Search results for: state of learning

Authors: Hsin-Ping Huang, Shyankay Jou

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Resistive switching of aluminum nitride (AlNx) thin film was demonstrated in a TaN/AlNx/TiN memory cell that was prepared by sputter deposition techniques. The memory cell showed bipolar switching of resistance between +3.5 V and –3.5 V. The resistance ratio of high resistance state (HRS) to low resistance state (HRS), RHRS/RLRS, was about 2 over 100 cycles of endurance test. Both the LRS and HRS of the memory cell exhibited ohmic conduction at low voltages and Poole-Frenkel emission at high voltages. The electrical conduction in the TaN/AlNx/TiN memory cell was possibly attributed to the interactions between charges and defects in the AlNx film.

Keywords: aluminum nitride, nonvolatile memory, resistive switching, thin films

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Authors: Pedro Martinez-Santos, Víctor Gómez-Escalonilla, Silvia Díaz-Alcaide, Esperanza Montero, Miguel Martín-Loeches

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Groundwater monitoring networks are critical in evaluating the vulnerability of groundwater resources to depletion and contamination, both in space and time. Groundwater monitoring networks typically grow over decades, often in organic fashion, with relatively little overall planning. The groundwater monitoring networks in the Madrid area, Spain, were reviewed for the purpose of identifying gaps and opportunities for improvement. Spatial analysis reveals the presence of various monitoring networks belonging to different institutions, with several hundred observation wells in an area of approximately 4000 km2. This represents several thousand individual data entries, some going back to the early 1970s. Major issues included overlap between the networks, unknown screen depth/vertical distribution for many observation boreholes, uneven time series, uneven monitored species, and potentially suboptimal locations. Results also reveal there is sufficient information to carry out a spatial and temporal analysis of groundwater vulnerability based on machine learning applications. These can contribute to improve the overall planning of monitoring networks’ expansion into the future.

Keywords: groundwater monitoring, observation networks, machine learning, madrid

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Authors: Cynthia Adlerstein

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Physical learning environment (PLE) has been considered, after family and educators, as the third teacher. There have been conflicting and converging viewpoints on the role of the physical dimensions of places to learn, in facilitating educational innovation and quality. Despite the different approaches, PLE has been widely recognized as a key factor in the quality of the learning experience , and in the levels of learning achievement in ECE . The conceptual frameworks of the field assume that PLE consists of a complex web of factors that shape the overall conditions for learning, and that much more interdisciplinary and complementary methodologies of research and development are required. Although the relevance of PLE attracts a broad international consensus, in Chile it remains under-researched and weakly regulated by public policy. Gaining deeper contextual understanding and more thoughtfully-designed recommendations require the use of innovative assessment tools that cross cultural and disciplinary boundaries to produce new hybrid approaches and improvements. When considering a PLE-based change process for ECE improvement, a central question is what dimensions, variables and indicators could allow a comprehensive assessment of PLE in Chilean kindergartens? Based on a grounded theory social justice inquiry, we adopted a mixed method design, that enabled a multivocal and interdisciplinary construction of data. By using in-depth interviews, discussion groups, questionnaires, and documental analysis, we elicited the PLE discourses of politicians, early childhood practitioners, experts in architectural design and ergonomics, ECE stakeholders, and 3 to 5 year olds. A constant comparison method enabled the construction of the dimensions, variables and indicators through which PLE assessment is possible. Subsequently, the instrument was applied in a sample of 125 early childhood classrooms, to test reliability (internal consistency) and validity (content and construct). As a result, an interdisciplinary and multivocal tool for assessing physical learning environments was constructed and validated, for Chilean kindergartens. The tool is structured upon 7 dimensions (wellbeing, flexible, empowerment, inclusiveness, symbolically meaningful, pedagogically intentioned, institutional management) 19 variables and 105 indicators that are assessed through observation and registration on a mobile app. The overall reliability of the instrument is .938 while the consistency of each dimension varies between .773 (inclusive) and .946 (symbolically meaningful). The validation process through expert opinion and factorial analysis (chi-square test) has shown that the dimensions of the assessment tool reflect the factors of physical learning environments. The constructed assessment tool for kindergartens highlights the significance of the physical environment in early childhood educational settings. The relevance of the instrument relies in its interdisciplinary approach to PLE and in its capability to guide innovative learning environments, based on educational habitability. Though further analysis are required for concurrent validation and standardization, the tool has been considered by practitioners and ECE stakeholders as an intuitive, accessible and remarkable instrument to arise awareness on PLE and on equitable distribution of learning opportunities.

Keywords: Chilean kindergartens, early childhood education, physical learning environment, third teacher

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Melody Mandevere, Roselyn Cheruiyot

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Through Corporate Social Responsibility (CSR), organizations contribute to a stable environment that leads to a predictable climate for investment and trade. Organizations are now deviating from traditional CSR, where it was believed that the only responsibility of an organization is to meet its shareholder's needs. Organizations and society now believe that an organization has many stakeholders that it must satisfy for it to be viable. The function of State-Owned Enterprises (SOEs) is not profit making but providing service and accomplishing public policy objectives. SOEs demand consideration in the current economic climate because they represent an important part of the economies of many countries. Given the importance and complex relationship of the stakeholders in SOE, the paper seeks to examine how full name first Zimra is implementing its CSR activities. SOE managers are responsible for CSR implementation and stakeholder engagement. ZIMRA is one of the parastatals that plays a crucial role in the Zimbabwean economy. It is, therefore, important to understand how Zimra is implementing CSR. Qualitative research was used for the research. Interviews were contacted with Zimra managers to understand how they are implementing CSR. Although Zimra managers understand the CSR concept, the organization does not have a CSR strategy that includes their stakeholders, which may have a negative impact on stakeholder perception and the organization's reputation. The funding of the CSR strategy is also not sustainable.

Keywords: corporate social responsibility, managers, stakeholders, state-owned enterprises

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Authors: Mariam Shalaby, Kayla Krause, Raisha Ismail, Daniel George

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Introduction: The Pennsylvania State College of Medicine Refugee Initiative is a student-run organization that works with eleven Syrian and Congolese refugee families. Since 2016, it has used grant funding to make weekly produce purchases at a local market, provide tutoring services, and develop trusting relationships. This case study explains how the Refugee Initiative shifted focus to face new challenges due to the COVID-19 pandemic in 2020. Methodology: When refugees who had previously attained stability found themselves unable to pay the bills, the organization shifted focus from food security to direct assistance such as applying for unemployment compensation since many had recently lost jobs. When refugee families additionally struggled to access hygiene supplies, funding was redirected to purchase them. Funds were also raised from the community to provide financial relief from unpaid rent and bills. Findings: Systemic challenges were encountered in navigating federal/state unemployment and social welfare systems, and there was a conspicuous absence of affordable, language-accessible assistance that could help refugees. Finally, as struggling public schools failed to maintain adequate English as a Second Language (ESL) education, the group’s tutoring services were hindered by social distancing and inconsistent access to distance-learning platforms. Conclusion: Ultimately, the pandemic highlighted that a charity-based arrangement is helpful but not sustainable, and challenges persist for refugee families. Based on the Refugee Initiative's experiences over the past year of the COVID-19 pandemic, several needs must be addressed to aid refugee families at this time, including: increased access to affordable and language-accessible social services, educational resources, and simpler options for grant-based financial assistance. Interventions to increase these resources will aid refugee families in need in Central Pennsylvania and internationally

Keywords: COVID-19, health, pandemic, refugees

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Authors: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

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Authors: Gak-Gyu Kim, Won Il Jung

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According to a remarkable development of science and technology, function and role of the system of engineering fields has recently been diversified. The system has become increasingly more complex and precise, and thus, system designers intended to maximize reliability concentrate more effort at the design stage. This study deals with the reliability redundancy optimization problem (RROP) for k-out-of-n: G system configuration with cold standby and warm standby components. This paper further intends to present the optimal mathematical model through which the following three elements of (i) multiple components choices, (ii) redundant components quantity and (iii) the choice of redundancy strategies may be combined in order to maximize the reliability of the system. Therefore, we focus on the following three issues. First, we consider RROP that there exists warm standby state as well as cold standby state of the component. Second, as eliminating an approximation approach of the previous RROP studies, we construct a precise model for system reliability. Third, given transition time when the state of components changes, we present not simply a workable solution but the advanced method. For the wide applicability of RROPs, moreover, we use absorbing continuous time Markov chain and matrix analytic methods in the suggested mathematical model.

Keywords: RROP, matrix analytic methods, k-out-of-n: G system, MTTF, absorbing continuous time Markov Chain

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Quiza Pearl Senilla, Hannah Mercado, Francis Angelo Erosa

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Underdog narratives not only provides viewers with models of determination and hard work but that inducing hope may increase the likelihood that viewers will pursue their own goals in life. Although it has been proven that underdog narratives not only create a positive motivational state to the viewers but can also induce hope, little attention has been given to know if this underdog narrative affect the health outcomes or the subjective well-being of the viewers and if their hopefulness mediates on it. To address this gap, using underdog narratives as a predictor and hope as mediator, this study determined the effect of underdog narratives to the subjective well-being of the respondents, the relationship of hope and subjective well-being and last is the mediating effect of hopefulness. This study is an experimental research that uses a between subject design. Purposeful random sampling was used wherein the respondents must meet the following criteria to be part of the study. One hundred and twenty (N=120) Local State University students were assigned to different treatment conditions— underdog narrative, comedy, nature scenes—and a no exposure control group. Results show that there is a minimal difference on the subjective well-being of the respondents when exposed to different treatment condition although it is not significant. A moderate positive correlation between hope and subjective well-being also reveals in this study. And last the result also shows that there is no mediating effect of hopefulness to the subjective well-being of the subjects through exposure to underdog narrative.

Keywords: hope, hope theory, subjective well-being, underdog narratives

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Authors: Gulnaz Erkomaishvili

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Introduction: The pandemic and the current economic crisis have created problems for entrepreneurship and, therefore for start-up entrepreneurs. The paper presents the challenges of start-up entrepreneurs in Georgia in the time of pandemic and the analysis of the state economic policy measures. Despite many problems, the study found that in 54.2% of start-ups surveyed under the pandemic, innovation opportunities were growing. It can be stated that the pandemic was a good opportunity to increase the innovative capacity of the enterprise. 52% of the surveyed start-up entrepreneurs managed to adapt to the current situation and increase the sale of their products/services through remote channels. As for the assessment of state support measures by start-up entrepreneurs, a large number of Georgian start-ups do not assess the measures implemented by the state positively. Methodology: The research process uses methods of analysis and synthesis, quantitative and qualitative, interview/survey, grouping, relative and average values, graphing, comparison, data analysis, and others. Main Findings: Studies have shown that for the start-up entrepreneurs, the main problem remains: inaccessible funding, workers' qualifications gap, inflation, taxes, regulation, political instability, inadequate provision of infrastructure, amount of taxes, and other factors. Conclusions: The state should take the following measures to support business start-ups: create an attractive environment for investment, availability of soft loans, creation of an insurance system, infrastructure development, increase the effectiveness of tax policy (simplicity of the tax system, clarity, optimal tax level ); promote export growth (develop strategy for opening up international markets, build up a broad marketing network, etc.).

Keywords: start-up entrepreneurs, startups, start-up entrepreneurs support programs, start-up entrepreneurs support economic policy

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Authors: Anvesh Gaddam, Amit Agrawal, Suhas Joshi, Mark Thompson

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An increase in surface area to volume ratio with a decrease in characteristic length scale, leads to a rapid increase in pressure drop across the microchannel. Texturing the microchannel surfaces reduce the effective surface area, thereby decreasing the pressured drop. Surface texturing introduces two wetting states: a metastable Cassie-Baxter state and stable Wenzel state. Predicting wetting transition in textured microchannels is essential for identifying optimal parameters leading to maximum drag reduction. Optical methods allow visualization only in confined areas, therefore, obtaining whole-field information on wetting transition is challenging. In this work, we propose a non-invasive method to capture wetting transitions in textured microchannels under flow conditions. To this end, we tracked the behavior of the Poiseuille number Po = f.Re, (with f the friction factor and Re the Reynolds number), for a range of flow rates (5 < Re < 50), and different wetting states were qualitatively demarcated by observing the inflection points in the f.Re curve. Microchannels with both longitudinal and transverse ribs with a fixed gas fraction (δ, a ratio of shear-free area to total area) and at a different confinement ratios (ε, a ratio of rib height to channel height) were fabricated. The measured pressure drop values for all the flow rates across the textured microchannels were converted into Poiseuille number. Transient behavior of the pressure drop across the textured microchannels revealed the collapse of liquid-gas interface into the gas cavities. Three wetting states were observed at ε = 0.65 for both longitudinal and transverse ribs, whereas, an early transition occurred at Re ~ 35 for longitudinal ribs at ε = 0.5, due to spontaneous flooding of the gas cavities as the liquid-gas interface ruptured at the inlet. In addition, the pressure drop in the Wenzel state was found to be less than the Cassie-Baxter state. Three-dimensional numerical simulations confirmed the initiation of the completely wetted Wenzel state in the textured microchannels. Furthermore, laser confocal microscopy was employed to identify the location of the liquid-gas interface in the Cassie-Baxter state. In conclusion, the present method can overcome the limitations posed by existing techniques, to conveniently capture wetting transition in textured microchannels.

Keywords: drag reduction, Poiseuille number, textured surfaces, wetting transition

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Authors: Aurélie Cassiers

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In this presentation, an overview is given of the freedom of states to legislate concerning abortion. Today, access to safe and legal abortion is still a hot topic in many countries in the world. Abortion policies try to strike a balance between women’s rights to self-determination and private life on the one hand, and the protection of the life of unborn children on the other. Each country has different religious, cultural and political views on abortion, and therefore specific legislations. However, citizens may submit a complaint at international courts when they find their national legislation too restrictive. The study is discussed of the development of the ECtHR, UNCHR, and IACHR case law, regarding the question of the ‘right to abort’ and indirectly of the protection of the unborn children. Each relevant case is analyzed to answer the following questions: Is the unborn child protected, and if so, how? Why does the woman want to abort and how is her interest or right protected? How is a fair balance reached between the different interests? Is the state completely free to write policies that restrict abortion? What are the factors to determine the margin of appreciation of the state? In conclusion, does this specific court recognize a right to abort, and if so, under which conditions? To conclude, this presentation shows that each court has its own perspective on and perception of abortion, and its own criteria to determine whether the state is complying with international norms regarding individual liberty and protection of the children.

Keywords: abortion, international courts, unborn children, women rights

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Authors: Tugce Duygu Koksal

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In recent years, given the fact that the acts of terrorism and the threat of the latter are taking place without any border and distinction, it has led the states to deal with the terrorism as a priority issue. More recently, as seen in different countries during state of emergency, the adoption of anti-terrorism measures motivated by the sole need of the prevention of terrorism targets directly the fundamental rights of individuals. Therefore, a contribution to the understanding of the value of the principle of legality is becoming more and more important nowadays. This paper aims to reflect the probable effects of the adoption of anti-terrorism measures regardless of the principle of legality, on the fundamental rights. In this respect, this paper will first discuss the margin of appreciation of the national authorities by countering terrorism, and then, the importance of the respect of the legality of the anti-terrorism measures will be examined in the light of actual examples. Indeed, one of the major findings of this study is the fact that the anti-terrorism laws and measures were taken in this framework must be subject to close scrutiny in democracies, which adopted the principle of the rule of law and respect human rights. Although the state's margin of appreciation in the field of counter-terrorism is broad, these measures which are based on the legitimate aim of a democracies’ legitimate right to protect itself against the activities of terrorist organizations should have the legal basis and be strictly required by the exigencies of the fight against terrorism. While combating terrorism, the legal basis shall only be achieved if the legal consequences of an individuals’ actions related to terrorism shall be clear and foreseeable by the individuals of a society. On the other hand, particularly during the state of emergency, the ambiguity of the law might be used to include a wide range of actions under acts of terrorism. This is becoming more dangerous where freedom of expression, freedom of the press, freedom of association and the right to information is in the substance of these actions. Disregarding the principle of legality is susceptible to create a chilling effect on the exercise of human rights, and therefore, the fight against terrorism can be transformed into a repressive regime on opponents. As a result, the efforts to counter terrorism of the national authorities irrespective of the principle of legality are susceptible to cause a transformation of the rule of law to a state of law which cannot be appreciated in a democratic society.

Keywords: anti-terrorism measures, chilling effect, predictability, the principle of legality, state of emergency

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Gezahegn Bekele Welldgiyorgise

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It is obvious that the economics, politics and social conditions of a country are determined by the quality and standard of its education. Indeed, education plays a vital role in changing the consciousness and awareness of society and transforming it on a large scale. Moreover, education contributes a lot to the advancement of science and technology, information and communication, and above all, it speeds up its progress in no time if it focuses mainly on the qualitative approach to education. Education brings about universal change and transformation and lightens mankind in all dimensions. It creates an educated, enlightened and brightened generation in society. The generation will be sharped, sharpened and well-oriented if it gets modern, sophisticated and standardized education in its field of study. The main goal of education is to produce well-qualified, well-trained and disciplined young offers in a given community. If the youth is well trained and well-mannered, he will certainly be enlightened, problem solvers and solution seekers, researchers, and innovators. In this respect, we have to provide the youth with modern education, a teaching-learning process led by active learning and a participatory approach with a new curriculum preparation for the age of children supported by modern facilities (ICT).In addition to that, the curriculum should have to give attention to mathematics and science lessons that include international experience in a comfortable school and classrooms. Therefore, the generation that will be created through such kinds of the guided education system will make the students active participants, self-confident, researchers and problem solvers, besides that result in changed life standards and a developed country. Similarly, our country, Ethiopia, has aimed to get such change in youth (generation) through modern education, designing a new educational policy and curriculum which was implemented for many years, although the goal of education has not reached the required level. To get the main idea of the article, I should have answered the question of why our country's educational goal had not reached the desired level because it is necessary to lay the foundation for research in finding out problems seen through students learning performance, the first task is selecting primary-school as a sample. Therefore, we selected “Dilla primary school (5-8)” which is a workplace for a teacher and gives me a chance to recognize students’ learning performance to recognize their learning grades (internal and external) and measure performance (achievement) of students easily’.

Keywords: curriculum, performance, innovation, learning

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Authors: Nune Ayvazyan

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Since the proclamation of monolingual foreign-language learning methods (the Berlitz Method in the early 20ᵗʰ century and the like), the dilemma has been to allow or not to allow learners’ mother tongue in the foreign-language learning process. The reason for not allowing learners’ mother tongue is reported to create a situation of immersion where students will only use the target language. It could be argued that this artificial monolingual situation is defective, mainly because there are very few real monolingual situations in the society. This is mainly due to the fact that societies are nowadays increasingly multilingual as plurilingual speakers are the norm rather than an exception. More recently, the use of learners’ mother tongue and translation has been put under the spotlight as valid foreign-language teaching tools. The logic dictates that if learners were permitted to use their mother tongue in the foreign-language learning process, that would not only be natural, but also would give them additional means of participation in class, which could eventually lead to learning. For example, when learners’ metalinguistic skills are poor in the target language, a question they might have could be asked in their mother tongue. Otherwise, that question might be left unasked. Attempts at empirically testing the role of translation as a didactic tool in foreign-language teaching are still very scant. In order to fill this void, this study looks into the interaction patterns between students in two kinds of English-learning classes: one with translation and the other in English only (immersion). The experiment was carried out with 61 students enrolled in a second-year university subject in English grammar in Spain. All the students underwent the two treatments, classes with translation and in English only, in order to see how they interacted under the different conditions. The analysis centered on four categories of interaction: teacher talk, teacher-initiated student interaction, student-initiated student-to-teacher interaction, and student-to-student interaction. Also, pre-experiment and post-experiment questionnaires and individual interviews gathered information about the students’ attitudes to translation. The findings show that translation elicited more student-initiated interaction than did the English-only classes, while the difference in teacher-initiated interactional turns was not statistically significant. Also, student-initiated participation was higher in comprehension-based activities (into L1) as opposed to production-based activities (into L2). As evidenced by the questionnaires, the students’ attitudes to translation were initially positive and mainly did not vary as a result of the experiment.

Keywords: foreign language, learning, mother tongue, translation

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Authors: Godiva Kwan, Winton Au, Fanny Cheung

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Authenticity, being true to oneself and acting in congruence with one’s values and beliefs, is a basic human strength, and is instrumental to understanding well-being. While dispositional authenticity was found to be associated with positive affect and subjective well-being, others have demonstrated that individuals assumed different levels of authenticity when they took up different social roles, suggesting that state authenticity can be an alternative mechanism. This study examined the relation between workplace authenticity and job satisfaction. We hypothesize that state authenticity at work will be predicted by psychological safety climate (organizational climate where employees feel safe to speak up without being embarrassed or rejected). Employees are expected to experience higher subjective well-being and job satisfaction as a result of being authentic at work. Survey results provided support to the hypotheses. Psychological safety climate enhanced employees’ authenticity state at work, which in turn improved well-being and job satisfaction. In conclusion, we found that employees become more authentic at work in an organizational climate where they feel safe to express themselves, leading to a higher job satisfaction and well-being. The current study contributes to the understanding of underlying mechanisms behind experiencing authenticity at work among employees in Hong Kong. Our findings are expected to provide insights and to raise organizations’ awareness of creating an open and trustful culture in order to enhance job satisfaction of employees through encouraging them to “be themselves”.

Keywords: authenticity, job satisfaction, psychological safety climate, organizational climate

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Authors: Ayad Al-Mahturi, Herman Wahid

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This paper presents an optimal state feedback controller based on Linear Quadratic Regulator (LQR) for a two-rotor aero-dynamical system (TRAS). TRAS is a highly nonlinear multi-input multi-output (MIMO) system with two degrees of freedom and cross coupling. There are two parameters that define the behavior of LQR controller: state weighting matrix and control weighting matrix. The two parameters influence the performance of LQR. Particle Swarm Optimization (PSO) is proposed to optimally tune weighting matrices of LQR. The major concern of using LQR controller is to stabilize the TRAS by making the beam move quickly and accurately for tracking a trajectory or to reach a desired altitude. The simulation results were carried out in MATLAB/Simulink. The system is decoupled into two single-input single-output (SISO) systems. Comparing the performance of the optimized proportional, integral and derivative (PID) controller provided by INTECO, results depict that LQR controller gives a better performance in terms of both transient and steady state responses when PSO is performed.

Keywords: LQR controller, optimal control, particle swarm optimization (PSO), two rotor aero-dynamical system (TRAS)

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Authors: Zhepeng Rui, Zhenyu Gu, Caitilin de Bérigny

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In the current study, we designed an interactive meditation and brain painting application to cultivate users’ creativity, promote meditation, reduce stress, and improve cognition while attempting to learn a foreign language. User tests and data analyses were conducted on 42 male and 42 female participants to better understand sex-associated psychological and aesthetic differences. Our method utilized brain-computer interfaces to import meditation and attention data to create artwork in meditation-based applications. Female participants showed statistically significantly different language learning outcomes following three meditation paradigms. The art style of brain painting helped females with language memory. Our results suggest that the most ideal methods for promoting memory attention were meditation methods and brain painting exercises contributing to language learning, memory concentration promotion, and foreign word memorization. We conclude that a short period of meditation practice can help in learning a foreign language. These findings provide new insights into meditation, creative language education, brain-computer interface, and human-computer interactions.

Keywords: brain-computer interface, creative thinking, meditation, mental health

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Authors: Elham Fakhrpoor, Daryush Mohammadi Zanjirani, Maziyar Nojaba

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Business process reengineering is one of the most important strategies in banks in recent years that not only it increases customers’ satisfaction, but also it increases performance of banks. The purpose of elementary (initial) business process reengineering is reinforcing banks abilities to obtain new customers and making long-term relationships with existed customers and increasing customers’ satisfaction among service quality in global level. Banks specially the private ones are the main streams of state, because cash flow is necessary to survive a state. What guarantees survival and permanency of financial institutes’ activities is providing favorite, certain, and proper services. Capital market being small and state financial system being bank-oriented needs optimum usage from banks. According to this fact and role and importance of developing banking system, the present study tried to offer a constructed model using Lisrel and also spss software to evaluate effects of business process reengineering on performance of private banks. We have one min hypothesis and four sub-hypotheses. The main hypothesis says reengineering factors have positive effects on bank performances (balanced- scores card aspects). These hypotheses were tested by structural equations modeling.

Keywords: effect, business, reengineering, private bank

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Authors: Mamta M. Barapatre, V. N. Sahare

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Since from long time two wheeled vehicle self-balancing has always been a back-breaking task for both human and robots. Leaning a bicycle driving is long time process and goes through building knowledge base for parameter decision making while balancing robots. In order to create this machine learning phase with embedded system the proposed system is designed. The system proposed aims to construct a bicycle automaton, power-driven by an electric motor, which could balance by itself and move along a specific path. This path could be wavy with bumps and varying widths. The key aim was to construct a cycle which self-balances itself by controlling its handle. In order to take a turn, the mass was transferred to the center. In order to maintain the stability, the bicycle bot automatically turned the handle and a turn. Some problems were faced by the team which were Speed, Steering mechanism through mass- distribution (leaning), Center of mass location and gyroscopic effect of its wheel. The idea proposed have potential applications in automation of transportation system and is most efficient.

Keywords: gyroscope-flywheel, accelerometer, servomotor-controller, self stability concept

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Ode Kenneth Ogbu

Abstract:

This study was designed as a survey to investigate the incidence of use and abuse of drug as a social problem among the Nigeria youths in the secondary schools in urban centres of Benue state. 500 SS 3 and fresh secondary school graduates in remedial science class of Benue State University Makurdi with mean age of 16.8 were randomly sampled for the study. An instrument called drug use and abuse perception questionnaire (DAPQ) with a reliability coefficient of 74 were administered to the students. Only 337 copies of the questionnaire were properly completed and returned which reduced the sample size of 337. The data were subjected to factor analysis. X2 statistic and frequency distribution using split half method. The result of the analysis showed that: the DAPQ yield seven baseline factors responsible for drug use and abuse; there was appreciable evidence that the study subjects used drugs (42.1%); alcohol topped the list of the drugs consumed; most students use their pocket money to buy drugs; drugs were purchased from unconventional, hidden places and 13 out of the 20 items of DAPQ were perceived as significant factors in drug use and abuse. The paper recommends proper intervention of government, parents and NGO’S among students to reduce cases of drug abuse.

Keywords: drug abuse, psychology, psychiatry, students

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

Abstract:

This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: Hanan Abufares Elkhimry

Abstract:

Looking for more effective teaching and learning approaches, teaching instructors have been telling trainee teachers to decrease their talking time, but the problem is how best to do this. Doing classroom research, specifically in the area of teacher talking time (TTT), is worthwhile, as it could improve the quality of teaching languages, as the learners are the ones who should be practicing and using the language. This work hopes to ascertain if teachers consider this need in a way that provides the students with the opportunities to increase their production of language. This is a question that is worthwhile answering. As many researchers have found, TTT should be decreased to 30% of classroom talking time and STT should be increased up to 70%. Other researchers agree with this, but add that it should be with awareness of the quality of teacher talking time. Therefore, this study intends to investigate the balance between quantity and quality of teacher talking time in the EFL classroom. For this piece of research and in order to capture the amount of talking in a four classrooms. The amount of talking time was measured. A Checklist was used to assess the quality of the talking time In conclusion, In order to improve the quality of TTT, the results showed that teachers may use more or less than 30% of the classroom talking time and still produce a successful classroom learning experience. As well as, the important factors that can affect TTT is the English level of the students. This was clear in the classroom observations, where the highest TTT recorded was with the lowest English level group.

Keywords: teacher talking time TTT, learning experience, classroom research, effective teaching

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Authors: Vivian Uchechi Okpala

Abstract:

Background: Millions of workers die every year as a result of occupational hazards, accidents and injuries, which are as a result of non- compliance to the laws or legislations guiding the health, safety and welfare of workers in the industries. This and many more lead to the assessment of implementation of the health and safety contents of the Nigerian Factories Act (NFA) by small and medium scale industries in Anambra State. Objectives: The study is aimed at achieving the following specific objectives; to assess the extent of implementation of Part-II Health and Part -III Safety (General Provisions), implementation of Part II Health and Part -III Safety (General Provisions Nigerian Factories Acts based on the age of the industries, locations of the industries and level of education of the workers of the small and medium scale industries Methods: the research design that was used for this study was descriptive survey research design, Area of this study was Anambra state, The population for this study comprised 180 chairmen/presidents of union workers of manufacturing industries in Anambra State, The instrument used for this study was structured questionnaire titled ‘assessment of implementation of NFA health and safety contents by small and medium scale industries, results: From the analysis, the following findings were made: Results: The medium scale industries implemented the Part-II Health and Part III Safety (General provisions) better than the small scale industries in Anambra state, the age of the industries, location of the industries and the level of education of the workers in the industries significantly influenced the implementation of the Part III Safety (General Provisions) of NFA, the location of the industries significantly influenced the implementation of the Part II-Health (General Provisions) of NFA. Conclusion: there was generally a certain level of implementation of the factories Act, there is need for more improvement, strict inspection by the regulatory agencies. Implications of the study were highlighted and several suggestions for further studies were made. Based on the findings, several recommendations were made including that the Ministry of Labour and Productivity and the Ministry of Health should strengthen planned information, strict policies to sanction the offenders. Keywords: Occupational Health and Safety, Nigerian Factories Act

Keywords: occupational health and safety, Nigerian factories act, workers, welfare

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Authors: Muhammad Abdullahi Mago

Abstract:

Islamic Banking began in Nigeria last three years and the sector has shown the sign of bright future for the sector and the Nigerian economy, within this very short time it is important to know the perception of the customers particularly learned or educated individuals for immediate evaluation and adjustment. This study investigates into the perception, awareness and attitudes of the academicians in the most populous state/place in Nigeria with more than 90% muslims, and the results has shown a relatively low levels results in all the variables of the study.The study recommends aggressive marketing strategy for the Banks operating within the sector.

Keywords: Islamic Banking Products, Islamic Financial Products, academicians, Islamic finance industry, perception, awareness and attitude

Procedia PDF Downloads 445