In Vietnam, prevalence of diabetes in adults is estimated at 5.5% and the total cases of diabetes in adults was over 3,5 million in 2017. Hypoglycaemia is one of the most-observed adverse reaction ...associated with the consumption of insulin and oral hypoglycaemia agents. The objective is to report the hypoglycaemia cases in type 2 diabetes mellitus (T2DM) patients with medication therapies attending a secondary health facility.
We conducted this retrospective research on 1,006 T2DM patients at Lam Dong General Hospital, Lam Dong province in three years between 2016 and 2018 using ICD-10 code of E11. A total of 523 eligible T2DM patients were enumerated to select hypoglycaemia cases while treated with insulin therapy or oral intake of hypoglycaemia agents. Information were extracted from hospital electronic databases, sensitive information was coded.
Among 523 eligible T2DM patients, 92.4% (n=483) patients experienced at least one symptom of hypoglycaemia. The mean age of the selected patients was 51.2}5.2. Females were dominated by males in terms of number. Frequency of hypoglycaemia symptoms was 0-1 time per week for most of patients. The main hypoglycaemia symptoms that most of patients with T2DM suffered were sweating and drowsiness (83% and 70% respectively). Seizures, headache and loss of consciousness accounted for lowest percentages.
The frequency of hypoglycaemia in T2DM patients in Lam Dong General Hospital was very high. Physician consideration and patient education are necessary in hypoglycaemia management.
A highly sensitive and flexible gas sensor that can detect a wide range of chemicals is crucial for wearable applications. However, conventional single resistance-based flexible sensors face ...challenges in maintaining chemical sensitivity under mechanical stress and can be affected by interfering gases. This study presents a versatile approach for fabricating a micropyramidal flexible ion gel sensor, which accomplishes sub-ppm sensitivity (<80 ppb) at room temperature and discrimination capability between various analytes, including toluene, isobutylene, ammonia, ethanol, and humidity. The discrimination accuracy of our flexible sensor is as high as 95.86%, enhanced by using machine learning-based algorithms. Moreover, its sensing capability remains stable with only a 2.09% change from the flat state to a 6.5 mm bending radius, further amplifying its universal usage for wearable chemical sensing. Therefore, we envision that a micropyramidal flexible ion gel sensor platform assisted by machine learning-based algorithms will provide a new strategy toward next-generation wearable sensing technology.
Quasihole excitations in fractional quantum Hall (FQH) systems exhibit fractional statistics and fractional spin, but how the spin-statistics relation emerges from many-body physics remains poorly ...understood. Here we prove a spin-statistics relation using only FQH wave functions, on both the sphere and disk geometry. In particular, the proof on the disk generalizes to all quasiholes in realistic systems, which have a finite size and could be deformed into arbitrary shapes. Different components of the quasihole spins are linked to different conformal Hilbert spaces (CHS), which are nullspaces of model Hamiltonians that host the respective FQH ground states and quasihole states. Understanding how the intrinsic spin of the quasiholes is linked to different CHS is crucial for the generalized spin-statistics relation that takes into account the effect of metric deformation. In terms of the experimental relevance, this enables us to study the effect of deformation and disorder that introduces an additional source of Berry curvature, an aspect of anyon braiding that has been largely neglected in previous literature.
Network slicing allows Mobile Network Operators to split the physical infrastructure into isolated virtual networks (slices), managed by Service Providers to accommodate customized services. The ...Service Function Chains (SFCs) belonging to a slice are usually deployed on a best-effort premise: nothing guarantees that network infrastructure resources will be sufficient to support a varying number of users, each with uncertain requirements. Taking the perspective of a network Infrastructure Provider (InP), this paper proposes a resource provisioning approach for slices, robust to a partly unknown number of users with random usage of the slice resources. The provisioning scheme aims to maximize the total earnings of the InP, while providing a probabilistic guarantee that the amount of provisioned network resources will meet the slice requirements. Moreover, the proposed provisioning approach is performed so as to limit its impact on low-priority background services, which may co-exist with slices in the infrastructure network. A Mixed Integer Linear Programming formulation of the slice resource provisioning problem is proposed. Optimal joint and suboptimal sequential solutions are proposed. These solutions are compared to a provisioning scheme that does not account for best-effort services sharing the common infrastructure network.
Applications for machine learning (ML), deep learning, and other artificial intelligence models have shown great promise in nuclear physics, including not only in classification systems but also in ...the analysis of numerical data. This study used various ML algorithms to estimate the concentrations of six rare earth elements (Sm, La, Ce, Sc, Eu, and Tb) in both archaeological and marine sediment samples. An interesting aspect of this analysis is that 80% of the 235 data points were used for training data, which included two parameters: specific activity (
A
sp
) and concentration (
ρ
) by the
k
0
-method for the purpose of model development. The remaining 20% of the dataset was held out for testing the model's accuracy. The fundamental principle of this approach is the use of regression analysis between
A
sp
and
ρ
to construct a machine learning regression model. This machine learning model was subsequently applied to estimate element concentrations based on
A
sp
values obtained from gamma spectra. The mean absolute error (MAE), root mean square error (RMSE) and the statistical measure
R
-squared (
R
2
) were employed for evaluating the accuracy of the predicted models. The random forest (RF) algorithm produces smaller MAE and RMSE values and achieves better
R
2
values compared to other algorithms. In addition, RF shows the lowest relative bias of the concentration values of elements in reference material (NIST 2711a) compared to other prediction models. The work focuses on demonstrating that machine learning models can effectively predict the concentrations of rare earth elements, even though this is a fundamental issue in NAA and one previous study has addressed this issue for one single element. The extension of the current work and potential directions for further development will be presented in the results and discussion section.
In this paper, we analyze the performance of cooperative spectrum sharing single-carrier (SC) relay systems. Taking into account the peak interference power at the primary user (PU) and the maximum ...transmit power at the secondary user (SU) network, two separate power allocation constraints are formed. For a two-hop decode-and-forward (DF) relaying protocol and two power allocation constraints, two relay selection schemes, namely, a full-channel state information (CSI)-based best relay selection (BRS) and a partial CSI-based best relay selection (PBRS), are proposed. The distributions of the end-to-end signal-to-noise ratios (e2e-SNRs) for the four cases are derived first, and then their outage probabilities and asymptotic outage probabilities are derived in closed-form. The derived asymptotic outage probabilities are utilized to see different diversity gains. Monte Carlo simulations have verified the derived diversity gains for the four different cases. We also present upper bounds on the ergodic capacities for two particular cases.
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