Search results for: data driven diagnosis

Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Harini Chakkera

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Background: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts with hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. Methods: Data on first-time solitary kidney transplantations were retrieved between September 2015 to December 2018. Information was linked to the electronic health record to determine a diagnosis of diabetes mellitus and extract glucometeric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on five bootstrapped partitions of the data to ensure statistical significance. Results: The cohort included 1036 patients who received kidney transplantation, and 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average AUC of 77.3% (95% CI 75.30-79.3%). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, and minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia and recipient and donor BMI values as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. Conclusions: Suboptimal glucose metrics during hospitalization after kidney transplantation is associated with an increased risk for 30-day hospital readmission. Optimizing the hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Keywords: kidney, transplant, diabetes, insulin

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Authors: Byomakesh Debata, Jitendra Mahakud

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The recent financial crisis has been characterized by massive monetary policy interventions by the Central bank, and it has amplified the importance of liquidity for the stability of the stock market. This paper empirically elucidates the actual impact of monetary policy interventions on stock market liquidity covering all National Stock Exchange (NSE) Stocks, which have been traded continuously from 2002 to 2015. The present study employs a multivariate VAR model along with VAR-granger causality test, impulse response functions, block exogeneity test, and variance decomposition to analyze the direction as well as the magnitude of the relationship between monetary policy and market liquidity. Our analysis posits a unidirectional relationship between monetary policy (call money rate, base money growth rate) and aggregate market liquidity (traded value, turnover ratio, Amihud illiquidity ratio, turnover price impact, high-low spread). The impulse response function analysis clearly depicts the influence of monetary policy on stock liquidity for every unit innovation in monetary policy variables. Our results suggest that an expansionary monetary policy increases aggregate stock market liquidity and the reverse is documented during the tightening of monetary policy. To ascertain whether our findings are consistent across all periods, we divided the period of study as pre-crisis (2002 to 2007) and post-crisis period (2007-2015) and ran the same set of models. Interestingly, all liquidity variables are highly significant in the post-crisis period. However, the pre-crisis period has witnessed a moderate predictability of monetary policy. To check the robustness of our results we ran the same set of VAR models with different monetary policy variables and found the similar results. Unlike previous studies, we found most of the liquidity variables are significant throughout the sample period. This reveals the predictability of monetary policy on aggregate market liquidity. This study contributes to the existing body of literature by documenting a strong predictability of monetary policy on stock liquidity in an emerging economy with an order driven market making system like India. Most of the previous studies have been carried out in developing economies with quote driven or hybrid market making system and their results are ambiguous across different periods. From an eclectic sense, this study may be considered as a baseline study to further find out the macroeconomic determinants of liquidity of stocks at individual as well as aggregate level.

Keywords: market liquidity, monetary policy, order driven market, VAR, vector autoregressive model

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Authors: Mengdi Zhang, Zhenji Gao, Ning Kang, Rongmei Liu

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Management and application of geological big data is an important part of China's national big data strategy. With the implementation of a national big data strategy, geological big data management becomes more and more critical. At present, there are still a lot of technology barriers as well as cognition chaos in many aspects of geological big data management and application, such as data sharing, intellectual property protection, and application technology. Therefore, it’s a key task to make better use of new technologies for deeper delving and wider application of geological big data. In this paper, we briefly introduce the basic principle of blockchain technology at the beginning and then make an analysis of the application dilemma of geological data. Based on the current analysis, we bring forward some feasible patterns and scenarios for the blockchain application in geological big data and put forward serval suggestions for future work in geological big data management.

Keywords: blockchain, intellectual property protection, geological data, big data management

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Authors: Akbar Gharbali, Ali Abbasian Ardekani, Afshin Mohammadi

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Introduction: Thyroid nodules have an incidence of 33-68% in the general population. More than 5-15% of these nodules are malignant. Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Between the medical imaging methods, Ultrasound is the chosen imaging technique for assessment of thyroid nodules. The confirming of the diagnosis usually demands repeated fine-needle aspiration biopsy (FNAB). So, current management has morbidity and non-zero mortality. Objective: To explore diagnostic potential of automatic texture analysis (TA) methods in differentiation benign and malignant thyroid nodules by ultrasound imaging in order to help for reliable diagnosis and monitoring of the thyroid nodules in their early stages with no need biopsy. Material and Methods: The thyroid US image database consists of 70 patients (26 benign and 44 malignant) which were reported by Radiologist and proven by the biopsy. Two slices per patient were loaded in Mazda Software version 4.6 for automatic texture analysis. Regions of interests (ROIs) were defined within the abnormal part of the thyroid nodules ultrasound images. Gray levels within an ROI normalized according to three normalization schemes: N1: default or original gray levels, N2: +/- 3 Sigma or dynamic intensity limited to µ+/- 3σ, and N3: present intensity limited to 1% - 99%. Up to 270 multiscale texture features parameters per ROIs per each normalization schemes were computed from well-known statistical methods employed in Mazda software. From the statistical point of view, all calculated texture features parameters are not useful for texture analysis. So, the features based on maximum Fisher coefficient and the minimum probability of classification error and average correlation coefficients (POE+ACC) eliminated to 10 best and most effective features per normalization schemes. We analyze this feature under two standardization states (standard (S) and non-standard (NS)) with Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA). The 1NN classifier was performed to distinguish between benign and malignant tumors. The confusion matrix and Receiver operating characteristic (ROC) curve analysis were used for the formulation of more reliable criteria of the performance of employed texture analysis methods. Results: The results demonstrated the influence of the normalization schemes and reduction methods on the effectiveness of the obtained features as a descriptor on discrimination power and classification results. The selected subset features under 1%-99% normalization, POE+ACC reduction and NDA texture analysis yielded a high discrimination performance with the area under the ROC curve (Az) of 0.9722, in distinguishing Benign from Malignant Thyroid Nodules which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Conclusions: Our results indicate computer-aided diagnosis is a reliable method, and can provide useful information to help radiologists in the detection and classification of benign and malignant thyroid nodules.

Keywords: ultrasound imaging, thyroid nodules, computer aided diagnosis, texture analysis, PCA, LDA, NDA

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Authors: Sung Hyun Kim

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Introduction: Sleep disturbance is common in children with developmental disorders (D.D.). Sleep disturbance has a variety of negative effects, such as behavior problems, medical problems, and even developmental problems in children with D.D. However, to our best knowledge, there has been no proper treatment for sleep disorders in children with D.D. Therefore, we conduct this study to know the positive effects of intensive rehabilitation therapy in children with D.D. on the degree of sleep disturbance. Method: We prospectively recruited 22 patients with a diagnosis of D.D. during the period of January 2022 through May 2022. The inclusion criteria were as follows: 1) a patient who would participate in the intensive rehabilitation therapy of our institution; 2) the age participant under 18 years at the time of assessment; 3) a child who has consented to participate in the study by signing the consent form by the legal guardian. We investigated the clinical characteristics of participants by the medical record, including sex, age, underlying diagnosis of D.D., and Gross Motor Function Measures (GMFM). Before starting the intensive rehabilitation therapy, we conducted a Sleep disturbance scale for children (SDSC). It contains 26 questions about children’s sleep, and those questions are grouped into six subscales, such as Disorders of initiating and maintaining sleep (DIMS), Sleep Breathing Disorders(SBD), Disorders of arousal(DOA), Sleep-Wake Transition Disorders(SWTD), Disorders of excessive somnolence(DOES) and Sleep Hyperhydrosis(SHY). We used the t-score, which was calculated by comparing the scores of normal children. Twenty two patients received 8 weeks of intensive rehabilitation, including daily physical and occupational therapy. After that, we did follow up with SDSC. The comparison between SDSC before and after intensive rehabilitation was calculated using the paired t-test, and P< 0.05 was considered statistically significant. Results: Demographic data and clinical characteristics of 22 patients are enrolled. Patients were 4.03 ± 2.91 years old, and of the total 22 patients, 14 (64%) were male, and 8 (36%) were female. Twelve patients(45%) were diagnosed with Cerebral palsy(C.P.), and the mean value of participants’ GMFM was 47.82 ± 20.60. Each mean value of SDSC’s subscales was also calculated. DIMS was 62.36 ± 13.72, SBD was 54.18 ± 8.39, DOA was 49.59 ± 7.01, SWTD was 58.95 ± 9.20, DOES was 53.09 ± 15.15, SHY was 52.14 ± 8.82, and the total was 59.86 ± 13.18. These values suggest that children with D.D. have sleep disorders. After 8 weeks of intensive rehabilitation treatment, the score of DIMS showed improvement(p=0.016), but not the other subscale and total score of SDSC. Conclusion: This result showed that intensive rehabilitation could be helpful to patients of D.D. with sleep disorders. Especially intensive rehabilitation therapy itself can be a meaningful treatment in inducing and maintaining sleep.

Keywords: sleep disorder, developmental delay, intensive rehabilitation therapy, cerebral palsy

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Authors: Narender Banwala

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The study critically evaluates that the politics of foreign aid and its effect on Afghanistan. The study argues that dynamics of foreign aid to Afghanistan are not driven solely by the Afghan political, social, and economic realities but much more by the ephemeral political goals of international donor countries. The objective of this paper is to find out the political reality of foreign aid given to Afghanistan in a post 9/11 era. The study analyses the gap between the donor countries' interests and the Afghan government's priorities in aid coordination and management. The aid given to Afghanistan has been accompanied by the political interests of the major powers and therefore violated the core principles of humanitarianism, i.e., humanity, impartiality, neutrality, and independence. This research attempts to explain the areas which are of high priority, extremely vulnerable, and have been a neglected part since 2001. The study focuses on how as a result of politicization, foreign aid could not yield the expected results even after prolong presence of international donors in Afghanistan. Methodologically, the study includes both qualitative and quantitative data, which are collected by interviews with government officials and other government documents.

Keywords: Afganistan, aid, politics, security

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Authors: Agampodi Promoda Perera, Yong Shin, Mi Kyoung Park

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The successful detection of pathogenic bacteria in blood provides important information for early detection, diagnosis and the prevention and treatment of infectious diseases. Silicon microring resonators are refractive-index-based optical biosensors that provide highly sensitive, label-free, real-time multiplexed detection of biomolecules. We demonstrate the technique of using GO (graphene oxide) to enhance the signal output of the silicon microring optical sensor. The activated carboxylic groups in GO molecules bind directly to single stranded DNA with an amino modified 5’ end. This conjugation amplifies the shift in resonant wavelength in a real-time manner. We designed a capture probe for strain Staphylococcus aureus of 21 bp and a longer complementary target sequence of 70 bp. The mismatched target sequence we used was of Streptococcus agalactiae of 70 bp. GO is added after the complementary binding of the probe and target. GO conjugates to the unbound single stranded segment of the target and increase the wavelength shift on the silicon microring resonator. Furthermore, our results show that GO could successfully differentiate between the mismatched DNA sequences from the complementary DNA sequence. Therefore, the proposed concept could effectively enhance sensitivity of pathogen detection sensors.

Keywords: label free biosensor, pathogenic bacteria, graphene oxide, diagnosis

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Authors: Oyebade K. Oyedotun

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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Abdou Elhendy

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Numerous study have shown the efficacy of the percutaneous intervention (PCI) and coronary stenting in improving left ventricular function and relieving exertional angina. Furthermore, PCI remains the main line of therapy in acute myocardial infarction. Improvement of procedural techniques and new devices have resulted in an increased number of PCI in those with difficult and extensive lesions, multivessel disease as well as total occlusion. Immediate and late outcome may be compromised by acute thrombosis or the development of fibro-intimal hyperplasia. In addition, progression of coronary artery disease proximal or distal to the stent as well as in non-stented arteries is not uncommon. As a result, complications can occur, such as acute myocardial infarction, worsened heart failure or recurrence of angina. In a stent, restenosis can occur without symptoms or with atypical complaints rendering the clinical diagnosis difficult. Routine invasive angiography is not appropriate as a follow up tool due to associated risk and cost and the limited functional assessment. Exercise and pharmacologic stress testing are increasingly used to evaluate the myocardial function, perfusion and adequacy of revascularization. Information obtained by these techniques provide important clues regarding presence and severity of compromise in myocardial blood flow. Stress echocardiography can be performed in conjunction with exercise or dobutamine infusion. The diagnostic accuracy has been moderate, but the results provide excellent prognostic stratification. Adding myocardial contrast agents can improve imaging quality and allows assessment of both function and perfusion. Stress radionuclide myocardial perfusion imaging is an alternative to evaluate these patients. The extent and severity of wall motion and perfusion abnormalities observed during exercise or pharmacologic stress are predictors of survival and risk of cardiac events. According to current guidelines, stress echocardiography and radionuclide imaging are considered to have appropriate indication among patients after PCI who have cardiac symptoms and those who underwent incomplete revascularization. Stress testing is not recommended in asymptomatic patients, particularly early after revascularization, Coronary CT angiography is increasingly used and provides high sensitive for the diagnosis of coronary artery stenosis. Average sensitivity and specificity for the diagnosis of in stent stenosis in pooled data are 79% and 81%, respectively. Limitations include blooming artifacts and low feasibility in patients with small stents or thick struts. Anatomical and functional cardiac imaging modalities are corner stone for the assessment of patients after PCI and provide salient diagnostic and prognostic information. Current imaging techniques cans serve as gate keeper for coronary angiography, thus limiting the risk of invasive procedures to those who are likely to benefit from subsequent revascularization. The determination of which modality to apply requires careful identification of merits and limitation of each technique as well as the unique characteristic of each individual patient.

Keywords: coronary artery disease, stress testing, cardiac imaging, restenosis

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Hussaini Garba Mohammed

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Background/Significance: The lack of data gathering is affecting NGOs world-wide in general to have good data information about educational and health related issues among communities in any country and around the world. For example, HIV/AIDS smoking (Tuberculosis diseases) and COVID-19 virus carriers is becoming a serious public health problem, especially among old men and women. But there is no full details data survey assessment from communities, villages, and rural area in some countries to show the percentage of victims and patients, especial with this world COVID-19 virus among the people. These data are essential to inform programming targets, strategies, and priorities in getting good information about data gathering in any society.

Keywords: reliable information, data assessment, data mining, data communication

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Authors: Hsien-Te Lin

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The purpose of this paper is to introduce the BCFES (building carbon footprint evaluation system), which is a LCA (life cycle assessment) tool developed by the Low Carbon Building Alliance (LCBA) in Taiwan. A qualified BCFES for the building industry should fulfill the function of evaluating carbon footprint throughout all stages in the life cycle of building projects, including the production, transportation and manufacturing of materials, construction, daily energy usage, renovation and demolition. However, many existing BCFESs are too complicated and not very designer-friendly, creating obstacles in the implementation of carbon reduction policies. One of the greatest obstacle is the misapplication of the carbon footprint inventory standards of PAS2050 or ISO14067, which are designed for mass-produced goods rather than building projects. When these product-oriented rules are applied to building projects, one must compute a tremendous amount of data for raw materials and the transportation of construction equipment throughout the construction period based on purchasing lists and construction logs. This verification method is very cumbersome by nature and unhelpful to the promotion of low carbon design. With a view to provide an engineer-oriented BCFE with pre-diagnosis functions, a component input/output (I/O) database system and a scenario simulation method for building energy are proposed herein. Most existing BCFESs base their calculations on a product-oriented carbon database for raw materials like cement, steel, glass, and wood. However, data on raw materials is meaningless for the purpose of encouraging carbon reduction design without a feedback mechanism, because an engineering project is not designed based on raw materials but rather on building components, such as flooring, walls, roofs, ceilings, roads or cabinets. The LCBA Database has been composited from existing carbon footprint databases for raw materials and architectural graphic standards. Project designers can now use the LCBA Database to conduct low carbon design in a much more simple and efficient way. Daily energy usage throughout a building's life cycle, including air conditioning, lighting, and electric equipment, is very difficult for the building designer to predict. A good BCFES should provide a simplified and designer-friendly method to overcome this obstacle in predicting energy consumption. In this paper, the author has developed a simplified tool, the dynamic Energy Use Intensity (EUI) method, to accurately predict energy usage with simple multiplications and additions using EUI data and the designed efficiency levels for the building envelope, AC, lighting and electrical equipment. Remarkably simple to use, it can help designers pre-diagnose hotspots in building carbon footprint and further enhance low carbon designs. The BCFES-LCBA offers the advantages of an engineer-friendly component I/O database, simplified energy prediction methods, pre-diagnosis of carbon hotspots and sensitivity to good low carbon designs, making it an increasingly popular carbon management tool in Taiwan. To date, about thirty projects have been awarded BCFES-LCBA certification and the assessment has become mandatory in some cities.

Keywords: building carbon footprint, life cycle assessment, energy use intensity, building energy

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Authors: Morteza Gharibi, Abdolghader Pakniat, Somayeh Bahrampouri

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Introduction: Emergency department (E.R.) crowding is a serious widespread problem in hospitals that leads to irregularities, a slower rate of delivery of services to patients, and a long-term stay. In addition, the long-term stay in the E.D. reduces the possibility of providing services with appropriate quality to other patients who are undergoing medical emergencies, which leads to dissatisfaction among patients. This study aimed to determine the relationship between ED-crowding and the mortality rate of the patients referred to the E.D. In a retrospective cohort study, all patients who expired in first 24 hours of admission were enrolled in the study. Crowding index at the moment of admission was calculated using Edwin Score. The data including history and physical examination, time of arrival in the E.D., diagnosis (using ICD 10 code), time of death, cause of death, demographic information was recoded based on triage forms on admission and patients’ medical files. Data analysis was performed by using descriptive statistics and chi square test, ANOVA tests using SPSS ver. 19. The time of arrival in E.D. to death in crowded E.D. conditions, with an average of five hours and 25 minutes, was significantly higher than the average admission Time of arrival in E.D. to death in active and crowded E.D. conditions. More physicians and nurses can be employed during crowded times to reduce staff fatigue and improve their performance during these hours.

Keywords: mortality, emergency, department, crowding

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Authors: Benbya Hind, Belbaly Nassim

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Markets and communities are often cast as alternative forms of knowledge sharing, but an open question is how and why people dynamically transition between them. To study these transitions, we design a technology that allows geographically distributed participants to either buy knowledge (using virtual points) or request it for free. We use a data-driven, inductive approach, studying 550 members in over 5000 interactions, during nine months. Because the technology offered participants choices between market or community forms, we can document both individual and collective transitions that emerge as people cycle between these forms. Our inductive analysis revealed that uncertainties endemic to knowledge sharing were the impetus for these transitions. Communities evoke uncertainties about knowledge sharing’s costs and benefits, which markets resolve by quantifying explicit prices. However, if people manipulate markets, they create uncertainties about the validity of those prices, allowing communities to reemerge to establish certainty via identity-based validation.

Keywords: knowledge sharing, communities, information technology design, transitions, markets

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Authors: Tali Capua, Karin Hermon, Miguel Glatstein, Oren Tavor, Ayelet Rimon

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Background: Electric bicycles (E-bikes) are one of a wide range of light electric vehicles that provide convenient local transportation and attractive recreational opportunities. Along with their growing use worldwide, the E-bike related injury rate increases. To the best of our knowledge, this study is the first to specifically compare E-bike with classic bicycle related injuries in children. Methods: Data of all pediatric ( < 16 years of age) bicycle related injuries presenting to an urban level I trauma center between 2014 and 2015 were collected and analyzed. The recorded data included age, gender, details of the accident, as well severity of injury, medical diagnosis, and the outcome. Abbreviated Injury Score (AIS) and Injury Severity Score (ISS) were calculated for each patient. Data of E-bike related injuries and classic bicycle were then compared. Results: A total of 124 bicycle related injuries and 97 E-bike related injuries presented to the emergency department. Once pedestrians and bicycle passengers were removed, the groups of riders consisted of 111 bikers and 85 E-bikers. The mean age of bikers was 9.9 years (range 3-16 years) and of E-bikers was 13.7 years (range 7.5-16 years). Injuries to the head and the extremities were common in both groups. Compared to bikers, E-bikers had significantly more injuries to intra-abdominal organs (p = 0.04). Twenty patients (16%) with bicycle related injuries were admitted, and 13 (15%) patients with E-bike related injuries, of the latter group four underwent surgical intervention. ISS scores were low overall, but the injuries of higher severity (ISS > 9) were among the E-bikers. Conclusions: This study provides unique information which suggests that injuries in E-bikers tend to be more severe than in classic bikers. There is a need for regulation regarding the use of E-bikes to enhance the safety of both bikers and other road and pavement users.

Keywords: bicycle, electric bicycle, injury, pediatric, trauma

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Authors: Oloniyo Kemisola Eunice, Odere Oladunni Oluwafeyikemi

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Technological change and globalization have created a new global economy with Information and Communication Technology (ICT) occupying a complex position in relation to globalization. The emergence of this new global economy has serious implications on the nature and purpose of educational institutions. The paper is basically a theoretical discourse. Data for analysis were obtained from secondary sources. The paper found that significant challenges confront the integration of ICTs in education in the areas of educational policy and planning, infrastructure, language and content, capacity building and financing in Nigeria. The paper concluded that business education needs to be well equipped to anticipate and respond to opportunities created by ICTs in order to participate productively and equitably in an increasingly technology-rich and knowledge-driven world. The paper recommended, among others, that the investments in ICTs should be used to promote the development of basic skills, problem-solving and communication skills and the professional development of teachers.

Keywords: information, communication, technology, business, education

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Authors: Jaspinder Kaur, Jatinder Pal Singh

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Introduction- Delusional parasitosis is a rare psychotic illness characterized by a fixed belief of manifesting a parasite in a body when in reality, it was not. Also known as Ekbom syndrome or delusional infestations, or acarophobia. Although the patient has no primary skin pathology, but all skin findings were secondary to skin manipulation by the patient itself, which is why up to 90% of patients first seek consultation from a dermatologist. Most commonly, it was seen in older people with female to male ratio is 2:1. For treatment, the patient first need to be investigated to rule all other possible causes, as Delusional parasitosis can be caused by Vitamin B12 deficiency, pellagra, hepatic and renal disease, diabetes mellitus, multiple sclerosis, and leprosy. When all possible causes ruled out, psychiatric referral to be done. Rule out other psychiatric comorbidities, and treatment should be done accordingly. Patient with delusional parasitosis responds well to second generation antipsychotics and need to continuous medication over years, and relapse is likely if treatment is stopped. Case Presentation- A 79-year-old female, belonging to lower socio-economic status, presented with complaints of itching sensation with erythematous patches over the scalp and multiple scratch excoriations lesion over the scalp, face and neck from the past 7-8 months. She had a feeling of small insect crawling under her skin and scalp area. To reduce the itching and kill the insect, she would scratch and squeeze her skin repeatedly. When the family tried to give her explanation that there was no insect in her body, she would not get convinced, rather got angry and abuse family members for not believing her. Gradually, her sleep would remain disturbed, she would be seen awake at night, seen to be scratching her skin, pull her scalp hair, even squeeze out her healed lesions. She collected her skin debris, scalp hairs and look out for insect. Because of her continuous illness, the patient started to remain sad and had crying spells. Her appetite decreased. She became socially isolated and stopped doing her activities of daily living. Family member’s first consulted dermatologist, investigated thoroughly with routine investigations, autoimmune and malignancy workup. As all investigations were normal, following which patient was referred for psychiatric evaluation. The patient was started on Tablet Olanzapine 2.5 mg, gradually increased to 7.5 mg. Over 1 month, there was reduction in itching, skin pricking. Lesions were gradually healed, and the patient continued to take other dermatological medications and ointment and was in regular follow up with psychiatric liaison from past 2 months with 70-80 % improvement in her symptoms. Conclusion- Delusional parasitosis is a psychiatric disorder of insidious onset, seen commonly in middle and old age people. Both psychiatric and dermatology consultation liaison will help the patient for an early diagnosis and adequate treatment. If a primary psychiatric diagnosis, the patient respond well to second generation antipsychotics but always require a further evaluation and treatment management if it is secondary to some physical or other psychiatric comorbidity.

Keywords: delusional parasitosis, delusional infestations, rare, primary psychiatric diagnosis, antipsychotic agents

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Authors: Getenet Shumet Birhan, Biruk Lelisa Eticha, Gizachew Tilahun Belete, Fisseha Admassu Ayele

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Context: Glaucoma is a significant public health concern globally, being the second leading cause of blindness. This study focuses on adult glaucoma patients in Ethiopia, specifically at the University of Gondar. Research Aim: The main objective is to assess the prevalence of visual impairment and identify associated factors among adult glaucoma patients at the University of Gondar. Methodology: The study used an institution-based cross-sectional design, collecting data from 423 glaucoma patients through interviews and medical chart reviews. Descriptive statistics and logistic regression were employed for analysis. Findings: The study found a high prevalence of visual impairment (77.6%) among adult glaucoma patients, with factors such as female sex, rural residence, glaucoma type, disease stage, and duration of diagnosis significantly associated with visual impairment. Theoretical Importance: This research adds valuable insights into the prevalence and determinants of visual impairment among glaucoma patients in Ethiopia, contributing to the existing literature on eye health in low-resource settings. Data Collection: Data were collected through face-to-face interviews and medical chart reviews at the University of Gondar, utilizing a structured questionnaire. Analysis Procedures: Descriptive statistics, frequency analysis, and binary logistic regression were employed to analyze the data and identify factors associated with visual impairment in adult glaucoma patients. Question Addressed: The study sought to answer the question of the prevalence of visual impairment and its associated factors among adult glaucoma patients at the University of Gondar in Northwest Ethiopia. Conclusion: The research concludes that visual impairment is significantly high among adult glaucoma patients in this setting, with several factors playing a role in its occurrence.

Keywords: visual impairment, glaucoma, Ethiopia, Gondar

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Authors: Zuzana Chaloupková, Zdeňka Marková, Václav Ranc, Radek Zbořil

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Prostate cancer is now one of the most serious oncological diseases in men with an incidence higher than that of all other solid tumors combined. Diagnosis of prostate cancer usually involves detection of related genes or detection of marker proteins, such as PSA. One of the new potential markers is PSMA (prostate specific membrane antigen). PSMA is a unique membrane bound glycoprotein, which is considerably overexpressed on prostate cancer as well as neovasculature of most of the solid tumors. Commonly applied methods for a detection of proteins include techniques based on immunochemical approaches, including ELISA and RIA. Magnetically assisted surface enhanced Raman spectroscopy (MA-SERS) can be considered as an interesting alternative to generally accepted approaches. This work describes a utilization of MA-SERS in a detection of PSMA in human blood. This analytical platform is based on magnetic nanocomposites Fe3O4@Ag, functionalized by a low-molecular selector labeled as JB303. The system allows isolating the marker from the complex sample using application of magnetic force. Detection of PSMA is than performed by SERS effect given by a presence of silver nanoparticles. This system allowed us to analyze PSMA in clinical samples with limits of detection lower than 1 ng/mL.

Keywords: diagnosis, cancer, PSMA, MA-SERS, Ag nanoparticles

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Authors: Christopher Leung, Guillermo Becerril-Martinez

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Amyloidosis is a rare systemic disease where abnormal proteins invade various organs and impede their function. Occasionally, they can manifest in a solidary organ such as the heart, lung, and nervous systems; rarely do they manifest in the gallbladder. Diagnosis often requires biopsy of the affected area and histopathology shows deposition of abnormally folded globular proteins called amyloid proteins. This case presents a 69-year-old male with a 3-month history of RUQ pain, diarrhea and non-specific symptoms of tiredness, etc. On imaging, both his US and CT abdomen showed gallbladder wall thickening and pericholecystic fluid, which may represent acute cholecystitis with hypodense lesions around the gallbladder, possibly representing liver abscesses. Given his symptoms of abdominal pain and imaging findings, this gentleman eventually had a laparoscopic cholecystectomy showing a gangrenous gallbladder with a mass on the liver bed. On histopathology, it showed amorphous hyaline eosinophilic material, which Congo-stained confirmed amyloidosis. Amyloidosis explained his non-specific symptoms, he avoided further biopsy, and he was commenced immediately on Lenalidomide. Involvement of the gallbladder is extremely rare, with less than 30 cases around the world. Half of the cases are reported as primary amyloidosis. This case adds to the current literature regarding primary gallbladder amyloidosis. Importantly, this case highlights how laparoscopic cholecystectomy can help with the diagnosis of gallbladder amyloidosis.

Keywords: amyloidosis, cholecystitis, gangrenous cholecystitis, gallbladder, systemic amyloidosis

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Authors: Chirag Raval, Jimmy Toussaint, Tieuvi Nguyen, Hadi Fadaifard, George Wolberg, Steven Quarfordt, Kung-ming Jan, David S. Rumschitzki

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Numerous studies examine aquaporins’ role in osmotic water transport in various systems but virtually none focus on aquaporins’ role in hydrostatically-driven water transport involving mammalian cells save for our laboratory’s recent study of aortic endothelial cells. Here we investigate aquaporin-1 expression and function in the aortic endothelium in two high-renin rat models of hypertension, the spontaneously hypertensive genomically altered Wystar-Kyoto rat variant and Sprague-Dawley rats made hypertensive by two kidney, one clip Goldblatt surgery. We measured aquaporin-1 expression in aortic endothelial cells from whole rat aortas by quantitative immunohistochemistry, and function by measuring the pressure driven hydraulic conductivities of excised rat aortas with both intact and denuded endothelia on the same vessel. We use them to calculate the effective intimal hydraulic conductivity, which is a combination of endothelial and subendothelial components. We observed well-correlated enhancements in aquaporin-1 expression and function in both hypertensive rat models as well as in aortas from normotensive rats whose expression was upregulated by 2h forskolin treatment. Upregulated aquaporin-1 expression and function may be a response to hypertension that critically determines conduit artery vessel wall viability and long-term susceptibility to atherosclerosis. Numerous studies examine aquaporins’ role in osmotic water transport in various systems but virtually none focus on aquaporins’ role in hydrostatically-driven water transport involving mammalian cells save for our laboratory’s recent study of aortic endothelial cells. Here we investigate aquaporin-1 expression and function in the aortic endothelium in two high-renin rat models of hypertension, the spontaneously hypertensive genomically altered Wystar-Kyoto rat variant and Sprague-Dawley rats made hypertensive by two kidney, one clip Goldblatt surgery. We measured aquaporin-1 expression in aortic endothelial cells from whole rat aortas by quantitative immunohistochemistry, and function by measuring the pressure driven hydraulic conductivities of excised rat aortas with both intact and denuded endothelia on the same vessel. We use them to calculate the effective intimal hydraulic conductivity, which is a combination of endothelial and subendothelial components. We observed well-correlated enhancements in aquaporin-1 expression and function in both hypertensive rat models as well as in aortas from normotensive rats whose expression was upregulated by 2h forskolin treatment. Upregulated aquaporin-1 expression and function may be a response to hypertension that critically determines conduit artery vessel wall viability and long-term susceptibility to atherosclerosis.

Keywords: acute hypertension, aquaporin-1, hydraulic conductivity, hydrostatic pressure, aortic endothelial cells, transcellular flow

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Authors: F. Ghandehari, H. Lu, L. El-Jahel, D. Jayasuriya

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Disagreement among investors is a fundamental aspect of financial markets, significantly influencing market dynamics. Measuring this disagreement has traditionally posed challenges, often relying on proxies like analyst forecast dispersion, which are limited by biases and infrequent updates. Recent movements in social media indicate that retail investors actively seek financial advice online and can influence the stock market. The evolution of the investing landscape, particularly the rise of social media as a hub for financial advice, provides an alternative avenue for real-time measurement of investor sentiment and disagreement. Platforms like Reddit offer rich, community-driven discussions that reflect genuine investor opinions. This research explores how social media empowers retail investors and the potential of leveraging textual analysis of social media content to capture daily fluctuations in investor disagreement. This study investigates the relationship between daily investor disagreement and trading volume, focusing on the role of social media platforms in shaping market dynamics, specifically using data from WallStreetBets (WSB) on Reddit. This paper uses data from 2020 to 2023 from WSB and analyses 4,896 firms with enough social media activity in WSB to define stock-day level disagreement measures. Consistent with traditional theories that disagreement induces trading volume, the results show significant evidence supporting this claim through different disagreement measures derived from WSB discussions.

Keywords: disagreement, retail investor, social finance, social media

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Authors: Mounir Adal, Baalal Azeddine, Afifi Moulay Larbi

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Numerical analysis is a branch of mathematics devoted to the development of iterative matrix calculation techniques. We are searching for operations optimization as objective to calculate and solve systems of equations of order n with time and energy saving for computers that are conducted to calculate and analyze big data by solving matrix equations. Furthermore, this scientific discipline is producing results with a margin of error of approximation called rates. Thus, the results obtained from the numerical analysis techniques that are held on computer software such as MATLAB or Simulink offers a preliminary diagnosis of the situation of the environment or space targets. By this we can offer technical procedures needed for engineering or scientific studies exploitable by engineers for water.

Keywords: numerical analysis methods, obstacles solving, engineering, simulation, numerical modeling, iteration, computer, MATLAB, water, underground, velocity

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Authors: Rujijan Vichivanives

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This research aims to create a knowledge-based system as a database for self-healthcare analysis, diagnosis of simple illnesses, and the use of Thai herbs instead of modern medicine by using principles of Thai traditional medication theory. These were disseminated by website network programs within Suan Sunandha Rajabhat University. The population used in this study was divided into two groups: the first group consisted of four experts of Thai traditional medication and the second group was 300 website users. The methods used for collecting data were paper questionnaires and poll questionnaires on the website. The statistics used for analyzing data was at an average level. The results were divided into three parts: the first part was the development of a knowledge-based system and the second part was applied programs on website. Both parts could be fulfilled and achieved according to the set goal. The third part was the evaluation of the study: The evaluation of the viewpoints of the experts towards website designs were evaluated at a good level of 4.20. The satisfaction evaluation of the users was found at a good level of average satisfactory level at 4.24. It was found that the young population of those under the age of 16 had less cares about their health than the population of other teenagers, working age adults and those of older age. The research findings should be extended in order to encourage the lifestyle modifications to people of all ages by using the self-healthcare principles.

Keywords: developing, herbs, knowledge-based system, medical treatment

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Authors: Mudassar Iqbal, Riaz Hussain, Khalid Mehmood, Farah Ali, Fazal Mahmood, Abdul Ghaffar

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Corynebacterium pseudotuberculosis is an important cause of caseous lymphadenitis (CL), a complex, chronic devastating and destructive disease of small ruminants. In present study, postmortem examination of Chinkara deer (n=25) was conducted in year 2014. Pus samples suggestive of CL were collected from the superficial lymph nodes, liver, spleen and lungs during necropsy and subjected to standard microbiological procedures for isolation and molecular analysis of bacterial pathogens. Pus samples collected from carcasses (25) presenting clinical lesions of C. pseudotuberculosis infection was identified in 19 (76%) carcasses on the basis of culture characteristics. The frequency of C. pseudotuberculosis bacterium was higher in older animals as compared to young animals. Grossly, multiple tubercles of variable size having caseous material were observed in liver, lungs, spleen and lymph nodes. Histopathologically, tissue sections from all the visceral organs were extensively plugged with abscess. In present study specific prolineiminopeptidase (PIP) gene of the C. pseudotuberculosis was amplified by the Polymerase chain reaction technique (PCR) in 17(25) cases. The efficient and reliable molecular analysis along with necropsy findings in present study can be used as valuable approach for diagnosis of caseous lymphadenitis in small ruminants.

Keywords: Chinkara deer, Corynebacterium pseudotuberculosis, Caseous lymphadenitis, PCR

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Authors: Mukhtar Ibrahim Bello, Ibrahim U. Aikawa, Abubakar Sadiq Muhammad, Muhammad Baballe Ahmad

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These vital signs can be tracked by wearable sensors, which can also be used to assess patients' health. As a result, they can be very beneficial to patients and healthcare professionals in the diagnosis of diseases, particularly when it comes to taking a patient's body temperature in infectious disorders.

Keywords: fire out-break, robots, saving, dangerous environments, impacts

Procedia PDF Downloads 78

Authors: Armaghan Moravej Aleali, Seyed Mahmoud Latifi, Homeira Rashidi

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Objective: High blood pressure in a risk factor for some disease like stroke, coronary heart disease, and renal failure. High blood pressure in children is an increasing health problem. The aim of this present was to determine prevalence of hypertension and pre-hypertension age between 10 to 17 years old. Material & Methods: This descriptive-analytic study was conducted using multiphase sampling method in Ahvaz (Southwest of Iran). A questionnaire include: height, weight, and body mass index, systolic and diastolic blood pressures filled for each participant. Blood pressure was measured twice for each person. For the diagnosis of hypertension, the fourth report of the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents of the National Health Institute of United States was used. Results: The subject participants of the study were 1707children and adolescents including 922 boys (54%) and 785 girls 46%). The prevalence of high blood pressure was 1.7% (boys 2.5% girls 0.8%). The prevalence of pre-hypertension was 9 % (7.6% in boys, 10.6% in girls). The mean systolic and diastolic blood pressures increased with increasing body mass index. Conclusion: In this study, the prevalence of high blood pressure was found to be lower than other studies in our country. The prevalence of the high blood pressure in boys was significantly higher than girls. This study, like other studies, showed a high correlation between being overweight and an increase in systolic and diastolic blood pressure.

Keywords: hypertension, pre-hypertension, childhood, adolescence

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Authors: Ibrahim Umar, Ashiru Rajab, Sumayya Chindo, Emmanuel Olashore

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INTRODUCTION: Kano is the most populous state in Nigeria and one of the two states with the highest TB burden in the country. The state notifies an average of 8,000+ TB cases quarterly and has the highest yearly notification of all the states in Nigeria from 2020 to 2022. The contribution of the state TB program to the National TB notification varies from 9% to 10% quarterly between the first quarter of 2022 and second quarter of 2023. The Kano State TB Situation Room is an innovative platform for timely data collection, collation and analysis for informed decision in health system. During the 2023 second National TB Testing week (NTBTW) Kano TB program aimed at early TB detection, prevention and treatment. The state TB Situation room provided avenue to the state for coordination and surveillance through real time data reporting, review, analysis and use during the NTBTW. OBJECTIVES: To assess the role of innovative information and communication technology platform for real-time TB reporting during second National TB Testing week in Nigeria 2023. To showcase the NTBTW data cascade analysis using TSR as innovative ICT platform. METHODOLOGY: The State TB deployed a real-time virtual dashboard for NTBTW reporting, analysis and feedback. A data room team was set up who received realtime data using google link. Data received was analyzed using power BI analytic tool with statistical alpha level of significance of <0.05. RESULTS: At the end of the week-long activity and using the real-time dashboard with onsite mentorship of the field workers, the state TB program was able to screen a total of 52,054 people were screened for TB from 72,112 individuals eligible for screening (72% screening rate). A total of 9,910 presumptive TB clients were identified and evaluated for TB leading to diagnosis of 445 TB patients with TB (5% yield from presumptives) and placement of 435 TB patients on treatment (98% percentage enrolment). CONCLUSION: The TB Situation Room (TBSR) has been a great asset to Kano State TB Control Program in meeting up with the growing demand for timely data reporting in TB and other global health responses. The use of real time surveillance data during the 2023 NTBTW has in no small measure improved the TB response and feedback in Kano State. Scaling up this intervention to other disease areas, states and nations is a positive step in the right direction towards global TB eradication.

Keywords: tuberculosis (tb), national tb testing week (ntbtw), tb situation rom (tsr), information communication technology (ict)

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