Psychotropic Polypharmacy among Elderly Patients with Mental Illness in a Malaysian University Hospital: A 10-Year Review of Hospital Databases

Psychotropic polypharmacy among elderly patients is problematic due to their multiple comorbidities. This study investigated psychotropic polypharmacy among elderly patients discharged from a Malaysian university hospital and its associated factors. Discharges of patients aged 65 years or above from the psychiatric wards from 2010 to 2019 were reviewed. Sociodemographic data, length of stay (LOS), psychiatric and other diagnoses, and psychotropic prescription upon discharge were extracted from electronic databases. Multiple logistic regression was conducted with age, gender, race, marital status, main psychiatric diagnosis, LOS, and the number of medical comorbidities as independent variables, and polypharmacy (>2 psychotropics) as the dependent variable. There were 354 discharges in this period. The mean age was 72.4 years (SD=5.9 years); 63.0% were female and 39.8% were Malays. Most were married (84.2%). The commonest category of psychiatric diagnosis was mood disorders (54.5%). A total of 76.8% of the discharges involved one or more medical comorbidities. The median number of psychotropics was two, with 38.1% prescribed three or more psychotropics. In the multiple regression model, female gender, Malay race, and being married were significantly associated with psychotropic polypharmacy. Sociocultural factors may contribute to psychotropic polypharmacy among elderly patients. Such factors require further investigations to elucidate their roles.

Addressing Climate Internal Variability on Future Intensity-Duration-Frequency Curves at Fine Scales across South Korea

Designing water infrastructure requires information about the magnitude and frequency of upcoming rainfall. A limited range of data offers just one of many realizations that occurred in the past or will occur in the future; thus, it cannot sufficiently explain climate internal variability (CIV). In this study, future relationships among rainfall intensity (RI), duration, and frequency (called the IDF curve) are established by addressing the CIV and tail characteristics with respect to frequency. Specifically, 100 ensembles of 30-year time series data were created to quantify that uncertainty. Then, the tail characteristics of future extreme rainfall events were investigated to determine whether they will remain similar to those in the present. From the RIs computed for control and future periods under two emission scenarios, following are the key results. Firstly, future RI will increase significantly for most locations, especially near the end of this century. Secondly, the spatial distributions and patterns indicate higher RI in coastal areas and lower RI for the central inland areas of South Korea, and those distributions are similar to those of the climatological mean (CM) and CIV. Thirdly, a straightforward way to reveal whether the tail characteristics of future extreme rainfall events are the same as those in the present is to inspect the slope value for the factor of change (FOC), mFOC. Fourthly, regionalizing with nearby values is very risky when investigating future changes in precipitation frequency estimates. Fifthly, the magnitude of uncertainty is large when the data length is short and gradually decreases as the data length increases for all return periods, but the uncertainty range becomes much greater as the return period becomes large. Lastly, inferring future changes in RI from the CM is feasible only for small return periods and at locations where mFOC is close to zero.

Effects of ECG Data Length on Heart Rate Variability among Young Healthy Adults

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann–Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60–100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers.

Analysis of Stock Price Data: Determinition of The Optimal Sliding-Window Length

Over the recent years, the study of time series visualization has attracted great interests. Numerous scholars spare their great efforts to analyze the time series using complex network technology with the intention to carry out information mining. While Visibility Graph and corresponding spin-off technologies are widely adopted. In this paper, we try to apply a couple of models derived from basic Visibility Graph to construct complex networks on one-dimension or multi-dimension stock price time series. As indicated by the results of intensive simulation, we can predict the optimum window length for certain time series for the network construction. This optimum window length is long enough to the majority of stock price SVG whose data length is 1-year. The optimum length is 70% of the length of stock price data series.

Estimating Phase Amplitude Coupling between Neural Oscillations Based on Permutation and Entropy

Cross-frequency phase–amplitude coupling (PAC) plays an important role in neuronal oscillations network, reflecting the interaction between the phase of low-frequency oscillation (LFO) and amplitude of the high-frequency oscillations (HFO). Thus, we applied four methods based on permutation analysis to measure PAC, including multiscale permutation mutual information (MPMI), permutation conditional mutual information (PCMI), symbolic joint entropy (SJE), and weighted-permutation mutual information (WPMI). To verify the ability of these four algorithms, a performance test including the effects of coupling strength, signal-to-noise ratios (SNRs), and data length was evaluated by using simulation data. It was shown that the performance of SJE was similar to that of other approaches when measuring PAC strength, but the computational efficiency of SJE was the highest among all these four methods. Moreover, SJE can also accurately identify the PAC frequency range under the interference of spike noise. All in all, the results demonstrate that SJE is better for evaluating PAC between neural oscillations.

Geopotential-based Multivariate MJO Index: Extending RMM-like Indices to Pre-Satellite Era

Model simulations suggest that Madden-Julian Oscillation (MJO) activity changes under the anthropogenic climate change background. However, satellite observations, which provide information of MJO convection activity, are not available before the 1970s, hindering research on the historical long-term variability of MJO. This study aims at extending the data length of MJO indices that include both MJO circulation and convection features, such as the widely used Real-Multivariate MJO (RMM) index, to the pre-satellite era. This paper introduces a new MJO index construction method, of which the outgoing longwave radiation (OLR) input is derived from upper-level geopotential, and names it as the Geopotential-based Multivariate MJO (GMM) index. The GMM index is derived over 1902–2008 and compared with the filtered version of RMM (FMM) index during 1981–2008. The GMM index is shown to (1) have the same climatological properties as the FMM index, (2) be statistically highly correlated to the FMM index, and (3) be able to indicate MJO activities and its convection features in the pre-satellite era. The overall bivariate correlation between FMM index and GMM index based on ERA-20C ranges from 0.959 to 0.968 in different phases. Evaluation results confirm the validity of the proposed MJO index construction method, which could capture MJO convection activity in the pre-satellite era and can be applied to all MJO indices that require input of OLR. This study provides an alternative way that overcomes the difficulty of historical MJO studies, and will be beneficial to our understanding of the long-term change of MJO.

Adaptive Data Length Method for GPS Signal Acquisition in Weak to Strong Fading Conditions

Satellite-based navigation is an essential part of all the technology-dependent applications, such as road transport, cell phones, the medical field, aviation or the shipping industry, etc. The performance of the navigation systems depends upon how quickly they can acquire and process the received signals for positioning solutions. However, in dense urban or indoor environments, signal acquisition can be a challenging task due to fading as a result of multipath and/or interference. This paper presents post-processing acquisition results on Global Positioning System (GPS) signals to study the relationship between data lengths used for signal acquisition and the achieved signal power using a Fast Fourier Transform (FFT)-based circular correlation method. Based on this study, the detection performance of the FFT-based method has also been analyzed by intentionally degrading the signal power levels. A new Adaptive Data length (ADL) method for acquisition has been proposed in this paper, which can be used for speeding up the acquisition process and uses adaptive data lengths rather than fixed data lengths. The ADL method works by estimating the threshold level based on the noise present in the signal and then comparing it with the signal power levels. Less difference between the threshold level and signal power level means less data length will be used while more difference means that more data length will be used for acquisition. The proposed algorithm can be used in commercially available receivers for adopting to an adaptive acquisition process for increased efficiency.

The Method for Determining Optimal Analysis Length of Vibration Data Based on Improved Multiscale Permutation Entropy

Research on damage diagnosis or safety monitoring based on structural vibration response is one of the hot issues in the engineering field. The characteristic information of the structure is obtained by analyzing the structure response data. In the process of data analysis, the choice of data length is very important, which is related to the validity of the structure monitoring results. At present, the selection of data length is usually subjective, which reduces the rigor of the structure monitoring process. Therefore, a method based on improved multiscale permutation entropy (IMPE) is proposed to determine the optimal data analytical length (ODAL) of vibration data. This method creatively applies multiscale permutation entropy (MPE) to the field of data length analysis when processing nonlinear and nonstationary signals and optimizes MPE with the help of the improved coarse-grained method to obtain IMPE. IMPE is sensitive to different data lengths, and the entropy changes with the increase of the data length and tends to be stable. Here, the stable value is defined as a standard entropy. The entropy satisfying 97% of the standard entropy is used as the effective entropy, and the corresponding data length value of the effective entropy is selected as the ODAL of the vibration data. This method is suitable for many fields, provides a reliable data analytical length for data analysis, and has good engineering practicability.