Search results for: fraud prevention and detection

Authors: Dylan Elliott, Katya Pertsova

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Preposition errors are some of the most common errors created by L2 speakers. In addition, improving error correction and detection methods remains an open issue in the realm of Natural Language Processing (NLP). This research investigates whether the bidirectional encoder representations from the transformers model (BERT) have the potential to correct preposition errors accurately enough to be useful in error correction software. This research finds that BERT performs strongly when the scope of its error correction is limited to preposition choice. The researchers used an open-source BERT model and over three hundred thousand edited sentences from Wikipedia, tagged for part of speech, where only a preposition edit had occurred. To test BERT’s ability to detect errors, a technique known as multi-level masking was used to generate suggestions based on sentence context for every prepositional environment in the test data. These suggestions were compared with the original errors in the data and their known corrections to evaluate BERT’s performance. The suggestions were further analyzed to determine if BERT more often agreed with the judgements of the Wikipedia editors. Both the untrained and fined-tuned models were compared. Finetuning led to a greater rate of error-detection which significantly improved recall, but lowered precision due to an increase in false positives or falsely flagged errors. However, in most cases, these false positives were not errors in preposition usage but merely cases where more than one preposition was possible. Furthermore, when BERT correctly identified an error, the model largely agreed with the Wikipedia editors, suggesting that BERT’s ability to detect misused prepositions is better than previously believed. To evaluate to what extent BERT’s false positives were grammatical suggestions, we plan to do a further crowd-sourcing study to test the grammaticality of BERT’s suggested sentence corrections against native speakers’ judgments.

Keywords: BERT, grammatical error correction, preposition error detection, prepositions

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Authors: C. Gopi Jinimole, A. Harsha

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For detecting strokes, Computed Tomography (CT) scan is preferred for imaging the abnormalities or infarction in the brain. Because of the problems in the window settings used to evaluate brain CT images, they are very poor in the early stage infarction detection. This paper presents an automatic estimation method for the window settings of the CT images for proper contrast of the hyper infarction present in the brain. In the proposed work the window width is estimated automatically for each slice and the window centre is changed to a new value of 31HU, which is the average of the HU values of the grey matter and white matter in the brain. The automatic window width estimation is based on the average of median of statistical central moments. Thus with the new suggested window centre and estimated window width, the hyper infarction or post-stroke regions in CT brain images are properly detected. The proposed approach assists the radiologists in CT evaluation for early quantitative signs of delayed stroke, which leads to severe hemorrhage in the future can be prevented by providing timely medication to the patients.

Keywords: computed tomography (CT), hyper infarction or post stroke region, Hounsefield Unit (HU), window centre (WC), window width (WW)

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Authors: Gopi Kandaswamy, P. Balamuralidhar

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Fully autonomous small Unmanned Aerial Vehicles (UAVs) are increasingly being used in many commercial applications. Although a lot of research has been done to develop safe, reliable and durable UAVs, accidents due to electronic and structural failures are not uncommon and pose a huge safety risk to the UAV operators and the public. Hence there is a strong need for an automated health monitoring system for UAVs with a view to minimizing mission failures thereby increasing safety. This paper describes our approach to monitoring the electronic and structural components in a small UAV without the need for additional sensors to do the monitoring. Our system monitors data from four sources; sensors, navigation algorithms, control inputs from the operator and flight controller outputs. It then does statistical analysis on the data and applies a rule based engine to detect failures. This information can then be fed back into the UAV and a decision to continue or abort the mission can be taken automatically by the UAV and independent of the operator. Our system has been verified using data obtained from real flights over the past year from UAVs of various sizes that have been designed and deployed by us for various applications.

Keywords: fault detection, health monitoring, unmanned aerial vehicles, vibration analysis

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Authors: K. Jahani, J. Razavi

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Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.

Keywords: computer aided engineering, predictive maintenance, fault detection, mining process line, slurry pump, hydrocyclone

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Authors: Simiao Ren, En Wei

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Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning.

Keywords: computer vision, deep learning, YOLOv8, detection, swimming pool, drowning, domain adaptation, generative adversarial network, GAN, GP-GAN

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Authors: Mojtaba Valinataj

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Reversible logic as a new favorable design domain can be used for various fields especially creating quantum computers because of its speed and intangible power consumption. However, its susceptibility to a variety of environmental effects may lead to yield the incorrect results. In this paper, because of the importance of multiplication operation in various computing systems, some novel reversible logic array multipliers are proposed with error detection capability by incorporating the parity-preserving gates. The new designs are presented for two main parts of array multipliers, partial product generation and multi-operand addition, by exploiting the new arrangements of existing gates, which results in two signed parity-preserving array multipliers. The experimental results reveal that the best proposed 4×4 multiplier in this paper reaches 12%, 24%, and 26% enhancements in the number of constant inputs, number of required gates, and quantum cost, respectively, compared to previous design. Moreover, the best proposed design is generalized for n×n multipliers with general formulations to estimate the main reversible logic criteria as the functions of the multiplier size.

Keywords: array multipliers, Baugh-Wooley method, error detection, parity-preserving gates, quantum computers, reversible logic

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Authors: S. Abdulkadir, I. Y. Moh’d, S. U. Kunya, U. Nuruddeen

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The basis of any contract formation between the client and contractor is the budgeted cost and the estimated duration of projects. These variables are paramount important to project's sponsor in a construction projects and in assessing the success or viability of construction projects. Despite the availability of various techniques of cost and time control, many projects failed to achieve their initial estimated cost and time. The paper evaluate the inhibiting factors of cost and time control in Nigerian construction practice and comparing the result with the United Kingdom practice as identified by one researcher. The populations of the study are construction professionals within Bauchi and Gombe state, Nigeria, a judgmental sampling employed in determining the size of respondents. Descriptive statistics used in analyzing the data in SPSS. Design change, project fraud and corruption, financing and payment of completed work found to be common among the top five inhibiting factors of cost and time control in the study area. Furthermore, the result had shown some comprising with slight contrast as in the case of United Kingdom practice. Study recommend the adaptation of mitigation measures developed in the UK prior to assessing its effectiveness and so also developing a mitigating measure for other top factors that are not within the one developed in United Kingdom practice. Also, it recommends a wider assessing comparison on the modify inhibiting factors of cost and time control as revealed by the study to cover almost all part of Nigeria.

Keywords: comparison, cost, inhibiting factor, United Kingdom, time

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Authors: Marta Skwarecka, Patrycja Bloch, Rafal Walkusz, Oliwia Urbanowicz, Grzegorz Zielinski, Sabina Zoledowska, Dawid Nidzworski

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Upper respiratory tract infections are one of the most common reasons for visiting a general doctor. Streptococci are the most common bacterial etiological factors in these infections. There are many different types of Streptococci and infections vary in severity from mild throat infections to pneumonia. For example, S. pyogenes mainly contributes to acute pharyngitis, palatine tonsils and scarlet fever, whereas S. Streptococcus pneumoniae is responsible for several invasive diseases like sepsis, meningitis or pneumonia with high mortality and dangerous complications. There are only a few diagnostic tests designed for detection Streptococci from the infected throat of patients. However, they are mostly based on lateral flow techniques, and they are not used as a standard due to their low sensitivity. The diagnostic standard is to culture patients throat swab on semi selective media in order to multiply pure etiological agent of infection and subsequently to perform antibiogram, which takes several days from the patients visit in the clinic. Therefore, the aim of our studies is to develop and implement to the market a Point of Care device for the rapid identification of Streptococcus pyogenes and Streptococcus pneumoniae with simultaneous identification of antibiotic resistance genes. In the course of our research, we successfully selected genes for to-species identification of Streptococci and genes encoding antibiotic resistance proteins. We have developed a reaction to amplify these genes, which allows detecting the presence of S. pyogenes or S. pneumoniae followed by testing their resistance to erythromycin, chloramphenicol and tetracycline. What is more, the detection of β-lactamase-encoding genes that could protect Streptococci against antibiotics from the ampicillin group, which are widely used in the treatment of this type of infection is also developed. The test is carried out directly from the patients' swab, and the results are available after 20 to 30 minutes after sample subjection, which could be performed during the medical visit.

Keywords: antibiotic resistance, Streptococci, respiratory infections, diagnostic test

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Authors: Varsha Gupta, Priya Datta, Gursimran Mohi, Jagdish Chander

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Fluoroquinolones form the mainstay of therapy for the treatment of infections due to Salmonella enterica subsp. enterica. There is a complex interplay between several resistance mechanisms for quinolones and various fluoroquinolones discs, giving varying results, making detection and interpretation of fluoroquinolone resistance difficult. For detection of fluoroquinolone resistance in Salmonella ssp., we compared the use of pefloxacin and nalidixic acid discs as surrogate marker. Using MIC for ciprofloxacin as the gold standard, 43.5% of strains showed MIC as ≥1 μg/ml and were thus resistant to fluoroquinoloes. Based on the performance of nalidixic acid and pefloxacin discs as surrogate marker for ciprofloxacin resistance, both the discs could correctly detect all the resistant phenotypes; however, use of nalidixic acid disc showed false resistance in the majority of the sensitive phenotypes. We have also tested newer antimicrobial agents like cefixime, imipenem, tigecycline and azithromycin against Salmonella spp. Moreover, there was a comeback of susceptibility to older antimicrobials like ampicillin, chloramphenicol, and cotrimoxazole. We can also use cefixime, imipenem, tigecycline and azithromycin in the treatment of multidrug resistant S. typhi due to their high susceptibility.

Keywords: salmonella, pefloxacin, surrogate marker, chloramphenicol

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Seyedeh Banafsheh Bagheri Marzouni, Sona Rostampour Yasouri

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Salmonella is one of the most important common pathogens between humans and animals worldwide. Globally, the prevalence of the disease in humans is due to the consumption of food contaminated with animal-derived Salmonella. These foods include eggs, red meat, chicken, and milk. Contamination of chicken and its products with Salmonella may occur at any stage of the chicken processing chain. Salmonella infection is usually not fatal. However, its occurrence is considered dangerous in some individuals, such as infants, children, the elderly, pregnant women, or individuals with weakened immune systems. If Salmonella infection enters the bloodstream, the possibility of contamination of tissues throughout the body will arise. Therefore, determining the potential risk of Salmonella at various stages is essential from the perspective of consumers and public health. The aim of this study is to isolate and identify Salmonella from chicken samples distributed in the Tehran market using the Gold standard culture method and PCR techniques based on specific genes, invA and ent. During the years 2022-2023, sampling was performed using swabs from the liver and intestinal contents of distributed chickens in the Tehran province, with a total of 120 samples taken under aseptic conditions. The samples were initially enriched in buffered peptone water (BPW) for pre-enrichment overnight. Then, the samples were incubated in selective enrichment media, including TT broth and RVS medium, at temperatures of 37°C and 42°C, respectively, for 18 to 24 hours. Organisms that grew in the liquid medium and produced turbidity were transferred to selective media (XLD and BGA) and incubated overnight at 37°C for isolation. Suspicious Salmonella colonies were selected for DNA extraction, and PCR technique was performed using specific primers that targeted the invA and ent genes in Salmonella. The results indicated that 94 samples were Salmonella using the PCR technique. Of these, 71 samples were positive based on the invA gene, and 23 samples were positive based on the ent gene. Although the culture technique is the Gold standard, PCR is a faster and more accurate method. Rapid detection through PCR can enable the identification of Salmonella contamination in food items and the implementation of necessary measures for disease control and prevention.

Keywords: culture, PCR, salmonella spp, salmonella enteritidis

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Authors: Y. Mohammed, A. I. Kabuga

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Sexually transmitted infections (STIs) have continued to be a major public health problem in sub-Saharan Africa especially with the recent resurgence of syphilis. Syphilis is a systemic disease caused by the bacterium, spirochete Treponema pallidum and has been reported as one of the common sexually transmitted infections (STIs) in Nigeria. Presence of genital ulcer disease from syphilis facilitates human immunodeficiency virus (HIV) transmission and their ¬diagnosis is essential for the proper management. Venereal Disease Research Laboratory (VDRL) test is used as a screening test for the diagnosis of syphilis. However, unusual VDRL test results have been reported in HIV-infected persons with syphilis. There are reports showing higher than expected VDRL titers as well as biological false positive in most of the studies. A negative Rapid Plasma Reagin (RPR) test or VDRL test result may not rule out syphilis in patients with HIV infection. For laboratory confirmation of syphilis, one specific Treponemal test, namely, Fluroscent Treponemal Antibody Absorption (FTA-ABS) test or Treponema Pallidum Haemagglutination Assay (TPHA) should be done along with VDRL. A prospective cross sectional study was conducted for 2 years from Jun, 2012 to Jun 2014 to determine the prevalence of syphilis in HIV-seroreactive patients at 5 selected HIV/AIDS treatment and counseling centers in Kano State, North Western, Nigeria. New HIV-Seroreactive patients who gave informed consent to participate in the study were recruited. Venereal Diseases Research Laboratory (VDRL) test for Syphilis screening was performed on the same sera samples which were collected for HIV testing. A total of 238 patients, 113 (47%) males and 125 (53%) females, were enrolled. In the present study, 238 HIV-seropositive patients were screened for syphilis by VDRL test. Out of these 238 cases, 72 (32%) patients were positive for TPHA and 8 (3.4%) patients were reactive for VDRL in various titers with an overall prevalence of 3.4%. All the eight patients who were reactive for VDRL test were also positive for TPHA test. In Conclusions, with high prevalence of syphilis among HIV-infected people from this study, it is recommended that serological testing for syphilis should be carried out in all patients with newly diagnosed HIV infection. Detection and treatment of STI should have a central role in HIV prevention and control. This will help in proper management of patients having STIs and HIV co infection.

Keywords: HIV, infections, STIs, syphilis

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Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

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Authors: Hossein Rassi, Alaheh Gholami Roud-Majany, Zahra Razavi, Massoud Hoshmand

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Cervical cancer is multi step disease that is thought to result from an interaction between genetic background and environmental factors. Human papillomavirus (HPV) infection is the leading risk factor for cervical intraepithelial neoplasia (CIN)and cervical cancer. In other hand, some of hTERT and miRNA polymorphism may plays an important role in carcinogenesis. This study attempts to clarify the relation of hTERT genotypes and miR-146a genotypes in cervical cancer. Forty two archival samples with cervical lesion retired from Khatam hospital and 40 sample from healthy persons used as control group. A simple and rapid method was used to detect the simultaneous amplification of the HPV consensus L1 region and HPV-16,-18, -11, -31, 33 and -35 along with the b-globin gene as an internal control. We use Multiplex PCR for detection of hTERT and miR-146a rs2910164 genotypes in our lab. Finally, data analysis was performed using the 7 version of the Epi Info(TM) 2012 software and test chi-square(x2) for trend. Cervix lesions were collected from 42 patients with Squamous metaplasia, cervical intraepithelial neoplasia, and cervical carcinoma. Successful DNA extraction was assessed by PCR amplification of b-actin gene (99bp). According to the results, hTERT ( rs 2736098) GG genotype and miR-146a rs2910164 CC genotype was significantly associated with increased risk of cervical cancer in the study population. In this study, we detected 13 HPV 18 from 42 cervical cancer. The connection between several SNP polymorphism and human virus papilloma in rare researches were seen. The reason of these differences in researches' findings can result in different kinds of races and geographic situations and also differences in life grooves in every region. The present study provided preliminary evidence that a p53 GG genotype and miR-146a rs2910164 CC genotype may effect cervical cancer risk in the study population, interacting synergistically with HPV 18 genotype. Our results demonstrate that the testing of hTERT rs 2736098 genotypes and miR-146a rs2910164 genotypes in combination with HPV18 can serve as major risk factors in the early identification of cervical cancers. Furthermore, the results indicate the possibility of primary prevention of cervical cancer by vaccination against HPV18 in Iran.

Keywords: polymorphism of hTERT gene, miR-146a rs2910164 polymorphism, cervical cancer, virus

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Authors: Syed Beenish Rufai, Sarman Singh

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Background: Performance of Genotype MTBDRsl (Hain Life science GmbH Germany) for detection of mutations associated with second-line drug resistance is well known. However, less evidence regarding the association of mutations and genetic background of strains is known which, in the future, is essential for clinical management of anti-tuberculosis drugs in those settings where the probability of particular genotype is predominant. Material and Methods: During this retrospective study, a total of 259 MDR-TB isolates obtained from pulmonary TB patients were tested for second-line drug susceptibility testing (DST) using Genotype MTBDRsl VER 1.0 and compared with BACTEC MGIT-960 as a reference standard. All isolates were further characterized using spoligotyping. The spoligo patterns obtained were compared and analyzed using SITVIT_WEB. Results: Of total 259 MDR-TB isolates which were screened for second-line DST by Genotype MTBDRsl, mutations were found to be associated with gyrA, rrs and emb genes in 82 (31.6%), 2 (0.8%) and 90 (34.7%) isolates respectively. 16 (6.1%) isolates detected mutations associated with both FQ as well as to AG/CP drugs (XDR-TB). No mutations were detected in 159 (61.4%) isolates for corresponding gyrA and rrs genes. Genotype MTBDRsl showed a concordance of 96.4% for detection of sensitive isolates in comparison with second-line DST by BACTEC MGIT-960 and 94.1%, 93.5%, 60.5% and 50% for detection of XDR-TB, FQ, EMB, and AMK/CAP respectively. D94G was the most prevalent mutation found among (38 (46.4%)) OFXR isolates (37 FQ mono-resistant and 1 XDR-TB) followed by A90V (23 (28.1%)) (17 FQ mono-resistant and 6 XDR-TB). Among AG/CP resistant isolates A1401G was the most frequent mutation observed among (11 (61.1%)) isolates (2 AG/CP mono-resistant isolates and 9 XDR-TB isolates) followed by WT+A1401G (6 (33.3%)) and G1484T (1 (5.5%)) respectively. On spoligotyping analysis, Beijing strain (46%) was found to be the most predominant strain among pre-XDR and XDR TB isolates followed by CAS (30%), X (6%), Unique (5%), EAI and T each of 4%, Manu (3%) and Ural (2%) respectively. Beijing strain was found to be strongly associated with D94G (47.3%) and A90V mutations by (47.3%) and 34.8% followed by CAS strain by (31.6%) and 30.4% respectively. However, among AG/CP resistant isolates, only Beijing strain was found to be strongly associated with A1401G and WT+A1401G mutations by 54.5% and 50% respectively. Conclusion: Beijing strain was found to be strongly associated with the most prevalent mutations among pre-XDR and XDR TB isolates. Acknowledgments: Study was supported with Grant by All India Institute of Medical Sciences, New Delhi reference No. P-2012/12452.

Keywords: tuberculosis, line probe assay, XDR TB, drug susceptibility

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Authors: Manal Kamel, Sara Maher

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Development of sensitive non-invasive tests for detection of SARS-CoV-2 antigens is imperative to manage the extent of infection throughout the population, yet, it is still challenging. Here, we designed and optimized a sandwich enzyme-linked immunosorbent assay (ELISA) for SARS-CoV-2 S1 antigen detection in saliva. Both saliva samples and nasopharyngeal swapswere collected from 170 PCR-confirmed positive and negative cases. Gold nanoparticles (AuNPs) were conjugated with S1protein receptor binding domain (RBD) nanobodies. Recombinant S1 monoclonal antibodies (S1mAb) as primery antibody and gold conjugated nanobodies as secondary antibody were employed in sandwich ELISA. Our developed system were optimized to achieve 87.5 % sensitivity and 100% specificity for saliva samples compared to 89 % and 100% for nasopharyngeal swaps, respectively. This means that saliva could be a suitable replacement for nasopharyngeal swaps No cross reaction was detected with other corona virus antigens. These results revealed that our developed ELISAcould be establishedas a new, reliable, sensitive, and non-invasive test for diagnosis of SARS-CoV-2 infection, using the easily collected saliva samples.

Keywords: COVID 19, diagnosis, ELISA, nanobodies

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Authors: Shih-Yu Lo

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Stereotypes exist in almost every society, affecting how people interact with each other. However, to our knowledge, the influence of stereotypes was rarely explored in the context of basic perceptual processes. This study aims to explore how the gender stereotype affects object recognition. Participants were presented with a series of scene pictures, followed by a target display with a man or a woman, holding a weapon or a non-weapon object. The task was to identify whether the object in the target display was a weapon or not. Although the gender of the object holder could not predict whether he or she held a weapon, and was irrelevant to the task goal, the participant nevertheless tended to identify the object as a weapon when the object holder was a man than a woman. The analysis based on the signal detection theory showed that the stereotype effect on object recognition mainly resulted from the participant’s bias to make a 'weapon' response when a man was in the scene instead of a woman in the scene. In addition, there was a trend that the participant’s sensitivity to differentiate a weapon from a non-threating object was higher when a woman was in the scene than a man was in the scene. The results of this study suggest that the irrelevant social cues implied in the visual scene can be very powerful that they can modulate the basic object recognition process.

Keywords: gender stereotype, object recognition, signal detection theory, weapon

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Authors: Muhammad R. Ahmed, Mohammed Aseeri

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Wireless Sensor Network (WSN) consists of low-cost and multi functional resources constrain nodes that communicate at short distances through wireless links. It is open media and underpinned by an application driven technology for information gathering and processing. It can be used for many different applications range from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. With its nature and application scenario, security of WSN had drawn a great attention. It is known to be valuable to variety of attacks for the construction of nodes and distributed network infrastructure. In order to ensure its functionality especially in malicious environments, security mechanisms are essential. Malicious or internal attacker has gained prominence and poses the most challenging attacks to WSN. Many works have been done to secure WSN from internal attacks but most of it relay on either training data set or predefined threshold. Without a fixed security infrastructure a WSN needs to find the internal attacks is a challenge. In this paper we present an internal attack detection method based on maximum entropy model. The final experimental works showed that the proposed algorithm does work well at the designed level.

Keywords: internal attack, wireless sensor network, network security, entropy

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Authors: Portia Muehlbauer

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This paper examines the evolving landscape of preventing and countering violent extremism (PCVE) by delving into the growing importance of integrating human rights principles into violence prevention strategies on the local, community level. This study sheds light on the underlying theoretical frameworks of violent extremism and the influence of gender while investigating the intersection between human rights preservation and violent extremism prevention. To gain practical insight, the research focuses on two prominent international non-governmental organizations, Women without Borders (WwB) and Hedayah, and their distinct PCVE initiatives. WwB adopts a gender-sensitive approach, implementing parental education programs that empower mothers in at-risk communities to prevent the spread of violent extremism. In contrast, Hedayah takes an indirect route, employing capacity building programs that enhance the capabilities of educators, social workers, and psychologists in early intervention, rehabilitation and reintegration efforts. Qualitative data for this comparative analysis was collected through an extensive four-month internship at WwB during the fall of 2020, a three-month internship at Hedayah in the spring of 2021, a thought-provoking semi-structured interview with the executive director of WwB, personal field notes, and a comprehensive discourse analysis of the prevailing literature on human rights considerations in PCVE practices. This study examines the merits and challenges of integrating human rights into PCVE programming through the lens of both organizations, WwB and Hedayah. The findings of this study will inform policymakers, practitioners, and researchers on the intricate relationship between human rights protection and effective PCVE strategies.

Keywords: preventing and countering violent extremism, human rights, counterterrorism, peacebuilding, capacity building programs, gender studies

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

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Authors: Obulesh Avuku, Satwik Sunnam, Sri Charan Mohan Janthuka, Keerthi Yalamaddi

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In the United States alone, there are over 1.5 million spine fractures per year, resulting in about 17,730 spinal cord injuries. The cervical spine is where fractures in the spine most frequently occur. The prevalence of spinal fractures in the elderly has increased, and in this population, fractures may be harder to see on imaging because of coexisting degenerative illness and osteoporosis. Nowadays, computed tomography (CT) is almost completely used instead of radiography for the imaging diagnosis of adult spine fractures (x-rays). To stop neurologic degeneration and paralysis following trauma, it is vital to trace any vertebral fractures at the earliest. Many approaches have been proposed for the classification of the cervical spine [2d models]. We are here in this paper trying to break the bounds and use the vision transformers, a State-Of-The-Art- Model in image classification, by making minimal changes possible to the architecture of ViT and making it 3D-enabled architecture and this is evaluated using a weighted multi-label logarithmic loss. We have taken this problem statement from a previously held Kaggle competition, i.e., RSNA 2022 Cervical Spine Fracture Detection.

Keywords: cervical spine, spinal fractures, osteoporosis, computed tomography, 2d-models, ViT, multi-label logarithmic loss, Kaggle, public score, private score

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Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

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Authors: Christian Fercher, Jiaul Islam, Simon R. Corrie

Abstract:

Fluorescence-based immunodiagnostics are an emerging field in biosensor development and exhibit several advantages over traditional detection methods. While various affinity biosensors have been developed to generate a fluorescence signal upon sensing varying concentrations of analytes, reagentless, reversible, and continuous monitoring of complex biological samples remains challenging. Here, we aimed to genetically engineer biosensors based on single-chain antibody fragments (scFv) that are site-specifically labeled with environmentally sensitive fluorescent unnatural amino acids (UAA). A rational design approach resulted in quantifiable analyte-dependent changes in peak fluorescence emission wavelength and enabled antigen detection in vitro. Incorporation of a polarity indicator within the topological neighborhood of the antigen-binding interface generated a titratable wavelength blueshift with nanomolar detection limits. In order to ensure continuous analyte monitoring, scFv candidates with fast binding and dissociation kinetics were selected from a genetic library employing a high-throughput phage display and affinity screening approach. Initial rankings were further refined towards rapid dissociation kinetics using bio-layer interferometry (BLI) and surface plasmon resonance (SPR). The most promising candidates were expressed, purified to homogeneity, and tested for their potential to detect biomarkers in a continuous microfluidic-based assay. Variations of dissociation kinetics within an order of magnitude were achieved without compromising the specificity of the antibody fragments. This approach is generally applicable to numerous antibody/antigen combinations and currently awaits integration in a wide range of assay platforms for one-step protein quantification.

Keywords: antibody engineering, biosensor, phage display, unnatural amino acids

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Authors: D. P. Houhoula, J. Papaparaskevas, S. Konteles, A. Dargenta, A. Farka, C. Spyrou, M. Ziaka, S. Koussisis, E. Charvalos

Abstract:

Objectives: Nanotechnology is providing revolutionary opportunities for the rapid and simple diagnosis of many infectious diseases. Staphylococcus aureus, Listeria monocytogenes and Salmonella enteritis are important human pathogens. Diagnostic assays for bacterial culture and identification are time consuming and laborious. There is an urgent need to develop rapid, sensitive, and inexpensive diagnostic tests. In this study, a gold nanoprobe strategy developed and relies on the colorimetric differentiation of specific DNA sequences based approach on differential aggregation profiles in the presence or absence of specific target hybridization. Method: Gold nanoparticles (AuNPs) were purchased from Nanopartz. They were conjugated with thiolated oligonucleotides specific for the femA gene for the identification of members of Staphylococcus aureus, the mecA gene for the differentiation of Staphylococcus aureus and MRSA Staphylococcus aureus, hly gene encoding the pore-forming cytolysin listeriolysin for the identification of Listeria monocytogenes and the invA sequence for the identification of Salmonella enteritis. DNA isolation from Staphylococcus aureus Listeria monocytogenes and Salmonella enteritis cultures was performed using the commercial kit Nucleospin Tissue (Macherey Nagel). Specifically 20μl of DNA was diluted in 10mMPBS (pH5). After the denaturation of 10min, 20μl of AuNPs was added followed by the annealing step at 58oC. The presence of a complementary target prevents aggregation with the addition of acid and the solution remains pink, whereas in the opposite event it turns to purple. The color could be detected visually and it was confirmed with an absorption spectrum. Results: Specifically, 0.123 μg/μl DNA of St. aureus, L.monocytogenes and Salmonella enteritis was serially diluted from 1:10 to 1:100. Blanks containing PBS buffer instead of DNA were used. The application of the proposed method on isolated bacteria produced positive results with all the species of St. aureus and L. monocytogenes and Salmonella enteritis using the femA, mecA, hly and invA genes respectively. The minimum detection limit of the assay was defined at 0.2 ng/μL of DNA. Below of 0.2 ng/μL of bacterial DNA the solution turned purple after addition of HCl, defining the minimum detection limit of the assay. None of the blank samples was positive. The specificity was 100%. The application of the proposed method produced exactly the same results every time (n = 4) the evaluation was repeated (100% repeatability) using the femA, hly and invA genes. Using the gene mecA for the differentiation of Staphylococcus aureus and MRSA Staphylococcus aureus the method had a repeatability 50%. Conclusion: The proposed method could be used as a highly specific and sensitive screening tool for the detection and differentiation of Staphylococcus aureus Listeria monocytogenes and Salmonella enteritis. The use AuNPs for the colorimetric detection of DNA targets represents an inexpensive and easy-to-perform alternative to common molecular assays. The technology described here, may develop into a platform that could accommodate detection of many bacterial species.

Keywords: gold nanoparticles, pathogens, nanotechnology, bacteria

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Authors: Vivek Mahadev, Manoj Kumar, Neelu Mathur, Brahm Dutt Pandey

Abstract:

Perimeter guarding and protection of critical installations require prompt intrusion detection and assessment to take effective countermeasures. Currently, visual and electronic surveillance are the primary methods used for perimeter guarding. These methods can be costly and complicated, requiring careful planning according to the location and terrain. Moreover, these methods often struggle to detect stealthy and camouflaged insurgents. The object of the present work is to devise a surveillance technique using seismic sensors that overcomes the limitations of existing systems. The aim is to improve intrusion detection, assessment, and characterization by utilizing seismic sensors. Most of the similar systems have only two types of intrusion detection capability viz., human or vehicle. In our work we could even categorize further to identify types of intrusion activity such as walking, running, group walking, fence jumping, tunnel digging and vehicular movements. A virtual fence of 60 meters at GCNEP, Bahadurgarh, Haryana, India, was created by installing four underground geophones at a distance of 15 meters each. The signals received from these geophones are then processed to find unique seismic signatures called features. Various feature optimization and selection methodologies, such as LightGBM, Boruta, Random Forest, Logistics, Recursive Feature Elimination, Chi-2 and Pearson Ratio were used to identify the best features for training the machine learning models. The trained models were developed using algorithms such as supervised support vector machine (SVM) classifier, kNN, Decision Tree, Logistic Regression, Naïve Bayes, and Artificial Neural Networks. These models were then used to predict the category of events, employing weighted ensemble voting to analyze and combine their results. The models were trained with 1940 training events and results were evaluated with 831 test events. It was observed that using the weighted ensemble voting increased the efficiency of predictions. In this study we successfully developed and deployed the virtual fence using geophones. Since these sensors are passive, do not radiate any energy and are installed underground, it is impossible for intruders to locate and nullify them. Their flexibility, quick and easy installation, low costs, hidden deployment and unattended surveillance make such systems especially suitable for critical installations and remote facilities with difficult terrain. This work demonstrates the potential of utilizing seismic sensors for creating better perimeter guarding and protection systems using multiple machine learning models in weighted ensemble voting. In this study the virtual fence achieved an intruder detection efficiency of over 97%.

Keywords: geophone, seismic perimeter surveillance, machine learning, weighted ensemble method

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Authors: Ihn Sook Jeong, Eun Joo Lee, Jae Hyung Kim, Gun Ho Kim, Young Jun Hwang

Abstract:

This study investigated intravenous (IV) infiltration using bioelectrical impedance for 27 hospitalized patients in a long-term care hospital. Impedance parameters showed significant differences before and after infiltration as follows. First, the resistance (R) after infiltration significantly decreased compared to the initial resistance. This indicates that the IV solution flowing from the vein due to infiltration accumulates in the extracellular fluid (ECF). Second, the relative resistance at 50 kHz was 0.94 ± 0.07 in 9 subjects without infiltration and was 0.75 ± 0.12 in 18 subjects with infiltration. Third, the magnitude of the reactance (Xc) decreased after infiltration. This is because IV solution and blood components released from the vein tend to aggregate in the cell membrane (and acts analogously to the linear/parallel circuit), thereby increasing the capacitance (Cm) of the cell membrane and reducing the magnitude of reactance. Finally, the data points plotted in the R-Xc graph were distributed on the upper right before infiltration but on the lower left after infiltration. This indicates that the infiltration caused accumulation of fluid or blood components in the epidermal and subcutaneous tissues, resulting in reduced resistance and reactance, thereby lowering integrity of the cell membrane. Our findings suggest that bioelectrical impedance is an effective method for detection of infiltration in a noninvasive and quantitative manner.

Keywords: intravenous infiltration, impedance, parameters, resistance, reactance

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Authors: Yu-Chi Liao, Nai-Wen Guo, Chun‑Hung Lee, Yung-Chin Lu, Cheng-Hung Ko

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Objective: The incidence of behavioral addictions, especially substance use disorders (SUDs), is gradually be taken seriously with various physical health problems. Mindfulness-based relapse prevention (MBRP) is a treatment option for promoting long-term health behavior change in recent years. MBRP is a structured protocol that integrates formal meditation practices with the cognitive-behavioral approach of relapse prevention treatment by teaching participants not to engage in reappraisal or savoring techniques. However, considering SUDs as a complex brain disease, questionnaires and symptom evaluation are not sufficient to evaluate the effect of MBRP. Neurophysiological biomarkers such as quantitative electroencephalogram (QEEG) may improve accurately represent the curative effects. This study attempted to find out the neurophysiological indicator of MBRP in various severity SUD involuntary clients. Participants and Methods: Thirteen participants (all males) completed 8-week mindfulness-based treatment provided by trained, licensed clinical psychologists. The behavioral data were from the Severity of Dependence Scale (SDS) and Negative Mood Regulation Scale (NMR) before and afterMBRP treatment. The QEEG data were simultaneously recorded with executive attention tasks, called comprehensive nonverbal attention test(CNAT). The two-way repeated-measures (treatment * severity) ANOVA and independent t-test were used for statistical analysis. Results: Thirteen participants regrouped into high substance dependence (HS) and low substance dependence (LS) by SDS cut-off. The HS group showed more SDS total score and lower gamma wave in the Go/No Go task of CNAT at pretest. Both groups showed the main effect that they had a lower frontal theta/beta ratio (TBR) during the simple reaction time task of CNAT. The main effect showed that the delay errors of CNAT were lower after MBRP. There was no other difference in CNAT between groups. However, after MBRP, compared to LS, the HS group have resonant progress in improving SDS and NMR scores. The neurophysiological index, the frontal TBR of the HS during the Go/No Go task of CNATdecreased than that of the LS group. Otherwise, the LS group’s gamma wave was a significant reduction on the Go/No Go task of CNAT. Conclusion: The QEEG data supports the MBRP can restore the prefrontal function of involuntary addicts and lower their errors in executive attention tasks. However, the improvement of MBRPfor the addict with high addiction severity is significantly more than that with low severity, including QEEG’s indicators and negative emotion regulation. Future directions include investigating the reasons for differences in efficacy among different severity of the addiction.

Keywords: mindfulness, involuntary clients, QEEG, emotion regulation

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Authors: Jean Paul M. Milambo

Abstract:

Purpose: Aromatase inhibitors (AIs) are indicated in the treatment of hormone-receptive breast cancer in postmenopausal women in various settings. Studies have shown cardiovascular events in some developed countries. To date the data is sparce for evidence-based recommendations in African clinical settings due to lack of cancer registries, capacity building and surveillance systems. Therefore, this study was conducted to assess the feasibility of HyBeacon® probe genotyping adjunctive to standard care for timely prediction and diagnosis of Aromatase inhibitors (AIs) associated adverse events in breast cancer survivors in Africa. Methods: Cross sectional study was conducted to assess the knowledge of POCT among six African countries using online survey and telephonically contacted. Incremental cost effectiveness ratio (ICER) was calculated, using diagnostic accuracy study. This was based on mathematical modeling. Results: One hundred twenty-six participants were considered for analysis (mean age = 61 years; SD = 7.11 years; 95%CI: 60-62 years). Comparison of genotyping from HyBeacon® probe technology to Sanger sequencing showed that sensitivity was reported at 99% (95% CI: 94.55% to 99.97%), specificity at 89.44% (95% CI: 87.25 to 91.38%), PPV at 51% (95%: 43.77 to 58.26%), and NPV at 99.88% (95% CI: 99.31 to 100.00%). Based on the mathematical model, the assumptions revealed that ICER was R7 044.55. Conclusion: POCT using HyBeacon® probe genotyping for AI-associated adverse events maybe cost effective in many African clinical settings. Integration of preventive measures for early detection and prevention guided by different subtype of breast cancer diagnosis with specific clinical, biomedical and genetic screenings may improve cancer survivorship. Feasibility of POCT was demonstrated but the implementation could be achieved by improving the integration of POCT within primary health cares, referral cancer hospitals with capacity building activities at different level of health systems. This finding is pertinent for a future envisioned implementation and global scale-up of POCT-based initiative as part of risk communication strategies with clear management pathways.

Keywords: breast cancer, diagnosis, point of care, South Africa, aromatase inhibitors

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Authors: Kanchan J. Kakade, S. A. Annadate

Abstract:

This paper presents design and development of underground coal mine monitoring using mbed arm cortex controller and ZigBee communication. Coal mine is a special type of mine which is dangerous in nature. Safety is the most important feature of a coal industry for proper functioning. It’s not only for employees and workers but also for environment and nation. Many coal producing countries in the world face phenomenal frequently occurred accidents in coal mines viz, gas explosion, flood, and fire breaking out during coal mines exploitation. Thus, such emissions of various gases from coal mines are necessary to detect with the help of robot. Coal is a combustible, sedimentary, organic rock, which is made up of mainly carbon, hydrogen and oxygen. Coal Mine Detection Robot mainly detects mash gas and carbon monoxide. The mash gas is the kind of the mixed gas which mainly make up of methane in the underground of the coal mine shaft, and sometimes it abbreviate to methane. It is formed from vegetation, which has been fused between other rock layers and altered by the combined effects of heat and pressure over millions of years to form coal beds. Coal has many important uses worldwide. The most significant uses of coal are in electricity generation, steel production, cement manufacturing and as a liquid fuel.

Keywords: Zigbee communication, various sensors, hazardous gases, mbed arm cortex M3 core controller

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Authors: I. Parkova, A. Vališevskis, A. Viļumsone

Abstract:

Nocturnal enuresis or bed-wetting is intermittent incontinence during sleep of children after age 5 that may precipitate wide range of behavioural and developmental problems. One of the non-pharmacological treatment methods is the use of a bed-wetting alarm system. In order to improve comfort conditions of nocturnal enuresis alarm system, modular moisture sensor should be replaced by a textile sensor. In this study behaviour and moisture detection speed of woven and sewn sensors were compared by analysing change in electrical resistance after solution (salt water) was dripped on sensor samples. Material of samples has different structure and yarn location, which affects solution detection rate. Sensor system circuit was designed and two sensor tests were performed: system activation test and false alarm test to determine the sensitivity of the system and activation threshold. Sewn sensor had better result in system’s activation test – faster reaction, but woven sensor had better result in system’s false alarm test – it was less sensitive to perspiration simulation. After experiments it was found that the optimum switching threshold is 3V in case of 5V input voltage, which provides protection against false alarms, for example – during intensive sweating.

Keywords: conductive yarns, moisture textile sensor, industry, material

Procedia PDF Downloads 246