Search results for: English learning strategies

Authors: M. Černe, I. Palčić, D. Anđelini, D. Cvitan, N. Major, M. Lukić, S. Goreta Ban, D. Ban, T. Rijavec, A. Lapanje

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Context: In the Mediterranean basin, large quantities of lignocellulosic by-products, such as olive pomace (OP), are generated during olive processing on an annual basis. Due to the phytotoxic nature of OP, composting is recommended for its stabilisation to produce the end-product safe for agricultural use. Research Aim: This study aims to evaluate the applicability of olive pomace compost (OPC) for use as a soil amendment by considering its physical and chemical characteristics and microbiological parameters. Methodology: The OPC samples were collected from the surface and depth layers of the compost pile after 8 months. The samples were analyzed for their C/N, pH, EC, total phenolic content, residual oils, and elemental content, as well as colloidal properties and microbial community structure. The specific analytical approaches used are detailed in the poster. Findings: The results showed that the pH of OPC ranged from 7.8 to 8.6, while the electrical conductivity was from 770 to 1608 mS/cm. The levels of nitrogen (N), phosphorus (P), and potassium (K) varied within the ranges of 1.5 to 27.2 g/kg d.w., 1.6 to 1.8 g/kg d.w., and 6.5 to 7.5 g/kg d.w., respectively. The contents of potentially toxic metals such as chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) were below the EU limits for soil improvers. The microbial structure follows the changes of the gradient from the outer to the innermost layer with relatively low amounts of DNA. The gradient nature shows that it is needed to develop better strategies for composting surpassing the conventional approach. However, the low amounts of total phenols and oil residues indicated efficient biodegradation during composting. The carbon-to-nitrogen ratio (C/N) within the range of 13 to 16 suggested that OPC can be used as a soil amendment. Overall, the study suggests that composting can be a promising strategy for environmentally-friendly OP recycling. Theoretical Importance: This study contributes to the understanding of the use of OPC as a soil amendment and its potential benefits in resource recycling and reducing environmental burdens. It also highlights the need for improved composting strategies to optimize its process. Data Collection and Analysis Procedures: The OPC samples were taken from the compost pile and charasterised for selected chemical, physical and microbial parameters. The specific analytical procedures utilized are described in detail in the poster. Question Addressed: This study addresses the question of whether composting can be optimized to improve the biodegradation of OP. Conclusion: The study concludes that OPC has the potential to be used as a soil amendment due to its favorable physical and chemical characteristics, low levels of potentially toxic metals, and efficient biodegradation during composting. However, the results also suggest the need for improved composting strategies to improve the quality of OPC.

Keywords: olive pomace compost, waste valorisation, agricultural use, soil amendment

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Authors: Morolake Josephine Adeagbo

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South Africa is one of those countries that openly rejected racism, and this is entrenched in its Bill of Rights. Despite the acceptance and incorporation of racial integration into the South Africa Constitution, the implementation within some sectors, most especially the educational sector, seems difficult. Recent occurrences of racism in some higher institutions of learning in South Africa are indications that racial integration / racial transformation is still farfetched in the country’s higher educational sector. It is against this background that this study was conducted to understand how gender and residential background influence racial integration in a South African university which was predominantly a white Afrikaner institution. Using a quantitative method to test the attitude of different categories of undergraduate students at the university, this study found that the factors- residential background and gender- used in measuring student’s attitude do not necessarily have a significant relationship towards racial integration. However, this study concludes with a call for more research with a range of other factors in order to better understand how racial integration can be promoted in South African institutions of higher learning.

Keywords: racial integration, gender, residential background, transformation

Procedia PDF Downloads 427

Authors: Mohini Mohamed, Nurul Huda Mas’od

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This study was aimed at designing and developing an assistive mathematical teaching aid (courseware) in helping dyslexic students in learning multiplication. Computers and multimedia interactive courseware has benefits students in terms of increase learner’s motivation and engage them to stay on task in classroom. Most disability student has short attention span thus with the advantage offered by multimedia interactive courseware allows them to retain the learning process for longer period as compared to traditional chalk and talk method. This study was conducted in a public school at a primary level with the help of three special education teachers and six dyslexic students as participants. Qualitative methodology using interview with special education teachers and observations in classes were conducted. The development of the multimedia interactive courseware in this study was divided to three processes which were analysis and design, development and evaluation. The courseware was evaluated by using User Acceptance Survey Form and interview. Feedbacks from teachers were used to alter, correct and develop the application for a better multimedia interactive courseware.

Keywords: disability students, dyslexia, mathematics teaching aid, multimedia interactive courseware

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

Procedia PDF Downloads 325

Authors: Abdul Haq

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The goal of this research is to better understand how wind loads affect bridges and develop strategies for designing bridges that are more stable and resistant to wind. The effect of wind on bridges is essential to their safety and functionality, especially in areas that are prone to high wind speeds or violent wind conditions. The study looks at the aerodynamic forces and vibrations caused by wind and how they affect bridge construction. Part of the research method involves first understanding the underlying ideas influencing wind flow near bridges. Computational fluid dynamics (CFD) simulations are used to model and forecast the aerodynamic behaviour of bridges under different wind conditions. These models incorporate several factors, such as wind directionality, wind speed, turbulence intensity, and the influence of nearby structures or topography. The results provide significant new insights into the loads and pressures that wind places on different bridge elements, such as decks, pylons, and connections. Following the determination of the wind loads, the structural response of bridges is assessed. By simulating their dynamic behavior under wind-induced forces, Finite Element Analysis (FEA) is used to model the bridge's component parts. This work contributes to the understanding of which areas are at risk of experiencing excessive stresses, vibrations, or oscillations due to wind excitations. Because the bridge has inherent modes and frequencies, the study considers both static and dynamic responses. Various strategies are examined to maximize the design of bridges to withstand wind. It is possible to alter the bridge's geometry, add aerodynamic components, add dampers or tuned mass dampers to lessen vibrations, and boost structural rigidity. Through an analysis of several design modifications and their effectiveness, the study aims to offer guidelines and recommendations for wind-resistant bridge design. In addition to the numerical simulations and analyses, there are experimental studies. In order to assess the computational models and validate the practicality of proposed design strategies, scaled bridge models are tested in a wind tunnel. These investigations help to improve numerical models and prediction precision by providing valuable information on wind-induced forces, pressures, and flow patterns. Using a combination of numerical models, actual testing, and long-term performance evaluation, the project aims to offer practical insights and recommendations for building wind-resistant bridges that are secure, long-lasting, and comfortable for users.

Keywords: wind effects, aerodynamic forces, computational fluid dynamics, finite element analysis

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Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

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Authors: Elizabeth Perry, Jylana Sheats

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Scholars posit that there is an overlap between the fields of Behavioral Economics (BE) and Behavior Science (BSci)—and that consideration of concepts from both may facilitate a greater understanding of health decision-making processes. For example, the ‘intention-action gap’ is one BE concept to explain sup-optimal decision-making. It is described as having knowledge that does not translate into behavior. Complementary best BSci practices may provide insights into behavioral determinants and relevant behavior change techniques (BCT). Within the context of BSci, this exploratory study aimed to apply a BE concept with demonstrated effectiveness in financial decision-making to a health behavior: influenza (flu) vaccine uptake. Adults aged >18 years were recruited on Amazon’s Mechanical Turk, a digital labor market where anonymous users perform simple tasks at low cost. Eligible participants were randomized into 2 groups, reviewed a scenario, and then completed a survey on the likelihood of receiving a flu shot. The ‘usual care’ group’s scenario included standard CDC guidance that supported the behavior. The ‘intervention’ group’s scenario included messaging about people who did not receive the flu shot. The framing was such that participants could learn from others’ (strangers) mistakes and the subsequent health consequences: ‘Last year, other people who didn’t get the vaccine were about twice as likely to get the flu, and a number of them were hospitalized or even died. Don’t risk it.’ Descriptive statistics and chi-square analyses were performed on the sample. There were 648 participants (usual care, n=326; int., n=322). Among racial/ethnic minorities (n=169; 57% aged < 40), the intervention group was 22% more likely to report that they were ‘extremely’ or ‘moderately’ likely to get the flu vaccine (p = 0.11). While not statistically significant, findings suggest that framing messages from the perspective of learning from the mistakes of unknown others coupled with the BCT ‘knowledge about the health consequences’ may help influence flu vaccine uptake among the study population. With the widely documented disparities in vaccine uptake, exploration of the complementary application of these concepts and strategies may be critical.

Keywords: public health, decision-making, vaccination, behavioral science

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Kunto Nurcahyoko

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The fundamental perception used in this study is that teaching and learning activities in Indonesian classroom have potentially generated individual’s sensitivity on cross-cultural understanding. This study aims at investigating Indonesian university students’ perception on cross-cultural understanding after doing Cultural Snapshot Project. The data was critically analyzed through multicultural ideology and diversity theories. The subjects were 30 EFL college students in one of colleges in Indonesia. Each student was assigned to capture a photo which depicted the existence of any cultural manifestation in their surrounding such as discrimination, prejudice and stereotype. Students were then requested asked to reflect on the picture by writing a short description on the picture and make an exhibition using their pictures. In the end of the project, students were instructed to fill in questionnaires to show their perception before and after the project. The result reveals that Cultural Snapshot Project has given the opportunity for the students to better realize cross-cultural understanding in their environment. In conclusion, the study shows that Cultural Snapshot Project has specifically enhanced students’ perception of multiculturalism in three major areas: cultural sensitivity and empathy, social tolerance, and understanding of diversity.

Keywords: cultural snapshot, cross-cultural understanding, students’ perception, multiculturalism

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Authors: M. Yazdani, J. F. Carragher

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Citrus gall wasp (CGW), Bruchophagus fellis (Hym: Eurytomidae), is an Australian native insect pest. CGW has now become a problem of national concern, threatening the viability of the entire Australian citrus industry. However, CGW appears to exhibit a preference for certain citrus species; growers report that grapefruit and lemons are most severely infested, with oranges and mandarins affected to a lesser extent. Given the specificity of the host plant-insect interactions, it is speculated that plant volatiles may play a significant role in host recognition. To address whether plant volatiles is involved in host plant preference by CGW we tested the behavioral response of CGW to plants in a wind tunnel. The result showed that CGW had significantly higher preference to grapefruit and lemon than other cultivars and the least preference was recorded to mandarin (Chi-square test, P<0.001). Because CGW exhibited a detectable choice further studies were undertaken to identify the components of the volatiles from each species. We trapped the volatile chemicals emitted by a 30 cm tip of each plant onto a solid Porapak matrix. Eluted extracts were then analysed by Gas Chromatography-Mass Spectrometry (GCMS) and the presumptive identity of the major compounds from each species inferred from the MS library. Although the same major compounds existed in all of the cultivars, the relative ratios of them differed between species. Next, we will validate the identity of the key volatiles using authentic standards and establish their ability to elicit olfactory responses in CGW in wind tunnel and field experiments. Identification of semiochemicals involved in host location by CGW is of interest not only from an ecological perspective but also for the development of novel pest control strategies.

Keywords: Citrus gall wasp, Bruchophagus fellis, volatiles, semiochemicals, IPM

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Authors: Dharmpal Singh

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The objective of this proposed project is to relocation and resettlement of carnivores tribal communities who are currently residing in the protected forest land in all over the world just like resettlement project of the carnivores tribal families of Mongia who at past were residing in Ranthambhore Tiger Reserve (RTR) and had caused excess damage of endangered species of wildlife including Tigers. At present several tribal communities are residing in the another national parks and they not only consuming the wild animals but also involved in illegal trading of vital organs, skin and bones with National and international traders. Tribal are ideally suited for the job because they are highly skilled game trackers and due to having had a definite source of income over the years, they easily drawn in to the illegal wildlife trade and slaughter of wild animals. Their income is increasing but wild animals are on the brink of extinction. For the conservation of flora and fauna the rehabilitation process should be thought out according to the RTR project (which not only totally change the quality of life of mongia tribal community but also increased the conopy cover of forest and grass due to reduced the biotic pressure on protected land of forest in Rajasthan state) with appropriate understanding of the sociology of the people involved, their culture, education standard and the need of different skills to be acquired by them for sustenance such as agriculture, dairy, poultry, social forestry, job as forest guard and others eco-development programmes. Perhaps, the dimensions presented by me may generate discussion among the international wild life lovers and conservationists and remedies may be result oriented in the field of management of forest and conservation of wildlife on this planet.

Keywords: strategies, rehablety of tribals, conservation of forest, eco-development Programmes, wildlife

Procedia PDF Downloads 422

Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

Procedia PDF Downloads 69

Authors: Fangfang Wang, Tianjing Wang, Leyi Fu, Feng Yun, Ningning Xie, Jue Zhou, Fan Qu

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Background: Ferroptosis, a recently discovered form of programmed cell death characterized by iron-dependent lipid peroxidation, may be linked to polycystic ovary syndrome (PCOS). Diseases marked by iron overload have been correlated with ferroptosis. Coincidently, investigations have revealed anomalies in iron metabolism among women with PCOS; however, there were inconsistencies in the evidence. Objective and Rationale: This review aimed to comprehensively explore the potential relationship between ferroptosis and PCOS by investigating the differences in iron metabolism among women with PCOS in comparison to a control group. Additionally, a narrative synthesis was provided on the past research status regarding the association between PCOS and ferroptosis. Methods: A systematic search of the literature was performed using PubMed, Embase, Web of Science from inception up to December 2022. Search terms relating to assisted PCOS, ferroptosis, and iron metabolism were used. PRISMA guidance was followed. RevMan 5.4 was utilized for conducting the meta-analysis, wherein the investigated outcomes included iron status (ferritin, iron, transferrin saturation) and a systemic iron-regulatory hormone (hepcidin). A narrative synthesis was performed to explore the correlation between PCOS and ferroptosis. Results: In the meta-analysis comprising a total of 16 studies, significant differences in serum ferritin levels between the PCOS group and the control group were observed (15 studies, standardized mean difference (SMD): 0.41, 95% CI: 0.22 to 0.59, P<0.01). This indicates elevated serum ferritin levels in PCOS patients compared to women without PCOS. The transferrin saturation in PCOS patients was significantly higher than that in the control group (3 studies, mean difference (MD): 4.39, 95% CI: 1.67 to 7.11, P<0.01). Regarding serum iron (6 studies, SMD: 0.05, 95% CI: -0.24 to 0.33, P=0.75) and serum hepcidin (4 studies, SMD: -0.44, 95% CI: -1.41 to 0.52, P=0.37), no statistically significant differences were observed between the PCOS group and the control group. Other studies have found that ferroptosis is involved in the occurrence and development of PCOS, offering valuable insights for guiding potential treatment measures and prognosis evaluation of PCOS. In addition, ferroptosis is involved in the miscarriage of PCOS-like rats; thus, controlling ferroptosis might improve pregnancy outcomes in PCOS. Conclusions: The observation of a significant elevation in serum ferritin and transferrin saturation levels in women with PCOS may suggest an underlying disturbance in iron metabolism, potentially inducing the activation of ferroptosis. Further research is imperative to elucidate the underlying pathophysiology, providing insights for potential preventive measures and therapeutic strategies. Limitation: There are some limitations as follows: First, due to limited extractable information, we excluded purely abstract publications and non-English publications. Second, the majority of original articles were case-control studies, making it difficult to determine the causal relationship between iron metabolism abnormalities and the onset of PCOS. Third, there is substantial heterogeneity in the definition of PCOS.

Keywords: polycystic ovary syndrome, ferroptosis, iron metabolism, systematic review and meta-analysis

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Authors: Vilar, Katila

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Rapid urbanization have made of spontaneous settlements one of the dominant´s social subjects of the XXIst century. Currently, it´s recognized that these territories cannot easily be eradicated and are a way of life to many populations of emergent countries. Since late 90s, there is an urgent concern in finding planning and efficient urban design strategies to poverty reduction, spatial integration and social inclusion of low-income communities. The article aims to identify, understand and evaluate the social inclusion´s processes through the urban transformation that has been undertaken in Moravia and how they affected the community´s social capital. To achieve this objective, we start to analyse the PPMIM´s planning discourse in which prevails the sustainability´s concept, to further identify, through the analysis of the project carried out, the urban design strategies implemented and their impact on the perception and on the community´s experience, and, finally, how these focused on the social capital. It relies on concepts such as urban design, social capital, local development and sustainability. At the urban design level it starts on the current principles of “making places”, on the new urbanism concepts and on the practices on the ground carried out by a new generation of architects/planners whose have the main ethical approach in order to create more opportunities and greater social impact to these territories. At the social capital´s level and on the development´s theory, relies on authors such as Coleman, Putman Kliksberg and Amartya Sen. Finally, it aims to address a general discussion about the positive and negative implications of slum upgrading programmes and some necessary recommendations for urban design and social capital can really be translated into real resources for the self sustainable development of low-income communities and their future generations.

Keywords: local and sustainable development, social capital, spontaneous settlements, urban design

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: Yu-Chen Wei

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The relationship between organizational human capital and organizational effectiveness have been a common topic of interest to organization researchers. Much of this research has concluded that higher human capital can predict greater organizational outcomes. Whereas human capital research has traditionally focused on organizations, the current study turns to the team level human capital. In addition, there are no known empirical studies assessing the effect of human capital divergence on team performance. Team human capital refers to the sum of knowledge, ability, and experience embedded in team members. Team human capital divergence is defined as the variation of human capital within a team. This study is among the first to assess the role of human capital divergence as a moderator of the effect of team human capital on team performance. From the traditional perspective, team human capital represents the collective ability to solve problems and reducing operational risk of all team members. Hence, the higher team human capital, the higher the team performance. This study further employs social learning theory to explain the relationship between team human capital and team performance. According to this theory, the individuals will look for progress by way of learning from teammates in their teams. They expect to have upper human capital, in turn, to achieve high productivity, obtain great rewards and career success eventually. Therefore, the individual can have more chances to improve his or her capability by learning from peers of the team if the team members have higher average human capital. As a consequence, all team members can develop a quick and effective learning path in their work environment, and in turn enhance their knowledge, skill, and experience, leads to higher team performance. This is the first argument of this study. Furthermore, the current study argues that human capital divergence is negative to a team development. For the individuals with lower human capital in the team, they always feel the pressure from their outstanding colleagues. Under the pressure, they cannot give full play to their own jobs and lose more and more confidence. For the smart guys in the team, they are reluctant to be colleagues with the teammates who are not as intelligent as them. Besides, they may have lower motivation to move forward because they are prominent enough compared with their teammates. Therefore, human capital divergence will moderate the relationship between team human capital and team performance. These two arguments were tested in 510 team-seasons drawn from major league baseball (1998–2014). Results demonstrate that there is a positive relationship between team human capital and team performance which is consistent with previous research. In addition, the variation of human capital within a team weakens the above relationships. That is to say, an individual working with teammates who are comparable to them can produce better performance than working with people who are either too smart or too stupid to them.

Keywords: human capital divergence, team human capital, team performance, team level research

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Authors: Helga Martins, Idália Matias

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Background: Dementia is an important research topic since that life expectancy worldwide is increasing, so people are getting older. The aging of populations has a major impact on the increase in dementia, and nurses play a major role in taking care of these patients. Therefore, the implementation of nursing interventions based on evidence is vital so that we are aware of what we can do in clinical practice in order to provide patient cantered care to patients with dementia. Aim: To identify the nurse´s interventions in patients with dementia during clinical practice. Method: Literature review grounded on an electronic search in the EBSCOhost platform (CINAHL Plus with Full Text, MEDLINE with Full Text, and Nursing & Allied Health Collection), using the search terms of "dementia" AND "nurs*" AND “interventions” in the abstracts. The inclusion criteria were: original papers published up to June 2021. A total of 153 results after de duplicate removal we kept 104. After the application of the inclusion criteria, we included 15 studies This literature review was performed by two independent researchers. Results: A total of 15 results about nurses’ interventions in patients with dementia were included in the study. The major interventions are therapeutic communication strategies, environmental management of stressors involving family/caregivers; strategies to promote patient safety, and assistance in activities of daily living in patients who are clinically deteriorated. Conclusion: Taking care of people with dementia is a complex and demanding task. Nurses are required to have a set of skills and competences in order to provide nursing interventions. We highlight that is necessary an awareness in nursing education regarding providing nursing care to patients with dementia.

Keywords: dementia, interventions, nursing, review

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Authors: Patsy Tan, Dana Baltutis

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This presentation demonstrates the effectiveness of music therapy in co-treatment with speech pathology and occupational therapy as an innovative way when working with children and adolescents with complex individual differences to facilitate communication, emotional, motor and social skills development. Each child with special needs and their carer has an individual profile which encompasses their visual-spatial, auditory, language, learning, mental health, family dynamic, sensory-motor, motor planning and sequencing profiles. The most common issues among children with special needs, especially those diagnosed with Autism Spectrum Disorder, are in the areas of regulation, communication, and social-emotional development. The ability of children living with challenges to communicate and use language and understand verbal and non-verbal information, as well as move their bodies to explore and interact with their environments in social situations, depends on the children being regulated both internally and externally and trusting their communication partners and understanding what is happening in the moment. For carers, it is about understanding the tempo, rhythm, pacing, and timing of their own individual profile, as well as the profile of the child they are interacting with, and how these can sync together. In this study, music therapy is used in co-treatment sessions with a speech pathologist and/or an occupational therapist using the DIRFloortime approach to facilitate the regulation, attention, engagement, reciprocity and social-emotional capacities of children presenting with complex individual differences. Documented changes in 10 domains of children’s development over a 12-month period using the Individual Music Therapy Assessment Profile (IMTAP) were observed. Children were assessed biannually, and results show significant improvements in the social-emotional, musicality and receptive language domains indicating that co-treatment with a music therapist using the DIRFloortime framework is highly effective. This presentation will highlight strategies that facilitate regulation, social-emotional and communication development for children and adolescents with complex individual profiles.

Keywords: communication, shared attention, regulation, social emotional

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Anusha Sajjad, Sibtain Kazmi, Sadaf Sajjad, Ali Mohsin

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Shia Muslims, a distinct minority within the broader Muslim community, have a unique mourning practice set that remains underexplored in research. These practices hold cultural and religious significance and are essential to Shia spirituality. This study seeks to delve into the emotional and psychological dimensions of Shia Muslims' mourning rituals, specifically investigating how these practices serve as coping strategies during times of grief and adversity. The motivation for this research stems from a gap in understanding the psycho-spiritual aspects of Shia mourning, with a focus on the potential therapeutic value of these practices for individuals experiencing daily life stressors. This qualitative investigation employs an online survey conducted in September 2023 as the primary research method. Data was collected from a sample of 49 Shia Muslims who have actively participated in mourning rituals. Pearson's correlation test was applied to assess the relationship between participants’ reported feelings of mentally feeling ‘Lighter or Heavier' and their 'Coping' responses. Pearson's correlation analysis revealed a positive but relatively weak correlation between 'Mentally Feeling Lighter' and 'Coping' (r = 0.303) with a statistically significant p-value of 0.034. The findings suggest that there is a statistically significant positive correlation between feeling "Mentally Lighter" and coping, as reported by participants in Shia mourning rituals, although the strength of this correlation is relatively weak. This implies that individuals who feel "Mentally Lighter" are more likely to report coping effectively, but other factors likely contribute to the overall coping strategies employed by participants in this context. Further research with a larger sample size would be needed to strengthen this correlation further.

Keywords: shia, mourning, religion, coping

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Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Modupe Beatrice Adeyinka

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European Languages are languages of literature, science and technology. Whereas, African languages are of literature, both written and oral, making it difficult for Yoruba, the African language of Kwa linguistic classification, to neatly and accurately translate European scientific and technological words, expressions and technologies. Unless a pragmatic and communicative approach is adopted, equivalence of European technical and scientific texts might be a mission impossible for Yoruba scholars. In view of the aforementioned difficult task, this paper tends to highlight the need for a thorough study and evaluation of English or French words, expressions, idiomatic expressions, technical and scientific terminologies then, trying to find ways of adopting them to Yoruba environment through interpretative translation.

Keywords: communication, high technology, translation, Yoruba language

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Authors: S. M. Amin Hosseini, Oriol Pons, Albert de la Fuente

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An earthquake hit the Kermanshah province, which is located in the west of Iran, on 12th November 2017 at 18:18 UTC (21:48 Iran Standard Time). This earthquake caused several deaths and injured people. In this disaster, substantial homes were destroyed and many homes were damaged. The Iranian government, local authorities, and several non-governmental organizations responded to affected populations’ needs, such as foods, blanket, water, a tent as a temporary shelter, etc. Considerable national groups, including governmental, non-governmental organizations, and people from non-organized groups, directly and indirectly, tried to bring donated goods to the affected populations. However, some of these aids could not satisfy all the affected populations. Moreover, these impossibilities led to waste extensive resources. In this regard, this research study aims to assess the problems of the Kermanshah disaster responses. At the same time, this project searches possible solutions in order to increase emergency management efficiencies for encountering future events. To this end, this study assesses the problem from all beneficiaries´ point of views. In this regard, a survey and a questionnaire were designed for statistical analyses of the responses of people, who were involved in the Kermanshah earthquake recovery program. Additionally, this research study takes into account diverse strategies, which have been applied in other recovery programs, with the Kermanshah case in order to determine similarities and differences. Finally, this study presents possible solutions taken from other recovery programs that could be applied for the Kermanshah emergency responses. However, the results demonstrate that it is required to customize applied strategies based on local conditions and requirements.

Keywords: disaster response, Kermanshah earthquake, natural disasters, NGOs

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Authors: Ivan Orozco

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Introduction: Forced contraception constitutes part of an effort to control the life and reproductive capacity of women through public health institutions. This phenomenon has affected many Mexican women historically and still persists nowadays. The notion of reproductive governmentality refers to the mechanisms through which different historical configurations of social actors (state institutions, churches, donor agents, NGOs, etc.) use legislative controls, economic incentives, moral mandates, direct coercion, and ethical incitements, to produce, monitor and control reproductive behaviors and practices. This research focuses on the use of these mechanisms by the Mexican State to control women's contraceptive practices in a public hospital. Method: An Institutional Ethnography was carried out, with the objective of knowing women's experiences from their own perspective, as they occur in their daily lives, but at the same time, discovering the structural elements that shape the discourses that promote women's contraception, even against their will. The fieldwork consisted in an observation of the dynamics between different participants within a public hospital and the conduction of interviews with the medical and nursing staff in charge of family planning services, as well as women attending the family planning office. Results: Public health institutions in Mexico are state tools to control and regulate reproduction. There are several strategies that are used for this purpose, for example, health personnel provide insufficient or misleading information to ensure that women agree to use contraceptives; health institutions provide economic incentives to the members of the health staff who reach certain goals in terms of contraceptive placement; young women are forced to go to the family planning service, regardless of the reason they went to the clinic; health campaigns are carried out, consisting of the application of contraceptives outside the health facilities, directly in the communities of people who visit the hospital less frequently. All these mechanisms seek for women to use contraceptives, from the women’s perspective; however, the reception of these discourses is ambiguous. While, for some women, the strategies become coercive mechanisms to use contraceptives against their will, for others, they represent an opportunity to take control over their reproductive lives. Conclusion: Since 1974, the Mexican government has implemented campaigns for the promotion of family planning methods as a means to control population growth. Although it is established in several legislations that the counselling must be carried out with a gender and human rights perspective, always respecting the autonomy of people, these research testify that health personnel uses different strategies to force some women to use contraceptive methods, thereby violating their reproductive rights.

Keywords: feminist research, forced contraception, institutional ethnography, reproductive. governmentality

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Authors: Matthew Armitt

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The revolutionary school of architectural teaching – VKhUTEAMAS (1923-1926) was a new approach for a new society bringing architectural education to the masses and masses to the growing industrial production. The school's pedagogical contribution of the 1920s made it an important school of the modernist movement, engaging pedagogy as a mode of experimentation. The teachers and students saw design education not just as a process of knowledge transfer but as a vehicle for design innovation developing an approach without precedent. This process of teaching and learning served as a vehicle for venturing into the unknown through a discipline of architectural teaching called “Space” developed by the Soviet architect Nikolai Ladovskii (1881-1941). The creation of “Space” was paramount not only for its innovative pedagogy but also as an experimental laboratory for developing new architectural language. This paper discusses whether the historical teaching of “Space” can function in the construction of the modern teaching identity today to promote value, richness, quality, and diversity inherent in architectural design education. The history of “Space” teaching remains unknown within academic circles and separate from the current architectural teaching debate. Using VKhUTEMAS and the teaching of “Space” as a pedagogical lens and drawing upon research carried out in the Russian Federation, America, Canada, Germany, and the UK, this paper discusses how historically different models of teaching and learning can intersect through examining historical based educational research by exploring different design studio initiatives; pedagogical methodologies; teaching and learning theories and problem-based projects. There are strong arguments and desire for pedagogical change and this paper will promote new historical and educational research to widen the current academic debate by exposing new approaches to architectural teaching today.

Keywords: VKhUTEMAS, discipline space, modernist pedagogy, teaching identity

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Authors: Hongmei Chi

Abstract:

The Blockchain has become a necessity for many different societal industries and ordinary lives including cryptocurrency technology, supply chain, health care, public safety, education, etc. Therefore, training our future blockchain developers to know blockchain programming vulnerability and I.T. students' cyber security is in high demand. In this work, we propose a framework including learning modules and hands-on labs to guide future I.T. professionals towards developing secure blockchain programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle following the concept of Secure Software Development Life Cycle (SSDLC). In this research, our goal is to make blockchain programmers and I.T. students aware of the vulnerabilities of blockchains. In summary, we develop a framework that will (1) improve students' skills and awareness of blockchain source code vulnerabilities, detection tools, and mitigation techniques (2) integrate concepts of blockchain vulnerabilities for IT students, (3) improve future IT workers’ ability to master the concepts of blockchain attacks.

Keywords: software vulnerability detection, hands-on lab, static analysis tools, vulnerabilities, blockchain, active learning

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Authors: Rui Yin, Haojie Wang, Xun Li

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Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then, the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. We also explore contrastive learning as a method for training domain-adapted dense retrievers and show that it leads to strong performance in various retrieval settings. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data.

Keywords: dense retrieval, query generation, contrastive learning, unsupervised training

Procedia PDF Downloads 80