Detection of microalbuminuria in non-insulin dependent diabetes mellitus (NIDDM) patients without overt proteinuria by a semiquantitative albumin-creatinine urine strips

AbstractMicroalbuminuria is the hallmark of the reversible stage of incipient diabetic nephropathy. A cost- effective and convenient bedside screening test is essential to detect this phase. We used Clinitek 50® which is a semiquantitative strip test to check spot urine sample from 81 patients with albustix one plus or less. The incidence of Clinitek 50® microalbuminuria was 17%, 18.2% and 75% in 47, 22 and 12 patients with albustix negative, trace or one plus respectively. Nineteen and 13 of the 21 Clinitek 50® positive patients were checked for spot urine DCA 2000® and two 12-hour urine collection for immunoassay respectively. Around 60% of these samples fell into the microalbuminuria range and 40% into the overt albuminuria range by either technique. There was no false positive of Clinitek 50®. The lowest range of microalbuminuria detected by Clinitek 50® was 27 μg/minute (38 mg/day). We concluded that Clinitek 50® is a useful screening test as it is nonexpensive, easily operated and has a sensitivity close to the lower range of microalbuminuria

Applicability of variability response function for geotechnical risk assessment

This paper explores the use of variability response function (VRF) for risk assessment of geotechnical system under spatially variable soil properties, where the properties exhibit a range of possible autocorrelation characteristics. VRF only requires a single set of analysis, but traditional Monte Carlo simulation (MCS) requires separate sets of analyses. VRF can be estimated through a simple regression procedure, which does not require random field simulation. In a footing displacement analysis, the reliability assessments by VRF match well with those of MCS, when the soil property has relatively low variance.The work presented this paper is financially supported by the Research Grants Council of the Hong Kong Special Administrative Region Government (Project No. 15212418)

Reconfigurable Architecture for Noise Cancellation in Acoustic Environment Using Single Multiply Accumulate Adaline Filter

The creation of multiple applications with a higher level of complexity has been made possible by the usage of artificial neural networks (ANNs). In this research, an efficient flexible finite impulse response (FIR) filter structure called ADALINE (adaptive linear element) that makes use of a MAC (multiply accumulate) core is proposed. The least mean square (LMS) and recursive least square (RLS) algorithms are the most often used methods for maximizing filter coefficients. Despite outperforming the LMS, the RLS approach has not been favored for real-time applications due to its higher design arithmetic complexity. To achieve less computation, the fundamental filter has utilized an LMS-based tapping delay line filter, which is practically a workable option for an adaptive filtering algorithm. To discover the undiscovered system, the adjustable coefficient filters have been developed in the suggested work utilizing an optimal LMS approach. The 10-tap filter being considered here has been analyzed and synthesized utilizing field programmable gate array (FPGA) devices and programming in hardware description language. In terms of how well the resources were used, the placement and postrouting design performed well. If the implemented filter architecture is compared with the existing filter architecture, it reveals a 25% decrease in resources from the existing one and an increase in clock frequency of roughly 20%

A metaheuristic-based framework for index tracking with practical constraints

Recently, numerous investors have shifted from active strategies to passive strategies because the passive strategy approach affords stable returns over the long term. Index tracking is a popular passive strategy. Over the preceding year, most researchers handled this problem via a two-step procedure. However, such a method is a suboptimal global-local optimization technique that frequently results in uncertainty and poor performance. This paper introduces a framework to address the comprehensive index tracking problem (IPT) with a joint approach based on metaheuristics. The purpose of this approach is to globally optimize this problem, where optimization is measured by the tracking error and excess return. Sparsity, weights, assets under management, transaction fees, the full share restriction, and investment risk diversification are considered in this problem. However, these restrictions increase the complexity of the problem and make it a nondeterministic polynomial-time-hard problem. Metaheuristics compose the principal process of the proposed framework, as they balance a desirable tradeoff between the computational resource utilization and the quality of the obtained solution. This framework enables the constructed model to fit future data and facilitates the application of various metaheuristics. Competitive results are achieved by the proposed metaheuristic-based framework in the presented simulation

An intelligent system for trading signal of cryptocurrency based on market tweets sentiments

The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visu�alization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals

A BERT Framework to Sentiment Analysis of Tweets

Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant

Robust capped norm dual hyper-graph regularized non-negative matrix tri-factorization

Non-negative matrix factorization (NMF) has been widely used in machine learning and data mining fields. As an extension of NMF, non-negative matrix tri-factorization (NMTF) provides more degrees of freedom than NMF. However, standard NMTF algorithm utilizes Frobenius norm to calculate residual error, which can be dramatically affected by noise and outliers. Moreover, the hidden geometric information in feature manifold and sample manifold is rarely learned. Hence, a novel robust capped norm dual hyper-graph regularized non-negative matrix tri-factorization (RCHNMTF) is proposed. First, a robust capped norm is adopted to handle extreme outliers. Second, dual hyper-graph regularization is considered to exploit intrinsic geometric information in feature manifold and sample manifold. Third, orthogonality constraints are added to learn unique data presentation and improve clustering performance. The experiments on seven datasets testify the robustness and superiority of RCHNMTF

A portfolio recommendation system based on machine learning and big data analytics

This research paper introduces a portfolio recommendation system that utilizes machine learning and big data analytics to offer a profitable stock portfolio and stock analytics via a web ap- plication. The system’s effectiveness was evaluated through backtesting and user evaluation studies, which consisted of two parts: user evaluation and performance evaluation. The findings indicate that the development of a machine learning-based portfolio recommendation system and big data analytics can effectively meet the expectations of the majority of users and enhance users’ financial knowledge. This study contributes to the growing body of research on utilizing advanced technologies for portfolio recommendation and highlights the potential of machine learning and big data analytics in the financial industry