Search results for: deep belief net

Authors: Ardiansyah I. Ryan, Ashsholih K. R., Fathurrohman G. R., Kurniadi M. R., Huda P. A

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The burden of brain-related disease is very high. There are a lot of brain-related diseases with limited treatment result and thus raise the burden more. The Parkinson Disease (PD), Mental Health Problem, or Paralysis of extremities treatments had risen concern, as the patients for those diseases usually had a low quality of life and low chance to recover fully. There are also many other brain or related neural diseases with the similar condition, mainly the treatments for those conditions are still limited as our understanding of the brain function is insufficient. Brain Implant Technology had given hope to help in treating this condition. In this paper, we examine the current update of the brain implant technology. Neurotechnology is growing very rapidly worldwide. The United States Food and Drug Administration (FDA) has approved the use of Deep Brain Stimulation (DBS) as a brain implant in humans. As for neural implant both the cochlear implant and retinal implant are approved by FDA too. All of them had shown a promising result. DBS worked by stimulating a specific region in the brain with electricity. This device is planted surgically into a very specific region of the brain. This device consists of 3 main parts: Lead (thin wire inserted into the brain), neurostimulator (pacemaker-like device, planted surgically in the chest) and an external controller (to turn on/off the device by patient/programmer). FDA had approved DBS for the treatment of PD, Pain Management, Epilepsy and Obsessive Compulsive Disorder (OCD). The target treatment of DBS in PD is to reduce the tremor and dystonia symptoms. DBS has been showing the promising result in animal and limited human trial for other conditions such as Alzheimer, Mental Health Problem (Major Depression, Tourette Syndrome), etc. Every surgery has risks of complications, although in DBS the chance is very low. DBS itself had a very satisfying result as long as the subject criteria to be implanted this device based on indication and strictly selection. Other than DBS, there are several brain implant devices that still under development. It was included (not limited to) implant to treat paralysis (In Spinal Cord Injury/Amyotrophic Lateral Sclerosis), enhance brain memory, reduce obesity, treat mental health problem and treat epilepsy. The potential of neurotechnology is unlimited. When brain function and brain implant were fully developed, it may be one of the major breakthroughs in human history like when human find ‘fire’ for the first time. Support from every sector for further research is very needed to develop and unveil the true potential of this technology.

Keywords: brain implant, deep brain stimulation (DBS), deep brain stimulation, Parkinson

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: Aurela Jahja, Katarzyna Wątor, Arjan Beqiraj, Piotr Rusiniak, Nevton Kodhelaj

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Albanian geological terrains represent an important node of the Alpine – Mediterranean mountain belt and are divided into several predominantly NNW - SSE striking geotectonic units, which, based on the presence or lack of Cretaceous transgression and magmatic rocks, belong to Internal or External Albanides. The internal (Korabi, Mirdita and Gashi) units are characterized by the Lower Cretaceous discordance and the presence of abundant magmatic rocks whereas in the external (Alps, Krasta-Cukali, Kruja, Ionian, Sazani and Peri Adriatic Depression) units an almost continuous sedimentation from Triassic to Paleogene is evidenced. The internal and external units show relevant differences in both geothermal and heat flow density values. The gradient values vary from 15-21.3 to 36 mK/m, while the heat flow density ranges from 42 to 60 mW/m2, in the external (Preadriatic Depression) and internal (ophiolitic belt) units, respectively. The geothermal fluids, which are found in natural springs and deep oil wells of Albania, are located in four thermo-mineral provinces: a) Peshkopi (Korabi) province; b) Kruja province; c) Preadriatic basin province, and d) South Ionian province. Thirteen geothermal waters were sampled from 11 natural springs and 2 deep wells, of which 6 springs and 2 wells from Kruja, 1 spring from Peshkopia, 2 springs from Preadriatic basin and 2 springs South Ionian province. Temperature, pH and Electrical Conductivity were measured in situ, while in laboratory were analyzed by ICP method major anions and cations and several trace elements (B, Li, Sr, Rb, I, Br, etc.). The measured values of temperature, pH and electrical conductivity range within 17-63°C, 6.26-7.92 and 724- 26856µS/cm intervals, respectively. The chemical type of the Albania thermal waters is variable. In the Kruja province prevail the Cl-SO4-NaCa and Cl-Na-Ca water types; while SO4-Ca, HCO3-Ca and Cl-HCO3-Na-Ca, and Cl-Na are found in the provinces of Peshkopi, Ionian and Preadriatic basin, respectively. In the Cl-SO4-HCO3 triangular diagram most of the geothermal waters are close to the chloride corner that belong to “mature waters”, typical of geothermal deep and hot fluids. Only samples from the Ionian province are located within the region of high bicarbonate concentration and they can be classified as peripheral waters that may have mixed with cold groundwater. In the Na-Ca-Mg and Na-K-Mg triangular diagram the majority of waters fall in the corner of sodium, suggesting that their cation ratios are controlled by mineral-solution equilibrium. There is a linear relationship between Cl and B which indicates the mixing of geothermal water with cold water, where the low-chlorine thermal waters from Ionian basin and Preadriatic depression provinces are distinguished by high-chlorine thermal waters from Kruja province. The Cl/Br molar ration of the thermal waters from Kruja province ranges from 1000 to 2660 and separates them from the thermal waters of Ionian basin and Preadriatic depression provinces having Cl/Br molar ratio lower than 650. The apparent increase of Cl/Br molar ratio that correlates with the increasing of the chloride, is probably related with dissolution of the Halite.

Keywords: geothermal fluids, geotectonic units, natural springs, deep wells, mature waters, peripheral waters

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Authors: Lesley Clack, Rachel Ellison, Elizabeth Chambers

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A belief exists that there are huge gender and racial disparities among hospital CEO’s in the United States, and historically, male, Caucasian healthcare executives have made significantly larger salaries than females and other races. With a recent focus on reducing barriers and disparities in healthcare, it remains to be seen whether there have been changes in these disparities over time. The purpose of this study was to explore disparities among salaries of hospital executives in the United States. Analysis of salary data was conducted utilizing online hospital salary databases. Statistical analysis was conducted to examine the significance of the differences. Results indicated that there had been improvements in disparities among some ethnicities. Gender disparities remain the largest gap. The implications of this study are significant for the field of healthcare management as disparities can affect both social dynamics and organizational culture. Understanding where disparities lie is the first step towards bridging the gap and reducing barriers for cultural diversity within healthcare management.

Keywords: health care, disparities, management, executives

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Authors: Mohammad Saleem, Neeta Kumar, Anita Sharma, Sazina Muzammil

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It is observed that some of the dental students in our setting take less interest in medical subjects. Various teaching methods are focus of research interest currently and being tried to generate interest among students. An approach of interactive integrated tutorial was used to assess its feasibility in teaching medical subjects to dental undergraduates. The aim was to generate interest and promote active self-learning among students. The objectives were to (1) introduce the integrated interactive learning method through two departments, (2) get feedback from the students and faculty on feasibility and effectiveness of this method. Second-year students in Bachelor of Dental Surgery course were divided into two groups. Each group was asked to study physiology and pathology of a common and important condition (anemia and hypertension) in a week’s time. During the tutorial, students asked questions on physiology and pathology of that condition from each other in the presence of teachers of both physiology and pathology departments. The teachers acted only as facilitators. After the session, the feedback from students and faculty on this alternative learning method was obtained. Results: Majority of the students felt that this method of learning is enjoyable, helped to develop reasoning skills and ability to correlate and integrate the knowledge from two related fields. Majority of the students felt that this kind of learning led to better understanding of the topic and motivated them towards deep learning. Teachers observed that the study promoted interdepartmental cross-discipline collaboration and better students’ linkages. Conclusion: Interactive integrated tutorial is effective in motivating dental students for better and deep learning of medical subjects.

Keywords: active learning, education, integrated, interactive, self-learning, tutorials

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Authors: Christina Killian, Katie Wall, Séamus Fanning, Guerrino Macori

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Deep-level metagenomics offers a useful technical approach to explore the three dynamic One Health axes: healthcare, domiciliary and veterinary. There is currently limited understanding of the composition of complex biofilms, natural abundance of AMR genes and gene transfer occurrence in these ecological niches. By using a newly established small-scale complex biofilm model, COMBAT has the potential to provide new information on microbial diversity, antimicrobial resistance (AMR)-encoding gene abundance, and their transfer in complex biofilms of importance to these three One Health axes. Shotgun metagenomics has been used to sample the genomes of all microbes comprising the complex communities found in each biofilm source. A comparative analysis between untreated and biocide-treated biofilms is described. The basic steps include the purification of genomic DNA, followed by library preparation, sequencing, and finally, data analysis. The use of long-read sequencing facilitates the completion of metagenome-assembled genomes (MAG). Samples were sequenced using a PromethION platform, and following quality checks, binning methods, and bespoke bioinformatics pipelines, we describe the recovery of individual MAGs to identify mobile gene elements (MGE) and the corresponding AMR genotypes that map to these structures. High-throughput sequencing strategies have been deployed to characterize these communities. Accurately defining the profiles of these niches is an essential step towards elucidating the impact of the microbiota on each niche biofilm environment and their evolution.

Keywords: COMBAT, biofilm, metagenomics, high-throughput sequencing

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Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

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Authors: Mohammad Ghiasvand, Babak Khorsandi, Morteza Kolahdoozan

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Under the influence of waves, oil in the sea is subject to vertical scattering in the water column. Scientists' knowledge of how oil is dispersed in the water column is one of the lowest levels of knowledge among other processes affecting oil in the marine environment, which highlights the need for research and study in this field. Therefore, this study investigates the distribution of oil in the water column in a turbulent environment with zero velocity characteristics. Lack of laboratory results to analyze the distribution of petroleum pollutants in deep water for information Phenomenon physics on the one hand and using them to calibrate numerical models on the other hand led to the development of laboratory models in research. According to the aim of the present study, which is to investigate the distribution of oil in homogeneous and isotropic turbulence caused by the oscillating Grid, after reaching the ideal conditions, the crude oil flow was poured onto the water surface and oil was distributed in deep water due to turbulence was investigated. In this study, all experimental processes have been implemented and used for the first time in Iran, and the study of oil diffusion in the water column was considered one of the key aspects of pollutant diffusion in the oscillating Grid environment. Finally, the required oscillation velocities were taken at depths of 10, 15, 20, and 25 cm from the water surface and used in the analysis of oil diffusion due to turbulence parameters. The results showed that with the characteristics of the present system in two static modes and network motion with a frequency of 0.8 Hz, the results of oil diffusion in the four mentioned depths at a frequency of 0.8 Hz compared to the static mode from top to bottom at 26.18, 57 31.5, 37.5 and 50% more. Also, after 2.5 minutes of the oil spill at a frequency of 0.8 Hz, oil distribution at the mentioned depths increased by 49, 61.5, 85, and 146.1%, respectively, compared to the base (static) state.

Keywords: homogeneous and isotropic turbulence, oil distribution, oscillating grid, oil spill

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Authors: H. E. Amolo

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The concepts of pollution or contamination and purification or ritual cleansing are very important concepts among traditional Africans. This is because in relation to human behaviors and attitudes, they constitute on the one hand what could be referred to as moral demands and on the other, what results in the default of such demands. The many taboos which a man has to observe are not to be regarded as things mechanical which do not touch the heart, but that the avoidance is a sacred law respected by the community. In breaking it, you offend the divine power’. Researches have shown that, Africans tenaciously hold the belief that, moral values are based upon the recognition of the divine will and that sin in the community must be expelled if perfect peace is to be enjoyed. Sadly enough, these moral values are gradually eroding in contemporary times. Thus, this study proposal calls for a survey of the passage from an African cultural context; how it can enhance the understanding of the text, as well as how it can complement its scholarly interpretation with the view of institutionalizing the concept of holiness as a means of bringing the people closer to God, and also instilling ethical purity and righteousness.

Keywords: cultural practices, Igbo ideology, purification, rituals

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Authors: Afif J. Arabi

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On April 15, 2024, Iran announced its unprecedented military attack by sending waves of more than 300 drones and ballistic missiles toward Israel. The Attack lasted approximately five hours and was a widely covered, distributed, and followed media event. Iran’s military action against Israel was a long-awaited action across the Middle East since the early days of the October 7th war on Gaza and after a long history of verbal threats. While people in many Arab countries stayed up past midnight in anticipation of watching the disastrous results of this unprecedented attack, voices on traditional and social media alike started to question the timed public announcement of the attack, which gave Israel at least a two-hour notice to prepare its defenses. When live news coverage started showing that nearly all the drones and missiles were intercepted by Israel – with help from the U.S. and other countries – and no deaths were reported, the social media response to this media event turned toward sarcasm, mockery, irony, and humor. Social media users posted sarcastic pictures, jokes, and comments mocking the Iranian offensive. This research examines this unique media event and the sarcastic response it generated on social media. The study aims to investigate the causes leading to media sarcasm in militarized political conflict, the social function of such generated sarcasm, and the role of social media as a platform for consuming frustration, dissatisfaction, and outrage passively through various media products. The study compares the serious traditional media coverage of the event with the humorous social media response among Arab countries. The research uses an eclectic theoretical approach using framing theory as a paradigm for understanding and investigating communication social functionalism theory in media studies to examine sarcasm. Social functionalism theory is a sociological perspective that views society as a complex system whose parts work together to promote solidarity and stability. In the context of media and sarcasm, this theory would suggest that sarcasm serves specific functions within society, such as reinforcing social norms, providing a means for social critique, or functioning as a safety valve for expressing social tension.; and a qualitative analysis of specific examples including responses of SM commentators to such manifestations of political criticism. The preliminary findings of this study point to a heightened dramatization of the televised event and a widespread belief that this attack was a staged show incongruent with Iran’s official enmity and death threats toward Israel. The social media sarcasm reinforces Arab’s view of Iran and Israel as mutual threats. This belief stems from the complex dynamics, historical context, and regional conflict surrounding these three nations: Iran, Israel, and Arabs.

Keywords: social functionalism, social media sarcasm, Television news framing, live militarized conflict coverage, iran, israel, communication theory

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Authors: Marlin Mubarak

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

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

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Authors: M. Seguini, D. Nedjar

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An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.

Keywords: finite element method, geometric nonlinearity, material nonlinearity, soil-structure interaction, spatial variability

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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Authors: H. M. Haytham, E. A. Azza, E.S. Mohamed, E. G. Nesreen

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Thoracotomy is a great surgery that has serious pulmonary complications, so purpose of this study was to determine the response of diaphragmatic excursion to inspiratory muscle trainer post thoracotomy. Thirty patients of both sexes (16 men and 14 women) with age ranged from 20 to 40 years old had done thoracotomy participated in this study. The practical work was done in cardiothoracic department, Kasr-El-Aini hospital at faculty of medicine for individuals 3 days Post operatively. Patients were assigned into two groups: group A (study group) included 15 patients (8 men and 7 women) who received inspiratory muscle training by using inspiratory muscle trainer for 20 minutes and routine chest physiotherapy (deep breathing, cough and early ambulation) twice daily, 3 days per week for one month. Group B (control group) included 15 patients (8 men and 7 women) who received the routine chest physiotherapy only (deep breathing, cough and early ambulation) twice daily, 3 days per week for one month. Ultrasonography was used to evaluate the changes in diaphragmatic excursion before and after training program. Statistical analysis revealed a significant increase in diaphragmatic excursion in the study group (59.52%) more than control group (18.66%) after using inspiratory muscle trainer post operatively in patients post thoracotomy. It was concluded that the inspiratory muscle training device increases diaphragmatic excursion in patients post thoracotomy through improving inspiratory muscle strength and improving mechanics of breathing and using of inspiratory muscle trainer as a method of physical therapy rehabilitation to reduce post-operative pulmonary complications post thoracotomy.

Keywords: diaphragmatic excursion, inspiratory muscle trainer, ultrasonography, thoracotomy

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Authors: Gelila Tamyalew, Asres Abitie

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Community Pharmacists (CPs) have a significant part in consumer choice in the rational use of LMPPs. The opinion of pharmacists regarding branded and generic medications can offer a perception of the potential obstacles that might have to be overcome to advance generic medicine utilization. Many factors affect CPs' attitudes negatively toward LMPPs. Therefore, the current study assessed factors that can affect CPs' attitudes toward LMPPs. In the regression analysis of variables, three variables were associated with CPs' attitudes toward LMPPs. These are; maximum educational status, professional status, and year of experience in community pharmacy practice. Moreover, lack of belief in LMPPs, substitution agreement with the prescriber, cost-effectiveness of LMPPs, and consumer preference/demand were the most influencing reasons for the selection of LMPPs. In conclusion, the attitude of CPs seems suboptimal that requires an intervention to optimize LMPP utilization.

Keywords: locally manufactured pharmaceutical products, attitude, community pharmacist, Ethiopia

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Authors: Jiyeon Kim

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This conceptual paper examines the psychological drivers of green consumerism and sustainable consumption by integrating the Value-Belief-Norm (VBN) Theory and the Theory of Reasoned Action (TRA). With growing environmental concerns, green consumerism promotes eco-friendly choices such as purchasing sustainable products and supporting environmentally responsible companies. However, there remains a need for research that effectively guides strategies to encourage sustainable behaviors. This paper evaluates VBN Theory’s role in driving pro-environmental behaviors. By incorporating TRA, the paper proposes an enhanced model that improves understanding of the factors driving sustained pro-environmental actions. Focusing on values, beliefs, and norms, this integrated model provides a deeper understanding of the cognitive and motivational factors that influence sustainable consumption. The findings offer valuable theoretical and practical insights for developing strategies to support long-term responsible consumer behavior.

Keywords: green consumerism, sustainable behavior, TRA, VBN

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Renuka Mahadevan, Sharon Chang

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This exploratory study is based on potential cruisers’ monetary valuation of cruise attributes. Using choice experiment, monetary trade-offs between four different cruise attributes are examined with Australians as a case study. We found 50% of the sample valued variety of onboard cruise activities the least while 30% were willing to pay A$87 for cruise-organised activities per day, and the remaining 20% regarded an ocean view to be most valuable at A$125. Latent class modelling was then applied and results revealed that potential cruisers’ valuation of the attributes can be used to segment the market into adventurers, budget conscious and comfort lovers. Evidence showed that socio demographics are not as insightful as lifestyle preferences in developing cruise packages and pricing that would appeal to potential cruisers. Marketing also needs to counter the mindset of potential cruisers’ belief that cruises are often costly and that cruising can be done later in life.

Keywords: latent class modelling, choice experiment, potential cruisers, market segmentation, willingness to pay

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Authors: Xiaowen Zhang, Wanda Luen-Wun Siu

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The paper reviews the insights gained in combating COVID-19 in the US, Japan, and China. After evaluation and investigation, we found that China’s and Japan’s experience of fighting COVID-19 is commendable. The Chinese government and the Japanese administration have implemented highly effective governance and public health course of action to fight COVID-19. Government-led epidemic control with a staunch belief in science can roll out effective pandemic control strategies. In contrast, the US failed to react to COVID-19 effectively. The relaxed public health measures of ending shutdowns prematurely were not working. When the US keeps business open after the spring shutdown, COVID-19 cases are soaring. Such experiences inform us effective governance and a mandatory and stricter approach can better curb a pandemic than milder measures in handling a public health emergency. And China and Japan, where collectivistic culture reins, can better maneuver a public health crisis with collective efforts.

Keywords: US, China, Japan, COVID-19

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Authors: T. Garlan, I. Gabelotaud, S. Lucas, E. Marchès

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Production of a global sedimentological seabed map has been initiated in 1995 to provide the necessary tool for searches of aircraft and boats lost at sea, to give sedimentary information for nautical charts, and to provide input data for acoustic propagation modelling. This original approach had already been initiated one century ago when the French hydrographic service and the University of Nancy had produced maps of the distribution of marine sediments of the French coasts and then sediment maps of the continental shelves of Europe and North America. The current map of the sediment of oceans presented was initiated with a UNESCO's general map of the deep ocean floor. This map was adapted using a unique sediment classification to present all types of sediments: from beaches to the deep seabed and from glacial deposits to tropical sediments. In order to allow good visualization and to be adapted to the different applications, only the granularity of sediments is represented. The published seabed maps are studied, if they present an interest, the nature of the seabed is extracted from them, the sediment classification is transcribed and the resulted map is integrated in the world map. Data come also from interpretations of Multibeam Echo Sounder (MES) imagery of large hydrographic surveys of deep-ocean. These allow a very high-quality mapping of areas that until then were represented as homogeneous. The third and principal source of data comes from the integration of regional maps produced specifically for this project. These regional maps are carried out using all the bathymetric and sedimentary data of a region. This step makes it possible to produce a regional synthesis map, with the realization of generalizations in the case of over-precise data. 86 regional maps of the Atlantic Ocean, the Mediterranean Sea, and the Indian Ocean have been produced and integrated into the world sedimentary map. This work is permanent and permits a digital version every two years, with the integration of some new maps. This article describes the choices made in terms of sediment classification, the scale of source data and the zonation of the variability of the quality. This map is the final step in a system comprising the Shom Sedimentary Database, enriched by more than one million punctual and surface items of data, and four series of coastal seabed maps at 1:10,000, 1:50,000, 1:200,000 and 1:1,000,000. This step by step approach makes it possible to take into account the progresses in knowledge made in the field of seabed characterization during the last decades. Thus, the arrival of new classification systems for seafloor has improved the recent seabed maps, and the compilation of these new maps with those previously published allows a gradual enrichment of the world sedimentary map. But there is still a lot of work to enhance some regions, which are still based on data acquired more than half a century ago.

Keywords: marine sedimentology, seabed map, sediment classification, world ocean

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Authors: Suheyla Saritas

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No matter who we are or where we live, our lives revolve around food, which is much more than a merely sustenance. As a part of the human culture, food carries complex significance and symbolic meanings. Turkish people attribute great value to food and its usage specifically tied to rites of passages of human life. Traditions, especially the ones practiced during rites of passages, such as birth, circumcisions, weddings and funerals, have always been accompanied by food in Turkish culture. Since food celebrates and symbolizes human progress in life in the culture, it also surrounds by aspects of belief, custom, magic, ritual and religion and has always been used in ceremonial context during such rites. Even though that context may be different depending on the religious, economic and social nuances of the various Turkish regions, like wheat, meat and bread, certain kinds of food play key roles during Turkish rites, generally upholding traditions. This paper highlights the sociocultural aspect of food in the rites of passages in the Turkish culture. The importance of this work also is how the ceremonial food represents the identity of Turkish people.

Keywords: food, culture, rites of passages, ritual and identity

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Authors: David Tennison

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Epistemic injustice, an injustice done to a person specifically in their capacity as a “knower”, is a subtle form of discrimination, yet its effects can be as dehumanizing and damaging as more overt forms of discrimination. The lens of epistemic injustice has, in recent years, been fruitfully applied to the field of healthcare, examining questions of agency, power, credibility and belief in doctor-patient interactions. Contested illness patients (e.g., those with illnesses lacking scientific consensuses such as fibromyalgia (FM), Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) and Long Covid) face higher levels of scrutiny than other patient groups and are often disbelieved or dismissed when their ailments cannot be easily imaged or tested for- often encapsulated by the expression “it’s all in your head”. Using the case study of FM, the trials of contested illness patients in healthcare can be conceptualized in terms of epistemic injustice, and what is going wrong in these doctor-patient relationships can be effectively diagnosed. This case study also helps reveal epistemic dysfunction (structural epistemic issues embedded in the healthcare system), how this relates to stigma identity-based prejudice, and how the healthcare system upholds existing societal hierarchies and disenfranchises the most vulnerable. In the modern landscape, where cases of these chronic illnesses are not only on the rise but future pandemics threaten to add to their number, this conversation is crucial for the well-being of patients and providers. This presentation will cover what epistemic injustice is and how it can be applied to the politics of the doctor-patient interaction on a micro level and the politics of the healthcare system more broadly. Contested illnesses will be explored in terms of how the “contested” label causes the patient to experience disease stigma and lowers their credibility in healthcare and across other aspects of life. This will be explored in tandem with a discussion of existing identity-based prejudice in the healthcare system and how social identities (such as those of gender, race, and socioeconomic status) intersect with the contested illness label. The effects of epistemic injustice, which include worsening patients’ symptoms of mental health and potentially disenfranchising them from the healthcare system altogether, will be presented alongside the potential ethical quandaries this poses for providers. Finally, issues with the way healthcare appointments and the modern NHS function will be explored in terms of epistemic injustice and solutions to improve doctor-patient communication and patient care will be discussed. The relationship between contested illness patients and healthcare providers is notoriously poor, and while this can mean frustration or feelings of unfulfillment in providers, the negative effects for patients are much more severe. The purpose of this research, then, is to highlight these issues and suggest ways in which to improve the healthcare experience for these patients, along with improving doctor-patient communication and mending the doctor-patient relationship in a tangible and realistic way. This research also aims to provoke important conversations about belief and hierarchy in medical settings and how these aspects intersect with identity prejudices.

Keywords: epistemic injustice, fibromyalgia, contested illnesses, chronic illnesses, doctor-patient relationships, philosophy of medicine

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Authors: Judith Partouche-Sebban, Saeedeh Rezaee Vessal

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Because of the rising number of new cases of cancer, especially among women, it is more than essential to better understand how women experience cancer in order to bring them adapted to support and care and enhance their well-being and patient experience. Cancer stands for a traumatic experience in which the diagnosis, its medical treatments, and the related side effects lead to deep physical and psychological changes that may arouse considerable stress and anxiety. In order to reduce these negative emotions, women tend to use various defense mechanisms, among which denial has been defined as the most frequent mechanism used by breast cancer patients. This study aims to better understand the consequences of the experience of cancer and their link with the adoption of a denial strategy. The empirical research was done among female cancer survivors in France. Since the topic of this study is relatively unexplored, a qualitative methodology and open-ended interviews were employed. In total, 25 semi-directive interviews were conducted with a female with different cancers, different stages of treatment, and different ages. A systematic inductive method was performed to analyze data. The content analysis enabled to highlight three different denial-related behaviors among women with cancer, which serve a self-protective function. First, women who expressed high levels of anxiety confessed they tended to completely deny the existence of their cancer immediately after the diagnosis of their illness. These women mainly exhibit many fears and a deep distrust toward the medical context and professionals. This coping mechanism is defined by the patient as being unconscious. Second, other women deliberately decided to deny partial information about their cancer, whether this information is related to the stages of the illness, the emotional consequences, or the behavioral consequences of the illness. These women use this strategy as a way to avoid the reality of the illness and its impact on the different aspects of their life as if cancer does not exist. Third, some women tend to reinterpret and give meaning to their cancer as a way to reduce its impact on their life. To this end, they may use magical thinking or positive reframing, or reinterpretation. Because denial may lead to delays in medical treatments, this topic deserves a deep investigation, especially in the context of oncology. As denial is defined as a specific defense mechanism, this study contributes to the existing literature in service marketing which focuses on emotions and emotional regulation in healthcare services which is a crucial issue. Moreover, this study has several managerial implications for healthcare professionals who interact with patients in order to implement better care and support for the patients.

Keywords: cancer, coping mechanisms, denial, healthcare services

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: William C. Alexander

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The oscillating water column (OSC) wave energy converter (WEC) with deep water reactance (DWR) consists of a large hollow sphere filled with seawater at the base, referred to as the ‘stabilizer’, a hollow cylinder at the top of the device, with a said cylinder having a bottom open to the sea and a sealed top save for an orifice which leads to an air turbine, and a long, narrow rod connecting said stabilizer with said cylinder. A small amount of ballast at the bottom of the stabilizer and a small amount of floatation in the cylinder keeps the device upright in the sea. The floatation is set such that the mean water level is nominally halfway up the cylinder. The entire device is loosely moored to the seabed to keep it from drifting away. In the presence of ocean waves, seawater will move up and down within the cylinder, producing the ‘oscillating water column’. This gives rise to air pressure within the cylinder alternating between positive and negative gauge pressure, which in turn causes air to alternately leave and enter the cylinder through said top-cover situated orifice. An air turbine situated within or immediately adjacent to said orifice converts the oscillating airflow into electric power for transport to shore or elsewhere by electric power cable. Said oscillating air pressure produces large up and down forces on the cylinder. Said large forces are opposed through the rod to the large mass of water retained within the stabilizer, which is located deep enough to be mostly free of any wave influence and which provides the deepwater reactance. The cylinder and stabilizer form a spring-mass system which has a vertical (heave) resonant frequency. The diameter of the cylinder largely determines the power rating of the device, while the size (and water mass within) of the stabilizer determines said resonant frequency. Said frequency is chosen to be on the lower end of the wave frequency spectrum to maximize the average power output of the device over a large span of time (such as a year). The upper portion of the device (the cylinder) moves laterally (surge) with the waves. This motion is accommodated with minimal loading on the said rod by having the stabilizer shaped like a sphere, allowing the entire device to rotate about the center of the stabilizer without rotating the seawater within the stabilizer. A full-scale device of this type may have the following dimensions. The cylinder may be 16 meters in diameter and 30 meters high, the stabilizer 25 meters in diameter, and the rod 55 meters long. Simulations predict that this will produce 1,400 kW in waves of 3.5-meter height and 12 second period, with a relatively flat power curve between 5 and 16 second wave periods, as will be suitable for an open-ocean location. This is nominally 10 times higher power than similar-sized WEC spar buoys as reported in the literature, and the device is projected to have only 5% of the mass per unit power of other OWC converters.

Keywords: oscillating water column, wave energy converter, spar bouy, stabilizer

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Authors: Neha Adsul, Rohit Shah

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Background: The global history of nursing is exclusively a history of deep contradictions as it seeks to negotiate inclusion in an already gendered world. Although a powerful 'clinical gaze exists, nurses have toiled to re-negotiate and subvert the 'medical gaze' by practicing the 'therapeutic gaze' to tether back 'care into nursing practice.' This helps address the duality of the 'body' and 'mind' wherein the patient is not just limited to being an object of medical inquiry. Nevertheless, there has been a consistent effort to fit 'nursing' into being an art or an emerging science over the years. Especially with advances in hospital-based techno-centric medical practices, the boundaries between technology and nursing practices are becoming more blurred as the technical process becomes synonymous with nursing, eroding the essence of nursing care. Aim: This paper examines the history of nursing and offers insights into how gendered relations and the ideological belief of 'nursing as gendered work' have propagated to the subjugation of the nursing profession. It further aims to provide insights into the patriarchally imbibed techno-centrism that negates the gendered caregiving which lies at the crux of a nurse's work. Method: A literature search was carried out using Google Scholar, Web of Science and PubMed databases. Search words included: technology and nursing, medical technology and nursing, history of nursing, sociology and nursing and nursing care. The history of nursing is presented in a discussion that weaves together the historical events of the 'Birth of the Clinic' and the shift from 'bed-side medicine' to 'hospital-based medicine' that legitimizes exploitation of the bodies of patients to the 'medical gaze while the emergence of nursing as acquiescent to instrumental, technical, positivist and dominant views of medicine. The resultant power asymmetries, wherein in contemporary nursing, the constant struggle of nurses to juggle between being the physicians "operational right arm" to harboring that subjective understanding of the patients to refrain from de-humanizing nursing-care. Findings: The nursing profession suffers from being rendered invisible due to gendered relations having patrifocal societal roots. This perpetuates a notion rooted in the idea that emphasizes empiricism and has resulted in theoretical and epistemological fragmentation of the understanding of body and mind as separate entities. Nurses operate within this structure while constantly being at the brink of being pushed beyond the legitimate professional boundaries while being labeled as being 'unscientific' as the work does not always corroborate and align with the existing dominant positivist lines of inquiries. Conclusion: When understood in this broader context of how nursing as a practice has evolved over the years, it provides a particularly crucial testbed for understanding contemporary gender relations. Not because nurses like to live in a gendered work trap but because the gendered relations at work are written in a covert narcissistic patriarchal milieu that fails to recognize the value of intangible yet utmost necessary 'caring work in nursing. This research urges and calls for preserving and revering the humane aspect of nursing care alongside the emerging tech-savvy expectations from nursing work.

Keywords: nursing history, technocentric, power relations, scientific duality

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Joy Chadwick

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There is no doubt that classrooms of today are more complex and diverse than ever before. Preparing teacher candidates to meet these challenges is essential to ensure the retention of teachers within the profession and to ensure that graduates begin their teaching careers with the knowledge and understanding of how to effectively meet the diversity of students they will encounter. Creating inclusive classrooms requires teachers to have a repertoire of effective instructional skills and strategies. Teachers must also have the mindset to embrace diversity and value the uniqueness of individual students in their care. This qualitative study analyzed teacher candidates' experiences as they completed a fourteen-week teaching practicum while simultaneously completing a university course focused on inclusive pedagogy. The research investigated the challenges and successes teacher candidates had in navigating the translation of theory related to inclusive pedagogy into their teaching practice. Applying threshold concept theory as a framework, the research explored the troublesome concepts, liminal spaces, and transformative experiences as connected to inclusive practices. Threshold concept theory suggests that within all disciplinary fields, there exists particular threshold concepts that serve as gateways or portals into previously inaccessible ways of thinking and practicing. It is in these liminal spaces that conceptual shifts in thinking and understanding and deep learning can occur. The threshold concept framework provided a lens to examine teacher candidate struggles and successes with the inclusive education course content and the application of this content to their practicum experiences. A qualitative research approach was used, which included analyzing twenty-nine course reflective journals and six follow up one-to-one semi structured interviews. The journals and interview transcripts were coded and themed using NVivo software. Threshold concept theory was then applied to the data to uncover the liminal or stuck spaces of learning and the ways in which the teacher candidates navigated those challenging places of teaching. The research also sought to uncover potential transformative shifts in teacher candidate understanding as connected to teaching in an inclusive classroom. The findings suggested that teacher candidates experienced difficulties when they did not feel they had the knowledge, skill, or time to meet the needs of the students in the way they envisioned they should. To navigate the frustration of this thwarted vision, they relied on present and previous course content and experiences, collaborative work with other teacher candidates and their mentor teachers, and a proactive approach to planning for students. Transformational shifts were most evident in their ability to reframe their perceptions of children from a deficit or disability lens to a strength-based belief in the potential of students. It was evident that through their course work and practicum experiences, their beliefs regarding struggling students shifted as they saw the value of embracing neurodiversity, the importance of relationships, and planning for and teaching through a strength-based approach. Research findings have implications for teacher education programs and for understanding threshold concepts theory as connected to practice-based learning experiences.

Keywords: inclusion, inclusive education, liminal space, teacher education, threshold concepts, troublesome knowledge

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