Insulinoma management in a geriatric local dog

A 14-year-old, spayed female local dog was presented to University Veterinary Hospital-University Putra Malaysia (UVH-UPM) for the complaint of uncontrollable seizures. The intracranial neoplasia or trauma as a cause of seizures was ruled out based on magnetic resonance imaging conducted prior to presentation. Full diagnostic investigation inclusive of haematology, serum biochemistry, parasitology, thoracic and abdominal radiography and abdominal ultrasonography was conducted. A diagnosis of insulinoma was made from the persistent, low fasting blood glucose levels and the inappropriately high fasting insulin level, as well as the response to treatment with prednisolon

Can empirical biplots predict high entropy oxide phases?

High entropy oxides are entropy-stabilised oxides that adopt specific disordered structures due to entropy stabilisation. They are a new class of materials that utilises the high-entropy concept first discovered in metallic alloys. They can have interesting properties due to the interactions at the electronic level and can be combined with other materials to make composite structures. The design of new meta-materials that utilise this concept to solve real-world problems may be a possibility but further understanding of how their phase stabilisation is required. In this work, biplots of the composition’s mean electronegativity are plotted against the electron-per-atom ratio of the compounds. The test dataset accuracy in the resulting biplots improves from 78% to 100% when using atomic-number-per-atom Z/a ratios as a biplot parameter. Phase stability maps were constructed using a Voronoi tessellation. This can be of use in determining stability at composite material interfaces

Flat inkjet-printed copper induction coils for magnetostrictive structural health monitoring: A comparison with bulk air coils and an anisotropic magnetoresistive sensor (AMR) sensor

Structural health monitoring (SHM) represents the next generation of carbon fiber-reinforced composite nondestructive testing. One challenge facing the application of magnetostrictive SHM is the lightweighting and ease of installation of actuators and sensors. Inkjet printing (IJP) technology is well suited to produce miniaturized electronic induction sensors that can be paired with magnetostrictive actuators to detect strain. These sensors have several advantages: their thicknesses can be minimized, the surface area can be maximized to increase sensitivity, and complex multifilar coil configurations can be fabricated. A parametric study of the efficacy of IJP induction coils with different parameters (number of coils, monofilar/bifilar, size) tested on a number of actuator-functionalized composite coupons (FeSiB ribbon and impregnated epoxy sensors) is conducted. The samples are characterized by measuring their inductance response through induced strains. Increased sensitivity and accuracy of the 10-turn monofilar IJP sensor are shown with respect to 1) 70-turn hand-wound coils, 2) a three-axis AMR sensor, and 3) other IJP actuators with <10 turns. This is attributed to increased contact area to the composite surface and the requirement of minimum sensitivity (i.e., the number of turns and surface area) for strain detection

Optimal Pricing of Internet of Things: A Machine Learning Approach

© 1983-2012 IEEE. Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition

Radiological characterisation in view of nuclear reactor decommissioning: On-site benchmarking exercise of a biological shield

Nearly all decommissioning and dismantling (D&D) projects are steered by the characterisation of the plant being dismantled. This radiological characterisation is a complex process that is updated and modified during the course of the D&D. One of the tools for carrying out this characterisation is the performance of in-situ measurements. There is a wide variety of equipment and methodologies used to carry out on-site measurements, depending on the environment in which they are to be carried out and also on the specific objectives of the measurements and the financial and personnel resources available. The extent to which measurements carried out with different types of equipment or methodologies providing comparable results can be crucial in view of the D&D strategy development and the decision-making process. This paper concerns an on-site benchmarking exercise carried out at the activated biological shield of Belgian Reactor 3 (BR3). This activity allows comparison and validation of characterisation methodologies and different equipment used as well as future interpretation of final results in terms of uncertainties and sensitivities. This paper describes the measurements and results from the analysis of this exercise. Other aspects of this exercise will be reported in separate papers. This paper provides an overview of the on-site benchmarking exercise, outlines the participating organisations and the measurement equipment used for total gamma, dose rate and gamma spectrometry measurements and finally, results obtained and their interpretations are discussed for each type of measurement as a function of detector type. Regarding the dose measurements, results obtained by using a large variety of equipment are very consistent. In view of mapping the inner surface of the biological shield the most appropriate equipment tested might be the organic scintillator, the BGO or even the ionisation chamber. In addition, for mapping this surface, the most appropriate total gamma equipment tested might be the LaBr$_{3}$(Ce), the thick organic scintillator or the BGO. These measurements can only be used as a secondary parameter in a relative way. Results for the gamma spectrometry are very consistent for all the equipment used and the main parameters to be determined

Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder

ObjectiveMental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.MethodsTo overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.ResultsThe results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%

Radion and Holographic Brane Gravity

The low energy effective theory for the Randall-Sundrum two brane system is investigated with an emphasis on the role of the non-linear radion in the brane world. The equations of motion in the bulk is solved using a low energy expansion method. This allows us, through the junction conditions, to deduce the effective equations of motion for the gravity on the brane. It is shown that the gravity on the brane world is described by a quasi-scalar-tensor theory with a specific coupling function omega(Psi) = 3 Psi / 2(1-Psi) on the positive tension brane and omega(Phi) = -3 Phi / 2(1+Phi) on the negative tension brane, where Psi and Phi are non-linear realizations of the radion on the positive and negative tension branes, respectively. In contrast to the usual scalar-tensor gravity, the quasi-scalar-tensor gravity couples with two kinds of matter, namely, the matters on both positive and negative tension branes, with different effective gravitational coupling constants. In particular, the radion disguised as the scalar fields Psi and Phi couples with the sum of the traces of the energy momentum tensor on both branes. In the course of the derivation, it has been revealed that the radion plays an essential role to convert the non-local Einstein gravity with the generalized dark radiation to the local quasi-scalar-tensor gravity. For completeness, we also derive the effective action for our theory by substituting the bulk solution into the original action. It is also shown that the quasi-scalar-tensor gravity works as holograms at the low energy in the sense that the bulk geometry can be reconstructed from the solution of the quasi-scalar-tensor gravity.Comment: Revtex4, 18 pages, revised version, conclusions unchanged, references adde

Invisibility and indistinguishability in structural damage tomography

Structural damage tomography (SDT) uses full-field or distributed measurements collected from sensors or self-sensing materials to reconstruct quantitative images of potential damage in structures, such as civil structures, automobiles, aircraft, etc. In approximately the past ten years, SDT has increased in popularity due to significant gains in computing power, improvements in sensor quality, and increases in measurement device sensitivity. Nonetheless, from a mathematical standpoint, SDT remains challenging because the reconstruction problems are usually nonlinear and ill-posed. Inasmuch, the ability to reliably reconstruct or detect damage using SDT is seldom guaranteed due to factors such as noise, modeling errors, low sensor quality, and more. As such, damage processes may be rendered invisible due to data indistinguishability. In this paper we identify and address key physical, mathematical, and practical factors that may result in invisible structural damage. Demonstrations of damage invisibility and data indistinguishability in SDT are provided using experimental data generated from a damaged reinforced concrete beam

Performance limits of the three MEMS inertial energy generator transduction types

Accepted versio