Miracle-dz/UniFAR-FTUs huggingface dataset:
Pengaruh Pemberian Galaktomanan dari Daging Kelapa Lokal (Cocos nufitera L) terhadap Kadar Glucagon Like Peptide-1 (GLP-1) pada Tikus Wistar Jantan yang Hiperkolesterolemia

Hypercholesterolemia is a condition characterized by high cholesterol concentration in blood. This is a risk factor for many diseases. Glucagon Like Peptide-1 (GLP-1) is an incretin hormone that affects cholesterol and other diseases with hypercholesterolemia as a risk factor. Drug that widely used for hypercholesterolemia is simvastatin which can increase GLP-1. GLP-1 also can be increased by high fibrous foods consumption, one of which is galactomannan which can be found in coconut as much as 61%. This study aimed to determine the effect of galactomannan on GLP-1 and compared with simvastatin. Wistar rats were induced into hypercholesterolemia and than treated with galactomannan 70 mg/ 200gBW/day and 140 mg/ 200gBW/day and simvastatin 0,1 mg/ 200gBW/day . After 28 days, GLP-1 levels was measured. The result showed that GLP-1 in 140 mg/ 200gBW/day group was the highest and in 70 mg/ 200gBW/day group was the lowest. Data analysis revealed no significant difference between 140 mg/ 200gBW/day and simvastatin group. In conclusion, galactomannan 140 mg/ 200gBW/day had the same potency as simvastatin 0,1 mg/ 200gBW/day. Keywords : Hypercholesterolemia, GLP-1, Galactomannan, Simvastatin
Use of online delivery including CaptureCAM-PRO in teaching Applied Pharmacotherapeutics courses in the Pharmacy program

Conventional online delivery and more recently CaptureCAM-PRO have been used to present selected content modules in the Applied Pharmacotherapeutics courses in the Pharmacy program at the University of South Australia. This paper reports on student feedback to the trial use of CaptureCAM-PRO and is set in the context of student feedback about online delivery in the Applied Pharmacotherapeutics courses overall. Several feedback themes can be identified. Students reported that they ‘prefer face-to-face teaching’; however flexibility of place and time of learning is also highly valued. The information gathered has allowed staff to review the online resources to potentially improve overall student satisfaction.
Rang and Dale's pharmacology

A straightforward way to master a complex subject! This bestselling textbook presents all the knowledge you need to get through your pharmacology course and beyondusing a clear and accessible approach that makes the material easy and interesting to learn. Progressing logically from a molecular understanding of receptors and drug actions to the clinical uses of the most important groups of drugs, it delivers the latest information on cannabinoids and rimonabant, Cox 2 inhibitors, pharmacogenetics, biopharmaceuticals, and drug abuseas well as "lifestyle drugs" such as performance-enhancing substances, botulinum toxin, and Viagra. And now, online access via STUDENT CONSULT makes it an even more effective learning resource!
vrAIn: A Deep Learning Approach Tailoring Computing and Radio Resources in Virtualized RANs

The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic resource controller for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and signal quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map (encoded) contexts into resource control decisions. We have implemented vrAIn using an open-source LTE stack over different platforms. Our results show that vrAIn successfully derives appropriate compute and radio control actions irrespective of the platform and context: (i) it provides savings in computational capacity of up to 30% over CPU-unaware methods; (ii) it improves the probability of meeting QoS targets by 25% over static allocation policies using similar CPU resources in average; (iii) upon CPU capacity shortage, it improves throughput performance by 25% over state-of-the-art schemes; and (iv) it performs close to optimal policies resulting from an offline oracle. To the best of our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs, and the first approach to a model-free solution that does not need to assume any particular vRAN platform or system conditions.
Towards a statically scheduled parallel execution of an FRP language for embedded systems

This paper presents an automatic parallelization method for a pure FRP language designed for embedded systems. Recent advances in microprocessor technology make it possible to use multicore processors even in resource-constrained embedded systems. To fully utilize such processors with a pure FRP language, we propose a method for parallelizing signal graph updates. The method is based on a static scheduling algorithm because it should run with a simple RTOS or without the help of operating systems. To evaluate the proposed method, we implemented a parallelizing compiler for XFRP, a pure FRP language for embedded systems. In this paper, we describe the parallelizing method and show some preliminary evaluation results using Linux-based systems that demonstrate the effectiveness of the method.
Descripción clínico patológica y expresión del inmunomarcador SOX11 en pacientes con linfoma del manto

Objetivos: Describir los hallazgos clinico patologicos, la expresion del inmunomarcador SOX11 patron nuclear y la relacion entre estos, en pacientes con Linfoma de Celulas del Manto (LCM) diagnosticados en el Instituto Nacional de Cancerologia (INC) en el periodo comprendido entre 2008 y 2011. Diseno: Estudio de tipo observacional descriptivo serie de casos retrospectiva Materiales y metodos: Sesenta casos de LCM observados en el INC durante un periodo de 4 anos, fueron estudiados a partir de sus registros clinicos y de patologia. 29 muestras de tejidos ganglionares, 1 de paladar y 1 de carrillo, fueron seleccionadas para la aplicacion del inmunomarcador antiSOX11. La informacion recolectada fue descrita y analizada mediante tecnicas estadisticas convencionales y el analisis bivariado mediante la prueba x2 o la prueba exacta de Fisher. Se uso el programa estadistico Stata version 9.0. Resultados: En los 60 casos identificados y analizados, se encontro una relacion hombre a mujer de 1.7:1. La edad promedio al diagnostico fue de 63.1 anos. La mayoria de ellos (85.7%) se diagnosticaron en estadio Ann Arbor avanzado (III o IV) y 89.1% con sintomas B, aunque el estado funcional era bueno (ECOG ≤ 1) solo en el 66% de los casos. La mayor parte de los casos tenian compromiso extra nodal (93.7%) principalmente dado por infiltracion de medula osea (79%). La variante citomorfologica clasica y el patron de crecimiento difuso estuvieron presentes en el 82.7% y 73.4%, de los casos analizados, respectivamente. Solo se encontro una relacion entre el indice mitotico y el sexo (p=0.036, Prueba exacta de Fisher). En cuanto a la expresion del anticuerpo monoclonal anti SOX11 en las 31 muestras de tejidos analizadas, se encontro que el marcador fue no contributivo. Conclusiones: Las caracteristicas clinico patologicas de pacientes con LCM observados en el INC, son similares a las mundialmente descritas. La edad avanzada, un pobre estado funcional (ECOG ≥ 2), el sexo masculino y unos elevados indices de proliferacion pueden influir en el pronostico adverso de estos pacientes. La expresion del inmunomarcador antiSOX11 fue considerada como no contributiva en nuestro estudio, debido a diferentes factores o circunstancias que podrian haber alterado esta prueba. / Abstract. Objectives: To describe clinicopathological findings, the expression nuclear of SOX11 and the relationship between them, in patients with Mantle Cell Lymphoma (MCL) diagnosed at the National Cancer Institute (NCI) in the period between 2008 and 2011. Design: Observational descriptive retrospective case series. Material and methods: Sixty cases of MCL observed in the NCI for a period of 4 years, were studied from their medical records and pathology. 29 lymph node tissue samples, 1 palate tissue and 1 cheek tissue, were selected for the implementation of antiSOX11 immunolabeling. The information collected was described and analyzed by conventional statistical techniques and bivariate analysis using the x2 test or Fisher's exact test. We used Stata statistical software version…
Implications of Machine learning in Cyber Security

Cybersecurity is positioned to improve security concerns in cyberspace by utilizing potential algorithms. Cyber networks and platforms are prone to cyber attacks. The recent survey reveals that, 45% of cyber attacks has grew in the last 5 years. Identifying the threats like malware, ransomware and network breach is challenging, since the threats are hetrogenity in nature. Organizations are facing a daunting challenge of dealing with the millions of new attacks a day. Conventional security analysis fails to identify the threats due to the rapid development in new attacks and variants. Cyber Security may be measured and evaluated against multiple threats. While they are not the only method for securing, assessing the vulnerability and information leakage, automating the network security and platform play an integral role in the cyber space. Machine learning is a powerful technique to develop a system, which adapts to the data stream. In order to deal with huge volume of data more efficiently and precisely, machine learning algorithms will be used to distinguish threats and identifying the infected hosts. However, certain intrisic characteristics of machine learning algorithms makes the implementation more challenging. The agenda of the talk will be focused on the mitigation of cyber attacks with the help of machine learning approaches by automation.
Breaking parsers: mutation-based generation of programs with guaranteed syntax errors

Grammar-based test case generation has focused almost exclusively on generating syntactically correct programs (i.e., positive tests) from a context-free reference grammar but a positive test suite cannot detect when the unit under test accepts words outside the language (i.e., false positives). Here, we investigate the converse problem and describe two mutation-based approaches for generating programs with guaranteed syntax errors (i.e., negative tests). % Word mutation systematically modifies positive tests by deleting, inserting, substituting, and transposing tokens in such a way that at least one impossible token pair emerges. % Rule mutation applies such operations to the symbols of the right-hand sides of productions in such a way that each derivation that uses the mutated rule yields a word outside the language.
When Gamification Meets Sustainability: A Pervasive Approach to Foster Sustainable Mobility in Madeira

In this paper, an approach to increase the use of public transportation by tourists, exploiting pervasive technologies and a smart objects infrastructure, is presented. The idea behind our platform is to take advantage of gamification and smart technologies (such as iBeacons technologies and smartphones) to engage users and persuade them to use (more) sustainable means of transport. The Beacons, in fact, acts as a provider of authentic contents related to the story of Madeira, assisting and locating the user in his/her adventure. We carried out a case study on the Madeira island, a well-known touristic destination where several bus lines allow tourists to reach the different sight-seeing places. Moreover, a preliminary usability test and a SUS questionnaire were performed to assess the usability and facility of use of the application.
Formation Process of Traditional Service Firm's Platform Ecosystem Strategy: A Case Study of Colour Life

In the mobile Internet era, more and more traditional service firms are trying to build platform ecosystems. Most research about platform ecosystem focus on the element, structure, the relationship between members etc. However, there are little attention on the formation process of traditional service firm's strategy to build a platform ecosystem. This study seeks to fill the gap by examining a traditional service firm's platform ecosystem strategy, including both the behavior and decision rationale behind it. This paper presents a case study of Colour Life, the biggest community service platform in the world, which has grown rapidly by building a platform ecosystem. The case analysis uncovers several important lessons that can help traditional service firms to transfer to build a platform ecosystem.
Neuron-Level Fuzzy Memoization in RNNs

Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of future predictions and, therefore, they are very effective for sequence processing problems. For each application run, each recurrent layer is executed many times for processing a potentially large sequence of inputs (words, images, audio frames, etc.). In this paper, we make the observation that the output of a neuron exhibits small changes in consecutive invocations. We exploit this property to build a neuron-level fuzzy memoization scheme, which dynamically caches the output of each neuron and reuses it whenever it is predicted that the current output will be similar to a previously computed result, avoiding in this way the output computations. The main challenge in this scheme is determining whether the new neuron's output for the current input in the sequence will be similar to a recently computed result. To this end, we extend the recurrent layer with a much simpler Bitwise Neural Network (BNN), and show that the BNN and RNN outputs are highly correlated: if two BNN outputs are very similar, the corresponding outputs in the original RNN layer are likely to exhibit negligible changes. The BNN provides a low-cost and effective mechanism for deciding when fuzzy memoization can be applied with a small impact on accuracy. We evaluate our memoization scheme on top of a state-of-the-art accelerator for RNNs, for a variety of different neural networks from multiple application domains. We show that our technique avoids more than 24.2% of computations, resulting in 18.5% energy savings and 1.35x speedup on average.
Silicon micromachining and high speed gas chromatography

Through the use of silicon micromachining, micron dimensional features have been photolithographically patterned and subsequently etched and bonded to generate three-dimensional silicon microstructures which are incorporated into the world's smallest and fastest gas chromatograph (GC). The micromachined devices of the GC provide the only commercially available components which can match the enhanced performance efficiency of the microbore (i.e., <or=100 mu m) fused silica capillary columns. It is noted that the resulting high-speed micro-GC offers a multitude of features and advantages over conventional GCs. High-speed chemical separations (analyses requiring less than 120 s) can now be realized with the interfacing of silicon micromachined components and the commercially available microbore chromatographic columns. The authors describe device fabrication, specifications, and performance, as well as current and projected future applications of silicon micromachining in gas chromatography. <<ETX>>
Asphalion: trustworthy shielding against Byzantine faults

Byzantine fault-tolerant state-machine replication (BFT-SMR) is a technique for hardening systems to tolerate arbitrary faults. Although robust, BFT-SMR protocols are very costly in terms of the number of required replicas (3f+1 to tolerate f faults) and of exchanged messages. However, with "hybrid" architectures, where "normal" components trust some "special" components to provide properties in a trustworthy manner, the cost of using BFT can be dramatically reduced. Unfortunately, even though such hybridization techniques decrease the message/time/space complexity of BFT protocols, they also increase their structural complexity. Therefore, we introduce Asphalion, the first theorem prover-based framework for verifying implementations of hybrid systems and protocols. It relies on three novel languages: (1) HyLoE: a Hybrid Logic of Events to reason about hybrid fault models; (2) MoC: a Monadic Component language to implement systems as collections of interacting hybrid components; and (3) LoCK: a sound Logic of events-based Calculus of Knowledge to reason about both homogeneous and hybrid systems at a high-level of abstraction (thereby allowing reusing proofs, and capturing the high-level logic of distributed systems). In addition, Asphalion supports compositional reasoning, e.g., through mechanisms to lift properties about trusted-trustworthy components, to the level of the distributed systems they are integrated in. As a case study, we have verified crucial safety properties (e.g., agreement) of several implementations of hybrid protocols.
A Social Interaction Interface Supporting Affective Augmentation Based on Neuronal Data

In this demonstration we present a prototype for an avatar-mediated social interaction interface that supports the replication of head- and eye movement in distributed virtual environments. In addition to the retargeting of these natural behaviors, the system is capable of augmenting the interaction based on the visual presentation of affective states. We derive those states using neuronal data captured by electroencephalographic (EEG) sensing in combination with a machine learning driven classification of emotional states.
Predictive personalization of conversational customer communications with data protection by design

The goal of the research presented here is to support the development of a software platform allowing businesses to improve the way they communicate with consumers throughout the lifecycle of the customer relationship. The motivation is to make communication with consumers more personalized and relevant to each customer’s interests, more direct and interactive, while also ensuring data privacy compliance by design. Personalization is made possible by developing predictive models based on a combination of data from past purchase transactions and past exchanges of messages between the business and the customer. This paper provides an overview of the capabilities of the system and the platform architecture that makes use of predictive analytics, data mining, and machine learning technologies.
Cataract and Abnormalities of the Lens

A comprehensive treatise of lens abnormalities would require contributions from the basic sciences and the clinical sciences. It would embody the art of ophthalmology as well as the science. Moreover, it would provide the reader with not only one plan of management but would consider that acknowledged authorities may have diametrically opposed methods of management. In presenting these conflicting arguments, it would allow the reader to evaluate these recommendations in light of his or her own expertise. Dr Bellows has edited just such a book utilizing the knowledge of 47 contributors from 11 different countries. Beginning with the history of cataracts, the development, structure, composition, and metabolism of the normal and abnormal lens are explored. Abnormal lenses are discussed according to etiology, ie, developmental, toxic, metabolic, traumatic, or as a manifestation of aging. Cataract surgery in children, cataract with glaucoma, senile cataract surgery, and preoperative management are a few of
Users as Programmers: Developing a Vehicular Interface Notation for Older Users of Smart Vehicles

We present a research framework for studying the development of programming notation for control interfaces targeted at older consumers of smart connected vehicles in order to support future data collection at the early stages of development using the concept of inclusive design. By analyzing literature from perspectives on mobility/IoT technology, inclusive design and user experience, we hope to establish evidence (in-practice) in the process of developing notations for vehicular interfaces that are inclusive for older consumers. The research framework describes the relationship between the perspectives and relates that to notation development for older consumers. We see notations as a feature that embody "experience-driven' and "interaction-driven' visions with the capacity to help older users better control their driving experience and develop their cognitive ability.
Analysis of land change with parameterised multi-level class sets : exploring the semantic dimension

Een veelvoorkomend probleem in de landdynamiek is dat na verloop van tijd kennis vordert, technologie ontwikkelt en beleidsdoelstellingen veranderen. Dit betekent dat met elke kartering die wordt uitgevoerd, met een voor dat doel specifiek ontworpen classificatie, een nieuwe basis dataset wordt gemaakt in plaats van een continue gegevensreeks. Verschillen in de naamgeving van klassen, veranderingen in de definitie van de klasse, en de toevoeging of verwijdering van de klassen in de datasets over hetzelfde gebied in verschillende periodes leveren problemen op in de scheiding van de feitelijke veranderingen in de tijd van klaarblijkelijke veranderingen in de definities van categorieen. In de praktijk zullen de resultaten van verschillende onderzoeken echter moeten worden geharmoniseerd in tijd en ruimte.
Weakening WebAssembly

WebAssembly (Wasm) is a safe, portable virtual instruction set that can be hosted in a wide range of environments, such as a Web browser. It is a low-level language whose instructions are intended to compile directly to bare hardware. While the initial version of Wasm focussed on single-threaded computation, a recent proposal extends it with low-level support for multiple threads and atomic instructions for synchronised access to shared memory. To support the correct compilation of concurrent programs, it is necessary to give a suitable specification of its memory model. Wasm's language definition is based on a fully formalised specification that carefully avoids undefined behaviour. We present a substantial extension to this semantics, incorporating a relaxed memory model, along with a few proposed extensions. Wasm's memory model is unique in that its linear address space can be dynamically grown during execution, while all accesses are bounds-checked. This leads to the novel problem of specifying how observations about the size of the memory can propagate between threads. We argue that, considering desirable compilation schemes, we cannot give a sequentially consistent semantics to memory growth. We show that our model provides sequential consistency for data-race-free executions (SC-DRF). However, because Wasm is to run on the Web, we must also consider interoperability of its model with that of JavaScript. We show, by counter-example, that JavaScript's memory model is not SC-DRF, in contrast to what is claimed in its specification. We propose two axiomatic conditions that should be added to the JavaScript model to correct this difference. We also describe a prototype SMT-based litmus tool which acts as an oracle for our axiomatic model, visualising its behaviours, including memory resizing.