Search results for: wearing of mask

Authors: Adam Aboalkaz, Rana Shamoon, Duncan Meikle, James Lewis

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Background and aims: In orthopaedic theatres, radiological screening during operations is a commonly used and useful technique to visualise and guide the operating surgeon. Within any theatre using ionising radiation, it is imperative that the use of protective equipment and the wearing of a dosimeter at all times. 1. To assess compliance with use of protective equipment during orthopaedic procedures involving ionising radiation. 2. To assess the radiation risk knowledge of staff members regularly present in an orthopaedic theatre of a national major trauma centre, in accordance to the ionising radiation regulation (2000) guidelines. Method: We conducted an Observational study of 21 operations at the University Hospital of Wales, which is a major trauma centre, recording the compliance with use of protective equipment (lead aprons and thyroid shields) and dosimeters. The observations were performed sporadically over a two week period to ensure that all staff in monitored operating theatres were not aware of the ongoing study, as to avoid bias. A questionnaire testing the knowledge of trainees and staff within the orthopaedic department was given following completion of the initial phase of the study, with 19 responses. The questions were based on knowledge of ionising radiation exposure and monitoring. The questions also tested the general staff knowledge of what equipment should be worn and where to locate such equipment. Results: This study found that only 25% of staff members were wearing thyroid protectors when less than 1 meter from the radiation source and only 50% were wearing appropriate lead aprons whilst in this same vicinity. The study also showed that 0% of all staff members used a dosimeter whilst in an area of radiation exposure. From the distributed questionnaires, only 40% of staff understood where to stand whilst radiation was being used, and only 25% of staff knew where to find protective equipment. Conclusion: Overall our audit showed poor compliance with regards to the National and local policies, due to lack of awareness of the policy and lack of basic ionising radiation exposure knowledge. It was evident from the observational study and questionnaire that staff were not fully aware of what equipment should be worn, where to find such equipment and did not appreciate that the distance from the ionising radiation source altered its exposure effect. This lack of knowledge may affect the staff health and safety after long term exposure. Changes to clinical practice: From the outcome of this study, we managed to drastically increase awareness of ionising radiation within the orthopaedic department. A mandatory teaching session on the safety of ionising radiation has been incorporated into the orthopaedic induction week for all staff. The dosimeters have been moved to a visible location within the trauma operating theatre and all staff made aware of where to find protective equipment.

Keywords: audit, ionising radiation, observational study, protection

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Authors: Jayshree P. Zend, Ashatai B. Pawar

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The study was carried out in Parbhani district of Maharashtra state, India with the objectives to study environmental problems faced by flour mill workers, prevalence of work-related health hazards and the physiological cost of workers while performing work in flour mill in traditional method as well as improved method. The use of flour presser, dust controlling bag and noise and dust controlling mask developed by AICRP College of Home Science, VNMKV, Parbhani was considered as an improved method. This investigation consisted survey and experiment which was conducted in the respective locations of flour mills. Healthy, non-smoking 30 flour mill workers ranged between the age group of 20-50 yrs comprising 16 female and 14 male working at flour mill for 4-8 hrs/ day and 6 days/ week and had minimum five years experience of work in flour mill were selected for the study. Pulmonary function test of flour mill workers was carried out by trained technician at Dr. ShankarraoChavan Government Medical College, Nanded by using Electronic Spirometer. The data regarding heart rate (resting, working and recovery), energy expenditure, musculoskeletal problems and occupational health hazards and accidents were recorded by using pretested questionnaire. Scientific equipment used in the experiment were polar sport test heart rate monitor, Hygrometer, Goniometer, Dialed Thermometer, Sound Level Meter, Lux Meter, Ambient Air Sampler and Air Quality Monitor. The collected data were subjected to appropriate statistical analysis such as 't' test and correlation coefficient test. Results indicated that improved method i.e. use of noise and dust controlling mask, flour presser and dust controlling bag were effective in reducing physiological cost of work of flour mill workers. Lung function test of flour mill workers showed decreased values of all parameters, hence the results of present study support paying attention to use of personal protective noise and dust controlling mask by flour mill workers and also to the working conditions in flour mill especially ventilation and illumination level needs to be enhanced in flour mill. The study also emphasizes the need to develop some mechanism for lifting load of grains and unloading in the hopper. It is also suggested that the flour mill workers should use flour presser suitable to their height to avoid frequent bending and should use dust controlling bag to flour outlet of machine to reduce inhalable flour dust level in the flour mill.

Keywords: physiological cost, energy expenditure, musculoskeletal problems

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Authors: Bernard Eze Orji

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The masquerade, which is the most popular indigenous art form in Africa, is obviously identified by its elaborate, weird, and opulent costumes. The costume is the major essential accouterments in the art of the masquerade. From time past, masquerades have performed and enjoyed the freedom associated with its inscrutability and mystification solely because of its costumes. Noninitiates and women watched masquerades from a distance due to the reverence attached to its costumes and performances. In fact, whether in performance or as an item of art, the masquerade costume was seen as an embodiment of a tradition of liveliness, showiness, secrecy, and sacredness. This liveliness and showiness transformed masked characters who are believed to be possessed by spirits of ancestors and animals that inhabited the costumes. However, with the translocation of masquerade in new festivals such as carnival and state-sponsored cultural days, its costumes have been reduced to a mere item of entertainment and aesthetic values. The sacredness and reverence which hitherto elevated masquerade art to the point of wonderment have given way to an aesthetic appreciation of ingenious and individual creativity deployed in these festivals. This is as a result of the realistic and artistic creations that pervade masquerade costumes and masks in these festivals. It is a common sight to see such masquerades of animal and human genera like a lion, elephant, hippopotamus, and antelope; Agbogho Mmuo, Adamma, and Nchiekwa, respectively. This creative flair has emerged to expunge the ritual narratives associated with masquerades in the past. The study utilized performance analysis and aesthetic theory to establish that the creative ingenuity deployed by fine artists and mask designers who combine traditional artifacts to achieve modern masterpieces for the masquerades of the new festivals have reduced the ritual trappings and hype ascribed to masquerades in indigenous societies.

Keywords: costume and mask designs, entertainment, masquerade, ritual

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Authors: Marcela De Oliveira, Marina P. Da Silva, Fernando C. G. Da Rocha, Jorge M. Santos, Jaime S. Cardoso, Paulo N. Lisboa-Filho

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Multiple Sclerosis (MS) is an immune-mediated disease of the central nervous system characterized by neurodegeneration, inflammation, demyelination, and axonal loss. Magnetic resonance imaging (MRI), due to the richness in the information details provided, is the gold standard exam for diagnosis and follow-up of neurodegenerative diseases, such as MS. Brain atrophy, the gradual loss of brain volume, is quite extensive in multiple sclerosis, nearly 0.5-1.35% per year, far off the limits of normal aging. Thus, the brain volume quantification becomes an essential task for future analysis of the occurrence atrophy. The analysis of MRI has become a tedious and complex task for clinicians, who have to manually extract important information. This manual analysis is prone to errors and is time consuming due to various intra- and inter-operator variability. Nowadays, computerized methods for MRI segmentation have been extensively used to assist doctors in quantitative analyzes for disease diagnosis and monitoring. Thus, the purpose of this work was to evaluate the brain volume in MRI of MS patients. We used MRI scans with 30 slices of the five patients diagnosed with multiple sclerosis according to the McDonald criteria. The computational methods for the analysis of images were carried out in two steps: segmentation of the brain and brain volume quantification. The first image processing step was to perform brain extraction by skull stripping from the original image. In the skull stripper for MRI images of the brain, the algorithm registers a grayscale atlas image to the grayscale patient image. The associated brain mask is propagated using the registration transformation. Then this mask is eroded and used for a refined brain extraction based on level-sets (edge of the brain-skull border with dedicated expansion, curvature, and advection terms). In the second step, the brain volume quantification was performed by counting the voxels belonging to the segmentation mask and converted in cc. We observed an average brain volume of 1469.5 cc. We concluded that the automatic method applied in this work can be used for the brain extraction process and brain volume quantification in MRI. The development and use of computer programs can contribute to assist health professionals in the diagnosis and monitoring of patients with neurodegenerative diseases. In future works, we expect to implement more automated methods for the assessment of cerebral atrophy and brain lesions quantification, including machine-learning approaches. Acknowledgements: This work was supported by a grant from Brazilian agency Fundação de Amparo à Pesquisa do Estado de São Paulo (number 2019/16362-5).

Keywords: brain volume, magnetic resonance imaging, multiple sclerosis, skull stripper

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Authors: Zulkifli Ahmad, Mohd Razlan Azizan, Nasrul Hadi Johari

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This paper presents a study on development and conducting of a wearable sensor system for gait analysis measurement. For validation, the method of plantar surface measurement by force plate was prepared. In general gait analysis, force plate generally represents a studies about barefoot in whole steps and do not allow analysis of repeating movement step in normal walking and running. The measurements that were usually perform do not represent the whole daily plantar pressures in the shoe insole and only obtain the ground reaction force. The force plate measurement is usually limited a few step and it is done indoor and obtaining coupling information from both feet during walking is not easily obtained. Nowadays, in order to measure pressure for a large number of steps and obtain pressure in each insole part, it could be done by placing sensors within an insole. With this method, it will provide a method for determine the plantar pressures while standing, walking or running of a shoe wearing subject. Inserting pressure sensors in the insole will provide specific information and therefore the point of the sensor placement will result in obtaining the critical part under the insole. In the wearable shoe sensor project, the device consists left and right shoe insole with ten FSR. Arduino Mega was used as a micro-controller that read the analog input from FSR. The analog inputs were transmitted via bluetooth data transmission that gains the force data in real time on smartphone. Blueterm software which is an android application was used as an interface to read the FSR reading on the shoe wearing subject. The subject consist of two healthy men with different age and weight doing test while standing, walking (1.5 m/s), jogging (5 m/s) and running (9 m/s) on treadmill. The data obtain will be saved on the android device and for making an analysis and comparison graph.

Keywords: gait analysis, plantar pressure, force plate, earable sensor

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Rajlakshmi Banerjee, Sujoy Banerjee

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The prevalence of malnutrition, as the most common cause of morbidity and mortality increases with old age, especially in developing countries like India, because of many factors out of which dentate status is one. It affects diet, nutritional status and general health due to reduced chewing ability and poor food choices. Aim and Objective: This study aimed to evaluate the effect of nutritional status, dietary intake on the Oral Health-related Quality of life (OHRQoL) of elderly edentulous complete denture wearing patients and to know whether they are at a higher risk of malnutrition. The objective was to assess the need to include dietary and nutritional counselling during Prosthodontic rehabilitation of elderly edentulous patients. Materials and method: A cross-sectional study was conducted among 200 elderly denture wearing patients above 60 yrs of age from Nagpur, Maharashtra. The majority of study subjects (60%) were between the age group 60 and 75 years. Mini-Nutritional Assessment (MNA) Questionnaire was used to assess nutritional status and General Oral Health Assessment Index (GOHAI) questionnaire was used to determine the Oral health related Quality of life of these patients.Descriptive statistics was used to analyze data using SPSS version 21. Results: Among the assessed subjects, nearly 80% of them had total scores of GOHAI between 12 and 57 which require ‘needed dental care.’ As per MNA, 10.5% had adequate nutrition, 70 % were at risk of malnutrition, and remaining 19.5 % of subjects were malnourished. There was a significant correlation between GOHAI and MNA scores. A strong association was found between mean GOHAI and MNA scores and thereby nutritional status and OHRQOL.Conclusion: The use of conventional dentures increases the risk of malnutrition in the elderly due to inability to eat and chew food properly and therefore severely affecting the quality of life. Dietary analysis and counselling should be strictly incorporated into geriatric treatment planning during Prosthetic rehabilitation.

Keywords: general oral health assessment index, General Oral Health Assessment Index (GOHAI), nutritional assessment, mini-nutritional assessment, Mini-Nutritional Assessment (MNA), quality of life

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Authors: Aboli Gore, Aritra Das, Sunil Sonthalia, Tanmay Mahapatra, Sridhar Srikantiah, Hemant Shah

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AMANAT is an initiative, taken in collaboration with the Government of Bihar, aimed at improving the Quality of Maternal and Neonatal care services at Bihar’s public health facilities – those offering either the Basic Emergency Obstetric and Neonatal care (BEmONC) or Comprehensive Emergency Obstetric and Neonatal care (CEmONC) services. The effectiveness of this program is evaluated by conducting cross-sectional assessments at the concerned facilities prior to (baseline) and following completion (endline) of intervention. Direct Observation of Delivery (DOD) methodology is employed for carrying out the baseline and endline assessments – through which key obstetric and neonatal care practices among the Health Care Providers (especially the nurses) are assessed quantitatively by specially trained nursing professionals. Assessment of vitals prior to delivery improved during all three phases of BEmONC and all four phases of CEmONC training with statistically significant improvement noted in: i) pulse measurement in BEmONC phase 2 (9% to 68%), 3 (4% to 57%) & 4 (14% to 59%) and CEmONC phase 2 (7% to 72%) and 3 (0% to 64%); ii) blood pressure measurement in BEmONC phase 2 (27% to 84%), 3 (21% to 76%) & 4 (36% to 71%) and CEmONC phase 2 (23% to 76%) and 3 (2% to 70%); iii) fetal heart rate measurement in BEmONC phase 2 (10% to 72%), 3 (11% to 77%) & 4 (13% to 64%) and CEmONC phase 1 (24% to 38%), 2 (14% to 82%) and 3 (1% to 73%); and iv) abdominal examination in BEmONC phase 2 (14% to 59%), 3 (3% to 59%) & 4 (6% to 56%) and CEmONC phase 1 (0% to 24%), 2 (7% to 62%) & 3 (0% to 62%). Regarding infection control, wearing of apron, mask and cap by the delivery conductors improved significantly in all BEmONC phases. Similarly, the practice of handwashing improved in all BEmONC and CEmONC phases. Even on disaggregation, the handwashing showed significant improvement in all phases but CEmONC phase-4. Not only the positive practices related to handwashing improved but also negative practices such as turning off the tap with bare hands declined significantly in the aforementioned phases. Significant decline was also noted in negative maternal care practices such as application of fundal pressure for hastening the delivery process and administration of oxytocin prior to delivery. One of the notable achievement of AMANAT is an improvement in active management of the third stage of labor (AMTSL). The overall AMTSL (including administration of oxytocin or other uterotonics uterotonic in proper dose, route and time along with controlled cord traction and uterine massage) improved in all phases of BEmONC and CEmONC mentoring. Another key area of improvement, across phases, was in proper cutting/clamping of the umbilical cord. AMANAT mentoring also led to improvement in important immediate newborn care practices such as initiation of skin-to-skin care and timely initiation of breastfeeding. The next phase of the mentoring program seeks to institutionalize mentoring across the state that could potentially perpetuate improvement with minimal external intervention.

Keywords: capacity building, nurse-mentoring, quality of care, pregnancy, newborn care

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Authors: Salwa S. Dawaki, Init Ithoi, Sa’adatu I. Yelwa

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Introduction: Parasitic infections are major public health problems worldwide, particularly in developing countries. Two third of the world population is infected while about 3 billion are at risk of parasitic infections. It is demonstrated that most parasitic infections occur as multiple infections especially among poor and rural communities of most countries in the tropical regions. Parasitic infections are endemic in Nigeria, yet multiple infections are rarely reported. The study aimed to estimate the prevalence and identify factors associating with multiple parasitic infections among rural population in Kano State Nigeria. Methodology: A cross-sectional survey was conducted from June to August 2013 in rural Kano State, Nigeria. Three samples stool, urine, and blood were collected from each of the 551 volunteers aged between one and ninety years old recruited for the survey. A pre-tested questionnaire was used to obtain epidemiological data. Data were analysed using appropriate descriptive, univariate and multivariate logistic regression methods. Major findings: The participants were 61.7% male, 38.3% female, and 69.0% were adults of 15 years and above. Overall, 463 (84%) were infected with parasitic infections among which 60.9% had multiple infections. A total of 15 parasitic species were recovered, and up to 8 different parasitic species were found concurrently in a single host. Plasmodium was the most common parasite followed by Blastocystis, Entamoeba species, and hookworms. It was found that presence of an infected family member (P = 0.017; OR = 1.52; 95% CI = 1.08, 2.13) and not wearing shoes outside home (P = 0.043; OR = 1.50; 95% CI = 1.01, 2.18) significantly associated with higher risk of having multiple parasitic infections among the studied population. Conclusion: Parasitic infections pose a public health challenge in the rural community of Kano. Multiple parasitic infections are highly prevalent and presence of an infected family member as well as not wearing proper foot wear outside home increases the risk of infection. Poor hygiene, unfavourable socioeconomic conditions, and culture promote survival and transmission of parasites. There is a need for implementation of integrated approach aimed at controlling or eliminating the infections with emphasis on public awareness.

Keywords: multiple infections, parasitic infections, poor hygiene, risk of infection

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Authors: Riina I. Bray, Yifan Wang, Nikolas Argiropoulos, Stephanie Robins, John Molot, Kelly Tragash, Lynn M. Marshall, Margaret E. Sears, Marie-Andrée Pigeon, Michel Gaudet, Pierre Auger, Emily Bélanger, Rohini Peris

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Multiple chemical sensitivity (MCS) is a chronic medical condition characterized by intolerances to chemical substances. Since the arrival of the COVID-19 pandemic and associated health measures, people experiencing MCS (PEMCS) are at a heightened risk of environmental exposures associated with cleaners, disinfectants, and sanitizers. Little attention has been paid to the well-being of PEMCS in the context of the COVID-19 pandemic. Objective: This study assesses the lived experiences of Canadian adults with MCS in relation to their living environment, access to healthcare, and levels of perceived social support before and during the pandemic. Methods: A total of 119 PEMCS completed an online questionnaire. McNemar Chi-Squared and Wilcoxon Signed Rank tests were used to evaluate if there were statistically significant changes in participants’ perception of their living environment, access to healthcare, and levels of social support before and after March 11, 2020. Results: Both positive and negative outcomes were noted. Participants reported an increase in exposure to disinfectants/sanitizers that entered their living environment (p<.001). There was a reported decrease in access to a family doctor during the pandemic (p<0.001). Although PEMCS experienced increased social isolation (p<0.001), they also reported an increase in understanding from family (p<0.029) and a decrease in stigma for wearing personal protective equipment (p<0.001). Conclusion: PEMCS reported experiencing: increased exposure to disinfectants or sanitizers, a loss of social support, and barriers in accessing healthcare during the pandemic. However, COVID-19 provided an opportunity to normalize the living conditions of PEMCS, such as wearing masks and social isolation. These findings can guide decision-makers on the importance of implementing nontoxic alternatives for cleaning and disinfection, as well as improving accommodation measures for PEMCS.

Keywords: covid-19, multiple chemical sensitivity, MCS, quality of life, social isolation, physical environment, healthcare

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Authors: Jiazhen Chen, Yue Sun, Joanne Yip, Kit-Lun Yick

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The design of sports bras poses a considerable challenge due to the difficulty in accurately predicting the wearing result after computer-aided design (CAD). It needs repeated physical try-on or virtual try-on to obtain a comfortable pressure range during motion. Specifically, in the context of running, the exact support area and force exerted on the breasts remain unclear. Consequently, obtaining an effective method to design the sports bra pads shape becomes particularly challenging. This predicament hinders the successful creation and production of sports bras that cater to women's health needs. The purpose of this study is to propose an effective method to obtain the 3D shape of sports bra pads and to understand the relationship between the supporting force and the 3D shape parameters of the pads. Firstly, the static 3D shape of the sports bra pad and human motion data (Running) are obtained by using the 3D scanner and advanced 4D scanning technology. The 3D shape of the sports bra pad is parameterised and simplified by Free-form Deformation (FFD). Then the sub-models of sports bra and human body are constructed by segmenting and meshing them with MSC Apex software. The material coefficient of sports bras is obtained by material testing. The Marc software is then utilised to establish a dynamic contact model between the human breast and the sports bra pad. To realise the reverse design of the sports bra pad, this contact model serves as a forward model for calculating the inverse problem. Based on the forward contact model, the inverse problem of the 3D shape parameters of the sports bra pad with the target bra-wearing pressure range as the boundary condition is solved. Finally, the credibility and accuracy of the simulation are validated by comparing the experimental results with the simulations by the FE model on the pressure distribution. On the one hand, this research allows for a more accurate understanding of the support area and force distribution on the breasts during running. On the other hand, this study can contribute to the customization of sports bra pads for different individuals. It can help to obtain sports bra pads with comfortable dynamic pressure.

Keywords: sports bra design, breast motion, running, inverse problem, finite element dynamic model

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Authors: Arjita Biswas, Sandeep Potnis

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Plastic roads (bituminous roads using waste plastic in the wearing course ) have now become familiar in the Road Construction Sector in India. With the Indian Road Congress Code (IRC SP: 98 -2013), many agencies are coming forward to implement Plastic Roads in India. This paper discuss and compare about the various properties of bituminous mix with 8% waste plastic and normal bituminous mix. This paper also signifies the performance of both the types of roads after 4 months of age under loading conditions. Experiments were carried out to evaluate its performance. The result shows improved performance of plastic roads.

Keywords: bituminous roads, experiments, performance, plastic roads

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Authors: Hang Zhou, Limin Zhu

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This paper investigates the fabrication of microstructures on Si surfaces by using electrochemical anodic oxidation with agarose stamps. The fabricating process is based on a selective anodic oxidation reaction that occurs in the contact area between a stamp and a Si substrate. The stamp which is soaked in electrolyte previously acts as a current flow channel. After forming the oxide patterns as an etching mask, a KOH aqueous is used for the wet etching of Si. A complicated microstructure array of 1 cm2 was fabricated by the method with high accuracy.

Keywords: microstructures, anodic oxidation, silicon, agarose stamps

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Authors: Paris Binos

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The present work investigates developmental aspects of the duration of isolated vowels in infants with normal hearing compared to those who received cochlear implants (CIs) before two years of age. Infants with normal hearing produced shorter vowel duration since this find related with more mature production abilities. First isolated vowels are transparent during the protophonic stage as evidence of an increased motor and linguistic control. Vowel duration is a crucial factor for the transition of prelexical speech to normal adult speech. Despite current knowledge of data for infants with normal hearing more research is needed to unravel productions skills in early implanted children. Thus, isolated vowel productions by two congenitally hearing-impaired Greek infants (implantation ages 1:4-1:11; post-implant ages 0:6-1:3) were recorded and sampled for six months after implantation with a Nucleus-24. The results compared with the productions of three normal hearing infants (chronological ages 0:8-1:1). Vegetative data and vocalizations masked by external noise or sounds were excluded. Participants had no other disabilities and had unknown deafness etiology. Prior to implantation the infants had an average unaided hearing loss of 95-110 dB HL while the post-implantation PTA decreased to 10-38 dB HL. The current research offers a methodology for the processing of the prelinguistic productions based on a combination of acoustical and auditory analyses. Based on the current methodological framework, duration measured through spectrograms based on wideband analysis, from the voicing onset to the end of the vowel. The end marked by two co-occurring events: 1) The onset of aperiodicity with a rapid change in amplitude in the waveform and 2) a loss in formant’s energy. Cut-off levels of significance were set at 0.05 for all tests. Bonferroni post hoc tests indicated that difference was significant between the mean duration of vowels of infants wearing CIs and their normal hearing peers. Thus, the mean vowel duration of CIs measured longer compared to the normal hearing peers (0.000). The current longitudinal findings contribute to the existing data for the performance of children wearing CIs at a very young age and enrich also the data of the Greek language. The above described weakness for CI’s performance is a challenge for future work in speech processing and CI’s processing strategies.

Keywords: cochlear implant, duration, spectrogram, vowel

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Authors: Matthew Baucum

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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

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Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

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Authors: Ravi Nandu, Kuldeep Singh

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A helmet is a protective layer that is worn in order to prevent head injury. Helmet is the most important safety gear for two wheeler riders. However, due to carelessness of people, less importance toward safety, lot of causalities is every year. According to National Crime Records Bureau (NCRB) two wheelers claimed 92 lives every day out of which most were due to helmetless drive. The system design will be such that without wearing the helmet the rider cannot start two wheelers. The helmet will be connected to vehicle key ignition systems which will be electronically controlled. The smart helmet will be having proximity sensor fitted inside it, which will act as our switch for ignition and further with wireless connection the helmet sensor circuit will be connected to the vehicle ignition system.

Keywords: helmet, proximity sensor, microcontroller, head injury

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Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Mohammad Shameel, Peter K. K. Loh, James H. Ng

Abstract:

Cybercrime is a new category of crime which poses a different challenge for crime investigators and incident responders. Attackers can mask their identities using a suite of tools and with the help of the deep web, which makes them difficult to track down. Scouring the deep web manually takes time and is inefficient. There is a growing need for a tool to scour the deep web to obtain useful evidence or intel automatically. In this paper, we will explain the background and motivation behind the research, present a survey of existing research on related tools, describe the design of our own crime/threat intelligence hunting tool prototype, demonstrate its capability with some test cases and lastly, conclude with proposals for future enhancements.

Keywords: cybercrime, deep web, threat intelligence, web crawler

Procedia PDF Downloads 139

Authors: R. Vishnu, K. S. Reddy, Amrendra Kumar

Abstract:

The present guideline for the construction of flexible pavement in India, IRC 37: 2012 recommends to use viscous grade VG 40 bitumen in both wearing and binder bituminous layers. However, most of the bitumen production plants in India are unable to produce the air-blown VG40 grade bitumen. This requires plant’s air-blowing technique modification, and often the manufactures finds it as uneconomical. In this context, stiffer grade bitumen can be produced if bitumen is modified. Gilsonite, which is naturally occurring asphalt have been found to be used for increasing the stiffness of binders. The present study evaluates the physical, rheological characteristics of Gilsonite modified binders and the performance characteristics of these binders when used in the mix.

Keywords: bitumen, gilsonite, stiffness, laboratory evaluation

Procedia PDF Downloads 439

Authors: Yan Zhang, Jan Awrejcewicz, Lin Fu

Abstract:

High-heeled shoes force the foot into plantar flexion position resulting in foot arch rising and disturbance of the articular congruence between the talus and tibiofibular mortice, all of which may increase the challenge of balance maintenance. Plantar pressure distribution of the stance limb during the star excursion balance test (SEBT) contributes to the understanding of potential sources of reaching excursions in SEBT. The purpose of this study is to evaluate the dynamic posture control while wearing high-heeled shoes using SEBT in a combination of plantar pressure measurement. Twenty healthy young females were recruited. Shoes of three heel heights were used: flat (0.8 cm), low (4.0 cm), high (6.6 cm). The testing grid of SEBT consists of three lines extending out at 120° from each other, which were defined as anterior, posteromedial, and posterolateral directions. Participants were instructed to stand on their dominant limb with the heel in the middle of the testing grid and hands on hips and to reach the non-stance limb as far as possible towards each direction. The distal portion of the reaching limb lightly touched the ground without shifting weight. Then returned the reaching limb to the beginning position. The excursion distances were normalized to leg length. The insole plantar measurement system was used to record peak pressure, contact area, and pressure-time integral of the stance limb. Results showed that normalized excursion distance decreased significantly as heel height increased. The changes of plantar pressure in SEBT as heel height increased were more obvious in the medial forefoot (MF), medial midfoot (MM), rearfoot areas. At MF, the peak pressure and pressure-time integral of low and high shoes increased significantly compared with that of flat shoes, while the contact area decreased significantly as heel height increased. At MM, peak pressure, contact area, and pressure-time integral of high and low shoes were significantly lower than that of flat shoes. To reduce posture instability, the stance limb plantar loading shifted to medial forefoot. Knowledge of this study identified dynamic posture control deficits while wearing high-heeled shoes and the critical role of the medial forefoot in dynamic balance maintenance.

Keywords: dynamic posture control, high-heeled shoes, plantar pressure, star excursion balance test.

Procedia PDF Downloads 106

Authors: Ebrahim Shayesteh, Nikolaos Styliaras, Alin George Raducu, Ozan Sahin, Daniel Pombo VáZquez, Jonas Funkquist, Sotirios Thanopoulos

Abstract:

One of the important requirements in many code generation projects is defining some of the model parameters tunable. This helps to update the model parameters without performing the code generation again. This paper studies the concept of embedded code generation by MATLAB/Simulink coder targeting the TwinCAT Simulink system. The generated runtime modules are then tested and deployed to the TwinCAT 3 engineering environment. However, defining the parameters tunable in MATLAB/Simulink code generation targeting TwinCAT is not very straightforward. This paper focuses on this subject and reviews some of the techniques tested here to make the parameters tunable in generated runtime modules. Three techniques are proposed for this purpose, including normal tunable parameters, callback functions, and mask subsystems. Moreover, some test Simulink models are developed and used to evaluate the results of proposed approaches. A brief summary of the study results is presented in the following. First of all, the parameters defined tunable and used in defining the values of other Simulink elements (e.g., gain value of a gain block) could be changed after the code generation and this value updating will affect the values of all elements defined based on the values of the tunable parameter. For instance, if parameter K=1 is defined as a tunable parameter in the code generation process and this parameter is used to gain a gain block in Simulink, the gain value for the gain block is equal to 1 in the gain block TwinCAT environment after the code generation. But, the value of K can be changed to a new value (e.g., K=2) in TwinCAT (without doing any new code generation in MATLAB). Then, the gain value of the gain block will change to 2. Secondly, adding a callback function in the form of “pre-load function,” “post-load function,” “start function,” and will not help to make the parameters tunable without performing a new code generation. This means that any MATLAB files should be run before performing the code generation. The parameters defined/calculated in this file will be used as fixed values in the generated code. Thus, adding these files as callback functions to the Simulink model will not make these parameters flexible since the MATLAB files will not be attached to the generated code. Therefore, to change the parameters defined/calculated in these files, the code generation should be done again. However, adding these files as callback functions forces MATLAB to run them before the code generation, and there is no need to define the parameters mentioned in these files separately. Finally, using a tunable parameter in defining/calculating the values of other parameters through the mask is an efficient method to change the value of the latter parameters after the code generation. For instance, if tunable parameter K is used in calculating the value of two other parameters K1 and K2 and, after the code generation, the value of K is updated in TwinCAT environment, the value of parameters K1 and K2 will also be updated (without any new code generation).

Keywords: code generation, MATLAB, tunable parameters, TwinCAT

Procedia PDF Downloads 197

Authors: Adel Edwar Waheeb Louka

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Kristina Ancutiene

Abstract:

The goal of this research is to perform comparative analysis between the virtual fit of the woman's dress and the fit on a real person. The dress fitting was done using mechanical and structural parameters of the 100 % linen fabric and using Modaris_3D_Fit software (CAD Lectra). The dress was also sawn after which garment fit differences of real and virtual dress was researched. Four respondents whose figures were similar were used to evaluate the ease and strain deformations of the real and virtual dress. The scores that were given by the respondents wearing the real dress were compared to the ease and strain results that were given by the software. The main result was that respondents feel similar to the virtual stretch deformations but their ease feeling is not always matching the virtual ones. The results may be influenced by psychological factors and different understanding about purpose of garment.

Keywords: virtual garment, 3D CAD, garment fit, mechanical properties

Procedia PDF Downloads 295

Authors: H. V. H. De Soysa, N. D. Hiripitiya, H. S. U. Thrimavithana, B. R. Epitawala, K. A. D. D. Kuruppu, D. J. K. Lokupathirage

Abstract:

This research was based on developing a mechanism in order to optimize the landing distance. Short distance braking and long distance braking may cause several issues for the aircraft including tyre wearing. The worst case occurs with short distance landing. The issues related to short distance landing were identified after conducting interviews with pilots, aeronautical engineers and technicians. A model was constructed in order to optimize the landing distance. The device started to function at the point where the main wheels of the aircraft touchdown the runway. It was found that implementing this device to the aircraft benefits to optimize the landing distance. This could lead to rectifying several issues occurred due to improper braking distances.

Keywords: aircraft, mechanism, optimize landing distance, runway

Procedia PDF Downloads 283

Authors: Francisco Toledo-Castillo, María Josefa Sospedra-Baeza, Sergio Hidalgo-Fuentes

Abstract:

Motorcycles constitute about 9% of all motorized vehicles in Spain and are generally overrepresented in traffic accidents, accounting for 21% of total accidents. Six years of motorcycle accident data were obtained through the national accident database of Spain (Dirección General de Tráfico). Some of the studied factors are age, gender, helmet wearing, alcohol consumption, speeding, subject’s driving license and for how long it has been held, the trip purpose, and the presence of a passenger on the vehicle. Key findings show helmet use, unsafe speed, the trip purpose, weather, distraction, collision type and other variables play significant roles in crash-injury outcomes. Finally, we examine evolution of mortality and severity from 2006 to 2011.

Keywords: motorcycle accidents, risk factors, statistics, Spain

Procedia PDF Downloads 322

Authors: Yinjun Tu, Qiang Fang, Glenn I. Matthews, Shuenn-Yuh Lee

Abstract:

This paper reports an efficient and rigorous self-adaptive stimulus artifacts removal approach for a mixed surface EMG (Electromyography) and stimulus signal during muscle stimulation. The recording of EMG and the stimulation of muscles were performing simultaneously. It is difficult to generate muscle fatigue feature from the mixed signal, which can be further used in closed loop system. A self-adaptive method is proposed in this paper, the stimulation frequency was calculated and verified firstly. Then, a mask was created based on this stimulation frequency to remove the undesired stimulus. 20 EMG signal recordings were analyzed, and the ANOVA (analysis of variance) approach illustrated that the decreasing trend of median power frequencies was successfully generated from the 'cleaned' EMG signal.

Keywords: EMG, FES, stimulus artefacts, self-adaptive

Procedia PDF Downloads 370

Authors: M. Hadji, A. Haddad, Y. Hadji

Abstract:

The effects of boronizing treatment on the friction coefficient and wear behavior of 0.35 Vf TiC- Ti3 SiC2 composite were investigated. In order to modity the surface properties of Ti3SiC2, boronizing treatment was carried out through powder pack cementation in the 1150-1350 °C temperature range. After boronizing treatment, one mixture layer, composed of TiB2 and SiC, forms on the surface of Ti3SiC2. The growth of the coating is processed by inward diffusion of Boron and obeys a linear rule. The Boronizing treatment increases the hardness of Ti3SiC2 from 6 GPa to 13 GPa. In the pin-on-disc test, i twas found that the material undergoes a steady-state coefficient of friction of around 0.8 and 0.45 in case of Ti3SiC2/Al2O3 tribocouple under 7N load for the non treated and the boronized samples, respectively. The wear resistance of Ti3SiC2 underAl2O3 ball sliding has been significantly improved, which indicated that the boronizing treatment is a promising surface modification way of Ti3SiC2.

Keywords: MAX phase, wearing, friction, boronizing

Procedia PDF Downloads 424

Authors: Moustafa Osman Mohamed

Abstract:

This paper will explore the influence of energy sector in Arab Republic of Egypt which has shared its responsibilities of many environmental challenges as the second largest economy in the Middle East (after Iran). Air and water pollution, desertification, inadequate disposal of solid waste and damage to coral reefs are serious problems that influence environmental management in Egypt. The intensive reliance of high population density and strong industrial growth are wearing Egypt's resources, and the rapidly-growing population has forced Egypt to breakdown agricultural land to residential and relevant use of commercial ingestion. The depletion effects of natural resources impose the government to apply innovation techniques in emission control and focus on sustainability. The cogeneration will be presented to control thermal losses and increase efficiency of energy power system.

Keywords: cogeneration, environmental management, power electricity, energy indicators

Procedia PDF Downloads 238

Authors: Ravija Gunawardana, Banuka Athuraliya

Abstract:

Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

Procedia PDF Downloads 103