AC-Impedance, Dynamic mechanical analysis and DSC investigations on poly(methyl methacrylate)–LiTf polymer electrolyte systems

Gel polymer electrolytes synthesized from Poly(methylmethacrylate) (PMMA) as the host, ethylene carbonate (EC)/propylene carbonate (PC) as plasticizer and LiCF3SO3 (LiTf) as a salt has been prepared using solution casting technique. X-ray characterization confirms the complete dissociation of the LiTf salt in the gel polymer. The effect of LiTf salt on ionic conductivity, ionic transference number (tion) and mechanical characteristics were investigated. The ac impedance has been studied to evaluate the ionic conductivity. It was observed that the ionic conductivity of the prepared gel reached the highest value of  6.60 x 10-5 S/cm at 10.2 wt.% (1M) of LiTf salt. The temperature dependence studies showed that the samples were ionic conductors and seemed to obey the Vogel Tamman Fulcher (VTF) rule.  Dynamic mechanical  analysis (DMA) indicates that  the LiTf salt induces a remarkable increase in the storage modulus of the matrix at temperatures above the glass transition (rubbery plateau region)

Localised Skin Hyperpigmentation as a Presenting Symptom of Vitamin B12 Deficiency Complicating Chronic Atrophic Gastritis

Vitamin B12 deficiency is common in developing countries and should be suspected in patients with unexplained anaemia or neurological symptoms. Dermatological manifestations associated with this deficiency include skin hyper- or hypopigmentation, angular stomatitis and hair changes. We report a case of a 28-year-old man who presented to the Sultan Qaboos University Hospital in Muscat, Oman, in November 2013 with localised hyperpigmentation of the palmar and dorsal aspects of both hands of two months’ duration. Other symptoms included numbness of the hands, anorexia, weight loss, dizziness, fatigability and a sore mouth and tongue. There was no evidence of hypocortisolaemia and a literature search revealed a possible B12 deficiency. The patient had low serum B12 levels and megaloblastic anaemia. An intrinsic factor antibody test was negative. A gastric biopsy revealed chronic gastritis. After B12 supplementation, the patient’s symptoms resolved. Family physicians should familiarise themselves with atypical presentations of B12 deficiency. Many symptoms of this deficiency are reversible if detected and treated early

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Predicting Learning Behavior Using A Unified Framework: Looking Beyond the Clicks

Predicting learning and human behavior in general is a challenging endeavor. Machine learning driven predictive modeling have been an increasingly popular means to understand disparities in student performance. With more than a handful of approaches to predictive modeling, the current literature of predicting learning is plagued with issues such as lack of standards for predictions, lack of work to understand the lower and upper bounds to context specific predictions, and explanatory models perceived to be at odds with predictive models. To overcome these issues, I use a single predictive modeling framework across three different learning contexts that involve four key steps: using approaches that are prediction task specific while reporting all metrics; using a baseline model for comparison; using context-specific early learning to predict later learning; systematically introducing extrinsic feature sets to derive actionable insights. In my first study, I use this framework to predict learning in a working memory training context. Results suggest that later learning can be predicted from early learning behavior better when using extrinsic features. In the second study, this framework is applied in the context of a blended learning environment. Results suggest that students’ study spacing intentions, demographics, and past achievements predict later learning, while students’ click behaviors in the learning environment do not improve predictions. In the third study, this framework is applied in the context of a fully online learning environment. Results show that students’ self-reported motivational, affective, and social-emotional data are more predictive of early learning than context-agnostic click-behaviors. Overall, the current work proposes and evaluates a framework that can be used to compare results across learning contexts, between models, and approaches to make predictions that may inform future prescriptions. Specifically, the framework acts as a means to understand the relationship shared by the three key aspects to making successful predictions – ‘how well’, ‘how soon’, and ‘how much information’ – and relevance of looking beyond simple accuracies

Predicting Learning Behavior Using A Unified Framework: Looking Beyond the Clicks

Predicting learning and human behavior in general is a challenging endeavor. Machine learning driven predictive modeling have been an increasingly popular means to understand disparities in student performance. With more than a handful of approaches to predictive modeling, the current literature of predicting learning is plagued with issues such as lack of standards for predictions, lack of work to understand the lower and upper bounds to context specific predictions, and explanatory models perceived to be at odds with predictive models. To overcome these issues, I use a single predictive modeling framework across three different learning contexts that involve four key steps: using approaches that are prediction task specific while reporting all metrics; using a baseline model for comparison; using context-specific early learning to predict later learning; systematically introducing extrinsic feature sets to derive actionable insights. In my first study, I use this framework to predict learning in a working memory training context. Results suggest that later learning can be predicted from early learning behavior better when using extrinsic features. In the second study, this framework is applied in the context of a blended learning environment. Results suggest that students’ study spacing intentions, demographics, and past achievements predict later learning, while students’ click behaviors in the learning environment do not improve predictions. In the third study, this framework is applied in the context of a fully online learning environment. Results show that students’ self-reported motivational, affective, and social-emotional data are more predictive of early learning than context-agnostic click-behaviors. Overall, the current work proposes and evaluates a framework that can be used to compare results across learning contexts, between models, and approaches to make predictions that may inform future prescriptions. Specifically, the framework acts as a means to understand the relationship shared by the three key aspects to making successful predictions – ‘how well’, ‘how soon’, and ‘how much information’ – and relevance of looking beyond simple accuracies

Hyperuricemia and its association with carotid intima-media thickness in hypertensive and non hypertensive patients

AbstractCarotid intima-media thickness (C-IMT) measured noninvasively by ultrasonography is widely used as a marker for increased risk of cardiovascular disease. Also hyperuricemia (HU) is a well recognized risk factor for cardiovascular diseases. The study was designed to assess the relation between hyperuricemia and carotid intima-media thickness C-IMT in patients with and without hypertension (HTN).This study included 126 patients divided into four groups: (1) Group A, included 59 hypertensive patients with hyperuricemia. (2) Group B, included 29 hypertensive patients without hyperuricemia. (3) Group C, included 17 patients with hyperuricemia and normal blood pressure without history of hypertension. (4) Group D, included 21 control subjects.We measured carotid intima-media thickness by B-mode ultrasound in the common carotid and internal carotid artery. Routine echocardiography and uric acid level was assessed for all patients.We found that C-IMT was significantly higher in group A, B and C than group D; and it was significantly higher in group A than B. This means that C-IMT is significantly higher in all hypertensive groups than control group but it was significantly higher in hypertensive hyperuricemia (group A) than those hypertensives without hyperuricemia. We also observed a higher C-IMT in hyperuricemic non hypertensive patients than control group this means that hyperuricemia per se could be a risk factor for atherosclerosis.Uric acid levels among the whole number of patients included in the study and among the groups with hyperuricemia (group A and C) were positively correlated with the intimal-media thickness (IMT) while there were no correlations in the other two groups without hyperuricemia.We found that left ventricular hypertrophy (LVH) was significantly higher in hypertensive patients (group A&B) than normotensives (group C&D) either with or without hyperuricemia and this was evident in the hypertensive hyperuricemic patients (group A); but unexpectedly we observed the presence of LVH in the hyperuricemic non hypertensive patients (group C) which was significantly higher than the control group (group D). This means that hyperuricemia is a risk factor for development of LVH hypertrophy independently of hypertension.Therefore, higher serum uric acid levels are associated with increased C-IMT and left ventricular hypertrophy in hypertensive and even non hypertensive patients. So, early screening for hyperuricemia and lowering serum uric acid levels might be beneficial in slowing progression of atherogenesis