Search results for: real estate price prediction

Authors: Alba Prats-Bisbe, Jaume López-Carballo, David Leno-Colorado, Alberto García Molina, Alicia Romero Marquez, Elena Hernández Pena, Eloy Opisso Salleras, Raimon Jané Campos

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Neurological disorders are a leading cause of disability and premature mortality worldwide. Neurorehabilitation (NRHB) is a clinical process aimed at reducing functional impairment, promoting societal participation, and improving the quality of life for affected individuals. Virtual reality (VR) technology is emerging as a promising NRHB support tool. Its immersive nature fosters a strong sense of agency and embodiment, motivating patients to engage in meaningful tasks and increasing adherence to therapy. However, the clinical benefits of VR interventions are challenging to determine due to the high heterogeneity among health applications. This study explores a stepwise development approach for creating VR-based tools to assist individuals with neurological disorders in medical practice, aiming to enhance reproducibility, facilitate comparison, and promote the generalization of findings. Building on previous research, the step-by-step methodology encompasses: Needs Identification– conducting cross-disciplinary meetings to brainstorm problems, solutions, and address barriers. Intervention Definition– target population, set goals, and conceptualize the VR system (equipment and environments). Material Selection and Placement– choose appropriate hardware and software, place the device within the hospital setting, and test equipment. Co-design– collaboratively create VR environments, user interfaces, and data management strategies. Prototyping– develop VR prototypes, conduct user testing, and make iterative redesigns. Usability and Feasibility Assessment– design protocols and conduct trials with stakeholders in the hospital setting. Efficacy Assessment– conduct clinical trials to evaluate outcomes and long-term effects. Cost-Effectiveness Validation– assess reproducibility, sustainability, and balance between costs and benefits. NRHB is complex due to the multifaceted needs of patients and the interdisciplinary healthcare architecture. VR has the potential to support various applications, such as motor skill training, cognitive tasks, pain management, unilateral spatial neglect (diagnosis and treatment), mirror therapy, and ecologically valid activities of daily living. Following this methodology was crucial for launching a VR-based system in a real hospital environment. Collaboration with neuropsychologists lead to develop A) a VR-based tool for cognitive rehabilitation in patients with acquired brain injury (ABI). The system comprises a head-mounted display (HTC Vive Pro Eye) and 7 tasks targeting attention, memory, and executive functions. A desktop application facilitates session configuration, while database records in-game variables. The VR tool's usability and feasibility were demonstrated in proof-of-concept trials with 20 patients, and effectiveness is being tested through a clinical protocol with 12 patients completing 24-session treatment. Another case involved collaboration with nurses and paediatric physiatrists to create B) a VR-based distraction tool during invasive techniques. The goal is to alleviate pain and anxiety associated with botulinum toxin (BTX) injections, blood tests, or intravenous placements. An all-in-one headset (HTC Vive Focus 3) deploys 360º videos to improve the experience for paediatric patients and their families. This study presents a framework for developing clinically relevant and technologically feasible VR-based support tools for hospital settings. Despite differences in patient type, intervention purpose, and VR system, the methodology demonstrates usability, viability, reproducibility and preliminary clinical benefits. It highlights the importance approach centred on clinician and patient needs for any aspect of NRHB within a real hospital setting.

Keywords: neurological disorders, neurorehabilitation, stepwise development approach, virtual reality

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Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: Scholastic Nangila Adeli, Francisca Mbutitia Ngithi

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Stigma is commonly associated with mental health patients and may act as a barrier to individuals who may seek or engage in treatment services. Stigmatization among university students is common whether they know someone with a mental health problem, or have a good knowledge and experience of mental health issues. The objective of this study was to establish the various barriers that prevent university students who have mental health challenges from seeking treatment and care. The study was a descriptive in nature where 320 respondents helped to establish the barriers to treatment or participation in mental health care among university students. A questionnaire was used to help establish the barriers and attitude towards mental illness among the respondents. Results from this study revealed that mental illnesses are common among university students and they are manifested in different forms like; anxiety and panic attacks, mood and eating disorders, Impulse control leading to gambling, alcohol and drug addictions, anger and depression leading to loneliness. Mental stigma (both social and self) was the major barrier with 62% of the respondents stating that social stigma was worse than self-stigma. This is because of the social discrimination towards the victim of mental challenges. On issues of attitude, 71% of the respondents said that they can never admit that they have a mental issue and would rather secretly seek clinical or psychological help for fear of being discriminated or excluded by peers. This view is informed by the societal belief that people with mental health challenges were dangerous (associating them with criminal behavior) and hard to socialize with or help. From the findings of this study, it is concluded that mental health problems are real among university students in Kenya and it is important for the university environment to minimize or eradicate stigma within the social circles. Stigma can be minimized or eradicated by creating awareness among university students and fostering social inclusion so that the students who have mental health challenges can experience a sense of belonging and acceptance hence build their self-esteem.

Keywords: disorders, impulse control, mental health problems, stigma

Procedia PDF Downloads 263

Authors: Shalu Jain, Anju Bansal, Anup Kumar, Sunita Saxena

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Objectives: The novel TMPRSS2:ERG gene fusion is a common somatic event in prostate cancer that in some studies is linked with a more aggressive disease phenotype. Thus, this study aims to determine whether clinical variables are associated with the presence of TMPRSS2:ERG-fusion gene transcript in Indian patients of prostate cancer. Methods: We evaluated the clinical variables with presence and absence of TMPRSS2:ERG gene fusion in prostate cancer and BPH association of clinical patients. Patients referred for prostate biopsy because of abnormal DRE or/and elevated sPSA were enrolled for this prospective clinical study. TMPRSS2:ERG mRNA copies in samples were quantified using a Taqman chemistry by real time PCR assay in prostate biopsy samples (N=42). The T2:ERG assay detects the gene fusion mRNA isoform TMPRSS2 exon1 to ERG exon4. Results: Histopathology report has confirmed 25 cases as prostate cancer adenocarcinoma (PCa) and 17 patients as benign prostate hyperplasia (BPH). Out of 25 PCa cases, 16 (64%) were T2: ERG fusion positive. All 17 BPH controls were fusion negative. The T2:ERG fusion transcript was exclusively specific for prostate cancer as no case of BPH was detected having T2:ERG fusion, showing 100% specificity. The positive predictive value of fusion marker for prostate cancer is thus 100% and the negative predictive value is 65.3%. The T2:ERG fusion marker is significantly associated with clinical variables like no. of positive cores in prostate biopsy, Gleason score, serum PSA, perineural invasion, perivascular invasion and periprostatic fat involvement. Conclusions: Prostate cancer is a heterogeneous disease that may be defined by molecular subtypes such as the TMPRSS2:ERG fusion. In the present prospective study, the T2:ERG quantitative assay demonstrated high specificity for predicting biopsy outcome; sensitivity was similar to the prevalence of T2:ERG gene fusions in prostate tumors. These data suggest that further improvement in diagnostic accuracy could be achieved using a nomogram that combines T2:ERG with other markers and risk factors for prostate cancer.

Keywords: prostate cancer, genetic rearrangement, TMPRSS2:ERG fusion, clinical variables

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Authors: Mohammad Hossein Badamchi

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In Iran as well as other developing countries instituting innovation and entrepreneurship ecosystems by government around the universities is the new imported fashion of modernization and development in the 21st century. In recent decade various statesmen, policy makers, university administrations, economists and development theorists are emphasizing excitingly about the new “start-ups” which are going to solve all economic problems and backwardness of the country. However, critical study of modernization practices in Iran implies that this new trend is suffering from conventional deficiency of modernization planning in 20th century. This article is going to depict the misunderstandings of a situation which we can name “Pseudo-innovation in a closed society” by presenting these 12 paradoxes of this new system, actually happening in Iran: (1) Innovation without freedom? Fiction of innovation in a patriarchal state (2) Entrepreneurship without free market? Fiction of entrepreneurship in a rentier-state. (3) Ecosystem or a state-glasshouse? Is it possible to make and plan an innovation? (4) Innovation; epistemic or practical? How academic innovation could happen abstractly out of context? (5) Risk and Lucre: innovation to protect power and property?! (6) Silicon-valley mirage: what is in common between American-Iranian polity? (7) Information or Communication? ICT startups to restrict the internet (8) The elite paradox: new proletariat of private sector, new governmental clerk or a new path of brain drain? (9) Innovation or commercialization? Revisiting Schumpeterian creative destruction (10) The friendship of Jungle and fire: paradox of public science and market (11) Innovation and revolution: top-down or bottom-up paradox in an Iranian experience (12) Technology instead of civil society: ultimate result of innovation in a closed society. Through explaining these paradoxes we can gradually penetrate the real rationality of Pseudo-innovation ecosystem in a closed society, which can be understood as new-Neopatriarchy reconstruction of traditional patriarchal politics, economy and culture in Iran.

Keywords: innovation, critical sociology, modernisation, Iran, closed society

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Authors: Synthia A. Sari

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The transparent nomination and selection process is the first step to obtaining qualified members of board. It is believed as the representative (agent) of the owners, members of the board must consist of competent and professional people. However, the development of transparent and ideal nomination and selection process in Indonesian State-owned enterprises (SOEs) has been based on relatively little research. Considering the relative importance attached by boards to conduct their roles in their principal’s interest in a variety of governance tasks in state-owned enterprises, the primary aim of this paper is to shed light on the extent of nomination and selection process impact performance of the board in implementing good corporate governance in Indonesian SOEs. The exploratory nature of this study led to the adoption of a qualitative research methodology which uses semi-structured interviews and publically available documents to collect a range of data pertaining board nomination and selection and the work of boards. Interviews were conducted with four informants from three Indonesian SOEs and Ministry of SOEs. Findings in this study demonstrate unclear job description and expectations board members as a result of unclear functions of the board in Indonesian SOEs make transparent and accountable nomination and selection process hard to conduct. This situation is vulnerable to the influences from political interest and that even the process itself can degenerate into situations of political interference. In the end, it leads to choosing the wrong person for membership of the board. This study makes a significant contribution to several fields; the human resource management, corporate governance, and Southeast studies by addressing the basic research gaps of board selection process issues in Indonesian SOEs. The gap is addressed by providing a more coherent framework for effective nomination and selection system which reflects more clearly the real experiences of those actually involved at board level.

Keywords: board selection and nomination process, Indonesian stated-owned enterprises, good corporate governance, political influence

Procedia PDF Downloads 266

Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

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Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

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Authors: Nur Sahver Uslu, Ali̇ Hakan Büyüklü

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Maintenance, Repair, and Overhaul (MRO) costs are significant in the aviation industry. On the other hand, companies that provide MRO services to the aviation industry but are not dominant in the sector, need to determine the right strategies for sustainable profitability in a competitive environment. This study examined the operational real data of a small medium enterprise (SME) MRO company where analytical methods are not widely applied. The company's customers were divided into two categories: airline companies and non-airline companies, and the variables that best explained profitability were analyzed with Logistic Regression for each category and the results were compared. First, data reduction was applied to the transformed variables that went through the data cleaning and preparation stages, and the variables to be included in the model were decided. The misclassification rates for the logistic regression results concerning both customer categories are similar, indicating consistent model performance across different segments. Less profit margin is obtained from airline customers, which can be explained by the variables part description, time to quotation (TTQ), turnaround time (TAT), manager, part cost, and labour cost. The higher profit margin obtained from non-airline customers is explained only by the variables part description, part cost, and labour cost. Based on the two models, it can be stated that it is significantly more challenging for the MRO company, which is the subject of our study, to achieve profitability from Airline customers. While operational processes and organizational structure also affect the profit from airline customers, only the type of parts and costs determine the profit for non-airlines.

Keywords: aircraft, aircraft components, aviation, data analytics, data science, gini index, maintenance, repair, and overhaul, MRO, logistic regression, profit, variable clustering, variable reduction

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Authors: Diego Romero, Fabio Rojas, J. Alejandro Urrego

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The sounding rockets are aerospace vehicles that were developed in the mid-20th century, and Colombia has been involved in research that was carried out with the aim of innovating with this technology. The rockets are university research programs with the collaboration of the local government, with a simple strategy, develop and reduce the greatest costs associated with the production of a kind type of technology. In this way, in this document presents the failure analysis of a solid rocket motor, with the real compatibly to reach the thermosphere with a low-cost fuel. This solid rocket motor is the latest development of the Uniandes Aerospace Project (PUA for its Spanish acronym), an undergraduate and postgraduate research group at Universidad de los Andes (Bogotá, Colombia), dedicated to incurring in this type of technology. This motor has been carried out on Candy-type solid fuel, which is a compound of potassium nitrate and sorbitol, and the investigation has allowed the production of solid motors powerful enough to reach space, and which represents a unique technological advance in Latin America and an important development in experimental rocketry.To outline the main points the explosion in a static test is an important to explore and demonstrate the ways to develop technology, methodologies, production and manufacturing, being a solid rocket motor with 30 kN of thrust. In conclusion, this analysis explores different fields such as: design, manufacture, materials, production, first fire and more, with different engineering tools with principal objective find root failure. Following the engineering analysis methodology, was possible to design a new version of motor, with learned lessons new manufacturing specification, therefore, when publishing this project, it is intended to be a reference for future research in this field and benefit the industry.

Keywords: candy propellant, candy rockets, explosion, failure analysis, static test, solid rocket motor

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Authors: Alina Smalinskiene Janina Petkeviciene, Jurate Klumbiene, Vilma Kriaucioniene, Vaiva Lesauskaite

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The worldwide prevalence of obesity has been increasing dramatically in the last few decades, and Lithuania is no exception. In 2012, every fifth adult (19% of men and 20.5 % of women) was obese and every third was overweight Association studies have highlighted the influence of SNPs in obesity, with particular focus on FTO rs9939609. Thus far, no data on the possible association of this SNP to obesity in the adult Lithuanian population has been reported. Here, for the first time, we demonstrate an association between the FTO rs9939609 homozygous AA genotype and increased BMI when compared to homozygous TT. Furthermore, a positive association was determined between the FTO rs9939609 variant and risk of metabolic syndrome. Background: This study aimed to examine the associations between the fat mass and obesity associated (FTO) gene rs9939609 variant with obesity and metabolic syndrome in Lithuanian adult population. Materials and Methods: A cross-sectional health survey was carried out in randomly selected municipalities of Lithuania. The random sample was obtained from lists of 25–64 year-old inhabitants. The data from 1020 subjects were analysed. The rs9939609 SNP of the FTO gene was assessed using TaqMan assays (Applied Biosystems, Foster City, CA, USA). The Applied Biosystems 7900HT Real-Time Polymerase Chain Reaction System was used for detecting the SNPs. Results: The carriers of the AA genotype had the highest mean values of BMI and waist circumference (WC) and the highest risk of obesity. Interactions ‘genotype x age’ and ‘genotype x physical activity’ in determining BMI and WC were shown. Neither lipid and glucose levels, nor blood pressure were associated with the rs9939609 independently of BMI. In the age group of 25-44 years, association between the FTO genotypes and metabolic syndrome was found. Conclusion: The FTO rs9939609 variant was significantly associated with BMI and WC, and with the risk of obesity in Lithuanian population. The FTO polymorphism might have a greater influence on weight status in younger individuals and in subjects with a low level of physical activity.

Keywords: obesity metabolic syndrome, FTO gene, polymorphism, Lithuania

Procedia PDF Downloads 430

Authors: A. Aboubakr, E. Fehling, S. A. Mourad, M. Omar

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Wind energy is one of the most effective renewable sources especially offshore wind energy although offshore wind technology is more costly to produce. It is well known that offshore wind energy can potentially be very cheap once infrastructure and researches improve. Laterally, the trend is to construct offshore wind energy to generate the electricity form wind. This leads to intensive research in order to improve the infrastructures. Offshore wind energy is the construction of wind farms in bodies of water to generate electricity from wind. The most important part in offshore wind turbine structure is the foundation and its connection with the wind tower. This is the main difference between onshore and offshore structures. Grouted connection between the foundation and the wind tower is the most important part of the building process when constructing wind offshore turbines. Most attention should be paid to the actual grout connection as this transfers the loads safely from tower to foundations and the soil also. In this paper, finite element analyses have been carried out for studying the behaviour of offshore grouted connection for wind turbine structures. ATENA program have been used for non-linear analysis simulation of the real structural behavior thus demonstrating the crushing, cracking, contact between the two materials and steel yielding. A calibration of the material used in the simulation has been carried out assuring an accurate model of the used material by ATENA program. This calibration was performed by comparing the results from the ATENA program with experimental results to validate the material properties used in ATENA program. Three simple patch test models with different properties have been performed. The research is concluded with a result that the calibration showing a good agreement between the ATENA program material behaviors and the experimental results.

Keywords: grouted connection, 3D modeling, finite element analysis, offshore wind energy turbines, stresses

Procedia PDF Downloads 528

Authors: Abdela Bulbula

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Background: Marek’s disease virus is a devastating infection, causing high morbidity and mortality in chickens in Ethiopia. Methods: The current study was conducted from March to November, 2021 with the general objective of performing antemortem and postmortem, isolation, and molecular detection of Marek’s disease virus from outbreak cases in southwestern Ethiopia. Accordingly, based on outbreak information reported from the study sites namely, Bedelle, Yayo, and Bonga towns in southwestern Ethiopia, 50 sick chickens were sampled. The backyard and intensive farming systems of chickens were included in the sampling and priorities were given for chickens that showed clinical signs that are characteristics of Marek’s disease. Results: By clinical examinations, paralysis of legs and wings, gray eye, loss of weight, difficulty in breathing, and depression were recorded on all chickens sampled for this study and death of diseased chickens was observed. In addition, enlargement of the spleen and gross lesions of the liver and heart were recorded during postmortem examination. The death of infected chickens was observed in both vaccinated and non-vaccinated flocks. Out of 50 pooled feather follicle samples, Marek’s disease virus was isolated from 14/50 (28%) by cell culture method and out of six tissue samples, the virus was isolated from 5/6(83.30%). By Real time polymerization chain reaction technique, which was targeted to detect the Meq gene, Marek’s disease virus was detected from 18/50 feather follicles which accounts for 36% of sampled chickens. Conclusion: In general, the current study showed that the circulating Marek’s disease virus in southwestern Ethiopia was caused by the oncogenic Gallid herpesvirus-2 (Serotype-1). Further research on molecular characterization of revolving virus in current and other regions is recommended for effective control of the disease through vaccination.

Keywords: Ethioi, Marek's disease, isolation, molecular

Procedia PDF Downloads 69

Authors: I GD Yudha Partama, A. Kanno, Y. Akamatsu, R. Inui, M. Goto, M. Sekine

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The aerial photogrammetry of shallow water bottoms has the potential to be an efficient high-resolution survey technique for shallow water topography, thanks to the advent of convenient UAV and automatic image processing techniques Structure-from-Motion (SfM) and Multi-View Stereo (MVS)). However, it suffers from the systematic overestimation of the bottom elevation, due to the light refraction at the air-water interface. In this study, we present an empirical method to correct for the effect of refraction after the usual SfM-MVS processing, using common software. The presented method utilizes the empirical relation between the measured true depth and the estimated apparent depth to generate an empirical correction factor. Furthermore, this correction factor was utilized to convert the apparent water depth into a refraction-corrected (real-scale) water depth. To examine its effectiveness, we applied the method to two river sites, and compared the RMS errors in the corrected bottom elevations with those obtained by three existing methods. The result shows that the presented method is more effective than the two existing methods: The method without applying correction factor and the method utilizes the refractive index of water (1.34) as correction factor. In comparison with the remaining existing method, which used the additive terms (offset) after calculating correction factor, the presented method performs well in Site 2 and worse in Site 1. However, we found this linear regression method to be unstable when the training data used for calibration are limited. It also suffers from a large negative bias in the correction factor when the apparent water depth estimated is affected by noise, according to our numerical experiment. Overall, the good accuracy of refraction correction method depends on various factors such as the locations, image acquisition, and GPS measurement conditions. The most effective method can be selected by using statistical selection (e.g. leave-one-out cross validation).

Keywords: bottom elevation, MVS, river, SfM

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Authors: Paramvir Singh

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

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

Procedia PDF Downloads 89

Authors: Sally Ibrahim, Mohamed Hedia, Mohamed Taqi, Mohamed Derbala, Karima Mahmoud, Youssef Ahmed, Sayed Ismail, Mohamed El-Belely

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The identification and quantification of serum microRNA (miRNA) from mares with endometritis might serve as useful and implementable clinical biomarkers for the early diagnosis of endometiritis. Aims of the current study were (I) to study the expression pattern of eca-miR-155, eca-miR-223, eca-miR-17, eca-miR-200a, and eca-miR-205, and (II) to determine the levels of interleukin 6 (IL-6), prostaglandins (PGF₂α and PGE₂), in the serum of Arabian mares with healthy and abnormal uterine status (endometritis). This study was conducted on 80 Arabian mares (4-14 years old). Mares were divided into 48 sub-fertile mares suspected of endometritis and 32 fertile at stud farms. The criteria for mares to be enrolled in the endometritis group were that they had been bred three or more times unsuccessfully in the breeding season or had a history of more than one year of reproductive failure. In addition, two or more of the following criteria on a checklist were present: abnormal clinical findings, transrectal ultrasonographic uterine examination showed abnormal fluid in the uterus (echogenic or ≥2 cm in diameter), positive endometrial cytology; and bacterial and/or fungal growth. Serum samples were collected for measuring IL-6, PGF₂α, and PGE₂ concentrations, as well as serum miRNA isolation and quantitative real-time PCR. Serum concentrations of IL-6, PGE₂, and PGF₂α were higher (P ≤ 0.001) in mares with endometritis compared to the control healthy ones. The expression profile of eca-miR-155, eca-miR-223, eca-miR-17, eca-miR-200a, and eca-miR-205 increased (P≤0.001) in mares with endometritis compared to the control ones. To the best of our knowledge, this is the first study that revealed that serum miRNA and serum inflammatory mediators (IL-6, PGE₂, and PGF₂α) could be used as non-invasive gold standard biomarkers, and therefore might be served as an important additional diagnostic tool for endometritis in Arabian mares. Moreover, estimation of the serum concentrations of serum miRNA, IL-6, PGE₂, and PGF₂α is a promising recommended tool during the breeding soundness examination in mares.

Keywords: Arabian Mares, endometritis, inflammatory mediators, serum miRNA

Procedia PDF Downloads 180

Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

Procedia PDF Downloads 121

Authors: Daria Pashneva, Julija Pauraite, Agne Minderyte, Vadimas Dudoitis, Lina Davuliene, Kristina Plauskaite, Inga Garbariene, Steigvile Bycenkiene

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Black carbon (BC) has received particular attention around the world, not only for its impact on regional and global climate change but also for its impact on air quality and public health. In order to study the relationship between indoor and outdoor BC concentrations, studies were carried out in Vilnius, Lithuania. The studies are aimed at determining the relationship of concentrations, identifying dependencies during the day and week with a further opportunity to analyze the key factors affecting the indoor concentration of BC. In this context, indoor and outdoor continuous real-time measurements of optical BC-related light absorption by aerosol particles were carried out during the cold season (from October to December 2020). The measurement venue was an office located in an urban background environment. Equivalent black carbon (eBC) mass concentration was measured by an Aethalometer (Magee Scientific, model AE-31). The optical transmission of carbonaceous aerosol particles was measured sequentially at seven wavelengths (λ= 370, 470, 520, 590, 660, 880, and 950 nm), where the eBC mass concentration was derived from the light absorption coefficient (σab) at 880 nm wavelength. The diurnal indoor eBC mass concentration was found to vary in the range from 0.02 to 0.08 µgm⁻³, while the outdoor eBC mass concentration - from 0.34 to 0.99 µgm⁻³. Diurnal variations of eBC mass concentration outdoor vs. indoor showed an increased contribution during 10:00 and 12:00 AM (GMT+2), with the highest indoor eBC mass concentration of 0.14µgm⁻³. An indoor/outdoor eBC ratio (I/O) was below one throughout the entire measurement period. The weekend levels of eBC mass concentration were lower than in weekdays for indoor and outdoor for 33% and 28% respectively. Hourly mean mass concentrations of eBC for weekdays and weekends show diurnal cycles, which could be explained by the periodicity of traffic intensity and heating activities. The results show a moderate influence of outdoor eBC emissions on the indoor eBC level.

Keywords: black carbon, climate change, indoor air quality, I/O ratio

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Authors: Amir Sattari, Mac Panah, Naeim Rashidfarokhi

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To enhance the mixing of fluid in a rectangular enclosure with a circular inlet and outlet, an energy-efficient approach is further investigated through computational fluid dynamics (CFD). Particle image velocimetry (PIV) measurements help confirm that the pulsation of the inflow velocity improves the mixing performance inside the enclosure considerably without increasing energy consumption. In this study, multiple CFD simulations with different turbulent models were performed. The results obtained were compared with experimental PIV results. This study investigates small-scale representations of flow patterns in a ventilated rectangular room. The objective is to validate the concept of an energy-efficient ventilation strategy with improved thermal comfort and reduction of stagnant air inside the room. Experimental and simulated results confirm that through pulsation of the inflow velocity, strong secondary vortices are generated downstream of the entrance wall-jet. The pulsatile inflow profile promotes a periodic generation of vortices with stronger eddies despite a relatively low inlet velocity, which leads to a larger boundary layer with increased kinetic energy in the occupied zone. A real-scale study was not conducted; however, it can be concluded that a constant velocity inflow profile can be replaced with a lower pulsated flow rate profile while preserving the mixing efficiency. Among the turbulent CFD models demonstrated in this study, SST-kω is most advantageous, exhibiting a similar global airflow pattern as in the experiments. The detailed near-wall velocity profile is utilized to identify the wall-jet instabilities that consist of mixing and boundary layers. The SAS method was later applied to predict the turbulent parameters in the center of the domain. In both cases, the predictions are in good agreement with the measured results.

Keywords: CFD, PIV, pulsatile inflow, ventilation, wall-jet

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Authors: Basant Singh Sikarwar, Mukesh Roy, Ayush Goyal, Priya Ranjan

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This work combines two bodies of knowledge which includes biomedical basis of blood stain formation and fluid communities’ wisdom that such formation of blood stain depends heavily on physical properties. Moreover biomedical research tells that different patterns in stains of blood are robust indicator of blood donor’s health or lack thereof. Based on these valuable insights an innovative screening tool is proposed which can act as an aide in the diagnosis of diseases such Anemia, Hyperlipidaemia, Tuberculosis, Blood cancer, Leukemia, Malaria etc., with enhanced confidence in the proposed analysis. To realize this powerful technique, simple, robust and low-cost micro-fluidic devices, a micro-capillary viscometer and a pendant drop tensiometer are designed and proposed to be fabricated to measure the viscosity, surface tension and wettability of various blood samples. Once prognosis and diagnosis data has been generated, automated linear and nonlinear classifiers have been applied into the automated reasoning and presentation of results. A support vector machine (SVM) classifies data on a linear fashion. Discriminant analysis and nonlinear embedding’s are coupled with nonlinear manifold detection in data and detected decisions are made accordingly. In this way, physical properties can be used, using linear and non-linear classification techniques, for screening of various diseases in humans and cattle. Experiments are carried out to validate the physical properties measurement devices. This framework can be further developed towards a real life portable disease screening cum diagnostics tool. Small-scale production of screening cum diagnostic devices is proposed to carry out independent test.

Keywords: blood, physical properties, diagnostic, nonlinear, classifier, device, surface tension, viscosity, wettability

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Authors: Gertrude I. Hewapathirana, Loi A. Nguyen, Mohammed M. Mostafa

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Artificial intelligence (AI) technologies and tools are swiftly integrating into many functions of all organizations as a competitive drive to enhance innovations, productivity, efficiency, faster and precise decision making to keep up with rapid changes in the global business arena. Despite increasing research on AI technologies in production, manufacturing, and information management, AI in human resource disciplines is still lagging. Though a few research studies on HR informatics, recruitment, and HRM in general, how to integrate AI in other HR functional disciplines (e.g., compensation, training, mentoring and coaching, employee motivation) is rarely researched. Many inconsistencies of research hinder developing up-to-date knowledge on AI in HR disciplines. Therefore, exploring eight research questions, using bibliometric network analysis combined with a meta-analysis of published research literature. The authors attempt to generate knowledge on the role of AI in improving the efficiency of HR functional disciplines. To advance the knowledge for the benefit of researchers, academics, policymakers, and practitioners, the study highlights the types of AI innovations and outcomes, trends, gaps, themes and topics, fast-moving disciplines, key players, and future directions.AI in HR informatics in high tech firms is the dominant theme in many research publications. While there is increasing attention from researchers and practitioners, there are many gaps between the promise, potential, and real AI applications in HR disciplines. A higher knowledge gap raised many unanswered questions regarding legal, ethical, and morale aspects of AI in HR disciplines as well as the potential contributions of AI in HR disciplines that may guide future research directions. Though the study provides the most current knowledge, it is limited to peer-reviewed empirical, theoretical, and conceptual research publications stored in the WoS database. The implications for theory, practice, and future research are discussed.

Keywords: artificial intelligence, human resources, bibliometric analysis, research directions

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Authors: A. B. M. Rezaul Islam, Ernur Karadogan

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Shape memory alloys (SMAs) are known for their shape memory effect and pseudoelasticity behavior. Their thermomechanical behaviors are modeled by numerous researchers using microscopic thermodynamic and macroscopic phenomenological point of view. Tanaka, Liang-Rogers and Ivshin-Pence models are some of the most popular SMA macroscopic phenomenological constitutive models. They describe SMA behavior in terms of stress, strain and temperature. These models involve material parameters and they have associated uncertainty present in them. At different operating temperatures, the uncertainty propagates to the output when the material is subjected to loading followed by unloading. The propagation of uncertainty while utilizing these models in real-life application can result in performance discrepancies or failure at extreme conditions. To resolve this, we used probabilistic approach to perform the sensitivity and uncertainty analysis of Tanaka, Liang-Rogers, and Ivshin-Pence models. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods have been used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. As per the results, it is evident that the models vary due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most significant parameters at several temperatures. This work highlights the sensitivity and uncertainty analysis results and shows comparison of them at different temperatures and loading conditions for all these models. The analysis presented will aid in designing engineering applications by eliminating the probability of model failure due to the uncertainty in the input parameters. Thus, it is recommended to have a proper understanding of sensitive parameters and the uncertainty propagation at several operating temperatures and loading conditions as per Tanaka, Liang-Rogers, and Ivshin-Pence model.

Keywords: constitutive models, FAST sensitivity analysis, sensitivity analysis, sobol, shape memory alloy, uncertainty analysis

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Authors: Florence Ncube

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This paper examines the military and post-military experiences of soldiers who deserted from the Rwanda Defence Force (RDF) and tried to make a living in South Africa. Because they are deserters, they try to hide their military identity, yet it is simultaneously somewhat coercively ascribed to them by the Rwandan state and can put them in potential danger. The paper attends to the constructions, experiences, practices, and subjective understanding of the deserters’ being in exile to examine how, under circumstances of perceived threat, these men navigate real or perceived state-sponsored surveillance and threat in non-military settings in South Africa where they have become potential political and disciplinary targets. To make sense of the deserters’ experiences in these circumstances, the paper stitches together a number of useful theoretical concepts, including Bourdieu’s (1992) theory of practice and Vigh’s (2009; 2018) concept of social navigation because no single approach can coherently analyze the specificity of this study. Conventional post-military literature privileges an understanding of army desertion as a malignancy and somewhat problematic. Little is known about the military and post-military experiences of deserters who believe that army desertion is in fact a building block towards achieving subjective peace, even in the context of exile. The paper argues that the presence of Rwandan state agents in South Africa strips the context of the exile of its capacity to provide the deserters with peace, safety, and security. This paper recenters army desertion in analyses of militarism, soldiering, and transition in African contexts and complicates commonsense understandings of army desertion which assume that it is entirely problematic. This paper is drawn from an ethnography conducted among 30 junior-rank Rwandan army deserters exiled in Johannesburg and Cape Town. The researcher employed life histories, in-depth interviews, and deep hangouts to collect data.

Keywords: army deserter, military, identity, exile, peacebuilding, South Africa

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Authors: Itsuo Yokoyama, Rikako Kikuti, Miti Sekikawa, Tosinori Asai, Sarai Tsuyoshi

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Introduction: Vascular access for hemodialysis therapy is often difficult, even for experienced medical personnel. Ultrasound guided needle placement have been performed occasionally but is not always helpful in certain cases with complicated vascular anatomy. Obtaining precise anatomical knowledge of the vascular structure is important to prevent access-related complications. With augmented reality (AR) device such as AR glasses, the virtual vascular structure is shown superimposed on the actual patient vessels, thus enabling the operator to maneuver catheter placement easily with free both hands. We herein report our method of AR guided vascular access method in dialysis treatment Methods: Three dimensional (3D) object of the arm with arteriovenous fistula is computer graphically created with 3D software from the data obtained by computer tomography, ultrasound echogram, and image scanner. The 3D vascular object thus created is viewed on the screen of the AR digital display device (such as AR glass or iPad). The picture of the vascular anatomical structure becomes visible, which is superimposed over the real patient’s arm, thereby the needle insertion be performed under the guidance of AR visualization with ease. By this method, technical difficulty in catheter placement for dialysis can be lessened and performed safely. Considerations: Virtual reality technology has been applied in various fields and medical use is not an exception. Yet AR devices have not been widely used among medical professions. Visualization of the virtual vascular object can be achieved by creation of accurate three dimensional object with the help of computer graphical technique. Although our experience is limited, this method is applicable with relative easiness and our accumulating evidence has suggested that our method of vascular access with the use of AR can be promising.

Keywords: abdominal-aorta, calcification, extraskeletal, dialysis, computer graphics, 3DCG, CT, calcium, phosphorus

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Authors: Ali̇ Bayram, Semih Dalkilic, Remzi Yigiter

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N-methyl-D-aspartate (NMDA) receptor is a subtype of glutamate receptor and plays a pivotal role in learning, memory, neuronal plasticity, neurotoxicity and synaptic mechanisms. Animal experiments were suggested that glutamate-induced excitotoxic injuriy and NMDA receptor blockage lead to amnesia and other neurodegenerative diseases including Alzheimer’s disease (AD), Huntington’s disease, amyotrophic lateral sclerosis. Aim of this study is to investigate association between NMDA receptor coding gene GRIN2B expression level and Alzheimer disease. The study was approved by the local ethics committees, and it was conducted according to the principles of the Declaration of Helsinki and guidelines for the Good Clinical Practice. Peripheral blood was collected 50 patients who diagnosed AD and 49 healthy control individuals. Total RNA was isolated with RNeasy midi kit (Qiagen) according to manufacturer’s instructions. After checked RNA quality and quantity with spectrophotometer, GRIN2B expression levels were detected by quantitative real time PCR (QRT-PCR). Statistical analyses were performed, variance between two groups were compared with Mann Whitney U test in GraphpadInstat algorithm with 95 % confidence interval and p < 0.05. After statistical analyses, we have determined that GRIN2B expression levels were down regulated in AD patients group with respect to control group. But expression level of this gene in each group was showed high variability. İn this study, we have determined that NMDA receptor coding gene GRIN2B expression level was down regulated in AD patients when compared with healthy control individuals. According to our results, we have speculated that GRIN2B expression level was associated with AD. But it is necessary to validate these results with bigger sample size.

Keywords: Alzheimer’s disease, N-methyl-d-aspartate receptor, NR2B, GRIN2B, mRNA expression, RT-PCR

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Authors: Boris Blostotsky, Elia Efraim

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Testing of columns in sway mode is performed in order to determine the maximal allowable load limited by plastic deformations or their end connections and a critical load limited by columns stability. Motivation to determine accurate value of critical force is caused by its using as follow: - critical load is maximal allowable load for given column configuration and can be used as criterion of perfection; - it is used in calculation prescribed by standards for design of structural elements under combined action of compression and bending; - it is used for verification of theoretical analysis of stability at various end conditions of columns. In the present work a new non-destructive method for determination of columns critical buckling load in sway mode is proposed. The method allows performing measurements during the tests under loads that exceeds the columns critical load without losing its stability. The possibility of such loading is achieved by structure of the loading system. The system is performed as frame with rigid girder, one of the columns is the tested column and the other is additional two-hinged strut. Loading of the frame is carried out by the flexible traction element attached to the girder. The load applied on the tested column can achieve a values that exceed the critical load by choice of parameters of the traction element and the additional strut. The system lateral stiffness and the column critical load are obtained by the dynamic method. The experiment planning and the comparison between the experimental and theoretical values were performed based on the developed dependency of lateral stiffness of the system on vertical load, taking into account a semi-rigid connections of the column's ends. The agreement between the obtained results was established. The method can be used for testing of real full-size columns in industrial conditions.

Keywords: buckling, columns, dynamic method, semi-rigid connections, sway mode

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: David B. Tsuanyo, Didier Aussel, Yao Azoumah, Pierre Neveu

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An innovative concept called “Flexy-Energy”is developing at 2iE. This concept aims to produce electricity at lower cost by smartly mix different available energies sources in accordance to the load profile of the region. With a higher solar irradiation and due to the fact that Diesel generator are massively used in sub-Saharan rural areas, PV/Diesel hybrid systems could be a good application of this concept and a good solution to electrify this region, provided they are reliable, cost effective and economically attractive to investors. Presentation of the developed approach is the aims of this paper. The PV/Diesel hybrid system designed consists to produce electricity and/or heat from a coupling between Diesel gensets and PV panels without batteries storage, while ensuring the substitution of gasoil by bio-fuels available in the area where the system will be installed. The optimal design of this system is based on his technical performances; the Life Cycle Cost (LCC) and Levelized Cost of Energy are developed and use as economic criteria. The Net Present Value (NPV), the internal rate of return (IRR) and the discounted payback (DPB) are also evaluated according to dual electricity pricing (in sunny and unsunny hours). The PV/Diesel hybrid system obtained is compared to the standalone Diesel gensets. The approach carried out in this paper has been applied to Siby village in Mali (Latitude 12 ° 23'N 8 ° 20'W) with 295 kWh as daily demand. This approach provides optimal physical characteristics (size of the components, number of component) and dynamical characteristics in real time (number of Diesel generator on, their load rate, fuel specific consumptions, and PV penetration rate) of the system. The system obtained is slightly cost effective; but could be improved with optimized tariffing strategies.

Keywords: investments criteria, optimization, PV hybrid, sizing, rural electrification

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Authors: Emily C. Shepard, Caroline Sweeney, Jessica Grimm, Sophie Jacobs, Lauren Thompson, Lisa L. Weyandt

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Adolescence is a critical phase of development in which individuals are prone to more risky behavior while also facing potentially life-changing decisions. The balance of increased behavioral risk and responsibility indicates the importance of executive functioning ability. In recent years, executive function training has emerged as a technique to enhance this cognitive ability. The aim of the present systematic review was to discuss the reported efficacy of executive functioning training techniques among adolescents. After reviewing 3110 articles, a total of 24 articles were identified which examined the role of executive functioning training techniques among adolescents (age 10-19). Articles retrieved demonstrated points of comparison across psychiatric and medical diagnosis, location of training, and stage of adolescence. Typically developing samples, as well as those with attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), conduct disorder, and physical health concerns were found, allowing for the comparison of the efficacy of techniques considering physical and psychological heterogeneity. Among typically developing adolescents, executive functioning training yielded nonsignificant or low effect size improvements in executive functioning, and in some cases executive functioning ability was decreased following the training. In special populations, including those with ADHD, (ASD), conduct disorder, and physical health concerns significant differences and larger effect sizes in executive functioning were seen following treatment, particularly among individuals with ADHD. Future research is needed to identify the long-term efficacy of these treatments, as well as their generalizability to real-world conditions.

Keywords: adolescence, attention-deficit hyperactivity disorder, executive function, executive function training, traumatic brain injury

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Authors: Rosario Carmona

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This article aims to discuss the importance of indigenous people participation in climate change mitigation and adaptation. Specifically, it analyses different understandings of climate vulnerability among diverse actors involved in climate change policies in Chile: indigenous people, state officials, and academics. These data were collected through participant observation and interviews conducted during October 2017 and January 2019 in Chile. Following Karen O’Brien, there are two types of vulnerability, outcome vulnerability and contextual vulnerability. How vulnerability to climate change is understood determines the approach, which actors are involved and which knowledge is considered to address it. Because climate change is a very complex phenomenon, it is necessary to transform the institutions and their responses. To do so, it is fundamental to consider these two perspectives and different types of knowledge, particularly those of the most vulnerable, such as indigenous people. For centuries and thanks to a long coexistence with the environment, indigenous societies have elaborated coping strategies, and some of them are already adapting to climate change. Indigenous people from Chile are not an exception. But, indigenous people tend to be excluded from decision-making processes. And indigenous knowledge is frequently seen as subjective and arbitrary in relation to science. Nevertheless, last years indigenous knowledge has gained particular relevance in the academic world, and indigenous actors are getting prominence in international negotiations. There are some mechanisms that promote their participation (e.g., Cancun safeguards, World Bank operational policies, REDD+), which are not absent from difficulties. And since 2016 parties are working on a Local Communities and Indigenous Peoples Platform. This paper also explores the incidence of this process in Chile. Although there is progress in the participation of indigenous people, this participation responds to the operational policies of the funding agencies and not to a real commitment of the state with this sector. The State of Chile omits a review of the structure that promotes inequality and the exclusion of indigenous people. In this way, climate change policies could be configured as a new mechanism of coloniality that validates a single type of knowledge and leads to new territorial control strategies, which increases vulnerability.

Keywords: indigenous knowledge, climate change, vulnerability, Chile

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Authors: Daniel Tscharnuter, Eliza Truszkiewicz, Gerald Pinter

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High-quality surfaces of plastic parts can be achieved in a very cost-effective manner using in-mold processes, where e.g. scratch resistant or high gloss polymer films are pre-formed and subsequently receive their support structure by injection molding. The pre-forming may be done by high-pressure forming. In this process, a polymer sheet is heated and subsequently formed into the mold by pressurized air. Due to the heat transfer to the cooled mold the polymer temperature drops below its glass transition temperature. This ensures that the deformed microstructure is retained after depressurizing, giving the sheet its final formed shape. The development of a forming process relies heavily on the experience of engineers and trial-and-error procedures. Repeated mold design and testing cycles are however both time- and cost-intensive. It is, therefore, desirable to study the process using reliable computer simulations. Through simulations, the construction of the mold and the effect of various process parameters, e.g. temperature levels, non-uniform heating or timing and magnitude of pressure, on the deformation of the polymer sheet can be analyzed. Detailed knowledge of the deformation is particularly important in the forming of polymer films with integrated electro-optical functions. Care must be taken in the placement of devices, sensors and electrical and optical paths, which are far more sensitive to deformation than the polymers. Reliable numerical prediction of the deformation of the polymer sheets requires sophisticated material models. Polymer films are often either transversely isotropic or orthotropic due to molecular orientations induced during manufacturing. The anisotropic behavior affects the resulting strain field in the deformed film. For example, parts of the same shape but different strain fields may be created by varying the orientation of the film with respect to the mold. The numerical simulation of the high-pressure forming of such films thus requires material models that can capture the nonlinear anisotropic mechanical behavior. There are numerous commercial polymer grades for the engineers to choose from when developing a new part. The effort required for comprehensive material characterization may be prohibitive, especially when several materials are candidates for a specific application. We, therefore, propose a class of models for compressible hyperelasticity, which may be determined from basic experimental data and which can capture key features of the mechanical response. Invariant-based hyperelastic models with a reduced number of invariants are formulated in a semi-numerical way, such that the models are determined from a single uniaxial tensile tests for isotropic materials, or two tensile tests in the principal directions for transversely isotropic or orthotropic materials. The simulation of the high pressure forming of an orthotropic polymer film is finally done using an orthotropic formulation of the hyperelastic model.

Keywords: hyperelastic, anisotropic, polymer film, thermoforming

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