertaken on a survey group of 867 subjects over the age of 18 that were recruited by means of convenience sampling through social media, email, and text messaging. The central finding of this study is that only two of the Big Five personality traits had a significant correlation with perceiving interpersonal conflict in the workplace. Individuals who score higher on agreeableness and neuroticism, perceive more interpersonal conflict in the workplace compared to those that score lower on each dimension. The relationship between both constructs is worthy of research due to its everyday frequency and unique individual psycho-social consequences. This multimethod research associated the Big Five personality dimensions to interpersonal conflict. Its findings that can be utilized to further understand social cognition, person perception, complex social behavior and social relationships in the work environment.

Keywords: five-factor model, interpersonal conflict, personality, The Big Five personality traits

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Authors: S. M. Abdullah Al Mamun

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Human Computer Interaction (HCI) is an excellent area for the researchers to make daily life more simple and fast. Necessary hardware equipments for any BCI are generally expensive and not affordable for most of the people. Emotiv is one of the solutions for this problem, which can provide electroencephalograph (EEG) signal and explain the brain activities. BCI virtual speller was one of the important applications for the people who have lost their hand or speaking ability because of diseases or unexpected accident. In this paper, a matrix speller has been designed for the first time for Bengali speaking people around the world. Bengali is one of the most commonly spoken languages. Among them, a lot of disabled person will be able to express their desire in their mother tongue. This application is also usable for the social networks and daily life communications. For this virtual keyboard, the well-known matrix speller method with column flashing is applied and controlled by single Emokey only. Emokey is a great feature which translates emotional state for application inputs. In this paper, it is presented that the ITR (Information Transfer Rate) were 29.4 bits/min and typing speed achieved up to 7.43 char/per min.

Keywords: brain computer interface, Emotiv EPOC, EEG, virtual keyboard, matrix speller

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Authors: Jonas Colin

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Standing at the vanguard of AI development, Alpha represents an unprecedented synthesis of logical rigor and human abstraction, meticulously crafted to mirror the user's unique persona and personality, a feat previously unattainable in AI development. Alpha, an avant-garde artefact in the realm of artificial intelligence, epitomizes a paradigmatic shift in personalized digital interaction, amalgamating user-specific dialogic patterns with the sophisticated algorithmic prowess of OpenAI's GPT-3.5 to engender a platform for enhanced metacognitive engagement and individualized user experience. Underpinned by a sophisticated algorithmic framework, Alpha integrates vast datasets through a complex interplay of neural network models and symbolic AI, facilitating a dynamic, adaptive learning process. This integration enables the system to construct a detailed user profile, encompassing linguistic preferences, emotional tendencies, and cognitive styles, tailoring interactions to align with individual characteristics and conversational contexts. Furthermore, Alpha incorporates advanced metacognitive elements, enabling real-time reflection and adaptation in communication strategies. This self-reflective capability ensures continuous refinement of its interaction model, positioning Alpha not just as a technological marvel but as a harbinger of a new era in human-computer interaction, where machines engage with us on a deeply personal and cognitive level, transforming our interaction with the digital world.

Keywords: chatbot, GPT 3.5, metacognition, symbiose

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Authors: Erol Karaca

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This study aims to analyze ceramic employees’ occupational health and safety training expectations. To that general objective, the study tries to examine whether occupational health and safety training expectations of ceramic employees meaningfully differentiate depending on demographic features and professional, social and economic conditions. For this purpose, the research data was collected through “Questionnaire of Occupational Health and Safety Training Expectation” (QSOHSTE) consisting of 25 open and close-ended questions developed by the researcher on the base of the literature review. QSOHSTE was applied to 125 ceramic employees working in Kutahya, Turkey. Data obtained from questionnaires were analyzed via SPSS 21. The findings, obtained from the study, revealed that employees’ agreement level to occupational health and safety training expectation statements is generally high-level. These findings also reveals that employees have various expectations about occupational health and safety training. These expectations are increasing sensitivity towards occupational health and safety training about the prevention of occupational accidents and diseases, contributing occupational health and safety training in establishing healthy and safe working environment, requiring occupational health and safety training before starting work, in case of changing working equipment and new technological applications, necessity of measurement and evaluation after occupational health and safety training. Besides these findings, employees’ agreement level to occupational health and safety training expectation statements also varies in terms of educational level, professional seniority, income level and perception of economic condition.

Keywords: occupational health and safety, occupational training, occupational expectation, professional seniority

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Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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Authors: Chih-Chieh Yang, Chung-Neng Huang

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In power system, how to improve the fault diagnosis technology of transmission line has always been the primary goal of power grid operators. In recent years, due to the rise of green energy, the addition of all kinds of distributed power also has an impact on the stability of the power system. Because the smart meters are with the function of data recording and bidirectional transmission, the adaptive Fuzzy Neural inference system, ANFIS, as well as the artificial intelligence that has the characteristics of learning and estimation in artificial intelligence. For transmission network, in order to avoid misjudgment of the fault type and location due to the input of these unstable power sources, combined with the above advantages of smart meter and ANFIS, a method for identifying fault types and location of faults is proposed in this study. In ANFIS training, the bus voltage and current information collected by smart meters can be trained through the ANFIS tool in MATLAB to generate fault codes to identify different types of faults and the location of faults. In addition, due to the uncertainty of distributed generation, a wind power system is added to the transmission network to verify the diagnosis correctness of the study. Simulation results show that the method proposed in this study can correctly identify the fault type and location of fault with more efficiency, and can deal with the interference caused by the addition of unstable power sources.

Keywords: ANFIS, fault diagnosis, power system, smart meter

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Saifullah Khalid

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Governance and good governance are among the main topics in international discussions about the success factors for social and economic development. The image of developing countries as for example Pakistan in this respect is bad (in TI Corruption Index nr. among countries). Additionally, the police are among the sectors and organizations which are seen as most corrupt in many countries. However, in case of Pakistan there seem to be exceptions to the rule, and improvement can be brought in specific police departments. This paper represents the findings of Islamabad traffic police (ITP). In Pakistan, the police, in general, have been stigmatized for being the most corrupt department in the country. However, the few recent examples of Motorway police and its replicated model of Islamabad traffic police changed the perception about police and policing. These police forces have shown that Policing in Pakistan can be changed for better. In this paper, the research question that is addressed is: How corrupt are (traffic) police forces in Pakistan and what factors influence corruption within that police force? And What lessons can be learned from that to improve police integrity? Both qualitative and quantitative tools are utilized for data collection. The overall picture of the factors is not so easy to interpret and summarise. Nevertheless paying a better salary does not seem to limit integrity violations, neither does recruitment and selection and leadership, while supervision and control, training and stimulating the positive and limiting the negative elements of culture appear to be important in curbing (sometimes specific) integrity violations in the context of Pakistani police forces. The study also leads to a number of suggestions for curbing corruption and other integrity violations in the Pakistan police.

Keywords: corruption control, governance, integrity violations, Islamabad traffic police, Pakistan

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Authors: Akshaya P., Priyanjali Prabhkaran

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The paper examines the formulation of strategies to develop a comprehensive model of city level water utility management to addressing urban sanitation issues. The water is prime life sustaining natural resources and nature’s gifts to all living beings on the earth multiple urban sanitation issues are addressed in the supply of water in a city. Many of these urban sanitation issues are linked to population expansion and economic inequity. Increased usage of water and the development caused water scarcity. The lack of water supply results increases the chance of unhygienic situations in the cities. In this study, the urban sanitation issues are identified with respect to water supply and utility management. The study compared based on their best practices and initiatives. From this, best practices and initiatives identify suitable sustainable measures to address water supply issues in the city level. The paper concludes with the listed provision that should be considered suitable measures for water supply and utility management in city level to address the urban sanitation issues.

Keywords: water, benchmarking water supply, water supply networks, water supply management

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Authors: Muhammad Adil

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Women in different parts of the world confront several barriers to accomplishing their legal rights, particularly political rights. One of the common barriers in this respect is the indigenous culture of the locality. In the same way, women in Khyber Pakhtunkhwa are facing difficulties in accomplishing their political rights. The most significant obstacle in this context is Pakhtunwali, the traditional code of conduct in Pakhtun society, which is perceived as a substantial impediment for Pakhtun women in practicing their political rights as guaranteed by the Constitution of Pakistan and international legal instruments. Several codes of Pakhtunwali, like peghor (slander or abuse), tor (blame or disgraced), sharam (shame and dishonor), purdah (gender segregation), and ghayrat (honor) have a prominent role in this regard. The research approach employed a combination of both qualitative and quantitative methods to ensure a thorough exploration of the subject. Not only different documents have been analyzed but also a questionnaire has been developed to get accurate findings. Simultaneously, both primary and secondary data have been utilized. The finding shows that the Pakhtun culture is a formidable hurdle in accomplishing women’s political rights in Pakhtun society, particularly in rural areas. Observation reveals that a prevailing societal perception is that having women as their representatives would be viewed as a challenge to the honor of Pakhtun men. Consequently, women candidates who participated in the general elections in Khyber Pakhtunkhwa received only 1 percent or less than 1 percent of the votes compared to their male counterparts. It is recommended that certain codes of Pakhtunwali should be redefined and made compatible with international legal instruments.

Keywords: constitution, fundamental rights, honor, pakhtunwali.

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Authors: Rasha A.Ibrahim El-Ghazawy, Ashraf M. El-Saeed, Heusin El-Shafey, M. Abdel-Raheim, Maher A. El-Sockary

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Two thermotropic liquid crystalline curing agents based on abietic acid with different mesogens (LCC1 and LCC2) were synthesized for producing thermally stable liquid crystal networks suitable for high performance epoxy coatings. Differential scanning calorimetry (DSC) and polarized optical microscope (POM) was used to identify the liquid crystal phase transformation temperatures and texture, respectively. POM micro graphs for both LCCs revealing cholesteric texture. A multifunctional epoxy resin with two abietic acid moieties was also synthesized. Dynamic mechanical (DMA) and thermogravimetric (TGA) analyses show that the fully bio-based cured epoxies by either LCCs possess high glass transition temperature (Tg), high modulus (G`) and improved thermal stability. The chemical structure of the synthesized LCCs and epoxy resin was investigated through FTIR and 1HNMR spectroscopic techniques.

Keywords: abietic acid, dynamic mechanical analysis, epoxy resin, liquid crystal, thermo gravimetric analysis

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Authors: Iyabo Aremu

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The role of the teacher in stirring a qualitative and distinctive knowledge-driven and value-laden environment with modern teaching practices cannot be over accentuated. In spite of the myriad advantages the use of Information and Communication Technology (ICT) promises, many teachers are still at the rear of this archetypical transition. These teachers; notable forces needed to elicit positive academic performances of students of higher learning are ill-equipped for the task. In view of this, the research work sought to assess how teachers have been able to effectively apply ICT tools to improve students’ academic performance in the higher institution and to evaluate the challenges faced by teachers in using these tools. Thus, the research adopted descriptive survey research design and involved a sample of 25 lecturers from five schools in the study area: Adeniran Ogunsanya College of Education (AOCOED). The barrier to Teachers’ Use of ICT Questionnaire (BTUICTQ) was used to gather data from these respondents. The data gathered was tested with chi-square at 0.05 level of significance. The results revealed that the perception and attitude of teachers towards the use of ICT is not favourable. It was also discovered that teachers suffer from gaps in ICT knowledge and skills. Finally, the research showed that lack of training and inadequate support is a major challenge teacher contend with. The study recommended that teachers should be given adequate training and support and that teachers’ unrestricted access to ICT gadgets should be ensured by schools.

Keywords: ICT, teachers, AOCOED, academic performance

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Authors: Ahmed Al-Adaileh, Souheil Khaddaj

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Outcomes of exhaustion of natural resources started influencing each spirit on this planet. Energy is an essential factor in this aspect. To restore the circumstance to the appropriate track, all attempts must focus on two fundamental branches: producing electricity from clean and renewable reserves and decreasing the overall unnecessary consumption of energy. The focal point of this paper will be on lessening the power consumption in the household's segment. This paper is an attempt to give a clear understanding of a framework called Reduction of Energy Consumption in Household Sector (RECHS) and how it should help householders to reduce their power consumption by substituting their household appliances, turning-off the appliances when stand-by modus is detected, and scheduling their appliances operation periods. Technically, the framework depends on utilizing Z-Wave compatible plug-ins which will be connected to the usual house devices to gauge and control them remotely and semi-automatically. The suggested framework underpins numerous quality characteristics, for example, integrability, scalability, security and adaptability.

Keywords: smart energy management systems, internet of things, wireless mesh networks, microservices, cloud computing, big data

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Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: Yana Snegireva

Abstract:

Carbonate reservoirs are known for their heterogeneity, resulting from various geological processes such as diagenesis and fracturing. These complexities may cause great challenges in understanding fluid flow behavior and predicting the production performance of naturally fractured reservoirs. The investigation of carbonate reservoirs is crucial, as many petroleum reservoirs are naturally fractured, which can be difficult due to the complexity of their fracture networks. This can lead to geological uncertainties, which are important for global petroleum reserves. The problem outlines the key challenges in carbonate reservoir modeling, including the accurate representation of fractures and their connectivity, as well as capturing the impact of fractures on fluid flow and production. Traditional reservoir modeling techniques often oversimplify fracture networks, leading to inaccurate predictions. Therefore, there is a need for a modern approach that can capture the complexities of carbonate reservoirs and provide reliable predictions for effective reservoir management and production optimization. The modern approach to carbonate reservoir modeling involves the utilization of the hybrid fracture modeling approach, including the discrete fracture network (DFN) method and implicit fracture network, which offer enhanced accuracy and reliability in characterizing complex fracture systems within these reservoirs. This study focuses on the application of the hybrid method in the Nepsko-Botuobinskaya anticline of the Eastern Siberia field, aiming to prove the appropriateness of this method in these geological conditions. The DFN method is adopted to model the fracture network within the carbonate reservoir. This method considers fractures as discrete entities, capturing their geometry, orientation, and connectivity. But the method has significant disadvantages since the number of fractures in the field can be very high. Due to limitations in the amount of main memory, it is very difficult to represent these fractures explicitly. By integrating data from image logs (formation micro imager), core data, and fracture density logs, a discrete fracture network (DFN) model can be constructed to represent fracture characteristics for hydraulically relevant fractures. The results obtained from the DFN modeling approaches provide valuable insights into the East Siberia field's carbonate reservoir behavior. The DFN model accurately captures the fracture system, allowing for a better understanding of fluid flow pathways, connectivity, and potential production zones. The analysis of simulation results enables the identification of zones of increased fracturing and optimization opportunities for reservoir development with the potential application of enhanced oil recovery techniques, which were considered in further simulations on the dual porosity and dual permeability models. This approach considers fractures as separate, interconnected flow paths within the reservoir matrix, allowing for the characterization of dual-porosity media. The case study of the East Siberia field demonstrates the effectiveness of the hybrid model method in accurately representing fracture systems and predicting reservoir behavior. The findings from this study contribute to improved reservoir management and production optimization in carbonate reservoirs with the use of enhanced and improved oil recovery methods.

Keywords: carbonate reservoir, discrete fracture network, fracture modeling, dual porosity, enhanced oil recovery, implicit fracture model, hybrid fracture model

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

Abstract:

Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

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Authors: Tomas Trainys, Algimantas Venckauskas

Abstract:

Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

Procedia PDF Downloads 125

Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

Abstract:

As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

Procedia PDF Downloads 326

Authors: Shayan Mohajer Hamidi

Abstract:

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

Procedia PDF Downloads 53