Search results for: deep neural networks

Authors: Shadi Fathi, Moura Mehravar, Mujib Rahman

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The railwaytransportation network in the UK is over 100 years old and is known as one of the oldest mass transit systems in the world. This aged track network requires frequent closure for maintenance. One of the main reasons for closure is inadequate drainage due to the leakage in the buried drainage pipes. The leaking water can cause localised subgrade weakness, which subsequently can lead to major ground/substructure failure.Different condition assessment methods are available to assess the railway substructure. However, the existing condition assessment methods are not able to detect any local ground weakness/damageand provide details of the damage (e.g. size and location). To tackle this issue, a hybrid back-analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to predict the substructurelayers’ moduli and identify any soil weaknesses. At first, afinite element (FE) model of a railway track section under Falling Weight Deflection (FWD) testing was developed and validated against field trial. Then a drainage pipe and various scenarios of the local defect/ soil weakness around the buried pipe with various geometriesand physical properties were modelled. The impact of the soil local weaknesson the track surface deflection wasalso studied. The FE simulations results were used to generate a database for ANN training, and then a GA wasemployed as an optimisation tool to optimise and back-calculate layers’ moduli and soil weakness moduli (ANN’s input). The hybrid ANN-GA back-analysis technique is a computationally efficient method with no dependency on seed modulus values. The modelcan estimate substructures’ layer moduli and the presence of any localised foundation weakness.

Keywords: finite element (FE) model, drainage defect, falling weight deflectometer (FWD), hybrid ANN-GA

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Authors: Mahmoud Roushdy, Mohamed El Naggar, Ahmed Yehia Abdelaziz

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Anchored sheet pile has been used worldwide as front quay walls for decades. With the increase in vessel drafts and weights, those sheet pile walls need to be upgraded by increasing the depth of the dredging line in front of the wall. A system has recently been used to increase the depth in front of the wall by installing a separated platform supported on a deep foundation (so called Relieving Platform) behind the sheet pile wall. The platform is structurally separated from the front wall. This paper presents a numerical investigation utilizing finite element analysis on the behavior of separated relieve platforms installed within existing anchored sheet pile quay walls. The investigation was done in two steps: a verification step followed by a parametric study. In the verification step, the numerical model was verified based on field measurements performed by others. The validated model was extended within the parametric study to a series of models with different backfill soils, separation gap width, and number of pile rows supporting the platform. The results of the numerical investigation show that using stiff clay as backfill soil (neglecting consolidation) gives better performance for the front wall and the first pile row adjacent to sandy backfills. The degree of compaction of the sandy backfill slightly increases lateral deformations but reduces bending moment acting on pile rows, while the effect is minor on the front wall. In addition, the increase in the separation gap width gradually increases bending moments on the front wall regardless of the backfill soil type, while this effect is reversed on pile rows (gradually decrease). Finally, the paper studies the possibility of reducing the number of pile rows along with the separation to take advantage of the positive separation effect on piles.

Keywords: anchored sheet pile, relieving platform, separation gap, upgrade quay wall

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Authors: Feng Tao, Han Ye, Shaoyi Liao

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City constructions collapse after earthquake and people will be buried under ruins. Search and rescue should be conducted as soon as possible to save them. Therefore, according to the complicated environment, irregular aftershocks and rescue allow of no delay, a kind of target localization method based on RSSI (Received Signal Strength Indication) is proposed in this article. The target localization technology based on RSSI with the features of low cost and low complexity has been widely applied to nodes localization in WSN (Wireless Sensor Networks). Based on the theory of RSSI transmission and the environment impact to RSSI, this article conducts the experiments in five scenes, and multiple filtering algorithms are applied to original RSSI value in order to establish the signal propagation model with minimum test error respectively. Target location can be calculated from the distance, which can be estimated from signal propagation model, through improved centroid algorithm. Result shows that the localization technology based on RSSI is suitable for large-scale nodes localization. Among filtering algorithms, mixed filtering algorithm (average of average, median and Gaussian filtering) performs better than any other single filtering algorithm, and by using the signal propagation model, the minimum error of distance between known nodes and target node in the five scene is about 3.06m.

Keywords: signal propagation model, centroid algorithm, localization, mixed filtering, RSSI

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Authors: Ubaid Ullah Kamgar, Ajaz Ahmed Suhaff, Mohammad Maqbool Dar

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Objective: The aim is to study the association of MRI findings of T₂/FLAIR white matter hyperintensities among patients with psychiatric disorders. Background and Rationale: MRI findings in psychiatric disorders can vary widely depending on specific disorders and individual differences. However, some general patterns have been observed, such as, in Depression - reduced volume in areas such as the prefrontal cortex and hippocampus; in Schizophrenia - enlarged ventricles, abnormalities in frontal and temporal lobes, as well as hippocampus and thalamus; in Bipolar Disorder – reduced volume in the prefrontal cortex and hippocampus and abnormalities in the amygdala; in OCD – abnormalities in the orbitofrontal cortex, anterior cingulate cortex and striatum. However, many patients show findings of white-matter hyper-intensities, which are usually considered non-specific in psychiatry. These hyperintensities are low attenuation in the deep and white matter. The pathogenic mechanisms of white matter hyperintensities are not well-understood and have been attributed to cerebral small vessel disease. The aim of the study is to study the association of the above MRI findings in patients with psychiatric disorders after ruling out neurological disorders (if any are found). Methodology: Patients admitted to psychiatric hospitals or presenting to OPDs with underlying psychiatric disorders, having undergone MRI Brain as part of investigations, and having T₂/FLAIR white-matter hyperintensities on MRI were taken to study the association of the above MRI findings with different psychiatric disorders. Results: Out of the 22 patients having MRI findings of T₂/FLAIR white-matter hyper-intensities, the underlying psychiatric comorbidities were: Major Depressive Disorder in 7 pts; Obsessive Compulsive Disorder in 5 pts; Bipolar Disorder in 5 pts; Dementia (vascular type) in 5pts. Discussion and conclusion: In our study, the white matter hyper-intensities were found mostly in MDD (32%), OCD (22.7%), Bipolar Disorder (22.7%) and Dementia in 22.7% of patients. In conclusion, the presence of white-matter hyperintensities in psychiatric disorders underscores the complex interplay between vascular, neurobiological and psychosocial factors. Further research with a large sample size is needed to fully elucidate their clinical significance.

Keywords: white-matter hyperintensities, OCD, MDD, dementia, bipolar disorder.

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Authors: Wardatutthoyyibah, Satyawan Pudyatmoko, Sena Adi Subrata, Muhammad Ali Imron

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The proboscis monkey is an endemic species to the island of Borneo with conservation status IUCN (The International Union for Conservation of Nature) of endangered. The population of the monkey has a specific habitat and sensitive to habitat disturbances. As a consequence of increasing rates of land-use change in the last four decades, its population was reported significantly decreased. We quantified the effect of land use change on the proboscis monkey’s habitat through the species distribution modeling (SDM) approach with Maxent Software. We collected presence data and environmental variables, i.e., land cover, topography, bioclimate, distance to the river, distance to the road, and distance to the anthropogenic disturbance to generate predictive distribution maps of the monkeys. We compared two prediction maps for 2000 and 2015 data to represent the current habitat of the monkey. We overlaid the monkey’s predictive distribution map with the existing protected areas to investigate whether the habitat of the monkey is protected under the protected areas networks. The results showed that almost 50% of the monkey’s habitat reduced as the effect of land use change. And only 9% of the current proboscis monkey’s habitat within protected areas. These results are important for the master plan of conservation of the endangered proboscis monkey and provide scientific guidance for the future development incorporating biodiversity issue.

Keywords: endemic species, land use change, maximum entropy, spatial distribution

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Authors: Bruno Di Pace, Roxanne H. Padley

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Aims: The recent increase in remote video interaction has increased the number of requests for teleconsultations with plastic surgeons in private practice (70% in the UK and 64% in the USA). This study investigated the motivations for such an increase and the underlying psychological impact on patients. Method: An anonymous web-based poll of 8 questions was designed and distributed to patients seeking cosmetic surgery through social networks in both Italy and the UK. The questions gathered responses regarding 1. Reasons for pursuing cosmetic surgery; 2. The effects of delays caused by the SARS-COV-2 pandemic; 3. The effects on mood; 4. The influence of video conferencing on body-image perception. Results: 85 respondents completed the online poll. Overall, 68% of respondents stated that seeing themselves more frequently online had influenced their decision to seek cosmetic surgery. The types of surgeries indicated were predominantly to the upper body and face (82%). Delays and access to surgeons during the pandemic were perceived as negatively impacting patients' moods (95%). Body-image perception and self-esteem were lower than in the pre-pandemic, particularly during lockdown (72%). Patients were more inclined to undergo cosmetic surgery during the pandemic, both due to the wish to improve their “lockdown face” for video conferencing (77%) and also due to the benefits of home recovery while in smart working (58%). Conclusions: Overall, findings suggest that video conferencing has led to a significant increase in requests for cosmetic surgery and the so-called “Zoom Boom” effect.

Keywords: cosmetic surgery, remote communication, telehealth, zoom boom

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Authors: Amirhossein Chambari

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This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given.

Keywords: bi-objective, facility location, queueing, controlled NSGA-II, NRGA-I

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Authors: Arezoo Fallahi, Siamak Derakhshan, Parvaneh Taymoori, Babak Nematshahrbabaki

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Background: Osteoporosis, the most common metabolic bone disease, is an important health care issue. Not only the cost of disease is high but also is one of the causes of disability and mortality and effect on quality of life. Although self-care is effective on disease, s control and treatment but still effective factors on self-care of patient, s viewpoint have not been survey. The aim of this study was to explore effective factors on self-care in women with osteoporosis. Materials and methods: This study was done by conventional content analysis approach in year 2014. Through purposeful sampling 15 women referred to bone mass densitometry centers participated in this study. Inclusion criteria were: Women older than 50 years old with osteoporosis, final diagnosis of osteoporosis for over six –month period, T-score index below -2.5 (lower back or hip), drug use by patients with a physician’s prescription, ability in speaking and attending to participate in the study. Data was collected by face to face and group semi-structure deep interviews and analyzed via content analysis method. To support of rigor of data, criteria credibility, confirmability and transferability were used. Results: during data analysis five categories developed: “hope and disability in the face of illness”, “mutual roles of physician”, “role of family” and “administrative centers and organizations”. To perform self-care behaviors, the participations of this study emphasized on pay attention to their own healthy, regarding patients' rights by physician, pay attention to women's health by men, and the role of media especially radio and television. Conclusion: the finding of the study showed that women’s responsibility with osteoporosis for their health is not a factor but it is multifactorial. Increasing life expectancy in patients, attention to patients needs by physician, increasing health promotion programs in the media and enhancing role of family may provide conditions and infrastructure to empowerment women in doing self-care behavior.

Keywords: women, osteoporosis, self-care, content analysis

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Authors: Grzegorz Kubinski

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The proposed paper is focused on forms of identity expression in people with disabilities (PWD) in the social networks like Instagram. Theoretical analysis widely proposes using the new media as an assistive tool for improving wellbeing and labour activities of PWD. This kind of use is definitely important and plays a key role in all social inclusion processes. However, Instagram is not a place where PWD only express their own problems, but in the opposite, allows them to construct a new definition of disability. In the paper, the problem how this different than a classical approach to disability is created by PWD will be discussed. This issue will be scrutinized mainly in two points. Firstly, the question of how disability is changed by other everyday activities, like fashion or sport, will be described. Secondly, and this could be seen as more important, the point how PWD redefining their bodies creating a different form of aesthetic will be presented. The paper is based on content analysis of Instagram accounts. About 20 accounts created by PWD were analyzed for 6 month period, taking into account elements like photos, comments and discussions. All those information were studied in relation to 'everyday life' category and 'aesthetic' category. Works by T. Siebers, L. J. Davis or R. McRuer were used as theoretical background. Conclusions and interpretations presented in the proposed paper show that the Internet can be used by PWD not only as prosthetic and assistive tools. PWD willingly use them as modes of expression their independence, agency and identity. The paper proposes that in further research this way of using the Internet communication by PWD should be taken into account as an important part of the understanding of disability.

Keywords: body, disability, identity, new media

Procedia PDF Downloads 134

Authors: N. A. Parvin, E. B. Ferranti, L. A. Chapman, C. A. Pfrang

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This study intends to investigate the effects of the Urban Heat Island on public health in Birmingham. Birmingham is located at the center of the West Midlands and its weather is Highly variable due to geographical factors. Residential developments, road networks and infrastructure often replace open spaces and vegetation. This transformation causes the temperature of urban areas to increase and creates an "island" of higher temperatures in the urban landscape. Extreme heat in the urban area is influencing public health in the UK as well as in the world. Birmingham is a densely built-up area with skyscrapers and congested buildings in the city center, which is a barrier to air circulation. We will investigate the city regarding heat and cold-related human mortality and other impacts. We are using primary and secondary datasets to examine the effect of population shift and land-use change on the UHI in Birmingham. We will also use freely available weather data from the Birmingham Urban Observatory and will incorporate satellite data to determine urban spatial expansion and its effect on the UHI. We have produced a temperature map based on summer datasets of 2020, which has covered 25 weather stations in Birmingham to show the differences between diurnal and nocturnal summer and annual temperature trends. Some impacts of the UHI may be beneficial, such as the lengthening of the plant growing season, but most of them are highly negative. We are looking for various effects of urban heat which is impacting human health and investigating mitigation options.

Keywords: urban heat, public health, climate change

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Authors: Pallavi Bhogaram, Xiaolong Wu, Min He, Onyedikachi Okenwa

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A transportation network is a realization of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include road networks, railways, air routes, pipelines, and many more. The transportation network plays a vital role in maintaining the vigor of the nation’s economy. Hence, ensuring the network stays resilient all the time, especially in the face of challenges such as heavy traffic loads and large scale natural disasters, is of utmost importance. In this paper, we used the Neo4j application to develop the graph. Neo4j is the world's leading open-source, NoSQL, a native graph database that implements an ACID-compliant transactional backend to applications. The Southern California network model is developed using the Neo4j application and obtained the most critical and optimal nodes and paths in the network using centrality algorithms. The edge betweenness centrality algorithm calculates the critical or optimal paths using Yen's k-shortest paths algorithm, and the node betweenness centrality algorithm calculates the amount of influence a node has over the network. The preliminary study results confirm that the Neo4j application can be a suitable tool to study the important nodes and the critical paths for the major congested metropolitan area.

Keywords: critical path, transportation network, connectivity reliability, network model, Neo4j application, edge betweenness centrality index

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Authors: Umit Cali

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The integration of intermittent energy sources like wind farms into the electricity grid has become an important challenge for the utilization and control of electric power systems, because of the fluctuating behaviour of wind power generation. Wind power predictions improve the economic and technical integration of large amounts of wind energy into the existing electricity grid. Trading, balancing, grid operation, controllability and safety issues increase the importance of predicting power output from wind power operators. Therefore, wind power forecasting systems have to be integrated into the monitoring and control systems of the transmission system operator (TSO) and wind farm operators/traders. The wind forecasts are relatively precise for the time period of only a few hours, and, therefore, relevant with regard to Spot and Intraday markets. In this work predictive data mining techniques are applied to identify a statistical and neural network model or set of models that can be used to predict wind power output of large onshore and offshore wind farms. These advanced data analytic methods helps us to amalgamate the information in very large meteorological, oceanographic and SCADA data sets into useful information and manageable systems. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast allows grid operators to schedule economically efficient generation to meet the demand of electrical customers. This study is also dedicated to an in-depth consideration of issues such as the comparison of day ahead and the short-term wind power forecasting results, determination of the accuracy of the wind power prediction and the evaluation of the energy economic and technical benefits of wind power forecasting.

Keywords: renewable energy sources, wind power, forecasting, data mining, big data, artificial intelligence, energy economics, power trading, power grids

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Authors: Misbah Fatma, Anam Saiyeda

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This analysis charts the introduction and expansion of research into the filter bubble phenomena over the last 10 years using a large dataset of academic publications. This bibliometric study demonstrates how interdisciplinary filter bubble research is. The identification of key authors and organizations leading the filter bubble study sheds information on collaborative networks and knowledge transfer. Relevant papers are organized based on themes including algorithmic bias, polarisation, social media, and ethical implications through a systematic examination of the literature. In order to shed light on how these patterns have changed over time, the study plots their historical history. The study also looks at how research is distributed globally, showing geographic patterns and discrepancies in scholarly output. The results of this bibliometric analysis let us fully comprehend the development and reach of filter bubble research. This study offers insights into the ongoing discussion surrounding information personalization and its implications for societal discourse, democratic participation, and the potential risks to an informed citizenry by exposing dominant themes, interdisciplinary collaborations, and geographic patterns. In order to solve the problems caused by filter bubbles and to advance a more diverse and inclusive information environment, this analysis is essential for scholars and researchers.

Keywords: bibliometric analysis, social media, social networking, algorithmic personalization, self-selection, content moderation policies and limited access to information, recommender system and polarization

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Authors: Yichin Chen

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Taiwan is an island characterized by an active tectonics and high erosion rates. Monitoring the dynamic landscape of Taiwan is an important issue for disaster mitigation, geomorphological research, and watershed management. Long-term and high spatiotemporal landform data is essential for quantifying and simulating the geomorphological processes and developing warning systems. Recently, the advances in unmanned aerial vehicle (UAV) and computational photogrammetry technology have provided an effective way to rebuild and monitor the topography changes in high spatio-temporal resolutions. This study rebuilds the 30-years landform change in the Aiyuzi watershed in 1986-2017 by using the aerial photogrammetry-based techniques. The Aiyuzi watershed, located in central Taiwan and has an area of 3.99 Km², is famous for its frequent landslide and debris flow disasters. This study took the aerial photos by using UAV and collected multi-temporal historical, stereo photographs, taken by the Aerial Survey Office of Taiwan’s Forestry Bureau. To rebuild the orthoimages and digital surface models (DSMs), Pix4DMapper, a photogrammetry software, was used. Furthermore, to control model accuracy, a set of ground control points was surveyed by using eGPS. The results show that the generated DSMs have the ground sampling distance (GSD) of ~10 cm and ~0.3 cm from the UAV’s and historical photographs, respectively, and vertical error of ~1 m. By comparing the DSMs, there are many deep-seated landslides (with depth over 20 m) occurred on the upstream in the Aiyuzi watershed. Even though a large amount of sediment is delivered from the landslides, the steep main channel has sufficient capacity to transport sediment from the channel and to erode the river bed to ~20 m in depth. Most sediments are transported to the outlet of watershed and deposits on the downstream channel. This case study shows that UAV and photogrammetry technology are useful for topography change monitoring effectively.

Keywords: aerial photogrammetry, landslide, landform change, Taiwan

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Authors: Shima Taheri, Simon Clark

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Concrete is an essential building material used in the majority of structures. Degradation of concrete over time increases the life-cycle cost of an asset with an estimated annual cost of billions of dollars to national economies. Most of the concrete failure occurs due to cracks, which propagate through a structure and cause weakening leading to failure. Stopping crack propagation is thus the key to protecting concrete structures from failure and is the best way to prevent inconveniences and catastrophes. Furthermore, the majority of cracks occur deep within the concrete in inaccessible areas and are invisible to normal inspection. Few materials intrinsically possess self-healing ability, but one that does is concrete. However, self-healing in concrete is limited to small dormant cracks in a moist environment and is difficult to control. In this project, we developed a method for self-healing of nascent fractures in concrete components through the automatic release of self-curing healing agents encapsulated in breakable nano- and micro-structures. The Poly(methyl methacrylate) (PMMA) based two-part adhesive is encapsulated in core-shell structures with brittle/weak inert shell, synthesized via miniemulsion/solvent evaporation polymerization. Stress fields associated with propagating cracks can break these capsules releasing the healing agents at the point where they are needed. The shell thickness is playing an important role in preserving the content until the final setting of concrete. The capsules can also be surface functionalized with carboxyl groups to overcome the homogenous mixing issues. Currently, this formulated self-healing system can replace up to 1% of cement in a concrete formulation. Increasing this amount to 5-7% in the concrete formulation without compromising compression strength and shrinkage properties, is still under investigation. This self-healing system will not only increase the durability of structures by stopping crack propagation but also allow the use of less cement in concrete construction, thereby adding to the global effort for CO2 emission reduction.

Keywords: self-healing concrete, concrete crack, concrete deterioration, durability

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Authors: Ermias A. Tegegn, Million Meshesha

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Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

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Authors: Md. Shafiullah

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This study delves into the evolving landscape of urban childcare in Bangladesh, focusing on the experiences and challenges faced by parents in Dhaka city. This paper argues that the traditional childcare arrangement of city families is inadequate to meet the development needs of children. The study aims to explore the childcare challenges faced by urban parents as they transition from traditional family-based childcare networks to alternative caregiving arrangements amidst urbanization, economic shifts, and social transformations. Utilizing a mixed-method research approach, combining quantitative surveys (n = 200) and four qualitative interviews, the research examines the parental viewpoints on childcare practices and the role of societal norms and values. The study finds childcare crises in both the family and daycare settings. In family care, caregiving suffers from the less availability of grandparents, a lack of skills of caregivers, and a lack of child interaction. As for the daycare, it is affected by the absence of appropriate policies, a lack of quality, health and safety concerns, affordability issues, and cultural concerns. Additionally, the study highlights inadequacies in childcare policies and regulatory frameworks, calling for comprehensive reforms to address the childcare vacuum in urban areas. By shifting the focus from developed to developing countries, this study contributes to the literature and suggests policy implications for Bangladesh and beyond.

Keywords: childcare, child development, childcare policy, daycare, Bangladesh

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Authors: Mohd Ali Abbas Zaidi, Meenu Sachdeva, Jonaki Sen

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In the vertebrate embryo, the forebrain anlagen develops from the anterior-most region of the neural tube which is the precursor of the central nervous system (CNS). The roof plate located at the dorsal midline region of the forebrain anlagen, acts as a source of several secreted molecules involved in patterning and morphogenesis of the forebrain. One such key morphogenetic event is the invagination of the forebrain roof plate which results in separation of the single forebrain vesicle into two cerebral hemispheres. Retinoic acid (RA) signaling plays a key role in this process. Blocking RA signaling at the dorsal forebrain midline inhibits dorsal invagination and results in the absence of certain key features of this region, such as thinning of the neuroepithelium and a lowering of cell proliferation. At present we are investigating the possibility of other signaling pathways acting in concert with RA signaling to regulate this process. We have focused on BMP signaling, which we found to be active in a mutually exclusive domain to that of RA signaling within the roof plate. We have also observed that there is a change in BMP signaling activity on modulation of RA signaling indicating an antagonistic relationship between the two. Moreover, constitutive activation of BMP signaling seems to completely inhibit thinning and partially affect invagination, leaving the lowering of cell proliferation in the midline unaffected. We are employing in-silico modeling as well as molecular manipulations to investigate the relative contribution if any, of regional differences in rates of cell proliferation and thinning of the neuroepithelium towards the process of invagination. We have found expression of certain cell adhesion molecules in forebrain roof-plate whose mRNA localization across the thickness of neuroepithelium is influenced by Bmp and RA signaling, giving regional rigidity to roof plate and assisting invagination. We also found expression of certain cytoskeleton modifiers in a localized small domains in invaginating forebrain roof plate suggesting that midline invagination is under control of many factors.

Keywords: bone morphogenetic signaling, cytoskeleton, cell adhesion molecules, forebrain roof plate, retinoic acid signaling

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Authors: Carolyn Scott, Rachel Nugent

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The agricultural industry in Ireland and worldwide is one of the most dangerous occupations with respect to occupational health and safety accidents and fatalities. Many accident causation models have been developed in safety research to understand the underlying and contributory factors that lead to the occurrence of an accident. Due to the uniqueness of the agricultural sector, current accident causation theories cannot be applied. This paper presents an accident causation model named the Irish Farm Accident Causation Model (IFACM) which has been specifically tailored to the needs of Irish farms. The IFACM is a theoretical and practical model of accident causation that arranges the causal factors into a graphic representation of originating, shaping, and contributory factors that lead to accidents when unsafe acts and conditions are created that are not rectified by control measures. Causes of farm accidents were assimilated by means of a thorough literature review and were collated to form a graphical representation of the underlying causes of a farm accident. The IFACM was validated retrospectively through case study analysis and peer review. Participants in the case study (n=10) identified causes that led to a farm accident in which they were involved. A root cause analysis was conducted to understand the contributory factors surrounding the farm accident, traced back to the ‘root cause’. Experts relevant to farm safety accident causation in the agricultural industry have peer reviewed the IFACM. The accident causation process is complex. Accident prevention requires a comprehensive understanding of this complex process because to prevent the occurrence of accidents, the causes of accidents must be known. There is little research on the key causes and contributory factors of unsafe behaviours and accidents on Irish farms. The focus of this research is to gain a deep understanding of the causality of accidents on Irish farms. The results suggest that the IFACM framework is helpful for the analysis of the causes of accidents within the agricultural industry in Ireland. The research also suggests that there may be international applicability if further research is carried out. Furthermore, significant learning can be obtained from considering the underlying causes of accidents.

Keywords: farm safety, farm accidents, accident causation, root cause analysis

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Authors: Mpai Mokoena, Nsenda Lukumwena

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South Africa is one of the driest countries in the world and is facing a water crisis. In addition to inadequate infrastructure and poor planning, the country is experiencing high rates of water wastage due to pipe leaks. This study outlines the level of water wastage and develops a smart solution to efficiently manage and reduce the effects of pipe leaks, while monitoring the situation before and after fixing the pipe leaks. To understand the issue in depth, a literature review of journal papers and government reports was conducted. A questionnaire was designed and distributed to the general public. Additionally, the municipality office was contacted from a managerial perspective. The analysis from the study indicated that the majority of the citizens are aware of the water crisis and are willing to participate positively to decrease the level of water wasted. Furthermore, the response from the municipality acknowledged that more practical solutions are needed to reduce water wastage, and resources to attend to pipe leaks swiftly. Therefore, this paper proposes a specific solution for municipalities, local plumbers and citizens to minimize the effects of pipe leaks. The solution provides web and mobile application platforms to report and manage leaks swiftly. The solution is beneficial to the country in achieving water security and would promote a culture of responsibility toward water usage.

Keywords: urban distribution networks, leak management, mobile application, responsible citizens, water crisis, water security

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Krishan P. Sharma, T. P. Sharma

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Wireless sensor nodes are resource constrained battery powered devices usually deployed in hostile and ill-disposed areas to cooperatively monitor physical or environmental conditions. Due to their limited power supply, the major challenge for researchers is to utilize their battery power for enhancing the lifetime of whole network. Communication and sensing are two major sources of energy consumption in sensor networks. In this paper, we propose a deployment strategy for enhancing the average lifetime of a sensor network by effectively utilizing communication and sensing energy to provide full coverage. The proposed scheme is based on the fact that due to heavy relaying load, sensor nodes near to the sink drain energy at much faster rate than other nodes in the network and consequently die much earlier. To cover this imbalance, proposed scheme finds optimal communication and sensing ranges according to effective load at each node and uses a non-uniform deployment strategy where there is a comparatively high density of nodes near to the sink. Probable relaying load factor at particular node is calculated and accordingly optimal communication distance and sensing range for each sensor node is adjusted. Thus, sensor nodes are placed at locations that optimize energy during network operation. Formal mathematical analysis for calculating optimized locations is reported in present work.

Keywords: load factor, network lifetime, non-uniform deployment, sensing range

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Authors: Wang Chung-Kwei, Lo Kuo Ying

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This study is aimed to examine main and moderation effect of Chinese traditional social wisdom ‘Ren-qing Shi-kuo’ on the adjustment of insurance workers. Rationale: Ren-qing Shi-ku as a social wisdom has been emphasized and practiced by collective-oriented Chinese for thousand years. The concept of‘Ren-qing Shi-ku’includes values, beliefs and behavior rituals, which helps Chinese to cope with interpersonal conflicts in a sophisticated and closely tied collective society. Based on interview and literature review, we found out Chinese still emphasized the importance of ‘Ren-qing Shi-ku’. The concepts contains five factors, including ‘proper emotion display’, ‘social ritual abiding’, ‘ make empathetic concession’, ‘harmonious and proper behavior’ and ‘tolerance for the interest of the whole’. We developed an indigenous ‘Ren-qing Shi-ku’scale based on interview data and a survey on social worker students. Research methods: We conduct a dyad survey between 294 insurance worker and their supervisors. Insurance workers’ response on ‘Ren-qing Shi-ku,emotion labor, emotional exhaustion, work stress and load, work satisfaction and well-being were collected. We also ask their supervisors to rate these workers ‘empathy, social rule abiding, work performance, and Ren-qing Shi-ku performance. Results: Students’self-ratings on Ren-qing Shi-ku scale are positively correlated with rating from their supervisors on all above indexes. Workers who have higher Ren-qing Shi-ku score also have lower work stress and emotion exhaustion, higher work satisfaction and well-being, more emotion deep acting. They also have higher work performance, social rule abiding, and Ren-qing Shi-ku performance rating from their supervisor. The finding of this study suggested Ren-qing Shi-ku is an effective indicator on insurance workers ‘adjustment. Since Ren-qing Shi-ku is trainable, we suggested that Ren-qing Shi-ku training might be beneficial to service industry in a collective-oriented culture.

Keywords: work stress, Ren-qing Shi-ku, emotional exhaustion, work satisfaction, well-being

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Authors: Mahmoud Zakeri Niri, Saber Moazami, Arman Abdollahipour, Hossein Ghalkhani

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The objective of this research is to hydrological evaluation of four widely-used satellite precipitation products named PERSIANN, TMPA-3B42V7, TMPA-3B42RT, and CMORPH over Zarinehrood basin in Iran. For this aim, at first, daily streamflow of Sarough-cahy river of Zarinehrood basin was simulated using IHACRES rainfall-runoff model with daily rain gauge and temperature as input data from 1988 to 2008. Then, the model was calibrated in two different periods through comparison the simulated discharge with the observed one at hydrometric stations. Moreover, in order to evaluate the performance of satellite precipitation products in streamflow simulation, the calibrated model was validated using daily satellite rainfall estimates from the period of 2003 to 2008. The obtained results indicated that TMPA-3B42V7 with CC of 0.69, RMSE of 5.93 mm/day, MAE of 4.76 mm/day, and RBias of -5.39% performs better simulation of streamflow than those PERSIANN and CMORPH over the study area. It is noteworthy that in Iran, the availability of ground measuring station data is very limited because of the sparse density of hydro-meteorological networks. On the other hand, large spatial and temporal variability of precipitations and lack of a reliable and extensive observing system are the most important challenges to rainfall analysis, flood prediction, and other hydrological applications in this country.

Keywords: hydrological evaluation, IHACRES, satellite precipitation product, streamflow simulation

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Authors: Amy L. Irvine, Gabriel Reygondeau, Derek P. Tittensor

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Marine protected areas (MPAs) within Canada’s exclusive economic zone help ensure the conservation and sustainability of marine ecosystems and the continued provision of ecosystem services to society (e.g., food, carbon sequestration). With ongoing and accelerating climate change, however, MPAs may become undermined in terms of their effectiveness at fulfilling these outcomes. Many populations of species, especially those at their thermal range limits, may shift to cooler waters or become extirpated due to climate change, resulting in new species compositions and ecological interactions within static MPA boundaries. While Canadian MPA management follows international guidelines for marine conservation, no consistent approach exists for adapting MPA networks to climate change and the resulting altered ecosystem conditions. To fill this gap, projected climate-driven shifts in species distributions on Canada’s east coast were analyzed to identify when native species emigrate and novel species immigrate within the network and how high mitigation and carbon emission scenarios influence these timelines. Indicators of the ecological changes caused by these species' shifts in the biological community were also developed. Overall, our research provides projections of climate change impacts and helps to guide adaptive management responses within the Canadian east coast MPA network.

Keywords: climate change, ecosystem modeling, marine protected areas, management

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Authors: Josiel Delgadillo, Johnson Kinyua, Charles Mutigwe

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The purpose of sentiment analysis is to determine the sentiment strength (e.g., positive, negative, neutral) from a textual source for good decision-making. Natural language processing in domains such as financial markets requires knowledge of domain ontology, and pre-trained language models, such as BERT, have made significant breakthroughs in various NLP tasks by training on large-scale un-labeled generic corpora such as Wikipedia. However, sentiment analysis is a strong domain-dependent task. The rapid growth of social media has given users a platform to share their experiences and views about products, services, and processes, including financial markets. StockTwits and Twitter are social networks that allow the public to express their sentiments in real time. Hence, leveraging the success of unsupervised pre-training and a large amount of financial text available on social media platforms could potentially benefit a wide range of financial applications. This work is focused on sentiment analysis using social media text on platforms such as StockTwits and Twitter. To meet this need, SkyBERT, a domain-specific language model pre-trained and fine-tuned on financial corpora, has been developed. The results show that SkyBERT outperforms current state-of-the-art models in financial sentiment analysis. Extensive experimental results demonstrate the effectiveness and robustness of SkyBERT.

Keywords: BERT, financial markets, Twitter, sentiment analysis

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Authors: Shadatul Hanom Rashid, Xiaorong Zhou

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Hybrid sol-gel coatings are the blend of both advantages of inorganic and organic networks have been reported as environmentally friendly anti-corrosion surface pre-treatment for several metals, including aluminum alloys. In this current study, Si-Zr hybrid sol-gel coatings were synthesized from (3-glycidoxypropyl)trimethoxysilane (GPTMS), tetraethyl orthosilicate (TEOS) and zirconium(IV) propoxide (TPOZ) precursors and applied on AA6111 aluminum alloy by dip coating technique. The hybrid sol-gel coatings doped with different concentrations of cerium nitrate (Ce(NO3)3) as a corrosion inhibitor were also prepared and the effect of Ce(NO3)3 concentrations on the morphology and corrosion resistance of the coatings were examined. The surface chemistry and morphology of the hybrid sol-gel coatings were analyzed by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). The corrosion behavior of the coated aluminum alloy samples was evaluated by electrochemical impedance spectroscopy (EIS). Results revealed that good corrosion resistance of hybrid sol-gel coatings were prepared from hydrolysis and condensation reactions of GPTMS, TEOS and TPOZ precursors deposited on AA6111 aluminum alloy. When the coating doped with cerium nitrate, the properties were improved significantly. The hybrid sol-gel coatings containing lower concentration of cerium nitrate offer the best inhibition performance. A proper doping concentration of Ce(NO3)3 can effectively improve the corrosion resistance of the alloy, while an excessive concentration of Ce(NO3)3 would reduce the corrosion protection properties, which is associated with defective morphology and instability of the sol-gel coatings.

Keywords: AA6111, Ce(NO3)3, corrosion, hybrid sol-gel coatings

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Amira Mabrouk, Chokri Abdennadher

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The reduction of road congestion which is inherent to the use of vehicles is an obvious priority to public authority. Therefore, assessing the willingness to pay of an individual in order to save trip-time is akin to estimating the change in price which was the result of setting up a new transport policy to increase the networks fluidity and improving the level of social welfare. This study holds an innovative perspective. In fact, it initiates an economic calculation that has the objective of giving an estimation of the monetized time value during the trips made in Sfax. This research is founded on a double-objective approach. The aim of this study is to i) give an estimation of the monetized value of time; an hour dedicated to trips, ii) determine whether or not the consumer considers the environmental variables to be significant, iii) analyze the impact of applying a public management of the congestion via imposing taxation of city tolls on urban dwellers. This article is built upon a rich field survey led in the city of Sfax. With the use of the contingent valuation method, we analyze the “declared time preferences” of 450 drivers during rush hours. Based on the fond consideration of attributed bias of the applied method, we bring to light the delicacy of this approach with regards to the revelation mode and the interrogative techniques by following the NOAA panel recommendations bearing the exception of the valorization point and other similar studies about the estimation of transportation externality.

Keywords: willingness to pay, contingent valuation, time value, city toll

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Authors: Matthew Lee Turnbough

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In 2007 Spain began to experience the effects of a deep economic crisis, which would generate a situation characterised by instability and uncertainty. This has been an obstacle, especially acute for the youth of this country seeking to enter the workforce. As a result of the impact of COVID-19, the youth in Spain are now suffering the effects of a new crisis that has deepened an already fragile labour environment. In this paper, we analyse the discourses that have emerged from a precarious labour market, specifically from two companies dedicated to operating job portals and job listings in Spain, Job Today, and CornerJob. These two start-up businesses have developed mobile applications geared towards young adults in search of employment in the service sector, two of the companies with the highest user rates in Spain. Utilizing a discourse analysis approach, we explore the impact of individualization and how the process of psychologization may contribute to an increasing reliance on individual solutions to social problems. As such, we seek to highlight the expectations and demands that are placed upon young workers and the type of subjectivity that this dynamic could foster, all this within an unstable framework seemingly marked by chance, a context which is key for the emergence of individualization. Furthermore, we consider the extent to which young adults incorporate these discourses and the strategies they employ basing our analysis on the VULSOCU (New Forms of Socio-Existential Vulnerability, Supports, and Care in Spain) research project, specifically the results of nineteen in-depth interviews and three discussion groups with young adults in this country. Consequently, we seek to elucidate the argumentative threads rooted in the process of individualization and underline the implications of this dynamic for the young worker and his/her labour insertion while also identifying manifestations of the goddess of fortune as a representation of chance in this context. Finally, we approach this panorama of social change in Spain from the perspective of the individuals or young adults who find themselves immersed in this transition from one crisis to another.

Keywords: chance, crisis, discourses, individualization, work, youth

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