25 research outputs found
Origin and spread of rice cultivation within the Yangtze River Valley, southern China
No full textThe purpose of this paper is to provide a description of the origin and spread of rice
cultivation within the Yangtze River Valley. The region of eastern China, particularly the
Yangtze River is thought to be the point of origin for rice cultivation and domestication,
among many other theories. The data came from assemblages from various sites in Southeastern and central China. The oldest dates are associated with rice husks and grains being used as pottery temper, dated to 9500 ? 500 years B.P. According to one theory
Oryza rufipogon evolved into O. sativa japonica and O. sativa indica. However research
on the genetics from O. sativa indica indicated that this subspecies could be the result of
hybridization between wild rice and O. s. japonica
Lobe based risk analysis of discharges and in-hospital mortality of lung cancer patients
No full textLung cancer occurrences are more likely as compared to any other type of cancer in the world with very high mortality rate. Based on the past research, smoking and family history of smoking are the most common causes of lung cancer by per age, race and gender. Lung cancer is the second deadliest cancer in this world after prostate cancer and breast cancer. In this research, we have used the Nationwide Inpatient Sample Data from the year 2003 to 2007 to analyze current and predict future trend of lung cancer. Towards the analyses, we have included all the anatomical sub categories of lung to determine, which lobe of lung is having considerably higher risk of catching cancer. Our anatomical analysis concludes the risk occurrence of lung cancer follows an order from high to low: Upper lobe, Lower lobe, Other-parts of the lung, Main bronchus, and Middle lobe. We have also examined the association of each category of lung with race across the gender. The overall lung cancer analysis indicates, White American and Black have the higher risk of getting lung cancer as compared with other races. Further analysis of individual lobe of lung denotes, among the all races, White American is more susceptible to each lobe of lung except other-parts of lung. The odds ratio analysis for individual anatomical site concludes white male and female [odds ratio of all sub category- male/female 1.03 to 1.08] are equally susceptible to each lobe of lung. The black females [odds ratio of Lower lobe-male/female 1.08] are highly associated with lower lobe of lung cancer as compared with black males. Whereas Hispanic, Asian and Native American females are more associated with middle lobe lung cancer as compared with Hispanic, Asian and native American males [odds ratio for middle lobe - male/female 0.89 to 0.95]. Based on the past research, smoking is the primary cause of the death in the lung cancer by age, race and gender. In this research, we have used National Impatient Sample and census population for the state CA, Fl, TX, NY, IL, RI, VA, SC, and WI to determine the association of geographical variation with the risk factors by population. We have confirmed that incidence of lung cancer cases in CA (bigger state by population) is lower than RI and VA (smaller state by population). In other word, we confirmed our hypothesis that higher population, need not have to have higher incidence rate (In this research, incidence rate refers to discharged), but other factors like race, gender, pollution, exposure to chemical factor also play an important role to measure susceptibility of the occurrences. This research study was limited to the National Inpatient database across the United State. Lung cancer patients were extracted base on principle diagnosis of the lung cancer symptoms.Ph.D.Includes bibliographical referencesby Riddhi. J. Vya
Evaluation of carbon reduction options in industrial combined heat and power plants
No full textIndustry is a major contributor to the rise in global CO2 emissions, constituting one fifth of the global energy consumption, of which significant amount is provided by fossil fuel combustion. Following the Paris agreement, emphasis has been made on decarbonization of the industrial sector. This thesis focuses on industrial decarbonization by employing Carbon Capture and Storage for Combined Heat and Power (CHP) gas turbine plants. The scope of this thesis includes conceptual modelling and thermodynamic analysis of potential decarbonization options for zero carbon CHP plants. The studied options include post-combustion capture, exhaust gas recirculation, pre-combustion capture and oxyfuel combustion. As conventional air Brayton cycles are not applicable for oxy-fuel combustion in gas turbines, different working fluids and cycle configurations are proposed and thermodynamic performance is evaluated. Selected cycles were then compared based on thermodynamics, economics and off-design performance at a typical constant power to heat ratio of 0.78. It was observed that oxyfuel CHP cycle with CO2 working fluid is a promising solution for zero carbon CHP with 100% CO2 reduction. However, this solution requires new turbomachinery design. In view of this, a retrofit analysis is also performed in this thesis which evaluates if an existing air designed gas turbine can be used for CO2 operation. It was concluded from this analysis that it is possible to operate an air gas turbine on CO2 by incorporating proposed modifications of higher compressor inlet temperature (473K) and a turbine inlet nozzle area 20% larger than design. These modifications, however also lead to serious performance deterioration.Mechanical Engineering | Energy, Flow and Process Technolog
Comparative study for hospitalization characteristics and predictors of ovarian cancer of inpatients in the United States
No full textOvarian cancer is the second most common type of gynecologic cancer, and it causes more death than any other female reproductive cancer. It is the 7th most common women's cancer. The objective of the present study is to highlight the risk factors of ovarian cancer related to hospitalization outcomes such as mortality, length of stay, and total medical charges when there is a presence of Congestive heart failure and other complications. The study implemented a cross-sectional design to achieve the primary objectives. Data were downloaded and extracted, with permission, from Nationwide Inpatient Sample (NIS). The collected data included patient demographic characteristics, such as age, gender, race, and income. Statistical Package for the Social Sciences (SPSS) version 28.0 was used to analyze the present study's data, and all outcomes with a p-value less than 0.05 were found to be significant. Overall mortality showed a higher incidence of epithelial ovarian cancer. The incidence of mortality increased with congestive heart failure (CHF) and hypertension (HT), in which the patients with HT had a higher death rate with epithelial ovarian cancer than CHF. Regarding the length of stay, increasing age and being white results in an average decrease in length of stay. Those patients with weight loss comorbidities resulted in the greatest mean increase in length of stay. The increasing number of diagnoses, procedures, age, and being Hispanic results in an average increase in total cost. Being white or black results in an average decrease in total cost. The deceased risk of dying was associated with the number of procedures. Increased age and number of diagnoses were associated with an increased likelihood of dying. The increase in procedures and SES will increase the length of stay. And when it comes to comorbidities, among those with hypertension, these predictors are significant. The predictors of mortality were assessed by age and number of diagnoses, which were associated with an increased likelihood of dying.Ph.D.Includes bibliographical reference
Rising Above Themselves: Why Today’s Lawyers of Color Must Look Beyond Color
No full textThis way particular sympathies, biases, and prejudices are replaced by the judge\u27s non-discriminatory appreciation of the rule of law and the obligation of courts to both respect elected authorities and to behave as a check against democracy run amok. Lawyers of color could contribute to society by the awareness that their promise is greater than race or color and by channeling race as a means but not as an endpoint to understand (and to aspire to remedy inasmuch as the relevant constitutional, positive law, or regulatory provision permits) discrimination in all its forms. In other words, minority lawyers must rise above themselves. That will be a necessary, even if not a sufficient, fulfillment of a more perfect [U]nion. The Author argues that lawyers of color, just like women and other groups once heavily disadvantaged both by law and in the legal profession, do sometimes bring a comprehensive awareness benefiting the particular legal provision
Clinical decision support system for the diagnosis, analysis and management of Hepatitis C
No full textDiagnosing, analyzing and managing hepatitis C is an important task for physicians. Traditional diagnosis of chronic disease, like Hepatitis C, is time consuming and expensive. In order to understand the complete analysis of Hepatitis, an understanding of the overall impact of Hepatitis C Length of Stay in the hospital, total charges of the treatment, procedures, mortality rate, morbidity and how Hepatitis effects the liver so that an early diagnose of Hepatitis C Virus must be achieved. The research is divided into two parts, first part is data analysis of hepatitis and liver diseases, and the second part is Clinical Decision Support System (CDSS) for the diagnosis of Hepatitis C is proposed.
The objective of this study is to examine the hospitalization outcomes of total charges, length of stay in the hospital, cost of the treatment, died during hospitalization, procedures, for gender, race/ethnicity, Insurance type, income level, location of the hospital, age, region of the hospital, destination after discharge, admission source, mortality, morbidity, and admission to the hospital. The study focusses on the facts about the Hepatitis C and other Liver Diseases. A variety of statistical analysis are performed based on the NIS data from 2007 to 2012.
This study utilized the National (Nationwide) Impatient Sample (NIS) for the years 2007 to 2012. The data source is an inpatient dataset produced every year. The NIS is a publicly available all-payer inpatient health care dataset with national estimates of inpatient stays. NIS is a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ). The current study includes the estimation of Length of Stay, total charges of the treatment, total charges of the procedures and other elements using the SPSS statistical analysis software.
The study revealed several significant factors related to the Hepatitis and other Liver diseases. Hepatitis C cases remained highest among other categories of Hepatitis as Hepatitis A+E and Hepatitis B+D, Cirrhosis remained highest among Chronic Hepatitis and Hepatocellular Carcinoma for the entire period of analysis. Hepatitis C Virus was found highest among the age groups of 21 to 51 years old and males, and low-income population for the year 2007. In average Hepatitis C was found highest among the age group 52-65 years throughout the years 2008 to 2012. The White population had highest number of Hepatitis C patients, followed by the Black population and then the Hispanic population. The Medicaid and Medicare coverage were highest for hepatitis C patients among Private insurance, Self-Pay, No Charge and Other. Hepatitis C was highest in the Northeast region per 100,000 normalized populations as compared to any other region. Urban location noticed higher number of patients reported with Hepatitis C infection. Hospital Emergency admission was highest for Hepatitis C patients. Admission Source for the Hepatitis C patients showed highest from Emergency Department throughout the entire period of analysis. Destination after discharge showed that most of the patients stayed to home self-care after they discharged from the hospital, followed by short term hospital transfer and home health care. An average Length of stay for Hepatitis C patient for the entire period of analysis was around 5.73 days to 6.01 days each year. Trauma center charged most to Hepatitis C patient throughout the entire period of the analysis as compared to Elective, Urgent and emergency treatment centers. Cirrhosis shows the highest number of deaths in the hospital followed by Hepatitis C. Number of patients die of Hepatitis C is highest for age group 52 to 65 years. Biopsy Procedure is performed highest followed by Liver Transplant and Destruction of tissue throughout the entire period of the analysis. The highest number of biopsy procedures was performed for Cirrhosis patients followed by Chronic Liver disease and then Hepatitis C. The cost of Liver Transplant remained the most expensive procedure followed by Repair of Liver and Removal of Lobe throughout year 2007-2012. The results of the analysis help to determine national estimates of incidence, prevalence inpatient mortality, morbidity, severity of illness, reference for resources allocation, hospital utilization, and policy changes related treatment of Hepatitis C Virus and other liver diseases.
For the 2nd part, a new Clinical Decision Support System (CDSS) was developed using Exsys Corvid for expert analysis. CDSS is algorithmic method of data analysis to help healthcare providers make decision, improve diagnostic probabilities, patient care and reduce overall treatment expenses. Clinical Decision Support System was successfully developed for Hepatitis C diagnostics. This CDSS is medically accurate and can guide healthcare professionals through the diagnostic process. Corvid Exsys rule-based system is used for building automated expert systems. The software utilizes backward and forward chaining technique. Selected variables had been entered in decision making flow to get the final diagnosis outcome of the analysis.
The analysis is performed using Corvid Exsys software and the following variables were used in the analysis: Patient Age, Duration of disease, blood Transfusion year, Disease Symptom, liver related diseases, blood-borne reason for hepatitis, potential reason of disease by drugs, hepatitis due to other diseases and Test Performed.
Input variables: Age- “numeric”, Blood Transfusion- “More or equal to 15 years” or “Less than 15 years”, Disease symptoms- Static list with values “fatigue, weight loss, Joint/Belly pain, loss of appetite, dark urine, itchy skin/sour muscle, abdominal swelling, fever, nausea, fluid retention, confusion, jaundice, and metabolic problem.”
The symptoms include Hepatitis Free, Hepatitis Present or Inconclusive results. Confidence variable decides if hepatitis is present or not or more detailed test was needed. All the questions asked by the system during the diagnosis process are based on the clinical literature. The system can guide a clinician through the diagnostic process to achieve hepatitis results and decision-making expert system was successfully developed.Ph.D.Includes bibliographical reference
Data analysis and risk factors associated with prostate cancer
No full textProstate cancer is one of the leading causes of mortality across all types of cancer. In 2022, prostate cancer had the highest incidence of new cases (268,490) and the second highest number of fatalities (34,500) in the United States Demographic, socioeconomic and lifestyle factors have been associated with prostate cancer risk and diagnosis, as well as health care, prostate specific antigen (PSA) and co-morbidities. The total cost of cancer care in the United States was estimated to be 208.9 billion. Costs associated specifically with prostate cancer diagnosis and treatment were reported to be 4,573 higher treatment cost compared to non-TURP. Patients who underwent a prostatectomy had a lower cost of -$3,974 compared to those who did not have a prostatectomy.
This study highlights the importance of implementing a comprehensive approach which includes early detection, education and addressing health disparities leading to more positive outcomes and reduction of the economic burden of prostate cancer.Ph.D.Includes bibliographical reference
Decoding Individual and Shared Experiences of Media Perception Using CNN Architectures
No full textThe brain is an incredibly complex organ capable of perceiving and interpreting a wide range of stimuli. Depending on individual brain chemistry and wiring, different people decipher the same stimuli differently, conditioned by their life experiences and environment. This study’s objective is to decode how the CNN models capture and learn these differences and similarities in brain waves using three publicly available EEG datasets. While being exposed to a variety of media stimuli, each brain produces unique brain waves with some similarity to other neural signals to the same stimuli. However, to figure out whether our neural models are able to interpret and distinguish the common and unique signals correctly, we employed three widely used CNN architectures to interpret brain signals. We extracted the pre-processed versions of the EEG data and identified the dependency of time windows on feature learning for song and movie classification tasks, along with analyzing the performance of models on each dataset. While the minimum length snippet of 5 s was enough for the personalized model, the maximum length snippet of 30 s proved to be the most efficient in the case of the generalized model. The usage of a deeper architecture, i.e., DeepConvNet was found to be the best for extracting personalized and generalized features with the NMED-T and SEED datasets. However, EEGNet gave a better performance on the NMED-H dataset. Maximum accuracy of 69%, 100%, and 56% was achieved in the case of the personalized model on NMED-T, NMED-H, and SEED datasets, respectively. However, the maximum accuracies dropped to 18%, 37%, and 14% on NMED-T, NMED-H, and SEED datasets, respectively, in the generalized model. We achieved a 5% improvement over the state of the art while examining shared experiences on NMED-T. This marked the outof-distribution generalization problem and signified the role of individual differences in media perception, thus emphasizing the development of personalized models along with generalized models with shared features at a certain level.Design Aesthetic
Analyses of NSAID induced GI bleed related hospitalization discharges from 2016 to 2018 in the United States
No full textBACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are popularly used non-opioids for their anti-inflammatory, antipyretic, analgesic effects. Gastrointestinal (GI) bleeds induced by NSAIDs cause issues for patients in terms of high costs of medical services and poor quality of life. The objective of this study was to determine the predictors and their relations to length of hospital stay and total charges.
METHOD: The study data was downloaded and extracted from Nationwide Inpatient Sample (NIS). Patients with NSAID induced GI bleed were reviewed from the years 2016 to 2018. This data included patients' demographic characteristics and medical information such as age, gender, race, household income, and positive screening for NSAID induced GI bleed. The data also contained dependent variables such as risk factor types: alcohol, helicobacter pylori, and smoking. Statistical Package for the Social Sciences (SPSS) version 27 was utilized to analyze the NIS data of NSAID induced GI bleed. All outcomes with p values less than 0.05 were found to be significant. Chi-square test goodness-of-fit, binary logistic regression and multiple linear regression were the statistical tests administered to determine the predictors of study outcomes.
RESULTS: A descriptive analysis of this study showed the highest incidence of NSAID induced GI bleed patients as Non-Hispanic whites, older adults (45 years and older) with variation in household income. Risk factors studied include helicobacter pylori, alcohol, and smoking, in which the highest incidence was smoking (80.2%). Predictors for both length of hospital stay and total charges included risk factors and socio-demographic characteristics. The overall prediction of study outcomes showed associations between the independent and dependent variables such as age, race, and gender, in relation to length of hospital stay and total charges. There was a high influence detected in the interaction of predictors such as health and socio-demographic characteristics, like advanced age, gender, race and smoking in relation to NSAID induced GI bleeds.
CONCLUSION: This study found numerous significant results related to NSAID induced GI bleed such as age, race, gender, smoking, income level, length of hospital stay and total charges. The patients’ health information and demographics were important for comprehending the necessity of decreasing hospitalization costs and improving the quality of life for patients. Healthcare services and providers must aim to improve therapy plans to minimize occurrences of NSAID induced GI bleeds and decrease costs of medical therapy and hospital services provided to patients.Ph.D.Includes bibliographical reference
Utilizing robust statistical methods for maximum likelihood estimation in clinical informatics for obstetrics research in the community hospital setting
No full textBACKGROUND:
Research in the field of obstetrics can be very challenging because the nature of studying treatments and interventions in pregnancy patients poses several ethical and practical limitations. Pregnancy research in the community hospital setting provides unique challenges but so too provides much needed answers to niche clinical problems, necessitating understanding, development, and implementation of statistical methods that are best suited to this scenario.
OBJECTIVE:
The objective of this dissertation is to provide a solution to these limitations by defining and implementing a statistical framework utilizing robust methods for conducting high-quality, practical research in the field of obstetrics and prove its effectiveness though original research with novel statistical methods.
METHODS:
Two prospective observational cohort studies were conducted: one evaluating antibiotic regimens in the management of preterm prelabor rupture of membranes and the other studying alternative antibiotic regimens for surgical prophylaxis for non-elective cesarean deliveries during the COVID-19 pandemic. The results of both studies were analyzed using robust statistical analysis to yield original results.
RESULTS:
The first study demonstrated a decreased risk was noted for the development of clinical chorioamnionitis (p=0.003), neonatal sepsis (p<0.001), and postpartum endometritis (p=0.010) when comparing azithromycin to erythromycin regimens. Pregnancy latency by regimen was not significantly different (p=0.90). The second study demonstrated that patients receiving clarithromycin had significantly lower rates and a decreased risk of postpartum endometritis as compared to those who did not receive adjunct prophylaxis (p=0.034). When evaluating robust statistical methods, the recommended statistical analysis framework for generalized linear regression and survival analysis under these unique circumstances includes Welch two-sample t-tests for continuous variables, G-Test and Fischer’s exact test for categorical variables, Quasi-likelihood Poisson regression with robust error variance, robust Cox proportional hazards model, Aalen-Johansen estimator with IJ variance for survival curve, and direct approach to adjusted survival curves.
CONCLUSION:
Utilizing this novel approach to statistical analysis as demonstrated by original research in this dissertation proposal for clinical research in high risk obstetrics in the community hospital setting may provide more accurate and appropriate results.Ph.D.Includes bibliographical reference
