Search results for: bio-inspired search algorithms

Authors: Amanda Aliende da Matta

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In different fields, there are people who have something that stands out. In the educational world, for example, it is clear when some teachers have something: they are the best teachers, but this is not directly attributed to their disciplines, methodologies, etc. It is that they have something that captivates, inspires, and motivates. But we also find this something in other contexts. In this thesis, the interest is in something that some marginalized people, such as Ab (fictitious name), have. Ab was born in a rural community and saw the lifestyle of his family change drastically as a consequence of structural changes in his village. The community became impoverished, and together with a group of teenagers, he decided to migrate to Spain in search of opportunities. His best friend drowned during the crossing. After arriving, he lived in indecent conditions and felt unsafe. He now suffers from anxiety and frequently faints from it. Yet, he’s linked to Joves x la pau (a Christian project, although he is a Muslim), distributing food for people who live on the streets every Thursday afternoon. When he asked about what happens on cold and rainy days, he explained simply: "if it rains, I distribute the food, and immediately I get home, take a bath, and sleep warm under my roof. That is when we most have to go." This something he has will be called caring. And one of the general objectives of the thesis is to discover what are the meaning structures of this caring what is the lived experience of this caring. In this communication, preliminary results of an Applied Hermeneutic Phenomenology (AHP) study on the lived experience of caring as a vulnerable person are presented. The research means to answer what is the lived experience of caring as a vulnerable person. That is, to describe and explain what it is like to caregive for a vulnerable person, what it is, essentially, to caregive for a vulnerable person, what makes the lived experience of caregiving for a vulnerable person different from any other. In order to investigate the meaning of the phenomenon of caregiving as a vulnerable person, as already stated, the method used will be Applied Hermeneutic Phenomenology (AHP). We base ourselves, initially, on the proposal of Raquel Ayala-Carabajo and Max Van Manen. As Van Manen (1990) explains, AHP is a method that works essentially through fieldwork, with the collection of data on lived experience (experiential material). It is a phenomenology of practice. We here present the provisional themes we found: caregiving as a vulnerable person is seeing yourself in the other, identifying with the care-receiver; Caregiving as a vulnerable person is putting the other’s need before oneself’s; Caregiving as a vulnerable person is temporarily overcoming your weaknesses to make yourself strong for the other; Caregiving as a vulnerable person is going beyond the conventional approach; and Caregiving as a vulnerable person is taking responsibility even if it’s not yours.

Keywords: applied hermeneutic phenomenology, care ethics, hermeneutics, phenomenology

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Authors: Bonguen Onouck Rolande Carole, Ndongo Barthelemy

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The climate change effects on the Lake Chad, coupled with population growth, have pushed large masses of people of various origins towards the lower part of the lower Logonewatershed in search of the benefits of environmental services, causing pressure on the environment and its resources. Economic services are therefore threatened, and the decrease in resources contributes to the deterioration of the social wellbeing resulting to conflicts among/between local communities, immigrants, displaced people, and foreigners. This paper is an information contribution on ethnical conflicts drivers in the area and the provided local management mechanisms such can help mitigate present or future conflicts in similar areas. It also prints out the necessity to alleviate water access deficit and encourage good practices for the population wellbeing. In order to meet the objective, in 2018, through the interface of the World Bank-Cameroon project-PULCI, data were collected on the field directly by discussing with the population and visiting infrastructures, indirectly by a questionnaire survey. Two administrative divisions were chosen (Logoneet Chari, Mayo-Danay) in which targeted localities were Zina, Mazera, Lahai, Andirni near the Waza Park and Yagoua, Tekele, Pouss, respectively. Due to some sociocultural and religious reasons, some information were acquired through the traditional chiefs. A desk study analysis based on resources access and availability conflicts history, and management mechanism was done. As results, roots drivers of ethnical conflicts are struggles over natural resources access, and the possibility of conflicts increases as the scarcity and vulnerabilities persist, creating more sociocultural gaps and tensions. The mitigation mechanisms though fruitful, are limited. There is poor documentation on the topic, the resources management policies of this basin are unsuitable and ineffective for some. Therefore, the restoration of environmental and ecosystems, the mitigation of climate change effects, and food insecurity are the challenges that must be met to alleviate conflicts in these localities.

Keywords: ethnic, communities, conflicts, mitigation mechanisms, natural resources, logone basin

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Shveta Bathla, Preeti Rawat, Sudarshan Kumar, Rubina Baithalu, Jogender Singh Rana, Tushar Kumar Mohanty, Ashok Kumar Mohanty

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Pregnancy is a complex process which includes series of events such as fertilization, formation of blastocyst, implantation of embryo, placental formation and development of fetus. The success of these events depends on various interactions which are synchronized by endocrine interaction between a receptive dam and competent embryo. These interactions lead to change in expression of hormones and proteins. But till date no protein biomarker is available which can be used to detect successful completion of these events. We employed quantitative proteomics approach to develop putative serological biomarker which has diagnostic applicability for early detection of pregnancy in cows. For this study, sera were collected from control (non-pregnant, n=6) and pregnant animals on successive days of pregnancy (7, 19, 45, n=6). The sera were subjected to depletion for removal of albumin using Norgen depletion kit. The tryptic peptides were labeled with iTRAQ. The peptides were pooled and fractionated using bRPLC over 80 min gradient. Then 12 fractions were injected to nLC for identification and quantitation in DDA mode using ESI. Identification using Mascot search revealed 2056 proteins out of which 352 proteins were differentially expressed. Twenty proteins were upregulated and twelve proteins were down-regulated with fold change > 1.5 and < 0.6 respectively (p < 0.05). The gene ontology studies of DEPs using Panther software revealed that majority of proteins are actively involved in catalytic activities, binding and enzyme regulatory activities. The DEP'S such as NF2, MAPK, GRIPI, UGT1A1, PARP, CD68 were further subjected to pathway analysis using KEGG and Cytoscape plugin Cluego that showed involvement of proteins in successful implantation, maintenance of pluripotency, regulation of luteal function, differentiation of endometrial macrophages, protection from oxidative stress and developmental pathways such as Hippo. Further efforts are continuing for targeted proteomics, western blot to validate potential biomarkers and development of diagnostic kit for early pregnancy diagnosis in cows.

Keywords: bRPLC, Cluego, ESI, iTRAQ, KEGG, Panther

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Authors: Thomas Louis, Irina Primac, Florent Morfoisse, Tania Durre, Silvia Blacher, Agnes Noel

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In vitro and in vivo lymphangiogenesis assays are essential for the identification of potential lymphangiogenic agents and the screening of pharmacological inhibitors. In the present study, we analyse three biological models: in vitro lymphatic endothelial cell spheroids, in vivo ear sponge assay, and in vivo lymph node colonisation by tumour cells. These assays provide suitable 3D models to test pro- and anti-lymphangiogenic factors or drugs. 3D images were acquired by confocal laser scanning and light sheet fluorescence microscopy. Virtual scan microscopy followed by 3D reconstruction by image aligning methods was also used to obtain 3D images of whole large sponge and ganglion samples. 3D reconstruction, image segmentation, skeletonisation, and other image processing algorithms are described. Fixed and time-lapse imaging techniques are used to analyse lymphatic endothelial cell spheroids behaviour. The study of cell spatial distribution in spheroid models enables to detect interactions between cells and to identify invasion hierarchy and guidance patterns. Global measurements such as volume, length, and density of lymphatic vessels are measured in both in vivo models. Branching density and tortuosity evaluation are also proposed to determine structure complexity. Those properties combined with vessel spatial distribution are evaluated in order to determine lymphangiogenesis extent. Lymphatic endothelial cell invasion and lymphangiogenesis were evaluated under various experimental conditions. The comparison of these conditions enables to identify lymphangiogenic agents and to better comprehend their roles in the lymphangiogenesis process. The proposed methodology is validated by its application on the three presented models.

Keywords: 3D image segmentation, 3D image skeletonisation, cell invasion, confocal microscopy, ear sponges, light sheet microscopy, lymph nodes, lymphangiogenesis, spheroids

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Authors: Zahid Ullah, Atlas Khan

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In today's data-driven world, the role of mathematics in bridging the gap between theory and applications is becoming increasingly vital. This abstract highlights the significance of mathematics as a powerful tool for analyzing, interpreting, and extracting meaningful insights from vast amounts of data. By integrating mathematical principles with real-world applications, researchers can unlock the full potential of data-driven decision-making processes. This abstract delves into the various ways mathematics acts as a bridge connecting theoretical frameworks to practical applications. It explores the utilization of mathematical models, algorithms, and statistical techniques to uncover hidden patterns, trends, and correlations within complex datasets. Furthermore, it investigates the role of mathematics in enhancing predictive modeling, optimization, and risk assessment methodologies for improved decision-making in diverse fields such as finance, healthcare, engineering, and social sciences. The abstract also emphasizes the need for interdisciplinary collaboration between mathematicians, statisticians, computer scientists, and domain experts to tackle the challenges posed by the data-driven landscape. By fostering synergies between these disciplines, novel approaches can be developed to address complex problems and make data-driven insights accessible and actionable. Moreover, this abstract underscores the importance of robust mathematical foundations for ensuring the reliability and validity of data analysis. Rigorous mathematical frameworks not only provide a solid basis for understanding and interpreting results but also contribute to the development of innovative methodologies and techniques. In summary, this abstract advocates for the pivotal role of mathematics in bridging theory and applications in a data-driven world. By harnessing mathematical principles, researchers can unlock the transformative potential of data analysis, paving the way for evidence-based decision-making, optimized processes, and innovative solutions to the challenges of our rapidly evolving society.

Keywords: mathematics, bridging theory and applications, data-driven world, mathematical models

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Authors: Faizah Asiri, Mohammed Fathy, Saeed Alghamdi, Nahlah Ayoub, Faisal Asiri

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Background: - The strategies for this study were based on the toxic effect of long-term inhalation of Benzene on hormones and liver enzymes and various parameters related to it. The following databases were searched: benzene, hepatotoxic, benzene metabolism, hormones, testosterone, hemotoxic, and prolonged exposure. A systematic strategy is designed to search the literature that links benzene with the multiplicity and different types of intoxication or the medical abbreviations of diseases relevant to benzene exposure. Evidence suggests that getting rid of inhaled gasoline is by exhalation. Absorbed benzene is metabolized by giving phenolic acid as well as meconic acid, followed by urinary excretion of conjugate sulfates and glucuronides. Materials and Methods :- This work was conducted in the Al-Khadra laboratory in Taif 2020/2021 and aimed to measure some of the possible endocrinal and liver toxicities associated with benzene's long-term exposure in Saudi Arabia at the station workers who are considered the most exposed category to gasoline. One hundred ten station workers were included in this study. They were divided into four patient groups according to the chronic exposure rate to benzene, one control group, and three other groups of exposures. As follows: patient Group 1 (controlled group), patient Group 2 (exposed less than 1y), patient Group 3 (exposed 1-5 y), patient Group 4 (more than 5). Each group is compared with blood sample parameters (ALT, FSH and Testosterone, TSH). Blood samples were drawn from the participants, and statistical tests were performed. Significant change (p≤0.05) was examined compared to the control group. Workers' exposure to benzene led to a significant change in hematological, hormonal, and hepatic factors compared to the control group. Results:- The results obtained a relationship between long-term exposure to benzene and a decrease in the level of testosterone and FSH hormones, including that it poses a toxic risk in the long term (p≤0.05) when compared to the control. We obtained results confirming that there is no significant coloration between years of exposure and TSH level (p≤0.05) when compared to the control. Conclusion:- We conclude that some hormones and liver enzymes are affected by chronic doses of benzene through inhalation after our study was on the group most exposed to benzene, which is gas station workers.

Keywords: toxicities, benzene, hormones, station workers

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Authors: Katielly Vianna Polkowski, Rodrigo Denizarte de Oliveira Polkowski, Cristiano Grings Herbert

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The use of nanomaterials in the formulation of polymeric materials modifies their molecular structure, offering an infinite range of possibilities for the development of smart products, being of great importance for science and contemporary industry. Shape memory polymers are generally lightweight, have high shape recovery capabilities, they are easy to process and have properties that can be adapted for a variety of applications. Shape memory materials are active materials that have attracted attention due to their superior damping properties when compared to conventional structural materials. The development of methodologies capable of preparing new materials, which use graphene in their structure, represents technological innovation that transforms low-cost products into advanced materials with high added value. To obtain an improvement in the shape memory effect (SME) of polymeric materials, it is possible to use graphene in its composition containing low concentration by mass of graphene nanoplatelets (GNP), graphene oxide (GO) or other functionalized graphene, via different mixture process. As a result, there was an improvement in the SME, regarding the increase in the values of maximum strain. In addition, the use of graphene contributes to obtaining nanocomposites with superior electrical properties, greater crystallinity, as well as resistance to material degradation. The methodology used in the research is Systematic Review, scientific investigation, gathering relevant studies on influence of nanomaterials on the properties of shape memory polymeric, using the literature database as a source and study methods. In the present study, a systematic reviewwas performed of all papers published from 2014 to 2022 regarding graphene and shape memory polymeric througha search of three databases. This study allows for easy identification of themost relevant fields of study with respect to graphene and shape memory polymeric, as well as the main gaps to beexplored in the literature. The addition of graphene showed improvements in obtaining higher values of maximum deformation of the material, attributed to a possible slip between stacked or agglomerated nanostructures, as well as an increase in stiffness due to the increase in the degree of phase separation that results in a greater amount physical cross-links, referring to the formation of shortrange rigid domains.

Keywords: graphene, shape memory, smart materials, polymers, nanomaterials

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Authors: Meital Amzalag, Sharon Hardof-Jaffe

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

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

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Authors: Asma Zaib, Saeed Khan, Ayaz Ahmed Noonari, Sehrish Bint-e-Mohsin

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Breast cancer is the most common cancer among females worldwide and is estimated that more than 450,000 deaths are reported each year. It accounts for about 14% of all female cancer deaths. Angiogenesis plays an essential role in Breast cancer development, invasion, and metastasis. Breast cancer predominantly begins in luminal epithelial cells lining the normal breast ducts. Breast carcinoma likely requires coordinated efforts of both increased proliferation and increased motility to progress to metastatic stages.Resveratrol: a natural stilbenoid, has anti-inflammatory and anticancer effects that inhibits proliferation of variety of human cancer cell lines, including breast, prostate, stomach, colon, pancreatic, and thyroid cancers.The objective of this study is:To investigate anti-neoangiogenesis effects of Resveratrol in breast cancer and to analyze inhibitory effects of resveratrol on aromatase, Erα, HER2/neu, and VEGFR.Docking is the computational determination of binding affinity between molecule (protein structure and ligand).We performed molecular docking using Swiss-Dock and to determine docking effects of (1) Resveratrol with Aromatase, (2) Resveratrol with ERα (3) Resveratrol with HER2/neu and (4) Resveratrol with VEGFR2.Docking results of resveratrol determined inhibitory effects on aromatase with binding energy of -7.28 kcal/mol which shows anticancerous effects on estrogen dependent breast tumors. Resveratrol also show inhibitory effects on ERα and HER2/new with binging energy -8.02, and -6.74 respectively; which revealed anti-cytoproliferative effects upon breast cancer. On the other hand resveratrol v/s VEGFR showed potential inhibitory effects on neo-angiogenesis with binding energy -7.68 kcal/mol, angiogenesis is the important phenomenon that promote tumor development and metastasis. Resveratrol is an anti-breast cancer agent conformed by in silico studies, it has been identified that resveratrol can inhibit breast cancer cells proliferation by acting as competitive inhibitor of aromatase, ERα and HER2 neo, while neo-angiogemesis is restricted by binding to VEGFR which authenticates the anti-carcinogenic effects of resveratrol against breast cancer.

Keywords: angiogenesis, anti-cytoproliferative, molecular docking, resveratrol

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Authors: Alice Huffman, Joseph Hartland, Sam Gibbs

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Dermatology is often a neglected specialty in low-resource settings, despite the high morbidity associated with skin disease. This becomes even more significant when associated with HIV infection, as dermatological conditions are more common and aggressive in HIV positive patients. African countries have the highest HIV infection rates and skin conditions are frequently misdiagnosed and mismanaged, because of a lack of dermatological training and educational material. The frequent lack of diagnostic tests in the African setting renders basic clinical skills all the more vital. This project aimed to improve diagnosis and treatment of skin disease in the HIV population in a district hospital in Malawi. A basic dermatological clinical tool was developed and produced in collaboration with local staff and based on available literature and data collected from clinics. The aim was to improve diagnostic accuracy and provide guidance for the treatment of skin disease in HIV positive patients. A literature search within Embase, Medline and Google scholar was performed and supplemented through data obtained from attending 5 Antiretroviral clinics. From the literature, conditions were selected for inclusion in the resource if they were described as specific, more prevalent, or extensive in the HIV population or have more adverse outcomes if they develop in HIV patients. Resource-appropriate treatment options were decided using Malawian Ministry of Health guidelines and textbooks specific to African dermatology. After the collection of data and discussion with local clinical and pharmacy staff a list of 15 skin conditions was included and a booklet created using the simple layout of a picture, a diagnostic description of the disease and treatment options. Clinical photographs were collected from local clinics (with full consent of the patient) or from the book ‘Common Skin Diseases in Africa’ (permission granted if fully acknowledged and used in a not-for-profit capacity). This tool was evaluated by the local staff, alongside an educational teaching session on skin disease. This project aimed to reduce uncertainty in diagnosis and provide guidance for appropriate treatment in HIV patients by gathering information into one practical and manageable resource. To further this project, we hope to review the effectiveness of the tool in practice.

Keywords: dermatology, HIV, Malawi, skin disease

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Authors: John Michael Weber

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The following research results spanned six months and 175 people addicted to some form of substance, from alcohol to heroin. One question was asked, and the answers were amazing and consistent. The following work is the detailed results of this writer’s answer to his own question and the 175 that followed. A constant pattern took shape throughout the bio-psycho-social assessments, these addicts had “first memories,” the memories were vivid and took place between the ages of three to six years old, to a person those first memories were traumatic. This writer’s personal search into his childhood was not to find an excuse for the way he became, but to explain the reason for becoming an addict. To treat addiction, these memories that have caused Post Traumatic Stress Disorder (PTSD), must be recognized as the catalyst that sparked a predisposition. Cognitive Behavioral Therapy (CBT), integrated with treatment specifically focused on PTSD, gives the addict a better chance at recovery sans relapse. This paper seeks to give the findings of first memories of the addicts assessed and provide the best treatment plan for such an addict, considering, the childhood trauma in congruence with treatment of the Substance Use Disorder (SUD). The question posed was concerning what their first life memory wa It is the hope of this author to take the knowledge that trauma is one of the main catalysts for addiction, will allow therapists to provide better treatment and reduce relapse from abstinence from drugs and alcohol. This research led this author to believe that if treatment of childhood trauma is not a priority, the twelve steps of Alcoholics Anonymous, specifically steps 4 and 5, will not be thoroughly addressed and odds for relapse increase. With this knowledge, parents can be educated on childhood trauma and the effect it has on their children. Parents could be mindful of the fact that the things they perceive as traumatic, do not match what a child, in the developmental years, absorbs as traumatic. It is this author’s belief that what has become the status quo in treatment facilities has not been working for a long time. It is for that reason this author believes things need to change. Relapse has been woven into the fabric of standard operating procedure and that, in this authors view, is not necessary. Childhood Trauma is not being addressed early in recovery and that creates an environment of inevitable relapse. This paper will explore how to break away from the status -quo and rethink the current “evidencebased treatments.” To begin breaking away from status-quo, this ends the Abstract, with hopes an interest has been peaked to read on.

Keywords: childood, trauma, treatment, addiction, change

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Authors: Brahim Aissa

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Transparent conductive oxides (TCOs) used as front electrodes in solar cells must feature simultaneously high electrical conductivity, low contact resistance with the adjacent layers, and an appropriate refractive index for maximal light in-coupling into the device. However, these properties may conflict with each other, motivating thereby the search for TCOs with high performance. Additionally, due to the presence of temperature sensitive layers in many solar cell designs (for example, in thin-film silicon and silicon heterojunction (SHJ)), low-temperature deposition processes are more suitable. Several deposition techniques have been already explored to fabricate high-mobility TCOs at low temperatures, including sputter deposition, chemical vapor deposition, and atomic layer deposition. Among this variety of methods, to the best of our knowledge, magnetron sputtering deposition is the most established technique, despite the fact that it can lead to damage of underlying layers. The Sn doped In₂O₃ (ITO) is the most commonly used transparent electrode-contact in SHJ technology. In this work, we studied the properties of ITO thin films grown by RF sputtering. Using different oxygen fraction in the argon/oxygen plasma, we prepared ITO films deposited on glass substrates, on one hand, and on a-Si (p and n-types):H/intrinsic a-Si/glass substrates, on the other hand. Hall Effect measurements were systematically conducted together with total-transmittance (TT) and total-reflectance (TR) spectrometry. The electrical properties were drastically affected whereas the TT and TR were found to be slightly impacted by the oxygen variation. Furthermore, the time of flight-secondary ion mass spectrometry (TOF-SIMS) technique was used to determine the distribution of various species throughout the thickness of the ITO and at various interfaces. The depth profiling of indium, oxygen, tin, silicon, phosphorous, boron and hydrogen was investigated throughout the various thicknesses and interfaces, and obtained results are discussed accordingly. Finally, the extreme conditions were selected to fabricate rear emitter SHJ devices, and the photovoltaic performance was evaluated; the lower oxygen flow ratio was found to yield the best performance attributed to lower series resistance.

Keywords: solar cell, silicon heterojunction, oxygen content, optoelectronic properties

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Authors: Mahtab Makaremi Masouleh, Günter Wozniak

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This work is part of a major research project concerning the design of a light temporary installable textile flood control structure. The motivation for this work is the great need of applying light structures for the protection of coastal areas from detrimental effects of rapid water runoff. The prime objective of the study is the numerical analysis of the interaction among free surface water flow and slender shaped pliable structures, playing a key role in safety performance of the intended system. First, the behavior of down scale membrane is examined under hydrostatic pressure by the Abaqus explicit solver, which is part of the finite element based commercially available SIMULIA software. Then the procedure to achieve a stable and convergent solution for strongly coupled media including fluids and structures is explained. A partitioned strategy is imposed to make both structures and fluids be discretized and solved with appropriate formulations and solvers. In this regard, finite element method is again selected to analyze the structural domain. Moreover, computational fluid dynamics algorithms are introduced for solutions in flow domains by means of a commercial package of Star CCM+. Likewise, SIMULIA co-simulation engine and an implicit coupling algorithm, which are available communication tools in commercial package of the Star CCM+, enable powerful transmission of data between two applied codes. This approach is discussed for two different cases and compared with available experimental records. In one case, the down scale membrane interacts with open channel flow, where the flow velocity increases with time. The second case illustrates, how the full scale flexible flood barrier behaves when a massive flotsam is accelerated towards it.

Keywords: finite element formulation, finite volume algorithm, fluid-structure interaction, light pliable structure, VOF multiphase model

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Authors: Saeed Musaid H. Alzahrani

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The role of middle leaders, especially in primary schools, is a multi-faced role that has been subject to changes in nature over recent decades, with claims for more distributed leadership practices. This research examines the way 18 middle leaders in Saudi and English primary schools conceptualise their roles and responsibilities, and different factors influencing those roles and responsibilities. It begins from the premise that both the power of the role and the values of middle leaders are grounded in cultural and political bases, a belief held by the researcher as an 'insider' within the Saudi educational leadership context. The study consisted of a comparative analysis of the role and the responsibilities of middle leaders in Saudi primary schools and their equivalents in English primary schools. A purely qualitative methodological stance was adopted, using in-depth face-to-face semi-structured interviews, observations and document analysis. Middle leaders were asked to reflect deeply on their perceptions and understanding of their roles and explain what they thought influenced their daily practices and responsibilities. The findings suggest that the concept of middle leadership has been influenced by power imposed from above by political authority, via internal and external hierarchical structures, which shapes the nature of the role of the middle leaders and forces them to comply. Middle leaders seem to believe they have the power to make decisions and promote change, but these findings suggest that this is illusory. The power that keeps middle leaders performing is the power of their cultural and religious values. Those values are the resource to which they turn in their search for more energy when they lack support and are short of time taken. Middle leaders in Saudi, just like their equivalents in English schools must comply with the requirements of their role. However, Saudi middle leaders are given no leeway to make decisions or implement change, neither do they have the culture of collegiality that seems to give middle leaders in England more power over their resources and decisions. However, in neither educational setting have middle leaders been given the power to lead, so they remain managers rather than leaders. The findings of this research suggest that there are more similarities between the educational settings of Saudi and England than differences; and in the light of different factors identified in the study, suggest the establishment of a framework for middle leadership, in the hope of enhancing the way the role is practiced.

Keywords: middle leader, primary school, power, educational leadership, value, culture, model, Saudi Arabia, England

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Authors: Semachew M. Kassa, Africa M Geremew, Tezera F. Azmatch, Nandyala Darga Kumar

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Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events.

Keywords: artificial neural network, logistic regression, landslide susceptibility, naïve Bayes, random forest, support vector machine

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Authors: Deniz Guven, Wil Ward, Jinming Duan, Li Bai

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Over the past two decades or so, Optical Coherence Tomography (OCT) has been used to diagnose retina and optic nerve diseases. The retinal nerve fibre layer, for example, is a powerful diagnostic marker for detecting and staging glaucoma. With the advances in optical imaging hardware, the adoption of OCT is now commonplace in clinics. More and more OCT images are being generated, and for these OCT images to have clinical applicability, accurate automated OCT image segmentation software is needed. Oct image segmentation is still an active research area, as OCT images are inherently noisy, with the multiplicative speckling noise. Simple edge detection algorithms are unsuitable for detecting retinal layer boundaries in OCT images. Intensity fluctuation, motion artefact, and the presence of blood vessels also decrease further OCT image quality. In this paper, we introduce a new method for segmenting three-dimensional (3D) OCT images. This involves the use of a Kalman filter, which is commonly used in computer vision for object tracking. The Kalman filter is applied to the 3D OCT image volume to track the retinal layer boundaries through the slices within the volume and thus segmenting the 3D image. Specifically, after some pre-processing of the OCT images, points on the retinal layer boundaries in the first image are identified, and curve fitting is applied to them such that the layer boundaries can be represented by the coefficients of the curve equations. These coefficients then form the state space for the Kalman Filter. The filter then produces an optimal estimate of the current state of the system by updating its previous state using the measurements available in the form of a feedback control loop. The results show that the algorithm can be used to segment the retinal layers in OCT images. One of the limitations of the current algorithm is that the curve representation of the retinal layer boundary does not work well when the layer boundary is split into two, e.g., at the optic nerve, the layer boundary split into two. This maybe resolved by using a different approach to representing the boundaries, such as b-splines or level sets. The use of a Kalman filter shows promise to developing accurate and effective 3D OCT segmentation methods.

Keywords: optical coherence tomography, image segmentation, Kalman filter, object tracking

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Authors: Zineb Emrane

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This abstract addresses the issue of radicalization of young people in a context with background of radicalization, in North of Morocco, 5 terrorist of Madrid's Attacts on 11th March, were coming from this context. It were developed a study pilot that describing young people perception about the main causes that lead and motivate for radicalization. Whenever we talk about this topic, we obtain information from studies and investigations by specialists in field, but we don’t give voice to the protagonists who in many cases are victims, specifically, young people at social risk because of social factors. Extremist radicalization is an expanding phenomenon, that affect young people, in north of Morocco. They live in a context with radical background and at risk of social exclusion, their social, economic and familiar needs make them vulnerable. The extremist groups take advantage of this vulnerability to involve them in a process of radicalization, offering them an alternative environment where they can found all they are looking for. This study pilot approaches the main causes that lead and motivates young people to become radicals, analyzing their context with emphasis on influencing factors, and bearing in mind the analysis of young people about how the radical background affect them and their opinion this phenomenon. The pilot study was carried out through the following actions: - Group dynamics with young people to analyze the process of violent radicalization of young people. -A participatory workshop with members of organizations that work directly with young people at risk of radicalization. -Interviews with institutional managers -Participant observation. The implementation of actions has led to the conclusion that young people define violent radicalization as a sequential process, depending on the stage, it can be deconstructed. Young people recognize that they stop feeling belonging to their family, school and neighborhood when they see behavior contrary to what they consider good and evil. The emotional rupture and the search for references outside their circle, push them to sympathize with groups that have an extremist ideology and that offer them what they need. The radicalization is a process with different stages, the main causes and the factors which lead young people to use extremist violence are related their low level of belonging feeling to their context, and lack of critical thinking about important issues. The young people are in a vulnerable stage, searching their identity, a space in which they can be accepted, and when they don't find it they are easily manipulated and susceptible to being attracted by extremist groups.

Keywords: exclusion, radicalization, vulnerability, youth

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: A. Fathianpour, M. B. Jelodar, S. Wilkinson

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This paper outlines the approaches taken to assess multi-hazard assessments. There is currently confusion in assessing multi-hazard impacts, and so this study aims to determine which of the available options are the most useful. The paper uses an international literature search, and analysis of current multi-hazard assessments and a case study to illustrate the effectiveness of the chosen method. Findings from this study will help those wanting to assess multi-hazards to undertake a straightforward approach. The paper is significant as it helps to interpret the various approaches and concludes with the preferred method. Many people in the world live in hazardous environments and are susceptible to disasters. Unfortunately, when a disaster strikes it is often compounded by additional cascading hazards, thus people would confront more than one hazard simultaneously. Hazards include natural hazards (earthquakes, floods, etc.) or cascading human-made hazards (for example, Natural Hazard Triggering Technological disasters (Natech) such as fire, explosion, toxic release). Multi-hazards have a more destructive impact on urban areas than one hazard alone. In addition, climate change is creating links between different disasters such as causing landslide dams and debris flows leading to more destructive incidents. Much of the prevailing literature deals with only one hazard at a time. However, recently sophisticated multi-hazard assessments have started to appear. Given that multi-hazards occur, it is essential to take multi-hazard risk assessment under consideration. This paper aims to review the multi-hazard assessment methods through articles published to date and categorize the strengths and disadvantages of using these methods in risk assessment. Napier City is selected as a case study to demonstrate the necessity of using multi-hazard risk assessments. In order to assess multi-hazard risk assessments, first, the current multi-hazard risk assessment methods were described. Next, the drawbacks of these multi-hazard risk assessments were outlined. Finally, the improvements to current multi-hazard risk assessments to date were summarised. Generally, the main problem of multi-hazard risk assessment is to make a valid assumption of risk from the interactions of different hazards. Currently, risk assessment studies have started to assess multi-hazard situations, but drawbacks such as uncertainty and lack of data show the necessity for more precise risk assessment. It should be noted that ignoring or partial considering multi-hazards in risk assessment will lead to an overestimate or overlook in resilient and recovery action managements.

Keywords: cascading hazards, disaster assessment, mullti-hazards, risk assessment

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Authors: Ioannis Iglezakis, Theodoros D. Trokanas, Panagiota Kiortsi

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This paper aims to explore major legal issues arising from the collection and processing of Health Big Data in the light of the new European secondary legislation for the protection of personal data of natural persons, placing emphasis on the General Data Protection Regulation 679/2016. Whether Big Health Data can be characterised as ‘personal data’ or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Another key point is that the application of some provisions of GPDR to Big Health Data may both absolve the data controller of his legal obligations and deprive the data subject of his rights (e.g., the right to be informed), ultimately undermining the fundamental right to the protection of personal data of natural persons. Moreover, data subject’s rights (e.g., the right not to be subject to a decision based solely on automated processing) are heavily impacted by the use of AI, algorithms, and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have a substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial sources of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subject’s rights while embracing the opportunities that Big Health Data has to offer. In addition, particular attention is attached to the scope of ‘consent’ as a legal basis in the collection and processing of Big Health Data, as the application of data analytics in Big Health Data signals the construction of new data and subject’s profiles. Finally, the paper addresses the knotty problem of role assignment (i.e., distinguishing between controller and processor/joint controllers and joint processors) in an era of extensive Big Health data sharing. The findings are the fruit of a current research project conducted by a three-member research team at the Faculty of Law of the Aristotle University of Thessaloniki and funded by the Greek Ministry of Education and Religious Affairs.

Keywords: big health data, data subject rights, GDPR, pandemic

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 76

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Hasan Ustun Basaran

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Diesel engines are widely used in transportation sector due to their high thermal efficiency. However, they also release high rates of NOₓ and PM (particulate matter) emissions into the environment which have hazardous effects on human health. Therefore, environmental protection agencies have issued strict emission regulations on automotive diesel engines. Recently, these regulations are even increasingly strengthened. Engine producers search novel on-engine methods such as advanced combustion techniques, utilization of renewable fuels, exhaust gas recirculation, advanced fuel injection methods or use exhaust after-treatment (EAT) systems in order to reduce emission rates on diesel engines. Although those aforementioned on-engine methods are effective to curb emission rates, they result in inefficiency or cannot decrease emission rates satisfactorily at all operating conditions. Therefore, engine manufacturers apply both on-engine techniques and EAT systems to meet the stringent emission norms. EAT systems are highly effective to diminish emission rates, however, they perform inefficiently at low loads due to low exhaust gas temperatures (below 250°C). Therefore, the objective of this study is to demonstrate that engine-out temperatures can be elevated above 250°C at low-loaded cases via cylinder disablement. The engine studied and modeled via Lotus Engine Simulation (LES) software is a six-cylinder turbocharged and intercooled diesel engine. Exhaust temperatures and mass flow rates are predicted at 1200 rpm engine speed and several low loaded conditions using LES program. It is seen that cylinder deactivation results in a considerable exhaust temperature rise (up to 100°C) at low loads which ensures effective EAT management. The method also improves fuel efficiency through reduced total pumping loss. Decreased total air induction due to inactive cylinders is thought to be responsible for improved engine pumping loss. The technique reduces exhaust gas flow rate as air flow is cut off on disabled cylinders. Still, heat transfer rates to the after-treatment catalyst bed do not decrease that much since exhaust temperatures are increased sufficiently. Simulation results are promising; however, further experimental studies are needed to identify the true potential of the method on fuel consumption and EAT improvement.

Keywords: cylinder disablement, diesel engines, exhaust after-treatment, exhaust temperature, fuel efficiency

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Authors: Berkan Ural, Arif Eser, Sinan Apaydin

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Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs.

Keywords: image processing algorithms, magnetic resonance imaging, neural network, pattern recognition

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Authors: Charisse H. Tay

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Background: Schizophrenia is a severe chronic mental disorder that can result in hallucinations, delusions, reduced social engagement, and lack of motivation. While antipsychotic medications often provide the basis for treatment, psychosocial strategies complement the benefit of medications and can result in meaningful improvements in symptoms and functioning. The aim of the study was to investigate psychosocial strategies used by internet self-help forum participants to effectively manage symptoms caused by schizophrenia. Internet self-help forums are a resource for medical and psychological problems and are commonly used to share information about experiences with symptom management. Method: Three international self-help internet forums on schizophrenia were identified using a search engine. 1,181 threads regarding non-pharmacological, psychosocial self-management of schizophrenia symptoms underwent screening, resulting in the final identification and coding of 91 threads and 191 posts from 134 unique forum users that contained details on psychosocial strategies endorsed personally by users that allowed them to effectively manage symptoms of schizophrenia, including positive symptoms (e.g., auditory/visual/tactile hallucinations, delusions, paranoia), negative symptoms (e.g.., avolition, apathy, anhedonia), symptoms of distress, and cognitive symptoms (e.g., memory loss). Results: Effective symptom management strategies personally endorsed by online forum users were psychological skills (e.g., re-focusing, mindfulness/meditation, reality checking; n = 94), engaging in activities (e.g., exercise, working/volunteering, hobbies; n = 84), social/familial support (n = 48), psychotherapy (n = 33), diet (n = 18), and religion/spirituality (n = 14). 44.4% of users reported using more than one strategy to manage their symptoms. The most common symptoms targeted and effectively managed, as specified by users, were positive symptoms (n = 113), negative symptoms (n = 17), distress (n = 8), and memory loss (n = 6). 10.5% of users reported more than one symptom effectively targeted. 70.2% of users with positive symptoms reported that psychological skills were effective for symptom relief. 88% of users with negative symptoms and 75% with distress symptoms reported that engaging in activities was effective. Discussion: Individuals with schizophrenia rely on a variety of different psychosocial methods to manage their symptoms. Different symptomology appears to be more effectively targeted by different types of psychosocial strategies. This may help to inform treatment strategy and tailored for individuals with schizophrenia.

Keywords: psychosocial treatment, qualitative methods, schizophrenia, symptom management

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Authors: Irene Yi

Abstract:

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

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

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Authors: Ashhar Ahmed Shaikh, Ayush Tandon

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The swift reduction of fossil fuels from nature has crucial need for alternative energy sources to cater vital demand. It is essential to boost alternative energy sources to cover the continuously increasing demand for energy while minimizing the negative environmental impacts. Solar energy is one of the reliable sources that can generate energy. Solar energy is freely available in nature and is completely eco-friendly, and they are considered as the most promising power generating sources due to their easy availability and other advantages for the local power generation. This paper is to review the implementation of power electronic devices through Solar Energy Grid Integration System (SEGIS) to increase the efficiency. This paper will also concentrate on the future grid infrastructure and various other applications in order to make the grid smart. Development and implementation of a power electronic devices such as PV inverters and power controllers play an important role in power supply in the modern energy economy. Solar Energy Grid Integration System (SEGIS) opens pathways for promising solutions for new electronic and electrical components such as advanced innovative inverter/controller topologies and their functions, economical energy management systems, innovative energy storage systems with equipped advanced control algorithms, advanced maximum-power-point tracking (MPPT) suited for all PV technologies, protocols and the associated communications. In addition to advanced grid interconnection capabilities and features, the new hardware design results in small size, less maintenance, and higher reliability. The SEGIS systems will make the 'advanced integrated system' and 'smart grid' evolutionary processes to run in a better way. Since the last few years, there was a major development in the field of power electronics which led to more efficient systems and reduction of the cost per Kilo-watt. The inverters became more efficient and had reached efficiencies in excess of 98%, and commercial solar modules have reached almost 21% efficiency.

Keywords: solar energy grid integration systems, smart grid, advanced integrated system, power electronics

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Authors: Leszek Szpakowski, Daniel Celiński, Sławomir Pilip, Grzegorz Michalak

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The aim of the study was to analyse the usefulness of the 'E-rescuer' pilot project planned to be implemented in a chosen area of Eastern Poland in the cases of suspected sudden cardiac arrests in the extra-urban areas. Inventing an application allowing to dispatch simultaneously both Medical Emergency Teams and the E-rescuer to the place of the accident is the crucial assumption of the mentioned pilot project. The E-rescuer is defined to be the trained person able to take effective basic life support and to use automated external defibrillator. Having logged in using a smartphone, the E-rescuer's readiness is reported online to provide cardiopulmonary resuscitation exactly at the given location. Due to the accurately defined location of the E-rescuer, his arrival time is possible to be precisely fixed, and the substantive support through the displayed algorithms is capable of being provided as well. Having analysed the medical records in the years 2015-2016, cardiopulmonary resuscitation was considered to be effective when an early indication of circulation was provided, and the patient was taken to hospital. In the mentioned term, there were 2.291 cases of a sudden cardiac arrest. Cardiopulmonary resuscitation was taken in 621 patients in total including 205 people in the urban area and 416 in the extra-urban areas. The effectiveness of cardiopulmonary resuscitation in the extra-urban areas was much lower (33,8%) than in the urban (50,7%). The average ambulance arrival time was respectively longer in the extra-urban areas, and it was 12,3 minutes while in the urban area 3,3 minutes. There was no significant difference in the average age of studied patients - 62,5 and 64,8 years old. However, the average ambulance arrival time was 7,6 minutes for effective resuscitations and 10,5 minutes for ineffective ones. Hence, the ambulance arrival time is a crucial factor influencing on the effectiveness of cardiopulmonary resuscitation, especially in the extra-urban areas where it is much longer than in the urban. The key role of trained E-rescuers being nearby taking basic life support before the ambulance arrival can effectively support Emergency Medical Services System in Poland.

Keywords: basic life support, bystander, effectiveness, resuscitation

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Authors: Ibragim Issabekov, Byron Crape, Lyazzat Toleubekova

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Background: Palliative care substantially improves the quality of life of terminally-ill patients. Symptom control is one of the keystones in the management of patients in palliative care settings, lowering distress as well as improving the quality of life of patients with end-stage diseases. The most common symptoms causing significant distress for patients are pain, nausea and vomiting, increased respiratory secretions and mental health issues like depression. Aims are: 1. to identify best practices in symptom management in palliative patients in accordance with internationally approved guidelines and compare aforementioned with actual practices in Kazakhstan; to evaluate the criteria for assessing symptoms in terminally-ill patients, 2. to review the availability and utilization of pharmaceutical agents for pain control, management of excessive respiratory secretions, nausea, and vomiting, and delirium and 3. to develop recommendations for the systematic approach to end-of-life symptom management in Kazakhstan. Methods: The use of qualitative research methods together with systematic literature review have been employed to provide a rigorous research process to evaluate current approaches for symptom management of palliative patients in Kazakhstan. Qualitative methods include in-depth semi-structured interviews of the healthcare professionals involved in palliative care provision. Results: Obstacles were found in appropriate provision of palliative care. Inadequate education and training to manage severe symptoms, poorly defined laws and regulations for palliative care provision, and a lack of algorithms and guidelines for care were major barriers in the effective provision of palliative care. Conclusion: Assessment of palliative care in this medium-income transitional-economy country is one of the first steps in the initiation of integration of palliative care into the existing health system. Achieving this requires identifying obstacles and resolving these issues.

Keywords: end-of-life care, middle income country, palliative care, symptom control

Procedia PDF Downloads 197