Search results for: heart sound classification

Authors: Nasser Koleini Mamaghani, Sara Mostowfi

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The complexity of public space design makes it difficult for designers to simultaneously consider all issues for thorough decision-making. Among public spaces, the public space around people’s house is the most prominent space that affects and impacts people’s daily life. Considering recreational public spaces in cities, their main purpose would be to design for experiences that enable a deep feeling of peace and a moment of being away from the hectic daily life. Respecting human emotions and restoring natural environments, although difficult and to some extent out of reach, are key issues for designing such spaces. In this paper we propose to analyse the structure of recreational public spaces and the related emotional impressions. Furthermore, we suggest investigating how these structures influence people’s choice for public spaces by using differential semantics. According to Kansei methodology, in order to evaluate a situation appropriately, the assessment variables must be adapted to the user’s mental scheme. This means that the first step would have to be the identification of a space’s conceptual scheme. In our case study, 32 Kansei words and 4 different locations, each with a different sensual experience, were selected. The 4 locations were all parks in the city of Tehran (Iran), each with a unique structure and artifacts such as a fountain, lighting, sculptures, and music. It should be noted that each of these parks has different combination and structure of environmental and artificial elements like: fountain, lightning, sculpture, music (sound) and so forth. The first one was park No.1, a park with natural environment, the selected space was a fountain with motion light and sculpture. The second park was park No.2, in which there are different styles of park construction: ways from different countries, the selected space was traditional Iranian architecture with a fountain and trees. The third one was park No.3, the park with modern environment and spaces, and included a fountain that moved according to music and lighting. The fourth park was park No.4, the park with combination of four elements: water, fire, earth, wind, the selected space was fountains squirting water from the ground up. 80 participant (55 males and 25 females) aged from 20-60 years participated in this experiment. Each person filled the questionnaire in the park he/she was in. Five-point semantic differential scale was considered to determine the relation between space details and adjectives (kansei words). Received data were analyzed by multivariate statistical technique (factor analysis using SPSS statics). Finally the results of this analysis are criteria as inspiration which can be used in future space designing for creating pleasant feeling in users.

Keywords: environmental design, differential semantics, Kansei engineering, subjective preferences, space

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Authors: Asaiel Mubark Al Hadi

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Background: A frequent anorectal condition known as hemorrhoids, sometimes known as piles, is characterized by a weakening of the anal cushion and the supporting tissue as well as spasms of the internal sphincter. Hemorrhoids are most frequently identified by painless bright red bleeding, prolapse, annoying grape-like tissue prolapse, itching, or a combination of symptoms. digital rectal examination (DRE) and anoscope are used to diagnose it. Constipation, a low-fiber diet, a high body- mass index (BMI), pregnancy, and a reduced physical activity are among the factors that are typically thought to increase the risk of hemorrhoids. Golighers is the most commonly used hemorrhoid classification scheme It is 4 degrees, which determines the degree of the event. The purpose of this study is to assess knowledge and awareness level of the causes and stages of Hemorrhoids in the public of Saudi Arabia. Method: This cross-sectional study was conducted in the Saudi Arabia between Oct 2022- Dec 2022. The study group included at least 384 aged above 18 years. The outcomes of this study were analyzed using the SPSS program using a pre-tested questionnaire. Results: The study included 1410 participants, 69.9% of them were females and 30.1% were males. 53.7% of participants aged 20- 30 years old. 17% of participants had hemorrhoids and 42% had a relative who had hemorrhoids. 42.8% of participants could identify stage 1 of hemorrhoids correctly, 44.7% identified stage 2 correctly, 46.7% identified stage 3 correctly and 58.1% identified stage 4 correctly. Only 28.9% of participants had high level of knowledge about hemorrhoids, 62.7% had moderate knowledge and 8.4% had low knowledge. Conclusion: In conclusion, Saudi general population has poor knowledge of hemorrhoids, their causes and their management approach. There was a significant association between knowledge scores of hemorrhoids with age, gender, residence area and employment.

Keywords: hemorrhoids, external hemorrhoid, internal hemorrhoid, anal fissure, hemorrhoid stages, prolapse, rectal bleeding

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Authors: Nkereke M. Essien

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The intention of this paper is to examine stress in the Educated Nigerian Accent of English (ENAE) with the aim of analyzing stress and rhythmic patterns of Nigerian English. Our aim also is to isolate differences and similarities in the stress patterns studied and also know what forms the accent of these Educated Nigerian English (ENE) which marks them off from other groups or English’s of the world, to ascertain and characterize it and to provide documented evidence for its existence. Nigerian stress and rhythmic patterns are significantly different from the British English stress and rhythmic patterns consequently, the educated Nigerian English (ENE) features more stressed syllables than the native speakers’ varieties. The excessive stressed of syllables causes a contiguous “Ss” in the rhythmic flow of ENE, and this brings about a “jerky rhythm’ which distorts communication. To ascertain this claim, ten (10) Nigerian speakers who are educated in the English Language were selected by a stratified Random Sampling technique from two Federal Universities in Nigeria. This classification belongs to the education to the educated class or standard variety. Their performance was compared to that of a Briton (control). The Metrical system of analysis was used. The respondents were made to read some words and utterance which was recorded and analyzed perceptually, statistically and acoustically using the one-way Analysis of Variance (ANOVA). The Turky-Kramer Post Hoc test, the Wilcoxon Matched Pairs Signed Ranks test, and the Praat analysis software were used in the analysis. It was revealed from our findings that the Educated Nigerian English speakers feature more stressed syllables in their productions by spending more time in pronouncing stressed syllables and sometimes lesser time in pronouncing the unstressed syllables. Their overall tempo was faster. The ENE speakers used tone to mark prominence while the native speaker used stress to mark pronounce, typified by the control. We concluded that the stress pattern of the ENE speakers was significantly different from the native speaker’s variety represented by the control’s performance.

Keywords: accent, Nigerian English, rhythm, stress

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Authors: Constantine Uwaremwe, Wenjie Bao, Bachir Goudia Daoura, Sandhya Mishra, Xianxian Zhang, Lingjie Shen, Shangwen Xia, Xiaodong Yang

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Background: Plukenetia volubilis Linneo is an oleaginous plant belonging to the family Euphorbiaceae. Due to its seeds containing a high content of edible oil and rich in vitamins, P. volubilis is cultivated as an economical plant worldwide. However, the cultivation and growth of P. volubilis is challenged by phytopathogen invasion leading to production loss. Methods: In the current study, we tested the pathogenicity of fungal pathogens isolated from root rot infected P. volubilis plant tissues by inoculating them into healthy P. volubilis seedlings. Metagenomic sequencing was used to assess the shift in the fungal community of P. volubilis rhizosphere soil after root rot infection. Results: Four Fusarium isolates and two Rhizopus isolates were found to be root rot causative agents of P. volubilis as they induced typical root rot symptoms in healthy seedlings. The metagenomic sequencing data showed that root rot infection altered the rhizosphere fungal community. In root rot infected soil, the richness and diversity indices increased or decreased depending on pathogens. The four most abundant phyla across all samples were Ascomycota, Glomeromycota, Basidiomycota, and Mortierellomycota. In infected soil, the relative abundance of each phylum increased or decreased depending on the pathogen and functional taxonomic classification. Conclusions: Based on our results, we concluded that Fusarium and Rhizopus species cause root rot infection of P. volubilis. In root rot infected P. volubilis, the shift in the rhizosphere fungal community was pathogen-dependent. These findings may serve as a key point for a future study on the biocontrol of root rot of P. volubilis.

Keywords: fusarium spp., plukenetia volubilis l., rhizopus spp., rhizosphere fungal community, root rot

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Authors: A. Kavita Murugkar, B. Anurag Kashyap

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With the Rights for Persons with Disabilities Act (RPWD Act) 2016, the Indian government has made it mandatory for all establishments, including Heritage Sites, to be accessible for People with Disabilities. However, recent access audit surveys done under the Accessible India Campaign by Ministry of Culture indicate that there are very few accessibility measures provided in the Heritage sites for people with disabilities. Though there are some measures for the mobility impaired, surveys brought out that there are almost no provisions for people with vision impairment (PwVI) in heritage sites thus depriving them of a reasonable physical & intellectual access that facilitates an enjoyable experience and enriching interpretation of the Heritage Site. There is a growing need to develop multisensory interpretative tools that can help the PwVI in perceiving heritage sites in the absence of vision. The purpose of this research was to examine the usability of an audio-tactile model as a haptic and sound-based strategy for augmenting the perception and experience of PwVI in a heritage site. The first phase of the project was a multi-stage phenomenological experimental study with visually impaired users to investigate the design parameters for developing an audio-tactile model for PwVI. The findings from this phase included user preferences related to the physical design of the model such as the size, scale, materials, details, etc., and the information that it will carry such as braille, audio output, tactile text, etc. This was followed by the second phase in which a working prototype of an audio-tactile model is designed and developed for a heritage site based on the findings from the first phase of the study. A nationally listed heritage site from the author’s city was selected for making the model. The model was lastly tested by visually impaired users for final refinements and validation. The prototype developed empowers People with Vision Impairment to navigate independently in heritage sites. Such a model if installed in every heritage site, can serve as a technological guide for the Person with Vision Impairment, giving information of the architecture, details, planning & scale of the buildings, the entrances, location of important features, lifts, staircases, and available, accessible facilities. The model was constructed using 3D modeling and digital printing technology. Though designed for the Indian context, this assistive technology for the blind can be explored for wider applications across the globe. Such an accessible solution can change the otherwise “incomplete’’ perception of the disabled visitor, in this case, a visually impaired visitor and augment the quality of their experience in heritage sites.

Keywords: accessibility, architectural perception, audio tactile model , inclusive heritage, multi-sensory perception, visual impairment, visitor experience

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Authors: Gabrielle Peck, Ryan Hayes

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Polyurethane (PU) foam is typically sold as acoustic foam that is often used as sound insulation in settings such as night clubs and bars. As a construction product, PU is tested by being glued to the walls and ceiling of the ISO 9705 room corner test room. However, when heat is applied to PU foam, it melts and burns as a pool fire due to it being a thermoplastic. The current test layout is unable to accurately measure mass loss and doesn’t allow for the material to burn as a pool fire without seeping out of the test room floor. The lack of mass loss measurement means gas yields pertaining to smoke toxicity analysis can’t be calculated, which makes data comparisons from any other material or test method difficult. Additionally, the heat release measurements are not representative of the actual measurements taken as a lot of the material seeps through the floor (when a tray to catch the melted material is not used). This research aimed to modify the ISO 9705 test to provide the ability to measure mass loss to allow for better calculation of gas yields and understanding of decomposition. It also aimed to accurately measure smoke toxicity in both the doorway and duct and enable dilution factors to be calculated. Finally, the study aimed to examine if doubling the fuel loading would force under-ventilated flaming. The test layout was modified to be a combination of the SBI (single burning item) test set up inside oof the ISO 9705 test room. Polyurethane was tested in two different ways with the aim of altering the ventilation condition of the tests. Test one was conducted using 1 x SBI test rig aiming for well-ventilated flaming. Test two was conducted using 2 x SBI rigs (facing each other inside the test room) (doubling the fuel loading) aiming for under-ventilated flaming. The two different configurations used were successful in achieving both well-ventilated flaming and under-ventilated flaming, shown by the measured equivalence ratios (measured using a phi meter designed and created for these experiments). The findings show that doubling the fuel loading will successfully force under-ventilated flaming conditions to be achieved. This method can therefore be used when trying to replicate post-flashover conditions in future ISO 9705 room corner tests. The radiative heat generated by the two SBI rigs facing each other facilitated a much higher overall heat release resulting in a more severe fire. The method successfully allowed for accurate measurement of smoke toxicity produced from the PU foam in terms of simple gases such as oxygen depletion, CO and CO2. Overall, the proposed test modifications improve the ability to measure the smoke toxicity of materials in different fire conditions on a large-scale.

Keywords: flammability, ISO9705, large-scale testing, polyurethane, smoke toxicity

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Sapan Bhatnagar

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Micron size particles emitted from different sources and produced by combustion have serious negative effects on human health and environment. They can penetrate deep into our lungs through the respiratory system. Determination of the amount of particulates present in the atmosphere per cubic meter is necessary to monitor, regulate and model atmospheric particulate levels. Cascade impactor is used to collect the atmospheric particulates and by gravimetric analysis, their concentration in the atmosphere of different size ranges can be determined. Cascade impactors have been used for the classification of particles by aerodynamic size. They operate on the principle of inertial impaction. It consists of a number of stages each having an impaction plate and a nozzle. Collection plates are connected in series with smaller and smaller cutoff diameter. Air stream passes through the nozzle and the plates. Particles in the stream having large enough inertia impact upon the plate and smaller particles pass onto the next stage. By designing each successive stage with higher air stream velocity in the nozzle, smaller diameter particles will be collected at each stage. Particles too small to be impacted on the last collection plate will be collected on a backup filter. Impactor consists of 4 stages each made of steel, having its cut-off diameters less than 10 microns. Each stage is having collection plates, soaked with oil to prevent bounce and allows the impactor to function at high mass concentrations. Even after the plate is coated with particles, the incoming particle will still have a wet surface which significantly reduces particle bounce. The particles that are too small to be impacted on the last collection plate are then collected on a backup filter (microglass fiber filter), fibers provide larger surface area to which particles may adhere and voids in filter media aid in reducing particle re-entrainment.

Keywords: aerodynamic diameter, cascade, environment, particulates, re-entrainment

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Lei Zhou, Chao Li, Ruiqi Ren, Dan Li, Yali Wang, Daxin Ni, Zijian Feng, Qun Li

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Background: Since the first human infection with avian influenza A(H7N9) case was reported in China on 31 March 2013, five epidemics have been observed in China through February 2013 and September 2017. Though the overall mortality rate of H7N9 has remained as high as around 40% throughout the five epidemics, the specific mortality rate in Mainland China varied by provinces. We conducted a descriptive analysis of mortality rates of H7N9 cases to explore the various severity features of the disease and then to provide clues of further analyses of potential factors associated with the severity of the disease. Methods: The data for analysis originated from the National Notifiable Infectious Disease Report and Surveillance System (NNIDRSS). The surveillance system and identification procedure for H7N9 infection have not changed in China since 2013. The definition of a confirmed H7N9 case is as same as previous reports. Mortality rates of H7N9 cases are described and compared by time and location of reporting, age and sex, and genetic features of H7N9 virus strains. Results: The overall mortality rate, the male and female specific overall rates of H7N9 is 39.6% (608/1533), 40.3% (432/1072) and 38.2% (176/461), respectively. There was no significant difference between the mortality rates of male and female. The age-specific mortality rates are significantly varied by age groups (χ²=38.16, p < 0.001). The mortality of H7N9 cases in the age group between 20 and 60 (33.17%) and age group of over 60 (51.16%) is much higher than that in the age group of under 20 (5.00%). Considering the time of reporting, the mortality rates of cases which were reported in the first (40.57%) and fourth (42.51%) quarters of each year are significantly higher than the mortality of cases which were reported in the second (36.02%) and third (27.27%) quarters (χ²=75.18, p < 0.001). The geographic specific mortality rates vary too. The mortality rates of H7N9 cases reported from the Northeast China (66.67%) and Westeast China (56.52%) are significantly higher than that of H7N9 cases reported from the remained area of mainland China. The mortality rate of H7N9 cases reported from the Central China is the lowest (34.38%). The mortality rates of H7N9 cases reported from rural (37.76%) and urban (38.96%) areas are similar. The mortality rate of H7N9 cases infected with the highly pathogenic avian influenza A(H7N9) virus (48.15%) is higher than the rate of H7N9 cases infected with the low pathogenic avian influenza A(H7N9) virus (37.57%), but the difference is not statistically significant. Preliminary analyses showed that age and some clinical complications such as respiratory failure, heart failure, and septic shock could be potential risk factors associated with the death of H7N9 cases. Conclusions: The mortality rates of H7N9 cases varied by age, sex, time of reporting and geographical location in mainland China. Further in-depth analyses and field investigations of the factors associated with the severity of H7N9 cases need to be considered.

Keywords: H7N9 virus, Avian Influenza, mortality, China

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Authors: Thinakar Mani Balusamy, Ratnakar S. Kini, Bharat Narasimhan, Venkateswaran A. R, Pugazhendi Thangavelu, Mohammed Ali, Prem Kumar K., Kani Sheikh M., Sibi Thooran Karmegam, Radhakrishnan N., Mohammed Noufal

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Introduction: Pancreatic injury remains a complicated condition requiring an individualized case by case approach to management. In this study, we aim to analyze the varied presentations and treatment outcomes of traumatic pancreatic injury in a tertiary care center. Methods: All consecutive patients hospitalized at our center with traumatic pancreatic injury between 2013 and 2017 were included. The American Association for Surgery of Trauma (AAST) classification was used to stratify patients into five grades of severity. Outcome parameters were then analyzed based on the treatment modality employed. Results: Of the 35 patients analyzed, 26 had an underlying blunt trauma with the remaining nine presenting due to penetrating injury. Overall in-hospital mortality was 28%. 19 of these patients underwent exploratory laparotomy with the remaining 16 managed nonoperatively. Nine patients had a severe injury ( > grade 3) – of which four underwent endotherapy, three had stents placed and one underwent an endoscopic pseudocyst drainage. Among those managed nonoperatively, three underwent a radiological drainage procedure. Conclusion: Mortality rates were clearly higher in patients managed operatively. This is likely a result of significantly higher degrees of major associated non-pancreatic injuries and not just a reflection of surgical morbidity. Despite this, surgical management remains the mainstay of therapy, especially in higher grades of pancreatic injury. However we would like to emphasize that endoscopic intervention definitely remains the preferred treatment modality when the clinical setting permits. This is especially applicable in cases of main pancreatic duct injury with ascites as well as pseudocysts.

Keywords: endotherapy, non-operative management, surgery, traumatic pancreatic injury

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Authors: Akintunde Alo

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The incessant encroachments into the forest ecosystem by the farmers and local contractors constitute a major threat to the conservation of genetic resources and biodiversity in Nigeria. To propose a viable monitoring system, this study employed Geographic Information System (GIS) technology to assess the changes that occurred for a period of five years (between 2011 and 2016) in Ikere forest reserve. Landsat imagery of the forest reserve was obtained. For the purpose of geo-referencing the acquired satellite imagery, ground-truth coordinates of some benchmark places within the forest reserve was relied on. Supervised classification algorithm, image processing, vectorization and map production were realized using ArcGIS. Various land use systems within the forest ecosystem were digitized into polygons of different types and colours for 2011 and 2016, roads were represented with lines of different thickness and colours. Of the six land-use delineated, the grassland increased from 26.50 % in 2011 to 45.53% in 2016 of the total land area with a percentage change of 71.81 %. Plantations of Gmelina arborea and Tectona grandis on the other hand reduced from 62.16 % in 2011 to 27.41% in 2016. The farmland and degraded land recorded percentage change of about 176.80 % and 8.70 % respectively from 2011 to 2016. Overall, the rate of deforestation in the study area is on the increase and becoming severe. About 72.59% of the total land area has been converted to non-forestry uses while the remnant 27.41% is occupied by plantations of Gmelina arborea and Tectona grandis. Interestingly, over 55 % of the plantation area in 2011 has changed to grassland, or converted to farmland and degraded land in 2016. The rate of change over time was about 9.79 % annually. Based on the results, rapid actions to prevail on the encroachers to stop deforestation and encouraged re-afforestation in the study area are recommended.

Keywords: land use change, forest reserve, satellite imagery, geographical information system

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Authors: Emmanuel Ikechuckwu Onwubuya, Afees Adebayo Oladejo

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Background: The use of medicinal plants in the treatment of chronic diseases especially myocardial infarction, is gaining wide acceptance globally. Andrographis paniculata (Acanthaceae) is a medicinal plant commonly known as the king of bitters in Nigeria and has been acclaimed for several therapeutic activities. Materials and methods: This study investigated the cardio-protective effect of the leaf extract of A. paniculata in isoproterenol-induced myocardial infarction. Fresh green leaves of A paniculata were harvested from the Faculty of Agriculture farmland, Nnamdi Azikiwe University, Awka, Nigeria. Identification and authentication of the plant were carried out at the Department of Botany, Nnamdi Azikiwe University and a voucher specimen was deposited at the herbarium. The plant material was then shredded, air-dried under shade and pulverized. The fine powders obtained were weighed and extraction was done via a solvent combination of water and ethanol (3:7) for 72 hr via maceration. The filtrate gotten was evaporated to dryness to obtain the ethanol extract, which was used for further bioassay study. The bioactive constituents of the plant extract were quantitatively analyzed by Gas chromatography-mass spectrometry (GC-MS). The animals were administered the extract of A. paniculata orally for seven days at a divided dose of 100 mg/kg, 200 mg/kg and 400 mg/kg body weights. On the eighth day, myocardial infarction was induced through subcutaneous administration of isoproterenol at a dose of 150 mg/kg/day diluted in 2 ml of saline on two consecutive days. Subsequently, the blood pressures were monitored and blood was collected for bioassay studies. Results: The results of the study showed that the leaf extract of A. paniculata was rich in Dodecanoic acid (8.261%), 4-Dibenzofuranamine (6.03%), Cyclotrisiloxane (4.679 %). The findings also showed a significant decrease (p>0.05) in the Mean arterial blood pressure, heart rate, aspartate transaminase, alanine transaminase, creatinine kinase and lactate dehydrogenase activities of the treatment group compared with the untreated control group while the antioxidant (superoxide dismutase, catalase and glutathione) activities were significantly increased in the treatment group, compared with the untreated control group. Conclusion: The findings of this work have shown that the leaf of A. paniculata was rich in bioactive compounds, which could be synthesized to produce plant-based products to fight cardiovascular diseases, especially myocardial infarction.

Keywords: cardiovascular disease, myocardial infarction, medicinal plant, andrographis paniculata, isoproterenol

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Authors: Laura Helena Caicedo Lopez, Luis Miguel Contreras Medina, Ramon Gerardo Guevara Gonzales, Juan E. Andrade

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In this study, we evaluated the effect of three different hydric stress conditions: Low (LHS), medium (MHS), and high (HHS) on capsaicinoid content and enzyme regulation of C. annuum plants. Five main peaks were detected using a 2 Hz resolution vibrometer laser (Polytec-B&K). These peaks or “characteristic frequencies” were used as acoustic emissions (AEs) treatment, transforming these signals into audible sound with the frequency (Hz) content of each hydric stress. Capsaicinoids (CAPs) are the main, secondary metabolites of chili pepper plants and are known to increase during hydric stress conditions or short drought-periods. The AEs treatments were applied in two plant stages: the first one was in the pre-anthesis stage to evaluate the genes that encode the transcription of enzymes responsible for diverse metabolic activities of C. annuum plants. For example, the antioxidant responses such as peroxidase (POD), superoxide dismutase (Mn-SOD). Also, phenyl-alanine ammonia-lyase (PAL) involved in the biosynthesis of the phenylpropanoid compounds. The chalcone synthase (CHS) related to the natural defense mechanisms and species-specific aquaporin (CAPIP-1) that regulate the flow of water into and out of cells. The second stage was at 40 days after flowering (DAF) to evaluate the biochemical effect of AEs related to hydric stress on capsaicinoids production. These two experiments were conducted to identify the molecular responses of C. annuum plants to AE. Moreover, to define AEs could elicit any increase in the capsaicinoids content after a one-week exposition to AEs treatments. The results show that all AEs treatment signals (LHS, MHS, and HHS) were significantly different compared to the non-acoustic emission control (NAE). Also, the AEs induced the up-regulation of POD (~2.8, 2.9, and 3.6, respectively). The gene expression of another antioxidant response was particularly treatment-dependent. The HHS induced and overexpression of Mn-SOD (~0.23) and PAL (~0.33). As well, the MHS only induced an up-regulation of the CHs gene (~0.63). On the other hand, CAPIP-1 gene gas down-regulated by all AEs treatments LHS, MHS, and HHS ~ (-2.4, -0.43 and -6.4, respectively). Likewise, the down-regulation showed particularities depending on the treatment. LHS and MHS induced downregulation of the SOD gene ~ (-1.26 and -1.20 respectively) and PAL (-4.36 and 2.05, respectively). Correspondingly, the LHS and HHS showed the same tendency in the CHs gene, respectively ~ (-1.12 and -1.02, respectively). Regarding the elicitation effect of AE on the capsaicinoids content, additional treatment controls were included. A white noise treatment (WN) to prove the frequency-selectiveness of signals and a hydric stressed group (HS) to compare the CAPs content. Our findings suggest that WN and NAE did not present differences statically. Conversely, HS and all AEs treatments induced a significant increase of capsaicin (Cap) and dihydrocapsaicin (Dcap) after one-week of a treatment. Specifically, the HS plants showed an increase of 8.33 times compared to the NAE and WN treatments and 1.4 times higher than the MHS, which was the AEs treatment with a larger induction of Capsaicinoids among treatments (5.88) and compared to the controls.

Keywords: acoustic emission, capsaicinoids, elicitors, hydric stress, plant signaling

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Authors: Shusmita Khan, Shams El Arifeen, Kanta Jamil

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Background: Nutrition transition in Bangladesh has progressed across various relevant socio-demographic contextual issues. For a developing country like Bangladesh, its is believed that, overnutrition is less prevalent than undernutrition. However, recent evidence suggests that a rapid shift is taking place where overweight is subduing underweight. With this rapid increase, for Bangladesh, it will be challenging to achieve the global agenda on halting overweight and obesity. Methods: A secondary analysis was performed from six successive national demographic and health surveys to get the trend on undernutrition and overnutrition for women from reproductive age. In addition, national relevant policy papers were reviewed to determine the countries readiness for whole of the systems approach to tackle this epidemic. Results: Over the last decade, the proportion of women with low body mass index (BMI<18.5), an indicator of undernutrition, has decreased markedly from 34% to 19%. However, the proportion of overweight women (BMI ≥25) increased alarmingly from 9% to 24% over the same period. If the WHO cutoff for public health action (BMI ≥23) is used, the proportion of overweight women has increased from 17% in 2004 to 39% in 2014. The increasing rate of obesity among women is a major challenge to obstetric practice for both women and fetuses. In the long term, overweight women are also at risk of future obesity, diabetes, hyperlipidemia, hypertension, and heart disease. These diseases have serious impact on health care systems. Costs associated with overweight and obesity involves direct and indirect costs. Direct costs include preventive, diagnostic, and treatment services related to obesity. Indirect costs relate to morbidity and mortality costs including productivity. Looking at the Bangladesh Health Facility Survey, it is found that the country is bot prepared for providing nutrition-related health services, regarding prevention, screening, management and treatment. Therefore, if this nutrition transition is not addressed properly, Bangladesh will not be able to achieve the target of the NCD global monitoring framework of the WHO. Conclusion: Addressing this nutrition transition requires contending ‘malnutrition in all its forms’ and addressing it with integrated approaches. Whole of the systems action is required at all levels—starting from improving multi-sectoral coordination to scaling up nutrition-specific and nutrition-sensitive mainstreamed interventions keeping health system in mind.

Keywords: nutrition transition, Bangladesh, health system, undernutrition, overnutrition, obesity

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Authors: O. Onoriode Emoyan, E. Eyitemi Akporhonor, Charles Otobrise

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Polycyclic Aromatic Hydrocarbons (PAHs) are formed mainly as a result of incomplete combustion of organic materials during industrial, domestic activities or natural occurrence. Their toxicity and contamination of terrestrial and aquatic ecosystem have been established. Though with limited validity index, previous research has focused on PAHs isomer pair ratios of variable physicochemical properties in source identification. The objective of this investigation was to determine the empirical validity of Pearson correlation coefficient (PCC) and cluster analysis (CA) in PAHs source identification along soil samples of different land uses. Therefore, 16 PAHs grouped as endocrine disruption substances (EDSs) were determined in 10 sample stations in top and sub soils seasonally. PAHs was determined the use of Varian 300 gas chromatograph interfaced with flame ionization detector. Instruments and reagents used are of standard and chromatographic grades respectively. PCC and CA results showed that the classification of PAHs along kinetically and thermodyanamically-favoured and those derived directly from plants product through biologically mediated processes used in source signature is about the predominance PAHs are likely to be. Therefore the observed PAHs in the studied stations have trace quantities of the vast majority of the sixteen un-substituted PAHs which may ultimately inhabit the actual source signature authentication. Type and extent of bacterial metabolism, transformation products/substrates, and environmental factors such as: salinity, pH, oxygen concentration, nutrients, light intensity, temperature, co-substrates and environmental medium are hereby recommended as factors to be considered when evaluating possible sources of PAHs.

Keywords: comparative correlation, kinetically and thermodynamically-favored PAHs, pearson correlation coefficient, cluster analysis, sources evaluation

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Authors: Liliana Villavicencio, Yohn Garcia, Pallavi Singh, Luis Fernando Cruz, Wilfrido Moreno

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Psychophysiological adaptive automation is a concept that combines human physiological data and computer algorithms to create personalized interfaces and experiences for users. This approach aims to enhance human learning by adapting to individual needs and preferences and optimizing the interaction between humans and machines. According to neurosciences, the working memory demand during the student learning process is modified when the student is learning a new subject or topic, managing and/or fulfilling a specific task goal. A sudden increase in working memory demand modifies the level of students’ attention, engagement, and cognitive load. The proposed psychophysiological adaptive automation system will adapt the task requirements to optimize cognitive load, the process output variable, by monitoring the student's brain activity. Cognitive load changes according to the student’s previous knowledge, the type of task, the difficulty level of the task, and the overall psychophysiological state of the student. Scaling the measured cognitive load as low, medium, or high; the system will assign a task difficulty level to the next task according to the ratio between the previous-task difficulty level and student stress. For instance, if a student becomes stressed or overwhelmed during a particular task, the system detects this through signal measurements such as brain waves, heart rate variability, or any other psychophysiological variables analyzed to adjust the task difficulty level. The control of engagement and stress are considered internal variables for the hypermedia system which selects between three different types of instructional material. This work assesses the feasibility of a fuzzy controller to track a student's physiological responses and adjust the learning content and pace accordingly. Using an industrial automation approach, the proposed fuzzy logic controller is based on linguistic rules that complement the instrumentation of the system to monitor and control the delivery of instructional material to the students. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the delivery of academic content based on the working memory demand without compromising students’ health. This work has a potential application in the instructional design of virtual reality environments for training and education.

Keywords: fuzzy logic controller, hypermedia control system, personalized education, psychophysiological adaptive automation

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Authors: Chahrazed Zouaoui

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In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Xinyue Jiao, Yu-Ren Lin

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Argumentation is an essential aspect of scientific thinking which has been widely concerned in recent reform of science education. The purpose of the present studies was to explore the influences of two variables termed ‘the argumentation strategy’ and ‘the kind of science concept’ on student’s web-based argumentation. The first variable was divided into either monological (which refers to individual’s internal discourse and inner chain reasoning) or dialectical (which refers to dialogue interaction between/among people). The other one was also divided into either descriptive (i.e., macro-level concept, such as phenomenon can be observed and tested directly) or theoretical (i.e., micro-level concept which is abstract, and cannot be tested directly in nature). The present study applied the quasi-experimental design in which 138 7th grade students were invited and then assigned to either monological group (N=70) or dialectical group (N=68) randomly. An argumentation learning program called ‘the PWAL’ was developed to improve their scientific argumentation abilities, such as arguing from multiple perspectives and based on scientific evidence. There were two versions of PWAL created. For the individual version, students can propose argument only through knowledge recall and self-reflecting process. On the other hand, the students were allowed to construct arguments through peers’ communication in the collaborative version. The PWAL involved three descriptive science concept-based topics (unit 1, 3 and 5) and three theoretical concept-based topics (unit 2, 4 and 6). Three kinds of scaffoldings were embedded into the PWAL: a) argument template, which was used for constructing evidence-based argument; b) the model of the Toulmin’s TAP, which shows the structure and elements of a sound argument; c) the discussion block, which enabled the students to review what had been proposed during the argumentation. Both quantitative and qualitative data were collected and analyzed. An analytical framework for coding students’ arguments proposed in the PWAL was constructed. The results showed that the argumentation approach has a significant effect on argumentation only in theoretical topics (f(1, 136)=48.2, p < .001, η2=2.62). The post-hoc analysis showed the students in the collaborative group perform significantly better than the students in the individual group (mean difference=2.27). However, there is no significant difference between the two groups regarding their argumentation in descriptive topics. Secondly, the students made significant progress in the PWAL from the earlier descriptive or theoretical topic to the later one. The results enabled us to conclude that the PWAL was effective for students’ argumentation. And the students’ peers’ interaction was essential for students to argue scientifically especially for the theoretical topic. The follow-up qualitative analysis showed student tended to generate arguments through critical dialogue interactions in the theoretical topic which promoted them to use more critiques and to evaluate and co-construct each other’s arguments. More explanations regarding the students’ web-based argumentation and the suggestions for the development of web-based science learning were proposed in our discussions.

Keywords: argumentation, collaborative learning, scientific concepts, web-based learning

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Authors: Rasha Shoumar

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Background: Since the onset of the Syria crisis in 2011, Jordan has hosted many Syrian refugees, many of which are residing in urban and rural areas. Vulnerability of refugees has increased due to the COVID-19 pandemic, adding to their already existing challenge in access to medical services, rendering them vulnerable to the complications of untreated medical conditions and amplifying their risk for severe COVID-19 disease. To improve health outcomes and access to health care services in a COVID-19 context, IOM (The International Organization for Migration) provided health services including awareness raising, direct primary health care through mobile teams and referrals to secondary services were extended to the vulnerable populations of refugees. Method: 6 community health volunteers were trained and deployed to different governorates to provide COVID-19 and non-communicable disease awareness and collect data rated to non-communicable disease and access to medical health services. Primary health care services were extended to 7 governorates through a mobile medical team, providing medical management. The collected Data was reviewed and analyzed. Results: 2150 refugees in rural areas were reached out by community health volunteers, out of which 78 received their medications through the Ministry of Health, 121 received their medications through different non-governmental organizations, 665 patients couldn’t afford buying any medications, 1286 patients were occasionally buying their medications when they were able to afford it. 853 patients received medications and follow up through IOM mobile clinics, the most common conditions were hypertension, diabetes, hyperlipidemia, anemia, heart disease, thyroid disease, asthma, seizures, and psychiatric conditions. 709 of these patients had more than 3 of the comorbidities. Multiple cases were referred for secondary and tertiary lifesaving interventions. Conclusion: Non communicable diseases are highly prevalent among refugee population in Jordan, access to medical services have proven to be a challenge in rural areas especially during the COVID-19 era, many of the patients have multiple uncontrolled medical conditions placing them at risk for complications and risk for severe COVID-19 disease. Deployment of mobile clinics to rural areas plays an essential role in managing such medical conditions, thus improving the continuum of health care approach, physical and mental wellbeing of refugees and reducing the risk for severe COVID-19 disease among this group, taking us one step forward toward universal health access.

Keywords: COVID-19, refugees, mobile clinics, primary health care

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Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

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This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

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Authors: André Augusto Ceballos Melo

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

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

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Authors: Dana Alina Magdas, Nicoleta Simona Vedeanu, Ioana Feher, Rares Stiufiuc

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Food and beverages authentication using rapid and non-expensive analytical tools represents nowadays an important challenge. In this regard, the potential of vibrational techniques in food authentication has gained an increased attention during the last years. For wines discrimination, Raman spectroscopy appears more feasible to be used as compared with IR (infrared) spectroscopy, because of the relatively weak water bending mode in the vibrational spectroscopy fingerprint range. Despite this, the use of Raman technique in wine discrimination is in an early stage. Taking this into consideration, the wine discrimination potential of surface-enhanced Raman scattering (SERS) technique is reported in the present work. The novelty of this study, compared with the previously reported studies, concerning the application of vibrational techniques in wine discrimination consists in the fact that the present work presents the wines differentiation based on the individual signals obtained from deconvoluted spectra. In order to achieve wines classification with respect to variety, geographical origin and vintage, the peaks intensities obtained after spectra deconvolution were compared using supervised chemometric methods like Linear Discriminant Analysis (LDA). For this purpose, a set of 20 white Romanian wines from different viticultural Romanian regions four varieties, was considered. Chemometric methods applied directly to row SERS experimental spectra proved their efficiency, but discrimination markers identification found to be very difficult due to the overlapped signals as well as for the band shifts. By using this approach, a better general view related to the differences that appear among the wines in terms of compositional differentiation could be reached.

Keywords: chemometry, SERS, variety, wines discrimination

Procedia PDF Downloads 154

Authors: Svetlana Cojocaru, Mircea Petic, Inga Titchiev

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Humanity faces more and more often with different social disasters, which in turn can generate new accidents and catastrophes. To mitigate their consequences, it is important to obtain early possible signals about the events which are or can occur and to prepare the corresponding scenarios that could be applied. Our research is focused on solving two problems in this domain: identifying signals related that an accident occurred or may occur and mitigation of some consequences of disasters. To solve the first problem, methods of selecting and processing texts from global network Internet are developed. Information in Romanian is of special interest for us. In order to obtain the mentioned tools, we should follow several steps, divided into preparatory stage and processing stage. Throughout the first stage, we manually collected over 724 news articles and classified them into 10 categories of social disasters. It constitutes more than 150 thousand words. Using this information, a controlled vocabulary of more than 300 keywords was elaborated, that will help in the process of classification and identification of the texts related to the field of social disasters. To solve the second problem, the formalism of Petri net has been used. We deal with the problem of inhabitants’ evacuation in useful time. The analysis methods such as reachability or coverability tree and invariants technique to determine dynamic properties of the modeled systems will be used. To perform a case study of properties of extended evacuation system by adding time, the analysis modules of PIPE such as Generalized Stochastic Petri Nets (GSPN) Analysis, Simulation, State Space Analysis, and Invariant Analysis have been used. These modules helped us to obtain the average number of persons situated in the rooms and the other quantitative properties and characteristics related to its dynamics.

Keywords: lexicon of disasters, modelling, Petri nets, text annotation, social disasters

Procedia PDF Downloads 194

Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 139

Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: Hussah Alotaishan

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In today’s globally-driven modern cities diverse groups of new visitors face various challenges when attempting to find their desired location if culture and language are barriers. The most common way-showing tools such as directional and identificational signs are the most problematic and their usefulness can be limited or even non-existent. It is argued new methods should be implemented that could support or replace such conventional literacy and language dependent way-finding aids. It has been concluded in recent research studies that local urban features in complex pedestrian spaces are worthy of further study in order to reveal if they do function as way-showing clues. Some researchers propose a more comprehensive approach to the complex perception of buildings, façade design and surface patterns, while some have been questioning whether we necessarily need directional signs or can other methods deliver the same message but in a clearer manner for a wider range of users. This study aimed to test to what extent do existent environmental and urban features through the city center area of Swansea in the UK facilitate the way-finding process of a first time visitor. The three-hour experiment was set to attempt to find 11 visitor attractions ranging from recreational, historical, educational and religious locations. The challenge was attempting to find as many as possible when no prior geographical knowledge of their whereabouts was established. The only clues were 11 pictures representing each of the locations that had been acquired from the city of Swansea official website. An iPhone and a heart-rate tracker wristwatch were used to record the route was taken and stress levels, and take record photographs of destinations or decision-making points throughout the journey. This paper addresses: current limitations in understanding the ways that the physical environment can be intentionally deployed to facilitate pedestrians while finding their way around, without or with a reduction in language dependent signage; investigates visitor perceptions of their surroundings by indicating what urban elements manifested an impact on the way-finding process. The initial findings support the view that building facades and street features, such as width, could facilitate the decision-making process if strategically employed. However, more importantly, the anticipated features of a specific place construed from a promotional picture can also be misleading and create confusion that may lead to getting lost.

Keywords: pedestrian way-finding, environmental features, urban way-showing, environmental affordance

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Authors: Shu-Ching Chen, Li-Yun Lee

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The purpose of the study was to identify the factors associated with head and neck cancer-related lymphoedema (HNCRL)-related symptoms, body image, and HNCRL-related functional outcomes among patients with resected advanced head and neck cancer. A cross-sectional correlational design was conducted to examine the predictors of HNCRL-related functional outcomes in patients with resected advanced head and neck cancer. Eligible patients were recruited from a single medical center in northern Taiwan. Consecutive patients were approached and recruited from the Radiation Head and Neck Outpatient Department of this medical center. Eligible subjects were assessed for the Symptom Distress Scale–Modified for Head and Neck Cancer (SDS-mhnc), Brief International Classification of Functioning, Disability and Health (ICF) Core Set for Head and Neck Cancer (BCSQ-H&N), Body Image Scale–Modified (BIS-m), The MD Anderson Head and Neck Lymphedema Rating Scale (MDAHNLRS), The Foldi’s Stages of Lymphedema (Foldi’s Scale), Patterson’s Scale, UCLA Shoulder Rating Scale (UCLA SRS), and Karnofsky’s Performance Status Index (KPS). The results showed that the worst problems with body HNCRL functional outcomes. Patients’ HNCRL symptom distress and performance status are robust predictors across over for overall HNCRL functional outcomes, problems with body HNCRL functional outcomes, and activity and social functioning HNCRL functional outcomes. Based on the results of this period research program, we will develop a Cancer Rehabilitation and Lymphedema Care Program (CRLCP) to use in the care of patients with resected advanced head and neck cancer.

Keywords: head and neck cancer, resected, lymphedema, symptom, body image, functional outcome

Procedia PDF Downloads 249

Authors: Shawn P. Daly

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Communities heavily engaged in the tourism industry discover their values intersect, meld, and conflict with those of visitors. Maintaining cultural integrity in the face of powerful external pressures causes stress among society members. This effect represents a less studied aspect of sustainable tourism. The present paper brings a perspective unique to the tourism literature: biculturalism. The grounded theories, coherent hypotheses, and validated constructs and indicators of biculturalism represent a sound base from which to consider sociocultural issues in sustainable tourism. Five models describe the psychological state of individuals operating at cultural crossroads: assimilation (joining the new culture), acculturation (grasping the new culture but remaining of the original culture), alternation (varying behavior to cultural context), multicultural (maintaining distinct cultures), and fusion (blending cultures). These five processes divide into two units of analysis (individual and society), permitting research questions at levels important for considering sociocultural sustainability. Acculturation modelling has morphed into dual processes of acculturation (new culture adaptation) and enculturation (original culture adaptation). This dichotomy divides sustainability research questions into human impacts from assimilation (acquiring new culture, throwing away original), separation (rejecting new culture, keeping original), integration (acquiring new culture, keeping original), and marginalization (rejecting new culture, throwing away original). Biculturalism is often cast in terms of its emotional, behavioral, and cognitive dimensions. Required cultural adjustments and varying levels of cultural competence lead to physical, psychological, and emotional outcomes, including depression, lowered life satisfaction and self-esteem, headaches, and back pain—or enhanced career success, social skills, and life styles. Numerous studies provide empirical scales and research hypotheses for sustainability research into tourism’s causality and effect on local well-being. One key issue in applying biculturalism to sustainability scholarship concerns identification and specification of the alternative new culture contacting local culture. Evidence exists for tourism industry, universal tourist, and location/event-specific tourist culture. The biculturalism paradigm holds promise for researchers examining evolving cultural identity and integrity in response to mass tourism. In particular, confirmed constructs and scales simplify operationalization of tourism sustainability studies in terms of human impact and adjustment.

Keywords: biculturalism, cultural integrity, psychological and sociocultural adjustment, tourist culture

Procedia PDF Downloads 401