In this paper, trajectory tracking of a differential drive nonholonomic mobile robot is presented. In addition to the complex relations of the control system, the nonholonomic system adds complexity to the system which has been solved using the feed-forward and feedback fuzzy logic controllers. An innovative scheme has been developed to track the reference trajectory in the presence of model uncertainties and disturbances. The performance comparison of the proposed controller is done with the standard backstepping controller and the simulation results show that the developed controller is best suited for the tracking trajectory problems.

The purpose of this study is to determine the application of the picture and picture learning model to improve student learning outcomes in learning English with recount text material. The research method used is classroom action research with 2 cycles of action. The results of this study indicate that the average value of the pre-cycle is 59.60 with the number of students who complete as many as 9 people and the percentage of classical completeness of 36%. In Cycle I, the average score increased to 67.20 with 18 students completing and the percentage of classical completeness was 72%. Cycle II the average value of students is 71.60 with the number of students who complete as many as 22 people and the percentage of classical completeness is 88%. After going through 2 cycles students are able to determine the composition of the recount text. The application of the picture and picture model can improve learning outcomes and students' activeness in writing recount text material.

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Providing quality education to students is the main objective of higher education institutions. The need of identifying students with weak performances has been a rising problem and most teachers have relied on calculating the average of exam grades. The main objective of our project is to predict and identify the students who might fail in semester examinations. This would prove helpful for teachers in providing additional assistance to such students. The data which was analyzed consisted of students’ transcript data that included their CGPA and grades in all courses which were taken from a university. The machine learning algorithms which were used in this research include; Naïve Bayes classifier, Neural Network, Support Vector Machine, and Decision Tree classifier. A comparative analysis has been performed on the obtained accuracy results of the algorithms used. This research shows that machine learning proves useful in predictions, but there is a lot more work to be done using this technology.

Background: The incidence of both myeloid malignancies and cardiovascular disease (CVD) increases with age, and cardiac disease is the most frequent cause of non-hematologic morbidity in patients with myelodysplastic syndrome (MDS). We aimed to study whether the presence of CVD is associated with worse outcomes in patients with myeloid malignancies. Methods: We retrospectively reviewed 295 patients diagnosed with acute myeloid leukemia (AML), MDS or MDS/myeloproliferative neoplasm (MDS/MPN). CVD included congestive heart failure (CHF), coronary artery disease (CAD), arrhythmia, peripheral vascular disease, congenital heart disease and/or cerebrovascular accident. Endpoints included overall response rate (ORR), overall survival (OS), cardiac events and death within 60 days of treatment initiation. Patient characteristics were compared between those with CVD vs. no-CVD using the Fisher's exact and t-tests. The Kaplan-Meier approach was used to estimate OS. The multivariate Cox regression model was utilized to identify predictors of OS. Results: Patient characteristics are summarized in Table 1. The study population included 92 patients (31%) with CVD and 203 (69%) without CVD, with a median age of 66 years (range 19-89) at diagnosis. Patients with CVD were older (p <0.001) and had higher rates of hypertension (p <0.001), hyperlipidemia (p<0.001), diabetes (p=0.04) and obesity (p=0.02) (Table 1). Treatment was hypomethylating agent (HMA)-based, anthracycline-based or other in 78%, 20% and 2% of CVD patients, compared to 56%, 41% and 3%, respectively, in no-CVD patients (p=0.001). Among the 228 AML patients, 20% vs. 48% of patients in the CVD vs. no-CVD groups achieved complete remission (CR), 24% vs. 22% achieved CR with incomplete count recovery (CRi), 1% vs. 3% achieved partial remission (PR), 20% vs. 17% were primary refractory (p <0.001), and 33.3% vs. 10.5% had early death. Among the 67 MDS and MDS/MPN patients (grouped as MDS), 12% vs. 6% CVD vs. no-CVD patients achieved CR, 6% vs. 0 CVD patients achieved CRi; 6% vs. 16% achieved hematologic improvement (HI); 0 vs. 2% of no-CVD patients achieved PR; and 12% vs. 4% had early death (p= 0.04). For AML patients, 1-, 2- and 3-year OS in CVD vs. no-CVD groups (Figure 1) was 46% vs. 70%; 34% vs. 53%; and 26% vs. 43%, respectively [HR 1.9, 95% CI = 1.35, 2.67]. For MDS patients it was 44% vs. 81%, 26% vs. 51%, and 26% vs. 49%, respectively [HR 2.52, 95% CI = 1.27, 5]. In a multivariable regression model accounting for age, performance status and karyotype, CVD was an independent prognostic factor for worse OS (Table 2). Smoking history (pack-years) did not impact OS. There was a trend toward fewer CVD than no-CVD patients undergoing allogeneic transplantation (17.4% vs. 27.6%, p=0.08). With a median follow-up of 15 months in both groups, 75% of AML and MDS patients with CVD were deceased, compared to 54% of no-CVD patients (p <0.0001). Cardiac adverse events were more frequent in CVD compared to no-CVD patients, including myocardial infarction/demand ischemia (13% vs 2%, p= 0.0003), arrhythmia (20% vs 10%, p= 0.01), and CHF exacerbations (27% vs 10%, p= 0.002). Conclusions: Our study demonstrated that presence of CVD is a very important predictor of survival outcomes of patients with AML and MDS. As the incidence of both CVD and myeloid malignancies increases with age, a better understanding of their association and shared complications can translate into better therapeutic options. Disclosures Emadi: Amgen: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Research Funding; Servier: Membership on an entity's Board of Directors or advisory committees; KinaRx: Other: co-founder and scientific advisor; NewLink Genetics: Research Funding. Baer:Astellas: Other: Institutional research funding; AbbVie: Other: Institutional research funding; Incyte: Other: Institutional research funding; Forma: Other: Institutional research funding; Takeda: Other: Institutional research funding; Kite: Other: Institutional research funding; Oscotec: Other: Institutional research funding.