Search results for: AR technology as pedological tools

Authors: Ahmed Kiani

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The United Arab Emirates is clearly facing a multitude of challenges in curbing its greenhouse gas emissions to meet its pre-allotted framework of Kyoto protocol and COP21 targets due to its hunger for modernization, industrialization, infrastructure growth, soaring population and oil and gas activity. In this work, we focus on the bonafide zero emission electric vehicles market penetration in the country’s transport industry for emission reduction. We study the global electric vehicle market trends, the complementary battery technologies and the trends by manufacturers, emission standards across borders and prioritized advancements which will ultimately dictate the terms of future conditions for the United Arab Emirate transport industry. Based on our findings and analysis at every stage of current viability and state-of-transport-affairs, we postulate policy recommendations to local governmental entities from a supply and demand perspective covering aspects of technology, infrastructure requirements, change in power dynamics, end user incentives program, market regulators behavior and communications amongst key stakeholders. 

Keywords: electric vehicles, greenhouse gas emission reductions, market analysis, policy recommendations

Procedia PDF Downloads 299

Authors: Shashi Arya, Nand L. Singh, Samiksha Singh, Pradeep K. Mishra, Siddh N. Upadhyay

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The enormous generation of E- waste and its recycling have greater environmental concern especially in developing countries like India. A major part of this waste comprises printed circuit boards (PCBs). Edge trims of PCBs have high copper content ranging between 25-60%. The extraction of various metals out of these PCBs is more or less a proven technology, wherein various hazardous chemicals are being used in the resource recovery, resulting into secondary pollution. The current trend of extracting of valuable metals is the utilization of microbial strains to eliminate the problem of a secondary pollutant. Keeping the above context in mind, this work aims at the enhanced recovery of copper from edge trims, through bioleaching using bacterial strain Acidithiobacillus ferrooxidans. The raw material such as motherboards, hard drives, floppy drives and DVD drives were obtained from the warehouse of the University. More than 90% copper could be extracted through bioleaching using Acidithiobacillus ferrooxidans. Inoculate concentration has merely insignificant effect over copper recovery above 20% inoculate concentration. Higher concentration of inoculation has the only initial advantage up to 2-4 days. The complete recovery has been obtained between 14- 24 days.

Keywords: acidithiobacillus ferrooxidans, bioleaching, e-waste, printed circuit boards

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Authors: Jin Woong Lee, Kyuman Cho, Taehoon Kim

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The application of constructability is increasingly required not only in the construction phase but also in the whole project stage. In particular, the proper application of construction experience and knowledge during the design phase enables the minimization of inefficiencies such as design changes and improvements in constructability during the construction phase. In order to apply knowledge effectively, engineering technology efforts should be implemented with design progress. Among many engineering technologies, engineering for temporary works, including facilities, equipment, and other related construction methods, is important to improve constructability. Therefore, as basic research, this study investigates the applicability of temporary work engineering during the design phase in the building construction industry. As a result, application of temporary work engineering has a greater impact on construction cost reduction and constructability improvement. In contrast to the existing design-bid-build method, the turn-key and CM (construct management) procurement methods currently being implemented in Korea are expected to have a significant impact on the direction of temporary work engineering. To introduce temporary work engineering, expert/professional organization training is first required, and a lack of client awareness should be preferentially improved. The results of this study are expected to be useful as reference material for the development of more effective temporary work engineering tasks and work processes in the future.

Keywords: Temporary Work Engineering, Design Phase, Constructability, Building Construction

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Authors: Marlin Mubarak

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Background: Endometriosis is a complex disabling disease affecting young females in the reproductive period mainly. The aim of this project is to generate a diagnostic model to predict severity and stage of endometriosis prior to Laparoscopic surgery. This will help to improve the pre-operative diagnostic accuracy of stage 3 & 4 endometriosis and as a result, refer relevant women to a specialist centre for complex Laparoscopic surgery. The model is based on the British Society of Gynaecological Endoscopy (BSGE) pain questionnaire, clinical examination and ultrasound scan. Design: This is a prospective, observational, study, in which women completed the BSGE pain questionnaire, a BSGE requirement. Also, as part of the routine preoperative assessment patient had a routine ultrasound scan and when recto-vaginal and deep infiltrating endometriosis was suspected an MRI was performed. Setting: Luton & Dunstable University Hospital. Patients: Symptomatic women (n = 56) scheduled for laparoscopy due to pelvic pain. The age ranged between 17 – 52 years of age (mean 33.8 years, SD 8.7 years). Interventions: None outside the recognised and established endometriosis centre protocol set up by BSGE. Main Outcome Measure(s): Sensitivity and specificity of endometriosis diagnosis predicted by symptoms based on BSGE pain questionnaire, clinical examinations and imaging. Findings: The prevalence of diagnosed endometriosis was calculated to be 76.8% and the prevalence of advanced stage was 55.4%. Deep infiltrating endometriosis in various locations was diagnosed in 32/56 women (57.1%) and some had DIE involving several locations. Logistic regression analysis was performed on 36 clinical variables to create a simple clinical prediction model. After creating the scoring system using variables with P < 0.05, the model was applied to the whole dataset. The sensitivity was 83.87% and specificity 96%. The positive likelihood ratio was 20.97 and the negative likelihood ratio was 0.17, indicating that the model has a good predictive value and could be useful in predicting advanced stage endometriosis. Conclusions: This is a hypothesis-generating project with one operator, but future proposed research would provide validation of the model and establish its usefulness in the general setting. Predictive tools based on such model could help organise the appropriate investigation in clinical practice, reduce risks associated with surgery and improve outcome. It could be of value for future research to standardise the assessment of women presenting with pelvic pain. The model needs further testing in a general setting to assess if the initial results are reproducible.

Keywords: deep endometriosis, endometriosis, minimally invasive, MRI, ultrasound.

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Authors: Sakir Tasdemir, Mustafa Altin, Gamze Fahriye Pehlivan, Sadiye Didem Boztepe Erkis, Ismail Saritas, Selma Tasdemir

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Artificial intelligence applications are commonly used in industry in many fields in parallel with the developments in the computer technology. In this study, a fire room was prepared for the resistance of wooden construction elements and with the mechanism here, the experiments of polished materials were carried out. By utilizing from the experimental data, an artificial neural network (ANN) was modeled in order to evaluate the final cross sections of the wooden samples remaining from the fire. In modelling, experimental data obtained from the fire room were used. In the system developed, the first weight of samples (ws-gr), preliminary cross-section (pcs-mm2), fire time (ft-minute), fire temperature (t-oC) as input parameters and final cross-section (fcs-mm2) as output parameter were taken. When the results obtained from ANN and experimental data are compared after making statistical analyses, the data of two groups are determined to be coherent and seen to have no meaning difference between them. As a result, it is seen that ANN can be safely used in determining cross sections of wooden materials after fire and it prevents many disadvantages.

Keywords: artificial neural network, final cross-section, fire retardant polishes, fire safety, wood resistance.

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Authors: Jose A. Diaz-Olivares, Nafise Mahdavian, Farhad Abtahi, Kaj Lindecrantz, Abdelakram Hafid, Fernando Seoane

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Despite new recommendations and guidelines for the evaluation of occupational risk assessments and their prevention, work-related musculoskeletal disorders are still one of the biggest causes of work activity disruption, productivity loss, sick leave and chronic work disability. It affects millions of workers throughout Europe, with a large-scale economic and social burden. These specific efforts have failed to produce significant results yet, probably due to the limited availability and high costs of occupational risk assessment at work, especially when the methods are complex, consume excessive resources or depend on self-evaluations and observations of poor accuracy. To overcome these limitations, a pervasive system of risk assessment tools in real time has been developed, which has the characteristics of a systematic approach, with good precision, usability and resource efficiency, essential to facilitate the prevention of musculoskeletal disorders in the long term. The system allows the combination of different wearable sensors, placed on different limbs, to be used for data collection and evaluation by a software solution, according to the needs and requirements in each individual working environment. This is done in a non-disruptive manner for both the occupational health expert and the workers. The creation of this solution allows us to attend different research activities that require, as an essential starting point, the recording of data with ergonomic value of very diverse origin, especially in real work environments. The software platform is here presented with a complimentary smart clothing system for data acquisition, comprised of a T-shirt containing inertial measurement units (IMU), a vest sensorized with textile electronics, a wireless electrocardiogram (ECG) and thoracic electrical bio-impedance (TEB) recorder and a glove sensorized with variable resistors, dependent on the angular position of the wrist. The collected data is processed in real-time through a mobile application software solution, implemented in commercially available Android-based smartphones and tablet platforms. Based on the collection of this information and its analysis, real-time risk assessment and feedback about postural improvement is possible, adapted to different contexts. The result is a tool which provides added value to ergonomists and occupational health agents, as in situ analysis of postural behavior can assist in a quantitative manner in the evaluation of work techniques and the occupational environment.

Keywords: ergonomics, mobile technologies, risk assessment, smart textiles

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Authors: Hugo Ribadeau Dumas

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In India, cities represent intense battlefields where patriarchal norms are simultaneously defied and reinforced. While Indian metropolises have witnessed numerous initiatives where women boldly claimed their right to the city, urban spaces still remain disproportionately unfriendly to female city-dwellers. As a result, the presence of strees (women, in Hindi) in the streets remains a socially and politically potent phenomenon. This paper explores how, in India, women engage with the city as compared to men. Borrowing analytical tools from urban geography, it uses Hindi cinema as a medium to map the extent to which activities, attitudes and experiences in urban spaces are highly gendered. The sample consists of 30 movies, both mainstream and independent, which were released between 2010 and 2020, were set in an urban environment and comprised at least one pivotal female character. The paper adopts a quantitative approach, consisting of the scrutiny of close to 3,000 minutes of footage, the labeling and time count of every scene, and the computation of regressions to identify statistical relationships between characters and the way they navigate the city. According to the analysis, female characters spend half less time in the public space than their male counterparts. When they do step out, women do it mostly for utilitarian reasons; inversely, in private spaces or in pseudo-public commercial places – like malls – they indulge in fun activities. For male characters, the pattern is the exact opposite: fun takes place in public and serious work in private. The characters’ attitudes in the streets are also greatly gendered: men spend a significant amount of time immobile, loitering, while women are usually on the move, displaying some sense of purpose. Likewise, body language and emotional expressiveness betray differentiated gender scripts: while women wander in the streets either smiling – in a charming role – or with a hostile face – in a defensive mode – men are more likely to adopt neutral facial expressions. These trends were observed across all movies, although some nuances were identified depending on the character's age group, social background, and city, highlighting that the urban experience is not the same for all women. The empirical pieces of evidence presented in this study are helpful to reflect on the meaning of public space in the context of contemporary Indian cities. The paper ends with a discussion on the link between universal access to public spaces and women's empowerment.

Keywords: cinema, Indian cities, public space, women empowerment

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Authors: Stephanie Watson, Fabrice Stephenson, Conrad Pilditch, Carolyn Lundquist

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Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments.

Keywords: Benthic ecology, spatial modelling, multi-scalar modelling, marine conservation.

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Authors: Freddy Patricio Moncayo-Matute, Pablo Gerardo Peña-Tapia, Vázquez-Silva Efrén, Paúl Bolívar Torres-Jara, Diana Patricia Moya-Loaiza, Gabriela Abad-Farfán

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This study describes a clinical case and the results on the application of additive manufacturing for the surgical planning in the removal of a cranial spheno-orbital meningioma. It is verified that the use of personalized anatomical models and cutting guides helps to manage the cranial anomalies approach. The application of additive manufacturing technology: Fused Deposition Modeling (FDM), as a low-cost alternative, enables the printing of the test anatomical model, which in turn favors the reduction of surgery time, as well the morbidity rate reduction too. And the printing of the personalized cutting guide, which constitutes a valuable aid to the surgeon in terms of improving the intervention precision and reducing the invasive effect during the craniotomy. As part of the results, post-surgical follow-up is included as an instrument to verify the patient's recovery and the validity of the procedure.

Keywords: surgical planning, additive manufacturing, rapid prototyping, fused deposition modeling, custom anatomical model

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Authors: Fatemeh Asadi Farsad, Sima Modirkhameneh

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The purpose of this study was to explore the pattern of Psychological Well-Being (PWB) of Iranian male and female EFL teachers during the pandemic. It was intended to see if such a drastic change in the context and mode of teaching affects teachers' PWB. Furthermore, the possible difference between the six elements of PWB of Iranian EFL male vs. female teachers during the pandemic was investigated. The other purpose was to find out the EFL teachers’ perceptions of any modifications, and factors leading to such modifications in their PWB during pandemic. For the purpose of this investigation, a total of 81 EFL teachers (59 female, 22 male) with an age range of 25 to 35 were conveniently sampled from different cities in Iran. Ryff’s PWB questionnaire was sent to participant teachers through online platforms to elicit data on their PWB. As for their perceptions on the possible modifications and the factors involved in PWB during pandemic, a set of semi-structured interviews were run among both sample groups. The findings revealed that male EFL teachers had the highest mean on personal growth, followed by purpose of life, and self-acceptance and the lowest mean on environmental mastery. With a slightly similar pattern, female EFL teachers had the highest mean on personal growth, followed by purpose in life, and positive relationship with others with the lowest mean on environmental mastery. However, no significant difference was observed between the male and female groups’ overall means on elements of PWB. Additionally, participants perceived that their anxiety level in online classes altered due to factors like (1) Computer literacy skills, (2) Lack of social communications and interactions with colleagues and students, (3) Online class management, (4) Overwhelming workloads, and (5) Time management. The study ends with further suggestions as regards effective online teaching preparation considering teachers PWB, especially at severe situations such as covid-19 pandemic. The findings offer to determine the reformations of educational policies concerning enhancing EFL teachers’ PWB through computer literacy courses and stress management courses. It is also suggested that to proactively support teachers’ mental health, it is necessary to provide them with advisors and psychologists if possible for free. Limitations: One limitation is the small number of participants (81), suggesting that future replications should include more participants for reliable findings. Another limitation is the gender imbalance, which future studies should address to yield better outcomes. Furthermore, Limited data gathering tools suggest using observations, diaries, and narratives for more insights in future studies. The study focused on one model of PWB, calling for further research on other models in the literature. Considering the wide effect of the COVID-19 pandemic, future studies should consider additional variables (e.g., teaching experience, age, income) to understand Iranian EFL teachers’ vulnerabilities and strengths better.

Keywords: online teaching, psychological well-being, female and male EFL teachers, pandemic

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Authors: Laura Niño Cáceres, Daisy Yoo, Caroline Hummels

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COVID-19 accelerated digital healthcare technology usage in many countries, such as Colombia, whose digital healthcare vision and projects are proof of this. However, with a significant cultural indigenous and Afro-Colombian heritage, only some parts of the country are willing to follow the proposed digital Western approach to health. Our paper presents the national healthcare system’s digital narrative, which we contrast with the micro-narrative of an Afro-Colombian ethnomedicine unit in Bogota called Kilombo Yumma. This ethnomedical unit is building its mobile app to safeguard and represent its ancestral medicine practices in local and national healthcare information systems. Kilombo Yumma is keen on promoting their beliefs and practices, which have been passed on through oral traditions and currently exist in the hands of a few older women. We unraveled their ambition, core beliefs, and practices through asset-based design. These assets outlined pluriversal and decolonizing forms of digital healthcare to increase social justice and connect Western and ancestral medicine digital opportunities through HCI.

Keywords: asset-based design, mobile app, decolonizing HCI, Afro-Colombian ancestral medicine

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Authors: Zhijun Ren, Zhang Lin, Zhao Ye, Zuo Xiangyu, Mei Dongxing

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As a pretreatment process of ballast water treatment, the performance of high gradient magnetic separation (HGMS) technology for the removal of particulates and microorganisms was studied. The results showed that HGMS process could effectively remove suspended particles larger than 5 µm and had ability to resist impact load. Microorganism could also be effectively removed by HGMS process, and the removal effect increased with increasing magnetic field strength. The maximum removal rates for Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) were 4016.1% and 9675.3% higher, respectively, than without the magnetic field. In addition, the superoxide dismutase (SOD) activity of the microbes decreased by 32.2% when the magnetic field strength was 15.4 mT for 72 min. The microstructure of the stainless steel wool was investigated, and the results showed that particle removal by HGMS has common function by the magnetic force of the high-strength, high-gradient magnetic field on weakly magnetic particles in the water, and on the stainless steel wool.

Keywords: HGMS, particulates, superoxide dismutase (SOD) activity, steel wool magnetic medium

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Authors: Amir Andria Gad Shehata

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenanceneural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Mareen Brosterhaus, Antje Hammer, Steffen Kalina, Stefan Grau, Anjali A. Roeth, Hany Ashmawy, Thomas Gross, Marcel Binnebosel, Wolfram T. Knoefel, Tanja Manser

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Background: The identification of critical incidents in hospitals is an essential component of improving patient safety. To date, various methods have been used to measure and characterize such critical incidents. These methods are often viewed by physicians and nurses as external quality assurance, and this creates obstacles to the reporting events and the implementation of recommendations in practice. One way to overcome this problem is to use tools that directly involve staff in measuring indicators of quality and safety of care in the department. One such instrument is the global trigger tool (GTT), which helps physicians and nurses identify adverse events by systematically reviewing randomly selected patient records. Based on so-called ‘triggers’ (warning signals), indications of adverse events can be given. While the tool is already used internationally, its implementation in German hospitals has been very limited. Objectives: This study aimed to assess the feasibility and potential of the global trigger tool for identifying adverse events in German hospitals. Methods: A total of 120 patient records were randomly selected from two surgical, and one neurosurgery, departments of three university hospitals in Germany over a period of two months per department between January and July, 2017. The records were reviewed using an adaptation of the German version of the Institute for Healthcare Improvement Global Trigger Tool to identify triggers and adverse event rates per 1000 patient days and per 100 admissions. The severity of adverse events was classified using the National Coordinating Council for Medication Error Reporting and Prevention. Results: A total of 53 adverse events were detected in the three departments. This corresponded to adverse event rates of 25.5-72.1 per 1000 patient-days and from 25.0 to 60.0 per 100 admissions across the three departments. 98.1% of identified adverse events were associated with non-permanent harm without (Category E–71.7%) or with (Category F–26.4%) the need for prolonged hospitalization. One adverse event (1.9%) was associated with potentially permanent harm to the patient. We also identified practical challenges in the implementation of the tool, such as the need for adaptation of the global trigger tool to the respective department. Conclusions: The global trigger tool is feasible and an effective instrument for quality measurement when adapted to the departmental specifics. Based on our experience, we recommend a continuous use of the tool thereby directly involving clinicians in quality improvement.

Keywords: adverse events, global trigger tool, patient safety, record review

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Authors: Darius Singh

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This study adopts a research, develop, and deploy methodology to create a state-of-the-art forest preschool environment using technology and the Gaia (Earth) Education Philosophy as design support. The new philosophy adopts an ancient Greek terminology, “Gaia,” meaning “Mother Earth”, and it take its principle to model everything with the oldest living and breathing entity that it know – Earth. This includes using nature and biomimicry-based principles in building design, environments, curricula, teaching, learning, values and outcomes for children. The study highlights the potential effectiveness of the Gaia (Earth) Education Philosophy as a means of designing Earth-inspired environments for children’s learning. The discuss the strengths of biomimicry-based design principles and propose a curriculum that emphasizes natural outcomes for early childhood learning. Theoretical implications of the study are that the Gaia (Earth) Education Philosophy could serve as a strong foundation for educating young learners.it present a unique approach that promotes connections with Earth-principles and lessons that can contribute to the development of social and environmental consciousness among children and help educate generations to come into a stable and balanced future.

Keywords: earth science, nature education, sustainability, gaia, forest school, nature, inspirational teaching and learning

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Authors: C. C. Liang

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MSN used to be the most popular application for communicating among social networks, but Facebook chat is now the most popular. Facebook and MSN have similar characteristics, including usefulness, ease-of-use, and a similar function, which is the exchanging of information with friends. Facebook outperforms MSN in both of these areas. However, the adoption of Facebook and abandonment of MSN have occurred for other reasons. Functions can be improved, but users’ willingness to use does not just depend on functionality. Flow status has been established to be crucial to users’ adoption of cyber applications and to affects users’ adoption of software applications. If users experience flow in using software application, they will enjoy using it frequently, and even change their preferred application from an old to this new one. However, no investigation has examined choice behavior related to switching from Facebook to MSN based on a consideration of flow experiences and functions. This investigation discusses the flow experiences and functions of social-networking applications. Flow experience is found to affect perceived ease of use and perceived usefulness; perceived ease of use influences information ex-change with friends, and perceived usefulness; information exchange influences perceived usefulness, but information exchange has no effect on flow experience.

Keywords: consumer behavior, social media, technology acceptance model, flow experience

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Authors: Nazma Sultana

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Social media has become an important part of our daily life. It has a significance influences on the people who use them in their daily life frequently. The number of people using social network sites has been increasing continuously. For this frequent utilization has started to affect our social life. This study examine whether the use of social network sites affects the psychosocial interaction between children and their parents. At first parents introduce their children to the internet and different type of device in their early childhood. Many parents use device for feeding their children by watching rhyme or cartoon. As a result children are habituate with it. In Bangladesh 70% people are heavy internet users. About 23 percent of them spend more than five hours on the social networking sites a day. Media are increasing pervasive in the lives of children-roughly the average child today spends nearly about 45 hours per week with media, compared with 17 hours with parents and 30 hours in school. According to a social learning theory, children & adolescents learn by observing & imitating what they see on screen particularly when these behaviors are realistic or are rewarded. The influence of the media on the psychosocial development of children is profound. Thus it is important for parents to provide guidance on age-appropriate use of all media, including television, radio, music, video games and the internet.

Keywords: social media, psychosocial, Technology, Parent, Social Relationship, Adolescents, Teenage, Youth

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Authors: Loreta Axhami

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Media literacy in the digital age emerges not only as a set of skills to generate true knowledge and information but also as a pedagogy methodology, as a kind of educational philosophy. In addition to such innovations as information integration and communication technologies, media infrastructures, and web usage in the educational system, media literacy enables the change in the learning methods, pedagogy, teaching programs, and school curriculum itself. In this framework, this study focuses on ICT's impact on teaching and learning methods and the degree they are reflected in the Albanian education system. The study is based on a combination of quantitative and qualitative methods of scientific research. Referring to the study findings, it results that student’s limited access to the internet in school, focus on the hardcopy textbooks and the role of the teacher as the only or main source of knowledge and information are some of the main factors contributing to the implementation of authoritarian pedagogical methods in the Albanian education system. In these circumstances, the implementation of media literacy is recommended as an apt educational process for the 21st century, which requires a reconceptualization of textbooks as well as the application of modern teaching and learning methods by integrating information and communication technologies.

Keywords: authoritarian pedagogic model, education system, ICT, media literacy

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Authors: Mourad Makhlouf Sabrina Bouriche, Zoubir Benmaamar, Didier Villemin

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Background: The increasing presence of electromagnetic interference (EMI) requires the development of effective protection solutions. Intelligent textiles offer a promising approach due to their wear ability and the possibility of integration into everyday clothing. In this study, the use of graphene and polyaniline for EMI shielding on cotton fabrics was examined. Methods: In this study, the continuous dyeing of recycled graphite-derived graphene and polyaniline was examined. Bottom-reforming technology was adopted to improve adhesion and achieve uniform distribution of conductive material on the fiber surface. The effect of material weight ratio on fabric performance and X-band EMI shielding effectiveness (SE) was evaluated. Significant Findings: The dyed cotton fabrics incorporating graphene, polyaniline, and their combination exhibited improved conductivity. Notably, these fabrics achieved EMI SE values ranging from 9 to 16 dB within the X-band frequency range (8-9 GHz). These findings demonstrate the potential of this approach for developing intelligent textiles with effective EMI shielding capabilities. Additionally, the utilization of recycled materials contributes to a more sustainable shielding solution.

Keywords: Intelligent textiles, graphene, polyaniline, electromagnetic shielding, conductivity, recycling

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Authors: Josephine Wanja Nyatia

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The relationship between vegetation and soil erosion deserves attention due to its scientific importance and practical applications. A great deal of information is available about the mechanisms and benefits of vegetation in the control of soil erosion, but the effects of soil erosion on vegetation development and succession is poorly documented. Research shows that soil erosion is the most important driving force for the degradation of upland and mountain ecosystems. Soil erosion interferes with the process of plant community development and vegetation succession, commencing with seed formation and impacting throughout the whole growth phase and affecting seed availability, dispersal, germination and establishment, plant community structure and spatial distribution. There have been almost no studies on the effects of soil erosion on seed development and availability, of surface flows on seed movement and redistribution, and their influences on soil seed bank and on vegetation establishment and distribution. However, these effects may be the main cause of low vegetation cover in regions of high soil erosion activity, and these issues need to be investigated. Moreover, soil erosion is not only a negative influence on vegetation succession and restoration but also a driving force of plant adaptation and evolution. Consequently, we need to study the effects of soil erosion on ecological processes and on development and regulation of vegetation succession from the points of view of pedology and vegetation, plant and seed ecology, and to establish an integrated theory and technology for deriving practical solutions to soil erosion problems

Keywords: soil erosion, vegetation, development, seed availability

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Authors: Rahimullah Habibzai

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To achieve a sustainable concrete industry, reduce exploitation of the natural aggregate resources, and mitigate waste concrete environmental burden, one way is to use recycled concrete aggregate. The utilization of low-quality fine aggregate inclusively recycled concrete sand that is produced from crushing waste concrete recently has become a popular and challenging topic among researchers nowadays. This study provides a scientific base for promoting the application of concrete waste as fine aggregate in producing concrete by conducting a comprehensive laboratory program. The mechanical properties of mortar made from recycled concrete fine aggregate (RCFA), that is produced by pulse power crushing concrete waste are satisfactory and capable of being utilized in the construction industry. A better treatment of RCFA particles and enhancing its quality will make it possible to be utilized in producing structural concrete. Pulse power discharge technology is proposed in this research to produce RCFA, which is a more effective and promising technique compared to other recycling methods to generate medium to high-quality recycled concrete fine aggregate with a reduced amount of powder, mitigate the environmental burden, and save more space.

Keywords: construction and demolition waste, concrete waste recycle fine aggregate, pulse power discharge

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Authors: Jingyuan Hu, Zhandong Liu

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CRISPR genome editing technology has transformed molecular biology by accurately targeting and altering an organism’s DNA. Despite the state-of-art precision of CRISPR genome editing, the imprecise mutation outcome and off-target effects present considerable risk, potentially leading to unintended genetic changes. Targeted deep sequencing, combined with bioinformatics sequence alignment, can detect such unwanted mutations. Nevertheless, the classical method, Needleman-Wunsch (NW) algorithm may produce false alignment outcomes, resulting in inaccurate mutation identification. The key to precisely identifying CRISPR-induced mutations lies in determining optimal parameters for the sequence alignment algorithm. Hidden Markov models (HMM) are ideally suited for this task, offering flexibility across CRISPR systems by leveraging forward-backward algorithms for parameter estimation. In this study, we introduce CRISPR-HMM, a statistical software to precisely call CRISPR-induced mutations. We demonstrate that the software significantly improves precision in identifying CRISPR-induced mutations compared to NW-based alignment, thereby enhancing the overall understanding of the CRISPR gene-editing process.

Keywords: CRISPR, HMM, sequence alignment, gene editing

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Authors: Amina Naidja, Zedira Khammar, Ines Soltani

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This study investigates the impact of urban development on the urban heat island (UHI) effect in the semi-arid Aures region of Algeria, integrating remote sensing data with statistical analysis and community surveys to examine the interconnected environmental and social dynamics. Using Landsat 8 satellite imagery, temporal variations in the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and land use/land cover (LULC) changes are analyzed to understand patterns of urbanization and environmental transformation. These environmental metrics are correlated with land surface temperature (LST) data derived from remote sensing to quantify the UHI effect. To incorporate the social dimension, a structured questionnaire survey is conducted among residents in selected urban areas. The survey assesses community perceptions of urban heat, its impacts on daily life, health concerns, and coping strategies. Statistical analysis is employed to analyze survey responses, identifying correlations between demographic factors, socioeconomic status, and perceived heat stress. Preliminary findings reveal significant correlations between built-up areas (NDBI) and higher LST, indicating the contribution of urbanization to local warming. Conversely, areas with higher vegetation cover (NDVI) exhibit lower LST, highlighting the cooling effect of green spaces. Social survey results provide insights into how UHI affects different demographic groups, with vulnerable populations experiencing greater heat-related challenges. By integrating remote sensing analysis with statistical modeling and community surveys, this study offers a comprehensive understanding of the environmental and social implications of urban development in semi-arid climates. The findings contribute to evidence-based urban planning strategies that prioritize environmental sustainability and social well-being. Future research should focus on policy recommendations and community engagement initiatives to mitigate UHI impacts and promote climate-resilient urban development.

Keywords: urban heat island, remote sensing, social analysis, NDVI, NDBI, LST, community perception

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

Procedia PDF Downloads 111

Authors: Rohith J. K., Amir Nazemi, Abbas S. Milani

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In Paralympic sports, athletes often depend on some form of equipment to enable competitive sporting, where most of this equipment would only allow passive physiological supports and discrete physiological measurements. Active feedback physiological support and continuous detection of performance indicators, without time or space constraints, would be beneficial in more effective training and performance measures of Paralympic athletes. Moreover, occasionally the athletes suffer from fatigue and muscular stains due to improper monitoring systems. The latter challenges can be overcome by using Smart Composites technology when manufacturing, e.g., knee brace and other sports wearables utilities, where the sensors can be fused together into the fabric and an assisted system actively support the athlete. This paper shows how different sensing functionality may be created by intrinsic and extrinsic modifications onto different types of composite fabrics, depending on the level of integration and the employed functional elements. Results demonstrate that fabric sensors can be well-tailored to measure muscular strain and be used in the fabrication of a smart knee brace as a sample potential application. Materials, connectors, fabric circuits, interconnects, encapsulation and fabrication methods associated with such smart fabric technologies prove to be customizable and versatile.

Keywords: smart composites, sensors, smart fabrics, knee brace

Procedia PDF Downloads 167

Authors: Shrey Sharma, Gunjan Kumar Verma

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High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used for data acquisition, processing and inversion of electrical resistivity data based on compilation of academic literature. In recent times there had been a significant evolution in field survey designs and data inversion techniques for the resistivity method. In general 2-D inversion for resistivity data is carried out using the linearized least-square method with the local optimization technique .Multi-electrode and multi-channel systems have made it possible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structures that were not possible with traditional 1-D surveys. 3-D surveys play an increasingly important role in very complex areas where 2-D models suffer from artifacts due to off-line structures. Continued developments in computation technology, as well as fast data inversion techniques and software, have made it possible to use optimization techniques to obtain model parameters to a higher accuracy. A brief discussion on the limitations of the electrical resistivity method has also been presented.

Keywords: inversion, limitations, optimization, resistivity

Procedia PDF Downloads 346

Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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Visible light communication is an emerging technology for almost a decade now; there is a growing need for VLC systems to overcome the challenges faced by radio frequency RF communication systems. With the advancement in the development of solid-state sources, in the future would replace incandescent and fluorescent light sources. These solid-state devices are not only to be used for illumination but can also be employed for communication and navigational purposes. The replacement of conventional illumination sources with highly efficient light-emitting diodes (LED's) (generally white light) will reduce energy consumption as well as environmental pollution. White LEDs dissipate very less power as compared to conventional light sources. The use of LED's is not only beneficial in terms of power consumption, but it also has an intrinsic capability for indoor wireless communication as compared to indoor RF communication. It is considerably low in cost to operate than the RF systems such as Wi-Fi routers, allows convenient means of reusing the bandwidth, and there is a huge potential for high data rate transmissions with enhanced data security. This paper provides an overview of some of the current challenges with VLC and proposes a possible solution to deal with these challenges; it also examines some joint protocols to optimize the joint illumination and communication functionality.

Keywords: visible light communication, line of sight, root mean square delay spread, light emitting diodes

Procedia PDF Downloads 56

Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

Procedia PDF Downloads 25

Authors: Fetah Eren, Ismail Sahin, Ismail Celik, Ahmet Oguz Akturk

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The aim of this study is to discover secondary school students’ perceptions related to information technologies and the connections between concepts in their cognitive structures. A word association test consisting of six concepts related to information technologies is used to collect data from 244 secondary school students. Concept maps that present students’ cognitive structures are drawn with the help of frequency data. Data are analyzed and interpreted according to the connections obtained as a result of the concept maps. It is determined students associate most with these concepts—computer, Internet, and communication of the given concepts, and associate least with these concepts—computer-assisted education and information technologies. These results show the concepts, Internet, communication, and computer, are an important part of students’ cognitive structures. In addition, students mostly answer computer, phone, game, Internet and Facebook as the key concepts. These answers show students regard information technologies as a means for entertainment and free time activity, not as a means for education.

Keywords: word association test, cognitive structure, information technology, secondary school

Procedia PDF Downloads 397

Authors: Fatemeh Deldarabdolmaleki, Mohamad Fakri Zaky Bin Ja'afar

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Having a daylit space together with view results in a pleasant and productive environment for office employees. A daylit space is a space which utilizes daylight as a basic source of illumination to fulfill user’s visual demands and minimizes the electric energy consumption. Malaysian weather is hot and humid all over the year because of its location in the equatorial belt. however, because most of the commercial buildings in Malaysia are air-conditioned, huge glass windows are normally installed in order to keep the physical and visual relation between inside and outside. As a result of climatic situation and mentioned new trend, an ordinary office has huge heat gain, glare, and discomfort for occupants. Balancing occupant’s comfort and energy conservation in a tropical climate is a real challenge. This study concentrates on evaluating a venetian blind system using per pixel analyzing tools based on the suggested cut-out metrics by the literature. Workplace area in a private office room has been selected as a case study. Eight-day measurement experiment was conducted to investigate the effect of different venetian blind angles in an office area under daylight conditions in Serdang, Malaysia. The study goal was to explore daylight comfort of a commercially available venetian blind system, its’ daylight sufficiency and excess (8:00 AM to 5 PM) as well as Glare examination. Recently developed software, analyzing High Dynamic Range Images (HDRI captured by CCD camera), such as radiance based Evalglare and hdrscope help to investigate luminance-based metrics. The main key factors are illuminance and luminance levels, mean and maximum luminance, daylight glare probability (DGP) and luminance ratio of the selected mask regions. The findings show that in most cases, morning session needs artificial lighting in order to achieve daylight comfort. However, in some conditions (e.g. 10° and 40° slat angles) in the second half of day the workplane illuminance level exceeds the maximum of 2000 lx. Generally, a rising trend is discovered toward mean window luminance and the most unpleasant cases occur after 2 P.M. Considering the luminance criteria rating, the uncomfortable conditions occur in the afternoon session. Surprisingly in no blind condition, extreme case of window/task ratio is not common. Studying the daylight glare probability, there is not any DGP value higher than 0.35 in this experiment.

Keywords: daylighting, energy simulation, office environment, Venetian blind

Procedia PDF Downloads 241