Search results for: college student learning experience

Authors: Christoph Opperer

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Space syntax is a theory and method for analyzing the spatial layout of buildings and urban environments to understand how they can influence patterns of human movement, social interaction, and behavior. While direct visibility is a key factor in space syntax research, important visual information such as light, color, texture, etc., are typically not considered, even though psychological studies have shown a strong correlation to the human perceptual experience within physical space – with light and color, for example, playing a crucial role in shaping the perception of spaciousness. Furthermore, these surface properties are often the visual features that are most salient and responsible for drawing attention to certain elements within the environment. This paper explores the potential of integrating these factors into general space syntax methods and visibility-based analysis of space, particularly for architectural spatial layouts. To this end, we use a combination of geometric (isovist) and topological (visibility graph) approaches together with image-based methods, allowing a comprehensive exploration of the relationship between spatial geometry, visual aesthetics, and human experience. Custom-coded ray-tracing techniques are employed to generate spherical panorama images, encoding three-dimensional spatial data in the form of two-dimensional images. These images are then processed through computer vision algorithms to generate saliency-maps, which serve as a visual representation of areas most likely to attract human attention based on their visual properties. The maps are subsequently used to weight the vertices of isovists and the visibility graph, placing greater emphasis on areas with high saliency. Compared to traditional methods, our weighted visibility analysis introduces an additional layer of information density by assigning different weights or importance levels to various aspects within the field of view. This extends general space syntax measures to provide a more nuanced understanding of visibility patterns that better reflect the dynamics of human attention and perception. Furthermore, by drawing parallels to traditional isovist and VGA analysis, our weighted approach emphasizes a crucial distinction, which has been pointed out by Ervin and Steinitz: the difference between what is possible to see and what is likely to be seen. Therefore, this paper emphasizes the importance of including surface properties in visibility-based analysis to gain deeper insights into how people interact with their surroundings and to establish a stronger connection with human attention and perception.

Keywords: space syntax, visibility analysis, isovist, visibility graph, visual features, human perception, saliency detection, raytracing, spherical images

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Authors: Dora Triki, Irena Vida, Claude Obadia

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Social networks such as e-Forums or dating sites often face the reluctance of key members to participate. Relying on the conation theory, this study investigates this phenomenon and proposes solutions to mitigate the issue. We show that highly experienced e-Forum members refuse to share business information in a peer to peer information exchange forums. However, forums managers can mitigate this behavior by developing a sentiment of belongingness to the network. Furthermore, by selecting only elite forum participants with ample experience, they can reduce the reluctance of key information providers to engage in information exchange. Our hypotheses are tested with PLS structural equations modeling using survey data from members of a French e-Forum dedicated to the exchange of business information about exporting.

Keywords: conation, e-Forum, information exchange, members participation

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Silvia C. Ambag, Racidon P. Bernarte, Jacquelyn B. Buccahi, Jessica R. Lacaron, Charlyn L. Mangulabnan

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This study aimed to answer the query, “What is the impact of Students Online Evaluation on PUP Faculty’s Performance?” The problem of the study was resolve through the objective of knowing the perceived impact of students’ online evaluation on PUP faculty’s performance. The objectives were carried through the application of quantitative research design and by conducting survey research method. The researchers utilized primary and secondary data. Primary data was gathered from the self-administered survey and secondary data was collected from the books, articles on both print-out and online materials and also other theses related study. Findings revealed that PUP faculty in general stated that students’ online evaluation made a highly positive impact on their performance based on their ‘Knowledge of Subject’ and ‘Teaching for Independent Learning’, giving a highest mean of 3.62 and 3.60 respectively., followed by the faculty’s performance which gained an overall means of 3.55 and 3.53 are based on their ‘Commitment’ and ‘Management of Learning’. From the findings, the researchers concluded that Students’ online evaluation made a ‘Highly Positive’ impact on PUP faculty’s performance based on all Four (4) areas. Furthermore, the study’s findings reveal that PUP faculty encountered many problems regarding the students’ online evaluation; the impact of the Students’ Online Evaluation is significant when it comes to the employment status of the faculty; and most of the PUP faculty recommends reviewing the PUP Online Survey for Faculty Evaluation for improvement. Hence, the researchers recommend the PUP Administration to revisit and revise the PUP Online Survey for Faculty Evaluation, specifically review the questions and make a set of questions that will be appropriate to the discipline or field of the faculty. Also, the administration should fully orient the students about the importance, purpose and impact of online faculty evaluation. And lastly, the researchers suggest the PUP Faculty to continue their positive performance and continue on being cooperative with the administrations’ purpose of addressing the students’ concerns and for the students, the researchers urged them to take the online faculty evaluation honestly and objectively.

Keywords: on-line Evaluation, faculty, performance, Polytechnic University of the Philippines (PUP)

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Authors: Silvio Araújo, Sérgio Maravilhas, Tamires Coutinho

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The aim of this paper is to present the experience of a project involving cooperation between the academic world and civil society to address the impact of the COVID-19 pandemic on the tourism sector in the Chapada Diamantina region, in Bahia state, Brazil. It collaborates with studies on organizational strategies and the monitoring of economic indicators in times of crisis, using data analysis to investigate associations between the variables studied. As a result, the economic, structural, and systemic factors that determine the resumption of activities after the pandemic are presented, as well as the results obtained and the general expectations for tourism activities in the region. The conclusion is that, even with government support, from the Brazilian authorities, the undesirable effects of the externalities of the pandemic threaten not only competitiveness but also business continuity itself.

Keywords: Chapada Diamantina, competitiveness, COVID-19, tourism

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Authors: Harumi Katayama, Mina Suzuki, Taeko Muramatsu, Yui Shimogawa, Yoshimi Mizushima, Mitsuo Hiramatsu, Kimitsugu Nakamura, Takeshi Suzue

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Purpose: There are very often footbaths in the hot-spring area as culture from old days in Japan. This culture moderately supported mental and physical health among people. In Japanese hospitals, nurses provide footbath for severe patients to mental comfortable. However, there are only a few evidences effect of footbath for mental comfortable. In this presentation, we show the effect of stress relief of the footbath using biomarker among 35 college students in volunteer. Methods: The experiment was designed in two groups of the footbath group and the simple relaxation group randomly. As mental load, Kraepelin test was given to the students beforehand. Ultra-weak chemiluminescence (UCL) in saliva and self-administered liner scale measurable emotional state were measured on four times concurrently; there is before and after the mental load, after the stress relief, and 30 minutes after the stress relief. The scale that measured emotional state was consisted of 7 factors; there is excitement, relaxation, vigorous, fatigue, tension, calm, and sleepiness with 22 items. ANOVA was calculated effect of the footbath for stress relief. Results: The level of UCL (photons/100sec) was significantly increased in response on both groups after mental load. After the two types of stress relief, UCL (photons/100sec) of footbath group was significantly decreased compared to simple relaxation group. Score of sleepiness and relaxation were significantly increased after the stress relief in the footbath group than the simple relaxation group. However, score of excitement, vigorous, tension, and calm were exhibit the same degree of decrease after the stress relief on both group. Conclusion: It was suggested that salivary UCL may be a sensitive biomarker for mild stress relief as nursing care. In the future, we will measure using UCL to evaluate as stress relief for inpatients, outpatients, or general public as the subjects.

Keywords: bio-marker, footbath, Japan, stress relief

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Authors: Tahira Imtiaz, Karim Ullah

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This paper aims to incorporate the banks employees’ perspective on acceptability of Islamic banking by the customers of Peshawar. A qualitative approach is adopted for which six in-depth interviews with employees of Islamic banks are conducted. The employees were asked to share their experience regarding customers’ acceptance attitude towards acceptability of Islamic banking. Collected data was analyzed through thematic analysis technique and its synthesis with the current literature. Through data analysis a theoretical framework is developed, which highlights the factors which drive customers towards Islamic banking, as witnessed by the employees. The practical implication of analyzed data evident that a new model could be developed on the basis of four determinants of human preference namely: inner satisfaction, time, faith and market forces.

Keywords: customers’ attraction, employees’ perspective, Islamic banking, Riba

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Authors: M. Shokry Rashwan

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This study follows a number of experiments conducted at Red River College (RRC) to investigate the short term properties of tire derived aggregate (TDA) produced from shredding off-the-road (OTR) wasted tires in a proposed new application. The application targets replacing natural material used under concrete slabs and as backfills for residential homes’ basement slabs and walls, respectively, with TDA. The experimental work included determining: compressibility, gradation distribution, unit weight, hydraulic conductivity and lateral pressure. Based on the results of those short term properties; it was decided to move forward to study the long-term performance of this otherwise waste material through on-site demonstration. A full-scale basement replicating a typical Manitoba home was therefore built at RRC where both TDA and Natural Materials (NM) were used side-by-side. A large number of sensing and measuring systems are used to compare between the performances of each material when exposed to the typical ground and weather conditions. Parameters monitored and measured include heat losses, moisture migration, drainage ability, lateral pressure, relative movements of slabs and walls, an integrity of ground water and radon emissions. Up-to-date results have confirmed part of the conclusions reached from the earlier laboratory experiments. However, other results have shown that construction practices; such as placing and compaction, may need some adjustments to achieve more desirable outcomes. This presentation provides a review of both short-term tests as well as up-to-date analysis of the on-site demonstration.

Keywords: tire derived aggregate (TDA), basement construction, TDA material properties, lateral pressure of TDA, hydraulic conductivity of TDA

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Fouad M. Bendimerad, Jerome B. Zayas, Michael Adrian T. Padilla

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With the increasing scale of severity and frequency of disasters worldwide, the performance of governance systems for disaster risk reduction and management in many countries are being put to the test. In the Philippines, the Disaster Risk Reduction and Management (DRRM) Act of 2010 (Republic Act 10121 or RA 10121) as the framework for disaster risk reduction and management was tested when Super Typhoon Haiyan hit the eastern provinces of the Philippines in November 2013. Typhoon Haiyan is considered to be the strongest recorded typhoon in history to make landfall with winds exceeding 252 km/hr. In assessing the performance of RA 10121 the authors conducted document reviews of related policies, plans, programs, and key interviews and focus groups with representatives of 21 national government departments, two (2) local government units, six (6) private sector and civil society organizations, and five (5) development agencies. Our analysis will argue that enhancements are needed in RA 10121 in order to meet the challenges of large-scale disasters. The current structure where government agencies and departments organize along DRRM thematic areas such response and relief, preparedness, prevention and mitigation, and recovery and response proved to be inefficient in coordinating response and recovery and in mobilizing resources on the ground. However, experience from various disasters has shown the Philippine government’s tendency to organize major recovery programs along development sectors such as infrastructure, livelihood, shelter, and social services, which is consistent with the concept of DRM mainstreaming. We will argue that this sectoral approach is more effective than the thematic approach to DRRM. The council-type arrangement for coordination has also been rendered inoperable by Typhoon Haiyan because the agency responsible for coordination does not have decision-making authority to mobilize action and resources of other agencies which are members of the council. Resources have been devolved to agencies responsible for each thematic area and there is no clear command and direction structure for decision-making. However, experience also shows that the Philippine government has appointed ad-hoc bodies with authority over other agencies to coordinate and mobilize action and resources in recovering from large-scale disasters. We will argue that this approach be institutionalized within the government structure to enable a more efficient and effective disaster risk reduction and management system.

Keywords: risk reduction and management, recovery, governance, typhoon haiyan response and recovery

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Authors: N. Anand, P. S. Nayyer, V. Rana, S. Verma

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Background: Diabetes mellitus is a group of disorders characterized by hyperglycemia and associated with microvascular and macrovascular complications. Among the lesser known complications is the involvement of respiratory system. Changes in pulmonary volume, diffusion and elastic properties of lungs as well as the performance of the respiratory muscles lead to a restrictive pattern in lung functions. The present study was aimed to determine the changes in various parameters of pulmonary function tests amongst patients with Type 2 Diabetes Mellitus and also try to study the effect of duration of Diabetes Mellitus on pulmonary function tests. Methods: It was a cross sectional study performed at Dr Baba Saheb Ambedkar Hospital and Medical College in, Delhi, A Tertiary care referral centre which included 200 patients divided into 2 groups. The first group included diagnosed patients with diabetes and the second group included controls. Cases and controls symptomatic for any acute or chronic Respiratory or Cardiovascular illness or a history of smoking were excluded. Both the groups were subjected to spirometry to evaluate for the pulmonary function tests. Result: The mean Forced Vital Capacity (FVC), Forced Expiratory Volume in first second (FEV1), Peak Expiratory Flow Rate(PEFR) was found to be significantly decreased ((P < 0.001) as compared to controls while the mean ratio of Forced Expiratory Volume in First second to Forced Vital Capacity was not significantly decreased( p>0.005). There was no correlation seen with duration of the disease. Conclusion: Forced Vital Capacity (FVC), Forced Expiratory Volume in first second (FEV1), Peak Expiratory Flow Rate(PEFR) were found to be significantly decreased in patients of Diabetes mellitus while ratio of Forced Expiratory Volume in First second to Forced Vital Capacity (FEV1/FVC) was not significantly decreased. The duration of Diabetes mellitus was not found to have any statistically significant effect on Pulmonary function tests (p > 0.005).

Keywords: diabetes mellitus, pulmonary function tests, forced vital capacity, forced expiratory volume in first second

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Authors: A. K. Singh, Chhotu Ram

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The present work relates to the corrosivity of distillery effluent and corrosion performance of mild steel and stainless steels SS304L, SS316L, and 2205. The report presents the results and conclusions drawn on the basis of (i) electrochemical polarization tests performed in distillery effluent and laboratory prepared solutions having composition similar to that of the effluent (ii) the surface examination by scanning electron microscope (SEM) of the corroded steel samples. It is observed that pH and presence of chloride, phosphate, calcium, nitrite and nitrate in distillery effluent enhance corrosion, whereas presence of sulphate and potassium inhibits corrosion. Among the materials tested, mild steel is observed to experience maximum corrosion followed by stainless steels SS304L, SS316L, and 2205.

Keywords: corrosion, distillery effluent, electrochemical polarization, steel

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Authors: Aisling Parkes, Fiona Donson

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There is no doubt that parental imprisonment directly alters the social experiences of childhood for many children worldwide today. Indeed, the extent to which meaningful contact with a parent in prison can positively impact on the life of a child is well documented as are the benefits for the prisoner, particularly in the long term and post-release. However, despite the growing acceptance of children’s rights in Ireland over the past decade in particular, it appears that children’s rights have not yet succeeded in breaking through the walls of Irish prisons when children are visiting an incarcerated parent. In a prison system that continues to prioritise security over all other considerations, little attention has been given to the importance of recognising and protecting the rights of children affected by parental imprisonment in Ireland for children, families and society in the long term. This paper will present the findings which have emerged from a national qualitative research project (the first of its kind to be conducted in Ireland) which examines the current visiting conditions for children and families, and the related culture of visitation within the Irish Prison system. This study investigated, through semi-structured interviews and focus groups, the unique and specialist perspectives of senior prison management, prison governors, prison officers, support organisations, prison child care workers, as well as those with a family member in prison who have direct experience of prison visits in Ireland which involve children and young people. The reality of the current system of visitation that operates in Irish prisons and its impact on children’s rights is presented from a variety of perspectives. The idea of what meaningful contact means from a children’s rights based perspective is interrogated as are the benefits long term for both the child and the offender. The current system is benchmarked against well-accepted international children’s rights norms as reflected under the UN Convention on the Rights of the Child 1989. The dissonance that continues to exist between the theory of children’s rights which includes the right to maintain meaningful contact with a parent in prison and current practice and procedure in Irish Prisons will be explored. In adopting a children’s rights based perspective combined with socio-legal research, this paper will explore the added value that this approach to prison visiting might offer in responding to this particularly marginalised group of children in terms of their social experience of childhood. Finally, the question will be raised as to whether or not there is a responsibility on prisons to view children as independent rights holders when they come to visit the prison or is the prison entitled to focus solely on the prisoner with their children being viewed as a circumstance of the offender? Do the interests of the child and the prisoner have to be exclusive or is there any way of marrying the two?

Keywords: children’s rights, prisoners, sociology, visitation

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Authors: David A. Harness

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Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: automated theorem proving, constructive quantum field theory, information theory, neural networks

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: K. Djellabi, M. E. H. Latreche

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Induction heating computer simulation is a powerful tool for process design and optimization, induction coil design, equipment selection, as well as education and business presentations. The authors share their vast experience in the practical use of computer simulation for different induction heating and heat treating processes. In this paper deals with mathematical modeling and numerical simulation of induction heating furnaces with axisymmetric geometries. For the numerical solution, we propose finite element methods combined with boundary (FEM) for the electromagnetic model using COMSOL® Multiphysics Software. Some numerical results for an industrial furnace are shown with high frequency.

Keywords: numerical methods, induction furnaces, induction heating, finite element method, Comsol multiphysics software

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Authors: Shinji Kajiwara, Tadamasa Fukuoka

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The formula project of Kinki University has been involved in the student Formula SAE of Japan (JSAE) since the second year the competition was held. The vehicle developed in the project uses a ZX-6R engine, which has been manufactured by Kawasaki Heavy Industries for the JSAE competition for the eighth time. The limited performance of the concept vehicle was improved through the development of a power train. The supercharger loading, engine dry sump, and engine cooling management of the vehicle were also enhanced. The supercharger loading enabled the vehicle to achieve a maximum output of 59.6 kW (80.6 PS)/9000 rpm and a maximum torque of 70.6 Nm (7.2 kgf m)/8000 rpm. We successfully achieved 90% of the engine’s torque band (4000–10000 rpm) with 50% of the revolutions in regular engine use (2000–12000 rpm). Using a dry sump system, we periodically managed hydraulic pressure during engine operation. A system that controls engine stoppage when hydraulic pressure falls was also constructed. Using the dry sump system at 80 mm reduced the required engine load and the vehicle’s center of gravity. Even when engine motion was suspended by the electromotive force exerted by the water pump, the circulation of cooling water was still possible. These findings enabled us to create a cooling system in accordance with the requirements of the competition.

Keywords: engine, combustion, cooling system, numerical simulation, power, torque, mechanical super charger

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Authors: Lynn Mcquarrie, Charlotte Enns

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The goal of this project was to develop a visually-based, signed language phonological awareness training program and to pilot the intervention with signing deaf children (ages 6 -10 years/ grades 1 - 4) who were beginning readers to assess the effects of systematic explicit American Sign Language (ASL) phonological instruction on both ASL vocabulary and English print vocabulary learning. Growing evidence that signing learners utilize visually-based signed language phonological knowledge (homologous to the sound-based phonological level of spoken language processing) when reading underscore the critical need for further research on the innovation of reading instructional practices for visual language learners. Multiple single-case studies using a multiple probe design across content (i.e., sign and print targets incorporating specific ASL phonological parameters – handshapes) was implemented to examine if a functional relationship existed between instruction and acquisition of these skills. The results indicated that for all cases, representing a variety of language abilities, the visually-based phonological teaching approach was exceptionally powerful in helping children to build their sign and print vocabularies. Although intervention/teaching studies have been essential in testing hypotheses about spoken language phonological processes supporting non-deaf children’s reading development, there are no parallel intervention/teaching studies exploring hypotheses about signed language phonological processes in supporting deaf children’s reading development. This study begins to provide the needed evidence to pursue innovative teaching strategies that incorporate the strengths of visual learners.

Keywords: American sign language phonological awareness, dual language strategies, vocabulary learning, word reading

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Authors: Donald Wright

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The ultimate goal of this study is to reinvigorate the philosophical underpinnings the study of urbanization with scientific data with the goal of circumventing what seems an inevitable future clash between rural and urban populations. To that end urban infrastructure must be sustainable economically, politically and ecologically over the course of several generations as cities continue to grow with the incorporation of climate refugees. Our research will provide data concerning the projected increase in population over the coming two decades in Morocco, and the population will shift from rural areas to urban centers during that period of time. As a result, urban infrastructure will need to be adapted, developed or built to fit the demand of future internal migrations from rural to urban centers in Morocco. This paper will also examine how past experiences of internally displaced people give insight into the challenges faced by future migrants and, beyond the gathering of data, how people react to internal migration. This study employs four different sets of research tools. First, a large part of this study is archival, which involves compiling the relevant literature on the topic and its complex history. This step also includes gathering data bout migrations in Morocco from public data sources. Once the datasets are collected, the next part of the project involves populating the attribute fields and preprocessing the data to make it understandable and usable by machine learning algorithms. In tandem with the mathematical interpretation of data and projected migrations, this study benefits from a theoretical understanding of the critical apparatus existing around urban development of the 20th and 21st centuries that give us insight into past infrastructure development and the rationale behind it. Once the data is ready to be analyzed, different machine learning algorithms will be experimented (k-clustering, support vector regression, random forest analysis) and the results compared for visualization of the data. The final computational part of this study involves analyzing the data and determining what we can learn from it. This paper helps us to understand future trends of population movements within and between regions of North Africa, which will have an impact on various sectors such as urban development, food distribution and water purification, not to mention the creation of public policy in the countries of this region. One of the strengths of this project is the multi-pronged and cross-disciplinary methodology to the research question, which enables an interchange of knowledge and experiences to facilitate innovative solutions to this complex problem. Multiple and diverse intersecting viewpoints allow an exchange of methodological models that provide fresh and informed interpretations of otherwise objective data.

Keywords: climate change, machine learning, migration, Morocco, urban development

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

Procedia PDF Downloads 55

Authors: Hozaifa Zaki, Ghada Soliman

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In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

Procedia PDF Downloads 57

Authors: Luciana Marrone

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Natural dyes are a great resource for today's artists and designers providing endless possibilities for design and sustainability. This research and development project focuses on the idea of making these dyeing or painting methodologies reach the widest possible range of students. The main objective is to inform and train, free of charge, teachers and students from different academic institutions, at different levels, kindergarten, primary, secondary, tertiary and university. In this research and dissemination project, in the first instance, institutions from Argentina, Chile, Uruguay, Mexico, Spain, Italy, Colombia, Paraguay, Venezuela, Brazil and Australia joined the project, reaching the grassroots of education from the very beginning. Natural dyes will become part of everyday life for more people, achieving their own colors for art, textiles or any other application. The knowledge of the techniques and resources of the student a fundamental tool, sustainable and opens endless possibilities even in places or homes with few economic resources, thus achieving that natural dyes are not only part of the world of designers but also that they are incorporated from the basics and can thus become a resource applicable in different areas even in places with few economic or development possibilities.

Keywords: art, education, natural dyes, sustainability, textile design.

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Authors: Ana Rita Conde, Pilar Mota, Tânia Botelho, Suzana Caldeira, Isabel Rego, Jessica Pacheco, Osvaldo Silva, Áurea Sousa

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Tourism promotes well-being and health to children with ASD and their families. Literature indicates the need to provide tourist activities that integrate the therapeutic component, to promote the development and well-being of children with ASD. The study aims to implement tourist offers in Azores that integrate the therapeutic feature, assess their suitability and impact on the well-being and health of the child and caregivers. Using a mixed methodology, the study integrates families that experience and evaluate the impact of tourism products developed specifically for them.

Keywords: austism spectrum disorder, children, therapeutic tourism activities, well-being, health, inclusive tourism

Procedia PDF Downloads 127

Authors: Marzena Świgoń

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Nowadays, graduates should be prepared for serious challenges in the internal and external labor market. The notion that a degree is a “passport to employment” has been relegated to the past. In the last few years a phenomenon in the form of the increasing unemployment of highly educated young people in EU countries, including Poland has been observed. Empirical studies were conducted among Polish students in the scope of the so-called self-perceived employability review. In this study, a special scale was used which consisted of 19 statements regarding five components: student’s perception of university; field of study; self-belief; state of the external labor market; and, personal knowledge management. The respondent group consisted of final-year master’s students of International Relations at the University of Warmia and Mazury in Olsztyn, Poland. The findings of the empirical studies were compiled using statistical methods: descriptive statistics and inferential statistics. In general, in light of the conducted studies, the self-perceived employability of the Polish students was not high. Limitations of the studies were discussed, as well as the implications for future research in the scope of the students’ employability.

Keywords: self-perceived employability, students of international relations, university students, students employability

Procedia PDF Downloads 314

Authors: Chang Li Yu

Abstract:

Resistances commonly happened during sexual offenders treatment program (SOTP), and removing resistances was one of the treatment goals on it. Studies concerning treatment effectiveness relied on pre- and post-treatment evaluations, however, no significant difference on resistance revealed after treatment, and the above consequences generally contributed to the low motivation for change instead. Therefore, the aim of this study was to investigate the resistance across each stage of change among sexual offenders (SO). The present study recruited prisoned SO in Taiwan, excluding those with literacy difficulties; finally, 272 participants were included. Of all participants completed revised version of URICA (University of Rhode Island Change Assessment) and resistance scale specifically for SO. The former included four stages of change: pre-contemplation (PC), contemplation (C), action (A), and maintain (M); the later composed eight types of resistance: system blaming, victims blaming, problems with treatment alliance, social justification, hopelessness, isolation, psychological reactance, and passive reactance. Both of the instruments were with well reliability and validity. Descriptive statistics and ANOVA were performed. All of 272 participants, age under 25 were 18(6.6%), 25-39 were 133(48.9%), 40-54 were 102(37.5%), and age over 55 were 19(7.0%); college level and above were 53(19.5%), high school level were 110(40.4%), and under high school level were 109(40.1%); first offended were 117(43.0%), and recidivist were 23(8.5%). Further deleting data with missing values and invalid questionnaires, SO with stage of change on PC were 43(18.9%), C were 109(47.8%), A were 70(30.7%), and on M were 6(2.6%). One-way ANOVA showed significant differences on every kind of resistances, excepting isolation and passive reactance. Post-hoc analysis showed that SO with different stages had their main resistance. There are two contributions to the present study. First, this study provided a clinical and theoretical measurement of evaluation that was never used in the past. Second, this study used an evidence-based methodology to prove a clinical perspective differed from the past, suggesting that resistances to treatment on SO appear the whole therapeutic process, when SO progress into the next stage of change, clinicians have to deal with their main resistance for working through the therapy.

Keywords: resistance, sexual offenders treatment program (SOTP), motivation for change, prisoned sexual offender

Procedia PDF Downloads 226

Authors: Noga Bregman

Abstract:

Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

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Authors: J. Luché-Thayer, C. Perronne, C. Meseko

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We reviewed how certain institutional policies and practices, as well as questionable research, are creating obstacles to care and informed consent for Lyme and relapsing fever Borreliosis patients. The interference is denying access to treatments that meet the internationally accepted standards as set by the Institute of Medicine. This obstruction to care contributes to significant human suffering, disability and negative economic effect across many nations and in many regions of the world. We note how evidence based medicine emphasizes the importance of clinical experience and patient-centered care and how these patients benefit significantly when their rights to choose among treatment options are upheld.  

Keywords: conflicts of interest, obstacles to healthcare accessibility, patient-centered care, the right to informed consent

Procedia PDF Downloads 190

Authors: Pei Xin Ker

Abstract:

The purpose of this study is to examine the influence of teachers’ self-efficacy (TSE) on teachers’ perceptions of the levels of implementation of the NPSC. A total of 187 respondents were selected by using purposive homogeneous sampling to represent preschool teachers in Selangor and Kuala Lumpur. This study involved a cross-sectional survey in which quantitative data were collected and analysed using descriptive statistics. The survey was containing 74 questionnaire items created using Google Form and distributed through online platforms such as WhatsApp, Telegram, and Facebook Messenger. The results indicated a high level of overall self-efficacy among the preschool teachers and the overall teachers' perceived level of NPSC. The findings also showed a significant and positive relationship at a high level between TSE and teachers' perceptions of the level of implementation of NPSC. Student involvement was one of the TSE factors that had the greatest influence in shaping teachers' perceptions of the level of implementation of NPSC. The findings of the predictors to teachers' perceptions of the implementation of NPSC within this study can be used as an indication to the researchers to reassure the validity of this study by repeating with similar research settings. Further studies to include other factors are also encouraged to explore the possible factors that may influence the teachers' perceptions of the implementation of NPSC.

Keywords: teachers’ self-efficacy, national preschool standard curriculum, preschool teachers, preschool education

Procedia PDF Downloads 178