Search results for: Hayatabad Medical Complex

Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

Abstract:

Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

Procedia PDF Downloads 224

Authors: Sultan Noman Qasem

Abstract:

This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature.

Keywords: radial basis function network, hybrid learning, multi-objective optimization, genetic algorithm

Procedia PDF Downloads 565

Authors: Jiujie Cai, Fengxia LI, Haibo Wang

Abstract:

After years of research, offshore oil and gas development now are shifted to unconventional reservoirs, where multi-stage hydraulic fracturing technology has been widely used. However, the simulation of complex hydraulic fractures in tight reservoirs is faced with geological and engineering difficulties, such as large burial depths, sand-shale interbeds, and complex stress barriers. The objective of this work is to simulate the hydraulic fracture propagation in the tight sandstone matrix of the marine-continental transitional reservoirs, where the Shanxi Formation in Tianhuan syncline of the Dongsheng gas field was used as the research target. The characteristic parameters of the vertical rock samples with rich beddings were clarified through rock mechanics experiments. The influence of rock mechanical parameters, vertical stress difference of pay-zone and bedding layer, and fracturing parameters (such as injection rates, fracturing fluid viscosity, and number of perforation clusters within single stage) on fracture initiation and propagation were investigated. In this paper, a 3-D fracture propagation model was built to investigate the complex fracture propagation morphology by boundary element method, considering the strength of bonding surface between layers, vertical stress difference and fracturing parameters (such as injection rates, fluid volume and viscosity). The research results indicate that on the condition of vertical stress difference (3 MPa), the fracture height can break through and enter the upper interlayer when the thickness of the overlying bedding layer is 6-9 m, considering effect of the weak bonding surface between layers. The fracture propagates within the pay zone when overlying interlayer is greater than 13 m. Difference in fluid volume distribution between clusters could be more than 20% when the stress difference of each cluster in the segment exceeds 2MPa. Fracture cluster in high stress zones cannot initiate when the stress difference in the segment exceeds 5MPa. The simulation results of fracture height are much higher if the effect of weak bonding surface between layers is not involved. By increasing the injection rates, increasing fracturing fluid viscosity, and reducing the number of clusters within single stage can promote the fracture height propagation through layers. Optimizing the perforation position and reducing the number of perforations can promote the uniform expansion of fractures. Typical curves of fracture height estimation were established for the tight sandstone of the Lower Permian Shanxi Formation. The model results have good consistency with micro-seismic monitoring results of hydraulic fracturing in Well 1HF.

Keywords: fracture propagation, boundary element method, fracture height, offshore oil and gas, marine-continental transitional reservoirs, rock mechanics experiment

Procedia PDF Downloads 127

Authors: Guanglin Song

Abstract:

(Objective) Situated within the context of China's tiered medical treatment system, this study aims to analyze spatial causes of urban healthcare access difficulties from the perspective of the configuration of healthcare facilities. (Methods) A social network analysis approach is employed to construct a healthcare demand and supply flow network between major residential clusters and various tiers of hospitals in the city.(Conclusion) The findings reveal that:1.there exists overall maldistribution and over-concentration of healthcare resources in Study Area, characterized by structural imbalance; 2.the low rate of primary care utilization in Study Area is a key factor contributing to congestion at higher-tier hospitals, as excessive reliance on these institutions by neighboring communities exacerbates the problem; 3.gradual optimization of the healthcare facility layout in Study Area, encompassing holistic, local, and individual institutional levels, can enhance systemic efficiency and resource balance.(Prospects) This research proposes a method for evaluating urban healthcare resource distribution structures based on service flows within hierarchical networks. It offers spatially targeted optimization suggestions for promoting the implementation of the tiered healthcare system and alleviating challenges related to accessibility and congestion in seeking medical care. Provide some new ideas for researchers and healthcare managers in countries, cities, and healthcare management around the world with similar challenges.

Keywords: flow of public services, urban networks, healthcare facilities, spatial planning, urban networks

Procedia PDF Downloads 71

Authors: Jakub Gren

Abstract:

The recent transfer of banking supervision to the ECB has expanded its influence as of a non-majoritarian and technocratic policy-shaper in EU supervisory policies. To fulfil the main policy objectives of the Single Supervisory Mechanism, the ECB has been tasked with building a single supervisory approach to supervised banks across the euro area and is now exclusively responsible for direct supervision of the largest ‘significant’ euro area banks and the oversight of the remaining ‘less significant’ banks. This enhanced supranational position of the ECB significantly alters the EU institutional order and creates powerful incentives to actively pursue integrationist agenda by the ECB. However, this drastic shift has a little impact upon adapting the ECB’s new supervisory mandate to the requirements of democratic legitimacy. Whereas the ECB’s strong pre-crisis independence and limited accountability could be reconciled with democratic principles through a clearly articulated price stability mandate, independence and limited accountability in the context of a more complex supervisory mandate is problematic. Hence, in order to ensure the democratic legitimacy of the ECB/SSM’s supervisory policies, the ECB’s supervisory mandate requires both a lower scope of independence and higher accountability requirements. To address this situation, organizational separation (“Chinese Wall”) between the ECB monetary and supervisory arms was introduced. This separation includes different reporting lines and the relocation of the ECB’s monetary function to a new building complex while leaving its supervisory function at the Euro-tower (“Two Towers”). This paper argues that these measures are not sufficient to establish proper checks and balances on the ECB’s powers to pursue euro zone’s wide supervisory policies. As a remedy, this contribution suggests that the ECB’s Treaties-embedded independence, as set out by art. 130 TFEU, designed to carry out its monetary function shall not be fully applicable to its supervisory function. Indeed functional and conditional reading of this provision to ECB supervisory function could enhance the legitimacy of future ECB’s supervisory action.

Keywords: accountability and transparency, democratic governance, financial management, rule of law

Procedia PDF Downloads 207

Authors: Sedigheh Kalantari, Yaping Huang

Abstract:

The historic area of cities has experienced different phases of transformation. The beginning of the twentieth century, modernism, and modern development changed the integrated pattern of change and the historic urban quarter were regarded as subject comprehensive redevelopment. In this respect, historic area of Iranian cities have not been safe from these changes and affected by widespread evolutions; in particular after Islamic Revolution eras (1978) cities have traveled through an evolution in conservation and development policies and practices. Moreover, moving toward a specific approach and specific attention paid to the regeneration of the historical urban centers in Iran has started since the 1990s. This reveals the great importance attached to the historical centers of cities. This paper is an approach to examine an experience on urban regeneration in Iran through a case study. The study relies on multiple source of evidence. The use of multiple sources of evidence can help substantially improve the validity and reliability of the research. The empirical core of this research, therefore, rests in the process of urban revitalization of the old square in Isfahan. Isfahan is one of the oldest city of Persia. The historic area of city encompasses a large number of valuable buildings and monuments. One of the cultural and historical region of Isfahan is Atiq Square (Old Square). It has been the backbone node of the city that in course of time has being ignored more and more and transformed negatively. The complex had suffered from insufficiencies especially with respect to social and spatial aspects. Therefore, reorganization of that complex as the main and most important urban center of Isfahan became an inevitable issue; So this paper except from reminding the value of such historic-cultural heritage and review of its transformation, focused on an experience of urban revitalization project in this heritage site. The outcome of this research shows that situated in different socio-economic political and historical contexts and in face of different urban regeneration issues, Iran have displayed significant differences in the way of urban regeneration.

Keywords: historic area, Iran, urban regeneration, revitalization

Procedia PDF Downloads 261

Authors: Lisa Handwerker

Abstract:

Using a Medical Anthropological lens, the paper will explore ideas elated to "aging in place" among Northern Californian older adults. Older adults seek independence, autonomy, flexibility, engagement, fulfillment and community in their pursuit of the highest quality of life. These values are at the heart of healthy and active "aging in place'. Drawing on a case study, the paper will examine one membership based non-profit organization for older adults united by the members' desire to be healthy and active while remaining in their homes for as long as possible. Relying on both volunteer and paid work, the paper explores the use of volunteer peer-to peer support, community building and advanced technologies toward this goal.

Keywords: aging in place, healthy and active aging, northern california, medical anthropologist, engagement, autonomy, flexibility, community, volunteers, quality of life

Procedia PDF Downloads 101

Authors: V. S. Bushkova

Abstract:

It is well known that in recent years magnetic materials have received increased attention due to their properties. For this reason a significant number of patents that were published during the last decade are oriented towards synthesis and study of such materials. The aim of this work is to create and study ferrite nanocrystalline materials with spinel structure, using sol-gel technology with participation of auto-combustion. This method is perspective in that it is a cheap and low-temperature technique that allows for the fine control on the product’s chemical composition.

Keywords: magnetic materials, ferrites, sol-gel technology, nanocrystalline powders

Procedia PDF Downloads 411

Authors: Paul J. McKay

Abstract:

Large-scale genome-wide association studies (GWAS) investigating genetic contributions to sexual orientation and gender identity are largely lacking and may reduce stigma experienced in the LGBTQ+ community by providing an underlying biological explanation for queerness. While there is a growing consensus within the scientific community that genetic makeup contributes – at least in part – to sexual orientation and gender identity, there is a marked lack of genomics research exploring polygenic contributions to queerness. Based on recent (2019) findings from a large-scale GWAS investigating the genetic architecture of same-sex sexual behavior, and various additional peer-reviewed publications detailing novel insights into the molecular mechanisms of sexual orientation and gender identity, we hypothesize that sexual orientation and gender identity are complex, multifactorial, and polygenic; meaning that many genetic factors contribute to these phenomena, and environmental factors play a possible role through epigenetic modulation. In recent years, large-scale GWAS studies have been paramount to our modern understanding of many other complex human traits, such as in the case of autism spectrum disorder (ASD). Despite possible benefits of such research, including reduced stigma towards queer people, improved outcomes for LGBTQ+ in familial, socio-cultural, and political contexts, and improved access to healthcare (particularly for trans populations); important risks and considerations remain surrounding this type of research. To mitigate possibilities such as invalidation of the queer identities of existing LGBTQ+ individuals, genetic discrimination, or the possibility of euthanasia of embryos with a genetic predisposition to queerness (through reproductive technologies like IVF and/or gene-editing in utero), we propose a community-engaged research (CER) framework which emphasizes the privacy and confidentiality of research participants. Importantly, the historical legacy of scientific research attempting to pathologize queerness (in particular, falsely equating gender variance to mental illness) must be acknowledged to ensure any future research conducted in this realm does not propagate notions of homophobia, transphobia or stigma against queer people. Ultimately, in a world where same-sex sexual activity is criminalized in 69 UN member states, with 67 of these states imposing imprisonment, 8 imposing public flogging, 6 (Brunei, Iran, Mauritania, Nigeria, Saudi Arabia, Yemen) invoking the death penalty, and another 5 (Afghanistan, Pakistan, Qatar, Somalia, United Arab Emirates) possibly invoking the death penalty, the importance of this research cannot be understated, as finding a biological basis for queerness would directly oppose the harmful rhetoric that “being LGBTQ+ is a choice.” Anti-trans legislation is similarly widespread: In the United States in 2022 alone (as of Oct. 13), 155 anti-trans bills have been introduced preventing trans girls and women from playing on female sports teams, barring trans youth from using bathrooms and locker rooms that align with their gender identity, banning access to gender affirming medical care (e.g., hormone-replacement therapy, gender-affirming surgeries), and imposing legal restrictions on name changes. Understanding that a general lack of knowledge about the biological basis of queerness may be a contributing factor to the societal stigma faced by gender and sexual orientation minorities, we propose the initiation of large-scale GWAS studies investigating the genetic basis of gender identity and sexual orientation.

Keywords: genome-wide association studies (GWAS), sexual and gender minorities (SGM), polygenicity, community-engaged research (CER)

Procedia PDF Downloads 70

Authors: Elidrissi Laila, El Rhaoussi Fatima-Zahra, Haddad Fouad, Tahiri Mohamed, Hliwa Wafaa, Bellabah Ahmed, Badre Wafaa

Abstract:

Introduction: Metabolic Dysfunction Associated with Non-Alcoholic Fatty Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD), is the leading cause of chronic liver disease. The cardiometabolic risk factors associated with MASLD represent common health issues and significant public health challenges. Medical students, being active participants in the healthcare system and a young demographic, are particularly relevant for understanding this entity to prevent its occurrence on a personal and collective level. The objective of our study is to assess the level of knowledge among medical students regarding MASLD, its risk factors, and its long-term consequences. Materials and Methods: We conducted a descriptive cross-sectional study using an anonymous questionnaire distributed through social media over a period of 2 weeks. Medical students from various faculties in Morocco answered 22 questions about MASLD, its etiological factors, diagnosis, complications, and principles of treatment. All responses were analyzed using the Jamovi software. Results: A total of 124 students voluntarily provided complete responses. 59% of our participants were in their 3rd year, with a median age of 21 years. Among the respondents, 27% were overweight, obese, or diabetic. 83% correctly answered more than half of the questions, and 77% believed they knew about MASLD. However, 84% of students were unaware that MASLD is the leading cause of chronic liver disease, and 12% even considered it a rare condition. Regarding etiological factors, overweight and obesity were mentioned in 93% of responses, and type 2 diabetes in 84%. 62% of participants believed that type 1 diabetes could not be implicated in MASLD. For 83 students, MASLD was considered a diagnosis of exclusion, while 41 students believed that a biopsy was mandatory for diagnosis. 12% believed that MASLD did not lead to long-term complications, and 44% were unaware that MASLD could progress to hepatocellular carcinoma. Regarding treatment, 85% included weight loss, and 19% did not consider diabetes management as a therapeutic approach for MASLD. At the end of the questionnaire, 89% of the students expressed a desire to learn more about MASLD and were invited to access an informative sheet through a hyperlink. Conclusion: MASLD represents a significant public health concern due to the prevalence of its risk factors, notably the obesity pandemic, which is widespread among the young population. There is a need for awareness about the seriousness of this emerging and long-underestimated condition among young future physicians.

Keywords: MASLD, medical students, obesity, diabetes

Procedia PDF Downloads 76

Authors: K. O'Malley

Abstract:

Background: The emergence and rapid evolution of large language models (LLMs) (i.e., models of generative artificial intelligence, or AI) has been unprecedented. ChatGPT is one of the most widely used LLM platforms. Using natural language processing technology, it generates customized responses to user prompts, enabling it to mimic human conversation. Responses are generated using predictive modeling of vast internet text and data swathes and are further refined and reinforced through user feedback. The popularity of LLMs is increasing, with a growing number of students utilizing these platforms for study and revision purposes. Notwithstanding its many novel applications, LLM technology is inherently susceptible to bias and error. This poses a significant challenge in the educational setting, where academic integrity may be undermined. This study aims to evaluate the performance of the latest iteration of ChatGPT (ChatGPT4.0) in standardized state medical licensing examinations. Methods: A considered search strategy was used to interrogate the PubMed electronic database. The keywords ‘ChatGPT’ AND ‘medical education’ OR ‘medical school’ OR ‘medical licensing exam’ were used to identify relevant literature. The search included all peer-reviewed literature published in the past five years. The search was limited to publications in the English language only. Eligibility was ascertained based on the study title and abstract and confirmed by consulting the full-text document. Data was extracted into a Microsoft Excel document for analysis. Results: The search yielded 345 publications that were screened. 225 original articles were identified, of which 11 met the pre-determined criteria for inclusion in a narrative synthesis. These studies included performance assessments in national medical licensing examinations from the United States, United Kingdom, Saudi Arabia, Poland, Taiwan, Japan and Germany. ChatGPT 4.0 achieved scores ranging from 67.1 to 88.6 percent. The mean score across all studies was 82.49 percent (SD= 5.95). In all studies, ChatGPT exceeded the threshold for a passing grade in the corresponding exam. Conclusion: The capabilities of ChatGPT in standardized academic assessment in medicine are robust. While this technology can potentially revolutionize higher education, it also presents several challenges with which educators have not had to contend before. The overall strong performance of ChatGPT, as outlined above, may lend itself to unfair use (such as the plagiarism of deliverable coursework) and pose unforeseen ethical challenges (arising from algorithmic bias). Conversely, it highlights potential pitfalls if users assume LLM-generated content to be entirely accurate. In the aforementioned studies, ChatGPT exhibits a margin of error between 11.4 and 32.9 percent, which resonates strongly with concerns regarding the quality and veracity of LLM-generated content. It is imperative to highlight these limitations, particularly to students in the early stages of their education who are less likely to possess the requisite insight or knowledge to recognize errors, inaccuracies or false information. Educators must inform themselves of these emerging challenges to effectively address them and mitigate potential disruption in academic fora.

Keywords: artificial intelligence, ChatGPT, generative ai, large language models, licensing exam, medical education, medicine, university

Procedia PDF Downloads 34

Authors: Laleh Boroumand, Muhammad Shiraz, Abdullah Gani, Rashid Hafeez Khokhar

Abstract:

Cloud computing being the use of hardware and software that are delivered as a service over a network has its application in the area of health care. Due to the emergency cases reported in most of the medical centers, prompt for an efficient scheme to make health data available with less response time. To this end, we propose a mobile global healthcare village (MGHV) model that combines the components of three deployment model which include country, continent and global health cloud to help in solving the problem mentioned above. In the creation of continent model, two (2) data centers are created of which one is local and the other is global. The local replay the request of residence within the continent, whereas the global replay the requirements of others. With the methods adopted, there is an assurance of the availability of relevant medical data to patients, specialists, and emergency staffs regardless of locations and time. From our intensive experiment using the simulation approach, it was observed that, broker policy scheme with respect to optimized response time, yields a very good performance in terms of reduction in response time. Though, our results are comparable to others when there is an increase in the number of virtual machines (80-640 virtual machines). The proportionality in increase of response time is within 9%. The results gotten from our simulation experiments shows that utilizing MGHV leads to the reduction of health care expenditures and helps in solving the problems of unqualified medical staffs faced by both developed and developing countries.

Keywords: cloud computing (MCC), e-healthcare, availability, response time, service broker policy

Procedia PDF Downloads 377

Authors: Ilana Margalith, Yaron Niv

Abstract:

One of the most important factors of professional health treatment is teamwork, in which each discipline contributes its expert knowledge, thus ensuring quality and a high standard of care as well as efficient communication (one of the International Patient Safety Goals). However, in most countries, students are educated separately by each health discipline. They are exposed to teamwork only during their clinical experience, which in some cases is short and skill-oriented. In addition, health organizations in most countries are hierarchical and although changes have occurred in the hierarchy of the medical system, there are still disciplines that underrate the unique contributions of other health professionals, thus, young graduates of health professions develop and base their perception of their peers from other disciplines on insufficient knowledge. In order to establish a wide-ranging perception among nursing students as to the contribution of different health professionals to the health of their patients, students at the Clalit Nursing Academy, Rabin Campus (Dina), Israel, participated in an interdisciplinary clinical discussion with students from several different professions, other than nursing, who were completing their clinical experience at Rabin Medical Center in medicine, health psychology, social work, audiology, physiotherapy and occupational therapy. The discussion was led by a medical-surgical nursing instructor. Their tutors received in advance, a case report enabling them to prepare the students as to how to present their professional theories and interventions regarding the case. Mutual stimulation and acknowledgment of the unique contribution of each part of the team enriched the nursing students' understanding as to how their own nursing interventions could be integrated into the entire process towards a safe and speedy recovery of the patient.

Keywords: health professions' students, interdisciplinary clinical discussion, nursing education, patient safety

Procedia PDF Downloads 173

Authors: Arda Ozkurt, Ozlem Bozkurt

Abstract:

Brain-computer interfaces (BCIs) are devices that output commands by interpreting the data collected from the brain. Electroencephalography (EEG) is a non-invasive method to measure the brain's electrical activity. Since it was invented by Hans Berger in 1929, it has led to many neurological discoveries and has become one of the essential components of non-invasive measuring methods. Despite the fact that it has a low spatial resolution -meaning it is able to detect when a group of neurons fires at the same time-, it is a non-invasive method, making it easy to use without possessing any risks. In EEG, electrodes are placed on the scalp, and the voltage difference between a minimum of two electrodes is recorded, which is then used to accomplish the intended task. The recordings of EEGs include, but are not limited to, the currents along dendrites from synapses to the soma, the action potentials along the axons connecting neurons, and the currents through the synaptic clefts connecting axons with dendrites. However, there are some sources of noise that may affect the reliability of the EEG signals as it is a non-invasive method. For instance, the noise from the EEG equipment, the leads, and the signals coming from the subject -such as the activity of the heart or muscle movements- affect the signals detected by the electrodes of the EEG. However, new techniques have been developed to differentiate between those signals and the intended ones. Furthermore, an EEG device is not enough to analyze the data from the brain to be used by the BCI implication. Because the EEG signal is very complex, to analyze it, artificial intelligence algorithms are required. These algorithms convert complex data into meaningful and useful information for neuroscientists to use the data to design BCI devices. Even though for neurological diseases which require highly precise data, invasive BCIs are needed; non-invasive BCIs - such as EEGs - are used in many cases to help disabled people's lives or even to ease people's lives by helping them with basic tasks. For example, EEG is used to detect before a seizure occurs in epilepsy patients, which can then prevent the seizure with the help of a BCI device. Overall, EEG is a commonly used non-invasive BCI technique that has helped develop BCIs and will continue to be used to detect data to ease people's lives as more BCI techniques will be developed in the future.

Keywords: BCI, EEG, non-invasive, spatial resolution

Procedia PDF Downloads 73

Authors: Saima Murtaza

Abstract:

Ethics and aesthetics have invariably remained the two closely integrated artistic appurtenances for the production of any work of art. These artistic devices configure themselves into a complex synthesis in our contemporary literature. The labyrinthine integration of ethics and aesthetics, operating in the lives of human characters, to the extent of transcending all limits has resulted in an artistic puzzle for the readers. Art, no doubt, is an extrinsic expression of the intrinsic life of man. The use of aesthetics in literature pertaining to human existence; aesthetic solipsism, has resulted in the artistic objectification of these characters. The practice of the like aestheticism deprives the characters of their souls, rendering them as mere objects of aesthetic gaze at the hands of their artists-creators. Artists orchestrate their lives founding it on a plot which deviates from normal social and ethical standards. Their perverse attitude can be seen in dealing with characters, their feelings and the incidents of their lives. Morality is made to appear not as a religious construct but as an individual’s private affair. Furthermore, the idea of beauty incarnated, in other words hedonistic aesthetic does not placate a true aesthete. Ethics and aesthetics are the two most recurring motifs of our contemporary literature, especially of Nabokov’s world. The purpose of this study is to peruse these aforementioned motifs in Nabokov’s most enigmatic novel Lolita, a story of pedophilia, which is in fact reflective of our complex individual psychic and societal patterns. The narrative subverts all the traditional and hitherto known notions of aesthetics and ethics. When applied to literature, aesthetic does not simply mean ‘beautiful’ in the text. It refers to an intricate relationship between feelings and perception and also incorporates within its range wide-ranging emotional reactions to text. The term aesthetics in literature is connected with the readers whose critical responses to the text determine the merit of any work to be really a piece of art. Aestheticism is the child of ethics. Morality sets the grounds for the production of any work and the idea of aesthetics gives it transcendence.

Keywords: ethics, aesthetics and hedonistic aesthetic, nymphet syndrome, pedophilia

Procedia PDF Downloads 158

Authors: Areej Al-Hamad, Yasin Yasin, Kateryna Metersky

Abstract:

This mixed-method systematic review and meta-analysis provide an encompassing understanding of the barriers, facilitators, and predictors of refugee women's employment and economic inclusion. The study sheds light on the complex interplay of sociocultural, personal, political, and environmental factors influencing these outcomes, underlining the urgent need for a multifaceted, tailored approach to devising strategies, policies, and interventions aimed at boosting refugee women's economic empowerment. Our findings suggest that sociocultural factors, including gender norms, societal attitudes, language proficiency, and social networks, profoundly shape refugee women's access to and participation in the labor market. Personal factors such as age, educational attainment, health status, skills, and previous work experience also play significant roles. Political factors like immigration policies, regulations, and rights to work, alongside environmental factors like labor market conditions, availability of employment opportunities, and access to resources and support services, further contribute to the complex dynamics influencing refugee women's economic inclusion. The significant variability observed in the impacts of these factors across different contexts underscores the necessity of adopting population and region-specific strategies. A one-size-fits-all approach may prove to be ineffective due to the diversity and unique circumstances of refugee women across different geographical, cultural, and political contexts. The study's findings have profound implications for policy-making, practice, education, and research. The insights garnered a call for coordinated efforts across these domains to bolster refugee women's economic participation. In policy-making, the findings necessitate a reassessment of current immigration and labor market policies to ensure they adequately support refugee women's employment and economic integration. In practice, they highlight the need for comprehensive, tailored employment services and interventions that address the specific barriers and leverage the facilitators identified. In education, they underline the importance of language and skills training programs that cater to the unique needs and circumstances of refugee women. Lastly, in research, they emphasize the need for ongoing investigations into the multifaceted factors influencing refugee women's employment experiences, allowing for continuous refinement of our understanding and interventions. Through this comprehensive exploration, the study contributes to ongoing efforts aimed at creating more inclusive, equitable societies. By continually refining our understanding of the complex factors influencing refugee women's employment experiences, we can pave the way toward enhanced economic empowerment for this vulnerable population.

Keywords: refugee women, employment barriers, systematic review, employment facilitators

Procedia PDF Downloads 83

Authors: Maryamsadat Habibi

Abstract:

Objective: pharmaceutical errors are avoidable occurrences that can result in inappropriate pharmaceutical use, patient harm, treatment failure, increased hospital costs and length of stay, and other outcomes that affect both the individual receiving treatment and the healthcare provider. This study aimed to perform a reconciliation of medications in the cardiovascular ward of Imam Reza Hospital in Mashhad, Iran, and evaluate the prevalence of medication discrepancies between the best medication list created for the patient by the pharmacist and the medication order of the treating physician there. Materials & Methods: The 97 patients in the cardiovascular ward of the Imam Reza Hospital in Mashhad were the subject of a cross-sectional study from June to September of 2021. After giving their informed consent and being admitted to the ward, all patients with at least one underlying condition and at least two medications being taken at home were included in the study. A medical reconciliation form was used to record patient demographics and medical histories during the first 24 hours of admission, and the information was contrasted with the doctors' orders. The doctor then discovered medication inconsistencies between the two lists and double-checked them to separate the intentional from the accidental anomalies. Finally, using SPSS software version 22, it was determined how common medical discrepancies are and how different sorts of discrepancies relate to various variables. Results: The average age of the participants in this study was 57.6915.84 years, with 57.7% of men and 42.3% of women. 95.9% of the patients among these people encountered at least one medication discrepancy, and 58.9% of them suffered at least one unintentional drug cessation. Out of the 659 medications registered in the study, 399 cases (60.54%) had inconsistencies, of which 161 cases (40.35%) involved the intentional stopping of a medication, 123 cases (30.82%) involved the stopping of a medication unintentionally, and 115 cases (28.82%) involved the continued use of a medication by adjusting the dose. Additionally, the category of cardiovascular pharmaceuticals and the category of gastrointestinal medications were found to have the highest medical inconsistencies in the current study. Furthermore, there was no correlation between the frequency of medical discrepancies and the following variables: age, ward, date of visit, type, and number of underlying diseases (P=0.13), P=0.61, P=0.72, P=0.82, P=0.44, and so forth. On the other hand, there was a statistically significant correlation between the number of medications taken at home (P=0.037) and the prevalence of medical discrepancies with gender (P=0.029). The results of this study revealed that 96% of patients admitted to the cardiovascular unit at Imam Reza Hospital had at least one medication error, which was typically an intentional drug discontinuance. According to the study's findings, patients admitted to Imam Reza Hospital's cardiovascular ward have a great potential for identifying and correcting various medication discrepancies as well as for avoiding prescription errors when the medication reconciliation method is used. As a result, it is essential to carry out a precise assessment to achieve the best treatment outcomes and avoid unintended medication discontinuation, unwanted drug-related events, and drug interactions between the patient's home medications and those prescribed in the hospital.

Keywords: drug combination, drug side effects, drug incompatibility, cardiovascular department

Procedia PDF Downloads 93

Authors: Sarah Nauwelaerts, Koen De Cremer, Alfred Bernard, Meredith Verlooy, Kristel Heremans, Natalia Bustos Sierra, Katrien Tersago, Tim Nawrot, Jordy Vercauteren, Christophe Stroobants, Sigrid C. J. De Keersmaecker, Nancy Roosens

Abstract:

Projected climate changes could lead to exacerbation of respiratory disorders associated with reduced air quality. Air pollution and climate changes influence each other through complex interactions. The poor air quality in urban and rural areas includes high levels of particulate matter (PM), ozone (O3) and nitrogen oxides (NOx), representing a major threat to public health and especially for the most vulnerable population strata, and especially young children. In this study, we aim to develop generic standardized policy supporting tools and methods that allow evaluating in future follow-up larger scale epidemiological studies the risks of the combined short-term effects of O3 and PM on the cardiorespiratory system of children. We will use non-invasive indicators of airway damage/inflammation and of genetic or epigenetic variations by using urine or saliva as alternative to blood samples. Therefore, a multi-phase field study will be organized in order to assess the sensitivity and applicability of these tests in large cohorts of children during episodes of air pollution. A first test phase was planned in March 2018, not yet taking into account ‘critical’ pollution periods. Working with non-invasive samples, choosing the right set-up for the field work and the volunteer selection were parameters to consider, as they significantly influence the feasibility of this type of study. During this test phase, the selection of the volunteers was done in collaboration with medical doctors from the Centre for Student Assistance (CLB), by choosing a class of pre-pubertal children of 9-11 years old in a primary school in Flemish Brabant, Belgium. A questionnaire, collecting information on the health and background of children and an informed consent document were drawn up for the parents as well as a simplified cartoon-version of this document for the children. A detailed study protocol was established, giving clear information on the study objectives, the recruitment, the sample types, the medical examinations to be performed, the strategy to ensure anonymity, and finally on the sample processing. Furthermore, the protocol describes how this field study will be conducted in relation with the prevision and monitoring of air pollutants for the future phases. Potential protein, genetic and epigenetic biomarkers reflecting the respiratory function and the levels of air pollution will be measured in the collected samples using unconventional technologies. The test phase results will be used to address the most important bottlenecks before proceeding to the following phases of the study where the combined effect of O3 and PM during pollution peaks will be examined. This feasibility study will allow identifying possible bottlenecks and providing missing scientific knowledge, necessary for the preparation, implementation and evaluation of federal policies/strategies, based on the most appropriate epidemiological studies on the health effects of air pollution. The research leading to these results has been funded by the Belgian Science Policy Office through contract No.: BR/165/PI/PMOLLUGENIX-V2.

Keywords: air pollution, biomarkers, children, field study, feasibility study, non-invasive

Procedia PDF Downloads 179

Authors: Kaoutar Lamrini Uahabi, Mohamed Atounti

Abstract:

In the present work, we consider one category of curves denoted by L(p, k, r, n). These curves are continuous arcs which are trajectories of roots of the trinomial equation zn = αzk + (1 − α), where z is a complex number, n and k are two integers such that 1 ≤ k ≤ n − 1 and α is a real parameter greater than 1. Denoting by L the union of all trinomial curves L(p, k, r, n) and using the box counting dimension as fractal dimension, we will prove that the dimension of L is equal to 3/2.

Keywords: feasible angles, fractal dimension, Minkowski sausage, trinomial curves, trinomial equation

Procedia PDF Downloads 190

Authors: Kimberly M. Cacciato, Steven J. Singer, Allison R. Shapiro, Julianna F. Kamenakis

Abstract:

This study examines the dynamics of communication among researchers and participants who have various levels of hearing, use multiple languages, have various disabilities, and who come from different social strata. This qualitative methodological study focuses on the strategies employed in an ethnographic research study examining the communication choices of six sets of parents who have Deaf-Disabled children. The participating families varied in their communication strategies and preferences including the use of American Sign Language (ASL), visual-gestural communication, multiple spoken languages, and pidgin forms of each of these. The research team consisted of two undergraduate students proficient in ASL and a Deaf principal investigator (PI) who uses ASL and speech as his main modes of communication. A third Hard-of-Hearing undergraduate student fluent in ASL served as an objective facilitator of the data analysis. The team created reflexive journals by audio recording, free writing, and responding to team-generated prompts. They discussed interactions between the members of the research team, their evolving relationships, and various social and linguistic power differentials. The researchers reflected on communication during data collection, their experiences with one another, and their experiences with the participating families. Reflexive journals totaled over 150 pages. The outside research assistant reviewed the journals and developed follow up open-ended questions and prods to further enrich the data. The PI and outside research assistant used NVivo qualitative research software to conduct open inductive coding of the data. They chunked the data individually into broad categories through multiple readings and recognized recurring concepts. They compared their categories, discussed them, and decided which they would develop. The researchers continued to read, reduce, and define the categories until they were able to develop themes from the data. The research team found that the various communication backgrounds and skills present greatly influenced the dynamics between the members of the research team and with the participants of the study. Specifically, the following themes emerged: (1) students as communication facilitators and interpreters as barriers to natural interaction, (2) varied language use simultaneously complicated and enriched data collection, and (3) ASL proficiency and professional position resulted in a social hierarchy among researchers and participants. In the discussion, the researchers reflected on their backgrounds and internal biases of analyzing the data found and how social norms or expectations affected the perceptions of the researchers in writing their journals. Through this study, the research team found that communication and language skills require significant consideration when working with multiple and complex communication modes. The researchers had to continually assess and adjust their data collection methods to meet the communication needs of the team members and participants. In doing so, the researchers aimed to create an accessible research setting that yielded rich data but learned that this often required compromises from one or more of the research constituents.

Keywords: American Sign Language, complex communication, deaf-disabled, methodology

Procedia PDF Downloads 119

Authors: Shende Suraj Balu, A. B. Deoghare, K. M. Pandey

Abstract:

The human middle ear (ME) is a delicate and vital organ. It has a complex structure that performs various functions such as receiving sound pressure and producing vibrations of eardrum and propagating it to inner ear. It consists of Tympanic Membrane (TM), three auditory ossicles, various ligament structures and muscles. Incidents such as traumata, infections, ossification of ossicular structures and other pathologies may damage the ME organs. The conditions can be surgically treated by employing prosthesis. However, the suitability of the prosthesis needs to be examined in advance prior to the surgery. Few decades ago, this issue was addressed and analyzed by developing an equivalent representation either in the form of spring mass system, electrical system using R-L-C circuit or developing an approximated CAD model. But, nowadays a three-dimensional ME model can be constructed using micro X-Ray Computed Tomography (μCT) scan data. Moreover, the concern about patient specific integrity pertaining to the disease can be examined well in advance. The current research work emphasizes to develop the ME model from the stacks of μCT images which are used as input file to MIMICS Research 19.0 (Materialise Interactive Medical Image Control System) software. A stack of CT images is converted into geometrical surface model to build accurate morphology of ME. The work is further extended to understand the dynamic behaviour of Harmonic response of the stapes footplate and umbo for different sound pressure levels applied at lateral side of eardrum using finite element approach. The pathological condition Cholesteatoma of ME is investigated to obtain peak to peak displacement of stapes footplate and umbo. Apart from this condition, other pathologies, mainly, changes in the stiffness of stapedial ligament, TM thickness and ossicular chain separation and fixation are also explored. The developed model of ME for pathologies is validated by comparing the results available in the literatures and also with the results of a normal ME to calculate the percentage loss in hearing capability.

Keywords: computed tomography (μCT), human middle ear (ME), harmonic response, pathologies, tympanic membrane (TM)

Procedia PDF Downloads 176

Authors: P. Selyshchev

Abstract:

The theoretical approach is developed to describe the change of drops in the atmosphere of own steam and buffer gas under irradiation. It is shown that the irradiation influences on size of stable droplet and on the conditions under which the droplet exists. Under irradiation the change of drop becomes more complex: the not monotone and periodical change of size of drop becomes possible. All possible solutions are represented by means of phase portrait. It is found all qualitatively different phase portraits as function of critical parameters: rate generation of clusters and substance density.

Keywords: irradiation, steam, plasma, cluster formation, liquid droplets, evolution

Procedia PDF Downloads 442

Authors: Mehrdad Shafiei Dizaji, Hoda Azari

Abstract:

The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.

Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven

Procedia PDF Downloads 43

Authors: Jian-Jun Shu

Abstract:

A cold, thin film of liquid impinging on an isothermal hot, horizontal surface has been investigated. An approximate solution for the velocity and temperature distributions in the flow along the horizontal surface is developed, which exploits the hydrodynamic similarity solution for thin film flow. The approximate solution may provide a valuable basis for assessing flow and heat transfer in more complex settings.

Keywords: flux, free impinging jet, solid-surface, uniform wall temperature

Procedia PDF Downloads 480

Authors: Wan Nur'ashiqin Wan Mohamad, Zarina Othman, Mohd Azman Abas, Azizah Ya'acob, Rozmel Abdul Latiff

Abstract:

Effective doctor-patient interaction is vital to both doctor and patient relationship. It is the cornerstone of good practice and an integral quality of a healthcare institution. This paper presented the hierarchy of the communication elements in doctor-patient interaction during medical consultations in a medical centre in Malaysia. This study adapted The Picker Patient Experience Questionnaire (2002) to obtain the information from patients. The questionnaire survey was responded by 100 patients between the ages of 20 and 50. Data collected were analysed using Rasch Measurement Model to yield the hierarchy of the communication elements in doctor-patient interaction. The findings showed that the three highest ranking on the doctor-patient interaction were doctor’s treatment, important information delivery and patient satisfaction of doctor’s responses. The results are valuable in developing the framework for communication ethics of doctors.

Keywords: communication elements, doctor-patient interaction, hierarchy, Rasch measurement model

Procedia PDF Downloads 164

Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

Abstract:

Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

Procedia PDF Downloads 101

Authors: Zahid Ullah, Atlas Khan

Abstract:

The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

Procedia PDF Downloads 95

Authors: Rajiv Ratan Singh, Sachin Kumar Tripathi, Pradeep Kumar Yadav

Abstract:

The timely evaluation, diagnosis, and treatment of people who have been exposed to toxic chemicals is a crucial component of emergency poison management in the medical field. The various substances that can poison include chemicals, medications, and naturally occurring poisons. The toxicology of the particular drug involved, as well as the symptoms and indicators of poisoning, must be thoroughly understood to handle poisoning emergencies effectively. One of the most important aspects of emergency poison management in medicine is the prompt examination, diagnosis, and treatment of persons who have been exposed to dangerous substances. To properly manage poisoning crises, one must have a good understanding of the toxicology of the particular medication concerned, as well as the signs and indicators of poisoning. Emergency management of poisoning includes not only prompt medical attention but also patient education, follow-up care, and monitoring for any long-term consequences. To achieve the greatest results for patients, the management of poisoning is a complicated and dynamic process that calls for collaboration between medical professionals, first responders, and toxicologists. All poisoned patients who present to the emergency room are assessed and diagnosed based on a collection of symptoms and a biochemical diagnosis, and they are then provided targeted, specialized treatment for the toxin identified. This article focuses on the loxodromic strategy as the primary method of treatment for poisoned patients. The authors of this article conclude that mortality and morbidity can be reduced if patients visit the emergency room promptly and receive targeted treatment.

Keywords: antidotes, blood poisoning, emergency medicine, gastric lavage, medico-legal aspects, patient care

Procedia PDF Downloads 102

Authors: Vikrant Gupta, Amrit Goswami

Abstract:

The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

Procedia PDF Downloads 137

Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

Abstract:

Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

Procedia PDF Downloads 156