Search results for: chronic training

Authors: Jeerasak Wisatphan, Nara Samattapapong

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This study's goals were to enhance nitroglycerin manufacturing efficiency through simulation, recover nitroglycerin from the storage facility, and enhance nitroglycerine recovery and purge systems. It was found that the problem was nitroglycerin reflux. Therefore, the researcher created three alternatives to solve the problem. The system of Nitroglycerine Recovery and Purge was then simulated using the FlexSim program, and each alternative was tested. The results demonstrate that the alternative system-led Nitroglycerine Recovery and Nitroglycerine Purge System collaborate to produce Nitroglycerine, which is more efficient than other alternatives and can reduce production time. It can also improve the recovery of nitroglycerin. It also serves as a guideline for developing a real-world system and modeling it for training staff without wasting raw chemical materials or fuel energy.

Keywords: efficiency increase, nitroglycerine recovery and purge system, production improvement, simulation

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Authors: Dipanwita Chakravortty

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Officially, the Israel Defence Forces (IDF) follows a policy of 'gender equality and partnership' which institutionalises norms regarding equal duty towards the nation. It reiterates the equality in unbiased opportunities and resources for Jewish men and women to participate in the military as equal citizens. At the same time, as a military institution, the IDF supports gender biases and crystallises the same through various interactions among women soldiers, male soldiers and the institution. These biases are expressed through various stages and processes in the military institution like biased training, discriminatory postings of women soldiers, lack of combat training and acceptance of sexual harassment. The gender-military debates in Israel is largely devoted to female emancipation and converting the militarised women’s experiences into mainstream debates. This critical scholarship, largely female-based and located in Israel, has been consistently critical of the structural policies of the IDF that have led to continued discriminatory practices against women soldiers. This has compelled the military to increase its intake of women soldiers and make its structural policies more gender-friendly. Nonetheless, the continued thriving of gender discrimination in the IDF resulted in scholars looking deep into the failure of these policies in bringing about a change. This article looks into two research objectives, firstly to analyse existing gender relations in the IDF which impact the practices and prejudices in the institution and secondly to look beyond the structural discrimination as part of the gender debates in the IDF. The proposed research uses the structural-functional model as a framework to study the discourses and norms emerging out of the interaction between gender and military as two distinct social institutions. Changing gender-military debates will be discussed in great detail to understanding the in-depth relation between the Israeli society and the military due to the conscription model. The main arguments of the paper deal with the functional aspect of the military service rather than the structural component of the institution. Traditional stereotypes of military institutions along with cultural notions of a female body restrict the complete integration of women soldiers despite favourable legislations and policies. These result in functional discriminations like uneven promotion, sexual violence, restructuring gender identities and creating militarised bodies. The existing prejudices encourage younger women recruits to choose from within the accepted pink-collared jobs in the military rather than ‘breaking the barriers.’ Some women recruits do try to explore new avenues and make a mark for themselves. Most of them face stiff discrimination but they accept it as part of military life. The cyclical logic behind structural norms leading to functional discrimination which then emphasises traditional stereotypes and hampers change in the institutional norms compels the IDF to continue to strive towards gender equality within the institution without practical realisation.

Keywords: women soldiers, Israel Defence Forces, gender-military debates, security studies

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Authors: Nur Atiqah Adam, Mor Jack Ng, Bernard Chern, Kok Hian Tan

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Introduction: Gestational diabetes (GDM) is a common pregnancy complication with short and long-term health consequences for both mother and fetus. Factors such as family history of diabetes mellitus, maternal obesity, maternal age, ethnicity and parity have been reported to influence the risk of GDM. In a multi-racial country like Singapore, it is worthwhile to study the GDM prevalences of different ethnicities. We aim to investigate the influence of ethnicity on the racial prevalences of GDM in Singapore. This is important as it may help us to improve guidelines on GDM healthcare services according to significant risk factors unique to Singapore. Materials and Methods: Obstetric cohort data of 926 singleton deliveries in KK Women’s and Children’s Hospital (KKH) from 2011 to 2013 was obtained. Only patients aged 18 and above and without complicated pregnancies or chronic illnesses were targeted. Factors such as ethnicity, maternal age, parity and maternal body mass index (BMI) at booking visit were studied. A multivariable logistic regression model, adjusted for confounders, was used to determine which of these factors are significantly associated with an increased risk of GDM. Results: The overall GDM prevalence rate based on WHO 1999 criteria & at risk screening (race alone not a risk factor) was 8.86%. GDM rates were higher among women above 35 years old (15.96%), obese (15.15%) and multiparous women (10.12%). Indians had a higher GDM rate (13.0 %) compared to the Chinese (9.57%) and Malays (5.20%). However, using multiple logistic regression model, variables that are significantly related to GDM rates were maternal age (p < 0.001) and maternal BMI at booking visit (p = 0.006). Conclusion: Maternal age (p < 0.001) and maternal booking BMI (p = 0.006) are the strongest risk factors for GDM. Ethnicity per se does not seem to have a significant influence on the prevalence of GDM in Singapore (p = 0.064). Hence we should tailor guidelines on GDM healthcare services according to maternal age and booking BMI rather than ethnicity.

Keywords: ethnicity, gestational diabetes, healthcare, pregnancy

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: K. M. Pratap Shankar, Dattatreya Rao, Sai Prasad

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Background: Skin diseases are among the most common health problems worldwide and are associated with a considerable burden. Eczema is such a skin ailment which cause psychological, social and financial burden on the patient and their families. Management of eczema with antibiotics, antihistamines, steroids etc., are available but even after their use relapses, recurrences and other complications are very common. Aim: The aim of this study was to assess the efficacy of leech application in the management of vicarcikā (Eczema) with Histopathological study. Methods: For the present study 10 patients having the classical symptoms of Vicarcikā, were randomly selected as per the inclusion and exclusion criteria from O.P.D. & I.P.D. sections of Śalya department, S.V. Āyurvedic Hospital, Tirupati. Minimum 4 sittings of Leech application was carried out with seven days interval. Total duration of treatment was 6 weeks. Biopsy samples were collected from the lesion site before and after treatment. Histopathological examination was done by the pathologist. Results: In eczema (dermatitis) the leech application therapy gives excellent response by reducing the inflammatory component, hyperkeratosis, spongiosis, irregular acanthosis and by evoking a granulation tissue response in the dermis and in most of the cases with complete recovery from the lesion. Most of the cases in the study were chronic dermatitis and sebhoric keratosis, almost all local/focal pigmented lesions is totally relieved by leech therapy especially in cases of sebhoric keratosis. Conclusion: In the present study it was found that, leech application evokes significant changes at histological level specifically in reduction of inflammatory component, hyperkeratosis, spongiosis and irregular acanthosis. It was also found that there was a considerable formation of granulation tissue, which helps in formation of healthy new tissues.

Keywords: acanthosis, eczema, hyperkeratosis, leech application, spongiosis

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: A. Monika, Manu Sharma, Hong Boo Lee, Richa Dhingra, Neelima Dhingra

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Nuclear factor-κappa B serve as a molecular lynchpin that links persistent infections and chronic inflammation to increased cancer risk. Inflammation has been recognized as a hallmark and cause of cancer. Natural products present a privileged source of inspiration for chemical probe and drug design. Herbal remedies were the first medicines used by humans due to the many pharmacologically active secondary metabolites produced by plants. Some of the metabolites like Lantadene (pentacyclic triterpenoids) from the weed Lantana camara has been known to inhibit cell division and showed anti-antitumor potential. The C-3 aromatic esters of lantadenes were synthesized, characterized and evaluated for cytotoxicity and inhibitory potential against Tumor necrosis factor alpha-induced activation of Nuclear factor-κappa B in lung cancer cell line A549. The 3-methoxybenzoyloxy substituted lead analogue inhibited kinase activity of the inhibitor of nuclear factor-kappa B kinase in a single-digit micromolar concentration. At the same time, the lead compound showed promising cytotoxicity against A549 lung cancer cells with IC50 ( half maximal inhibitory concentration) of 0.98l µM. Further, molecular docking of 3-methoxybenzoyloxy substituted analogue against Inhibitor of nuclear factor-kappa B kinase (Protein data bank ID: 3QA8) showed hydrogen bonding interaction involving oxygen atom of 3-methoxybenzoyloxy with the Arginine-31 and Glutamine-110. Encouraging results indicate the Lantadene’s potential to be developed as anticancer agents.

Keywords: anticancer, lantadenes, pentacyclic triterpenoids, weed

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Authors: J. Raub, H. Schillok, L. Kaupe, C. Jung-Sievers, G. Pitschel-Walz, M. Bühner, J. Gensichen, F. D. Pokal-Gruppe

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Introduction: In Germany, depression ranks among the top ten diseases with the highest disease burden and often occurs with comorbidities. Collaborative Care (CC), a concept developed in the United States for the primary care management of chronic diseases, has been identified as an efficient model for the treatment of depression in general medicine. A recent meta-analysis highlights research gaps regarding CC in patients with psychiatric multimorbidity. The highest prevalence of psychiatric comorbidities in depression is observed in anxiety disorders, post-traumatic stress disorder (PTSD), and substance use disorders. Methods: We conducted a literature search following the PRISMA guidelines with three components: Collaborative Care, Depression and randomized controlled trial on the common databases. We focused on the examination of psychiatric comorbidities in depression, specifically Posttraumatic Stress Disorder (PTSD) and Substance Use Disorder (SUD). Results: During the screening process, we identified nine relevant articles related to PTSD, the number of articles related to Substance Use Disorder (SUD) was ten. We examined a total of 8,634 individuals. Our literature review did not reveal any overall significant superiority of the Collaborative Care model compared to Usual Care in patients with depression with comorbid Substance Use Disorder (SUD) or Posttraumatic Stress Disorder (PTSD). Discussion: Five studies demonstrate a faster and statistically significant improvement in depression outcomes among patients with Substance Use Disorder (SUD) and Posttraumatic Stress Disorder (PTSD). Currently, several randomized controlled trials on the topic of Collaborative Care in depression with psychiatric comorbidity are ongoing, such as miCare, Claro and COMET.

Keywords: Depression, primary care, collaborative care, PTSD, Substance use Disorder

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Authors: Dora Martins, Susana Silva, Cândida Silva

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Human Resources Management (HRM) has been recognized by academics and practitioners as an important element in organizations. Therefore, this paper explores the best practices of HRM and seeks to understand the level of participation in the development of these practices by human resources managers in the hospitality industry and compare it with other industries. Thus, the study compared the HRM practices of companies in the hospitality sector with HRM practices of companies in other sectors, and identifies the main differences between their HRM practices. The results show that the most frequent HRM practices in all companies, independently of its sector of activity, are hiring and training. When comparing hospitality sector with other sectors of activity, some differences were noticed, namely in the adoption of the practices of communication and information sharing, and of recruitment and selection. According to these results, the paper discusses the major theoretical and practical implications. Suggestions for future research are also presented.

Keywords: exploratory study, human resources management practices, human resources manager, hospitality companies, Portuguese companies

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Helen Huang, Francisco Javier Quesada Ocet, Blanca Quesada Oce, Javier Jimenez Bello, Victor Palanca Gil, Alba Cervero Rubio, Ana Paya Chaume, Alejandro Herreros-Pomares, Fernando Vidal-Vanaclocha, Rafael Paya Serrano, Aurelio Quesada Dorador, Monica Soliman

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Background: Cancer favors both the pro-inflammatory state and autonomic dysfunction, two important mechanisms in the genesis of AF. Atrial remodeling might be caused as a result of paraneoplastic conditions or the result of direct expression of neoplasia. Here, we hypothesize that cancer, through inflammatory mediators, may favor the appearance of AF and patients with the first episode of AF could have a higher risk of developing cancer. Method: Data was collected from patients who attended the emergency department of our hospital for the first episode of AF, diagnosed electrocardiographically, between 2010-2015 (n = 712). The minimum follow-up was 2 years, recording the appearance of cancer, total mortality, recurrences of AF and other events. Patients who developed cancer and those who did not during the 2 years after the onset of AF were compared, as well as with the incidence of cancer in Spain in 2012. Results: After 2 years, 35 patients (4.91%) were diagnosed with cancer, with an annual incidence of 2.45%. Hematological neoplasms were the most frequent (34.28%). The cancer group was older (76.68 +/-12.75 years vs 74.16 +/-12.71; p <0.05) and had fewer typical symptoms (palpitations) (33.38% vs 14.28% , p <0.05). The incidence of cancer in Spain during 2012 was 0.46%, much lower than our sample. When comparing the incidence by age, these differences were maintained both in those over 65 years of age and in those under 65 years of age (2.17% vs. 0.28%; 0.28% vs. 0.18% respectively). Discussion: Therefore, a high incidence of cancer in patients with the first episode of AF was observed (the annual incidence of 2.45% after the onset of AF is 6.1 times that of the general population). After the evaluation of patients with AF in their first detected episode, surveillance of the appearance of cancer should be considered in clinical practice.

Keywords: cancer, cardiovascular outcomes, atrial fibrillation, inflammation

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Authors: Madeline Gibson

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Night shift workers make up an essential part of the modern workforce. However, night shift workers have higher incidences of late in life diseases and earlier mortality. Night shift workers are exposed to constant light and experience circadian rhythm disruption. Sleep disruption is thought to increase oxidative stress, defined as an imbalance of excess pro-oxidative factors and reactive oxygen species over anti-oxidative activity. Oxidative stress can damage cells, proteins and DNA and can eventually lead to varied chronic diseases such as cancer, diabetes, cardiovascular disease, Alzheimer’s and dementia. This review aimed to understand whether night shift workers were at greater risk of oxidative stress and to contribute to a consensus on this relationship. Twelve studies published in 2001-2019 examining 2,081 workers were included in the review. Studies compared both the impact of working a single shift and in comparisons between those who regularly work night shifts and only day shifts. All studies had evidence to support this relationship across a range of oxidative stress indicators, including increased DNA damage, reduced DNA repair capacity, increased lipid peroxidation, higher levels of reactive oxygen species, and to a lesser extent, a reduction in antioxidant defense. This research supports the theory that melatonin and the sleep-wake cycle mediate the relationship between shift work and oxidative stress. It is concluded that night shift work increases the risk for oxidative stress and, therefore, future disease. Recommendations are made to promote the long-term health of shift workers considering these findings.

Keywords: night shift work, coxidative stress, circadian rhythm, melatonin, disease, circadian rhythm disruption

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Authors: Noof Eid Al Shammari

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Background: Living with haemodialysis (HD) can impose several physical and social restrictions on the lives of individuals. Usually, the patient has three dialysis sessions per week that each run for three to four hours. This limits the social life of patients and causes a lower quality of life, in conjunction with the fact that people with chronic kidney disease must follow strict fluid and food regimens and use multiple medications. Given these factors, patients undergoing HD generally need psychological support. Objective: This scoping review study aims to evaluate the effectiveness of physical and breathing exercises on quality of life (QOL) and psychophysical status in patients undergoing HD. Methodology: Searches for relevant studies were performed in four databases (MEDLINE, CINAHL, Google Scholar, and PubMed) for articles published between 2011 and 2021. Out of all the searched literature, ten studies met the inclusion criteria (8 randomised controlled trials, one quasi-experimental study, and one pilot study), with a total of 588 patients. Different types of physical and breathing exercises were used (breathing, cardiopulmonary, and physical exercises). Results: All included studies in this scoping review revealed that most of the aerobic or anaerobic exercises, as well as breathing exercises, had a positive effect and significantly improved patients’ QOL, physical functioning, and psychological status. Conclusions: In this review, most of the articles demonstrated a positive effect of physical and breathing exercises on the QOL and psychophysical status of HD patients. Based on the findings of these studies, physical and breathing exercises were shown to improve muscle strength and other health-related aspects of QOL, including sexual, social, cognitive, and physical functions. However, more studies will need to be conducted with a larger sample to determine the best intervention that could be implemented and standardised in nursing care for patients undergoing HD.

Keywords: physical exercise, breathing exercises, quality of life, depression, hemodialysis

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Susmita Panda, Arnab Banerjee, Ajinkya Baxy, Bappaditya Manna

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The design of short to medium-span high-speed bridges in critical locations is an essential aspect of vehicle-bridge interaction. Due to dynamic interaction between moving load and bridge, mathematical models or finite element modeling computations become time-consuming. Thus, to reduce the computational effort, a universal approximator using an artificial neural network (ANN) has been used to evaluate the dynamic response of the bridge. The data set generation and training of surrogate models have been conducted over the results obtained from mathematical modeling. Further, the robustness of the surrogate model has been investigated, which showed an error percentage of less than 10% with conventional methods. Additionally, the dependency of the dynamic response of the bridge on various load and bridge parameters has been highlighted through a parametric study.

Keywords: artificial neural network, mode superposition method, moving load analysis, surrogate models

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Authors: Boudemagh Naime

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Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: Pocholo Rodriguez, Anne Bernadine Ocampo, Ian Benedict Chan, Janric Micah Gray

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The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system.

Keywords: back-propagation algorithm, load instability, neural network, power distribution system

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Authors: A. Chenafa, S. Boulenouar, M. Zitouni, M. Boukouria

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The sterilization of medical devices constitutes for all the medical professions, an inescapable obligation. It has for objective to prevent the infectious risk, both for the patient and for the medical team. The Dental surgeon as every healthcare professional has to master perfectly this subject and to train his staff to the various techniques of sterilization. It is the only way to assure the patients all the security for which they are entitled to wait when they undergo a dental care. It’s for it, that we undertook to lead an investigation aiming at estimating the sterilization practice at the dental surgeon of Sidi bel Abbes. The survey result showed a youth marked with the profession with a majority use of autoclave with cycle B and an almost total absence of the sterilization controls (test of Bowie and Dick). However, the majority of the dentists control and validate their sterilizers. Finally, our survey allowed us to describe some practices which must be improved regarding control, regarding qualification and regarding staff training. And suggestions were made in this sense.

Keywords: dental surgeon, medical devices, sterilization, survey

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Nitin Joseph

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Objectives: This study was done to assess emotional intelligence levels and to find out its association with socio demographic variables and perceived stress among medical students. Material and Methods: This study was done among first and second year medical students. Data was collected using a self-administered questionnaire. Results: Emotional intelligence scores was found to significantly increase with age of the participants (F=2.377, P < 0.05). Perceived stress was found to be significantly more among first year (t=1.997, P=0.05). Perceived stress was found to significantly decrease with increasing emotional intelligence scores (r = – 0.226, P < 0.001). Conclusion: First year students were found to be more vulnerable to stress than their seniors probably due to lesser emotional intelligence. As both these parameters are related, ample measures to improve emotional intelligence needs to be supported in the training curriculum of beginners so as to make them more stress free during early student life.

Keywords: emotional intelligence, medical students, perceived stress, socio demographic variables

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

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In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

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Authors: Tan Dexian Arthur, James Wei Ming Kwek, Ian Loh, Lee Tee Sin

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Statement of the Problem: Mucoceles most commonly affect the frontal sinus, which results from chronic obstruction of the sinus ostium or cystic dilatation of mucous glands with ductal obstruction. They are known to cause bony erosion of the sinus walls, which can lead to large defects. These defects were typically managed by obliteration or cranialization of the frontal sinus. Although short term outcomes of conservative management of significant posterior table defects from fractures are promising, there have been no studies on the long-term outcomes of large dehiscences in the posterior wall of the frontal sinus. Methodology & Findings : Computed Tomography (CT) Paranasal Sinuses images were analyzed and found complete spontaneous osteogenesis of a large dehiscent frontal sinus posterior wall, secondary to a large mucocele, 9 years from functional endoscopic sinus surgery with the defect managed conservatively. Conclusion & Significance: The dura is well known for its osteogenic properties. Prior studies have showed that dura could induce osteogenesis in cutaneous tissue in the absence of other central nervous system structures. It was also demonstrated that osteogenesis and chondrogenesis were possible in zygomatic fractures by transplanting neonatal dura grafts to the bony defects in rats. Extrapolating from these studies, the authors postulate that the presence of dura beneath the bony deformity of the posterior frontal sinus wall had likely initiated the osteogenesis and restored the bony defect in the patient. In our literature review, we did not find any reports of spontaneous osteogenesis of large frontal sinus defects. While our experience is incidental, it reinforces the osteogenetic potential of an intact dura and further highlights that selected large defects of the posterior wall of the frontal sinus can be conservatively managed.

Keywords: paranasal sinus mucocele, mucocele, osteogenesis, dehiscence

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Authors: ATM Emdadul Haque

Abstract:

Phosphorus is an essential nutrient which is regularly consumed with food and exists in the body as phosphate. Phosphate is an important component of cellular structures and needed for bone mineralization. Excessive accumulation of phosphate is an important driving factor of mortality in chronic renal failure patients; of relevance, these patients are usually provided health care by doctors, nurses, and pharmacists. Hence, this study was planned to determine the level of awareness of the future healthcare providers about the phosphate-containing foods and beverages and to access their knowledge on the harmful effects of excess phosphate consumption. A questionnaire was developed and distributed among the year-1 medical, nursing and pharmacy students. 432 medical, nursing and pharmacy students responded with age ranging from 18-24 years. About 70% of the respondents were female with a majority (90.7%) from Malay ethnicity. Among the respondents, 29.9% were medical, 35.4% were the pharmacy and 34.7% were nursing students. 79.2% students knew that phosphate was an important component of the body, but only 61.8% knew that consuming too much phosphate could be harmful to the body. Despite 97% of the students knew that carbonated soda contained high sugar, surprisingly 77% of them did not know the presence of high phosphate in the same soda drinks; in the similar line of observation, 67% did not know the presence of it in the fast food. However, it was encouraging that 94% of the students wanted to know more about the effects of phosphate consumption, 74.3% were willing to give up drinking soda and eating fast food, and 52% considered taking green coconut water instead of soda drinks. It is, therefore, central to take an educational initiative to increase the awareness of the future healthcare providers about phosphate-containing food and its harmful effects in excessive consumptions.

Keywords: high phosphate containing foods and beverages, excessive consumption, future health care providers, phosphorus

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Authors: Sikiru Ademola Adewale, Joe Thomas, Bolanle Hafiz Matti, Tosin Ige

Abstract:

This project demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness.

Keywords: decoder, encoder, many-to-many mapping, video captioning, 2-gram BLEU

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Authors: Viveka Jackson, Erouscilla Joseph, Denise Beckles, Thomas Christopher

Abstract:

Sulphur Dioxide (SO2) has been defined as a non-flammable, non-explosive, colourless gas, having a pungent, irritating odour, and is one of the main gases emitted from volcanoes. Sulphur dioxide has been recorded in concentrations hazardous to humans (0.25 – 0.5 ppm (~650 – 1300 μg/m3), downwind of many volcanoes and hence warrants constant air-quality monitoring around these sites. It has been linked to an increase in chronic respiratory disease attributed to long-term exposures and alteration in lung and other physiological functions attributed to short-term exposures. Sulphur Springs in Saint Lucia is a highly active geothermal area, located within the Soufrière Volcanic Centre, and is a park widely visited by tourists and locals. It is also a current source of continuous volcanic emissions via its many fumaroles and bubbling pools, warranting concern by residents and visitors to the park regarding the effects of exposure to these gases. In this study, we introduce a novel SO2 measurement system for the monitoring and quantification of ambient levels of airborne volcanic SO2 using low-cost technology. This work involves the extensive production of low-cost SO2 monitors/samplers, as well as field examination in tandem with standard commercial samplers (SO2 diffusion tubes). It also incorporates community involvement in the volcanic monitoring process as non-professional users of the instrument. We intend to present the preliminary monitoring results obtained from the low-cost samplers, to identify the areas in the Park exposed to high concentrations of ambient SO2, and to assess the feasibility of the instrument for non-professional use and application in volcanic settings

Keywords: ambient SO2, community-based monitoring, risk-reduction, sulphur springs, low-cost

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Authors: Kenneth Nhundu, Leocadia Zhou, Farhad Aghdasi, Voster Muchenje

Abstract:

Climate change poses increased risks to rural communities that rely on natural resources, such as forests, cropland and rangeland, waterways, and open spaces Because of their connection to the land and the potential for climate change to impact natural resources and disrupt ecosystems and seasons, rural livelihoods and well-being are disproportionately vulnerable to climate change. Climate change has the potential to affect the environment in a number of ways that place increased stress on everyone, but disproportionately on the most vulnerable populations, including the young, the old, those with chronic illness, and the poor. The communities in the study area are predominantly rural, resource-based and are generally surrounded by public or private lands that are dominated by natural resources, including forests, rangelands, and agriculture. The livelihoods of these communities are tied to natural resources. Therefore, targeted strategies to cope will be required. This paper assessed the household socio-economic characteristics and their implications to household vulnerability to climate change impacts in the rural Eastern Cape Province, South Africa. The results indicate that the rural communities are climate-vulnerable populations as they have a large proportion of people who are less economically or physically capable of adapting to climate change. The study therefore recommends that at each level, the needs, knowledge, and voices of vulnerable populations, including indigenous peoples and resource-based communities, deserve consideration and incorporation so that climate change policy (1) ensures that all people are supported and able to act, (2) provides as robust a strategy as possible to address a rapidly changing environment, and (3) enhances equity and justice.

Keywords: climate change, vulnerable, socio-economic, livelihoods

Procedia PDF Downloads 355

Authors: Teimuraz Manjapharashvili, Ketevani Manjaparashvili

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Fuzzy logic is a modeling method for complex or ill-defined systems and is a relatively new mathematical approach. Its basis is to consider overlapping cases of parameter values and define operations to manipulate these cases. Fuzzy logic can successfully create operative automatic management or appropriate advisory systems. Fuzzy logic techniques in various operational control technologies have grown rapidly in the last few years. Fuzzy logic is used in many areas of human technological activity. In recent years, fuzzy logic has proven its great potential, especially in the automation of industrial process control, where it allows to form of a control design based on the experience of experts and the results of experiments. The engineering of chemical technological processes uses fuzzy logic in optimal management, and it is also used in process control, including the operational control of continuous cycle chemical industrial, technological processes, where special features appear due to the continuous cycle and correct management acquires special importance. This paper discusses how intelligent systems can be developed, in particular, how fuzzy logic can be used to build knowledge-based expert systems in chemical process engineering. The implemented projects reveal that the use of fuzzy logic in technological process control has already given us better solutions than standard control techniques. Fuzzy logic makes it possible to develop an advisory system for decision-making based on the historical experience of the managing operator and experienced experts. The present paper deals with operational control and management systems of continuous cycle chemical technological processes, including advisory systems. Because of the continuous cycle, many features are introduced in them compared to the operational control of other chemical technological processes. Among them, there is a greater risk of transitioning to emergency mode; the return from emergency mode to normal mode must be done very quickly due to the impossibility of stopping the technological process due to the release of defective products during this period (i.e., receiving a loss), accordingly, due to the need for high qualification of the operator managing the process, etc. For these reasons, operational control systems of continuous cycle chemical technological processes have been specifically discussed, as they are different systems. Special features of such systems in control and management were brought out, which determine the characteristics of the construction of control and management systems. To verify the findings, the development of an advisory decision-making information system for operational control of a lime kiln using fuzzy logic, based on the creation of a relevant expert-targeted knowledge base, was discussed. The control system has been implemented in a real lime production plant with a lime burn kiln, which has shown that suitable and intelligent automation improves operational management, reduces the risks of releasing defective products, and, therefore, reduces costs. The special advisory system was successfully used in the said plant both for the improvement of operational management and, if necessary, for the training of new operators due to the lack of an appropriate training institution.

Keywords: chemical process control systems, continuous cycle industrial technological processes, fuzzy logic, lime kiln

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Authors: Rosemary Saunders, Courtney Glass, Karla Seaman, Karen Gullick, Julie Andrew, Anne Wilkinson, Ashwini Davray

Abstract:

Current literature demonstrates that most Australians receive end-of-life care in a hospital setting, despite most hoping to die within their own home. The necessity for high quality end-of-life care has been emphasised by the Australian Commission on Safety and Quality in Health Care and the National Safety and Quality in Health Services Standards depict the requirement for comprehensive care at the end of life (Action 5.20), reinforcing the obligation for continual organisational assessment to determine if these standards are suitably achieved. Limited research exploring clinical staff perspectives of end-of-life care delivery has been conducted within an Australian private health context. This study aimed to investigate clinical staff member perceptions of end-of-life care delivery at a private hospital in Western Australia. The study comprised of a multi-faceted mixed-methods methodology, part of a larger study. Data was obtained from clinical staff utilising surveys and focus groups. A total of 133 questionnaires were completed by clinical staff, including registered nurses (61.4%), enrolled nurses (22.7%), allied health professionals (9.9%), non-palliative care consultants (3.8%) and junior doctors (2.2%). A total of 14.7% of respondents were palliative care ward staff members. Additionally, seven staff focus groups were conducted with physicians (n=3), nurses (n=26) and allied health professionals including social workers (n=1), dietitians (n=2), physiotherapists (n=5) and speech pathologists (n=3). Key findings from the surveys highlighted that the majority of staff agreed it was part of their role to talk to doctors about the care of patients who they thought may be dying, and recognised the importance of communication, appropriate training and support for clinical staff to provide quality end-of-life care. Thematic analysis of the qualitative data generated three key themes: creating the setting which highlighted the importance of adequate resourcing and conducive physical environments for end-of-life care and to support staff and families; planning and care delivery which emphasised the necessity for collaboration between staff, families and patients to develop care plans and treatment directives; and collaborating in end-of-life care, with effective communication and teamwork leading to achievable care delivery expectations. These findings contribute to health professionals better understanding of end-of-life care provision and the importance of collaborating with patients and families in care delivery. It is crucial that health care providers implement strategies to overcome gaps in care, so quality end-of-life care is provided. Findings from this study have been translated into practice, with the development and implementation of resources, training opportunities, support networks and guidelines for the delivery of quality end-of-life care.

Keywords: clinical staff, end-of-life care, mixed-methods, private hospital.

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Authors: Anupama Sahoo, Bongyong Lee, Katia Boniface, Julien Seneschal, Sanjaya K. Sahoo, Tatsuya Seki, Chunyan Wang, Soumen Das, Xianlin Han, Michael Steppie, Sudipta Seal, Alain Taieb, Ranjan J. Perera

Abstract:

Vitiligo is a common, chronic skin disorder characterized by loss of epidermal melanocytes and progressive depigmentation. Vitiligo has a complex immune, genetic, environmental, and biochemical etiology, but the exact molecular mechanisms of vitiligo development and progression, particularly those related to metabolic control, are poorly understood. Here we characterized the human vitiligo cell line PIG3V and the normal human melanocytes, HEM-l by RNA-sequencing, targeted metabolomics, and shotgun lipidomics. Melanocyte-enriched miR-211, a known metabolic switch in non-pigmented melanoma cells, was severely downregulated in vitiligo cell line PIG3V and skin biopsies from vitiligo patients, while its novel predicted targets transcriptional co-activator PGC1-α (PPARGC1A), ribonucleotide reductase regulatory subunit M2 (RRM2), and serine-threonine protein kinase TAO1 (TAOK1) were reciprocally upregulated. miR-211 binds to PGC1-α 3’UTR locus and represses it. Although mitochondrial numbers were constant, mitochondrial complexes I, II, and IV and respiratory responses were defective in vitiligo cells. Nanoparticle-coated miR-211 partially augmented the oxygen consumption rate in PIG3V cells. The lower oxygen consumption rate, changes in lipid and metabolite profiles, and increased reactive oxygen species production observed in vitiligo cells appear to be partly due to abnormal regulation of miR-211 and its target genes. These genes represent potential biomarkers and therapeutic targets in human vitiligo.

Keywords: metabolism, microRNA, mitochondria, vitiligo

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Authors: Wentian Shi, Daming Shi, Maysam Orouskhani, Feng Tian

Abstract:

Whereas currently the most prevalent deep learning methods require a large amount of data for training, few-shot learning tries to learn a model from limited data without extensive retraining. In this paper, we present a loss function based on triplet loss for solving few-shot problem using metric based learning. Instead of setting the margin distance in triplet loss as a constant number empirically, we propose an adaptive margin distance strategy to obtain the appropriate margin distance automatically. We implement the strategy in the deep siamese network for deep metric embedding, by utilizing an optimization approach by penalizing the worst case and rewarding the best. Our experiments on image recognition and co-segmentation model demonstrate that using our proposed triplet loss with adaptive margin distance can significantly improve the performance.

Keywords: few-shot learning, triplet network, adaptive margin, deep learning

Procedia PDF Downloads 171