This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model.
Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.
The timing of ABG procedure in a cleft patient is crucial to provide room and bony
support for the eruption of canine. However, there seems to be a delay in execution of this
procedure in certain centres. Material and Methods: Sample consists of records of cleft patients
treated from 2000-2016. The date and age for commencement of active orthodontic treatment,
date referred for ABG and date ABG done were retrieved. The centres that conducted these
surgeries identified. (Copied from article).
Waiting is a common phenomenon in the doctor's waiting room. The purpose of this audit is to assess patient waiting time and doctor consultation time in a primary healthcare clinic and to formulate strategies for improvement. This audit was conducted at a primary care clinic for 4 weeks using the universal sampling method. All patients who attended the clinic during this period was included in the study except for those who required more time to be seen such as those who were critically ill, aggressive or those who came for repeat medication or procedures only without needing to see the doctor. The time of arrival was captured using the queue management system (QMS) and then the patient was given a timing chit which had to be manually filled by the staff at every station. The waiting time for registration, pre-consultation, consultation, appointment, payment and pharmacy were recorded as well as consultation time. The data were entered into the statistical software SPSS version 17 for analysis. version 17. Results showed that more than half of the patients were registered within 15 minutes (53%) and the average total waiting time from registration to seeing a doctor was 41 minutes. Ninety-nine percentage of patients waited less than 30 minutes to get their medication. The average consultation time was 18.21 minutes. The problems identified in this audit were addressed and strategies formulated to improve the waiting and consultation time were carried out including increasing the number of staff at the registration counter, enforcing the staggered appointment system for follow-up patients and improving the queuing system for walk-in patients.
'Causal' direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of 'causal' direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.
Newtonian and special-relativistic predictions, based on the same model parameters and initial conditions for the trajectory of a low-speed scattering system are compared. When the scattering is chaotic, the two predictions for the trajectory can rapidly diverge completely, not only quantitatively but also qualitatively, due to an exponentially growing separation taking place in the scattering region. In contrast, when the scattering is nonchaotic, the breakdown of agreement between predictions takes a very long time, since the difference between the predictions grows only linearly. More importantly, in the case of low-speed chaotic scattering, the rapid loss of agreement between the Newtonian and special-relativistic trajectory predictions implies that special-relativistic mechanics must be used, instead of the standard practice of using Newtonian mechanics, to correctly describe the scattering dynamics.
Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerning moving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents the coherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.
Schizophrenia is a common and devastating illness. Patients with schizophrenia may develop many disabilities both due to the disease process as well as due to side effects of the medication used. There are many advances in the treatment of schizophrenia, which can effectively reduce many of these disabilities. Treatment of schizophrenia is a primary health care responsibility and thus all health care personnel need to equip themselves with the latest knowledge on management issues. This article outlines the current management issues in schizophrenia.
Temporary cardiac pacing is an established therapeutic modality in the treatment of heart block and bradyarrythmias due to various causes like drugs and metabolic causes and prior to permanent pacing. Temporary pacing using fluoroscopy is best but the image intensifier is not available to all medical units. The method of temporary pacing at the bedside is described in detail.
Mobile Augmented Reality (MAR) requires a descriptor that is robust to changes in viewing conditions in real time application. Many different descriptors had been proposed in the literature for example floating-point descriptors (SIFT and SURF) and binary descriptors (BRIEF, ORB, BRISK and FREAK). According to literature, floating-point descriptors are not suitable for real-time application because its operating speed does not satisfy real-time constraints. Binary descriptors have been developed with compact sizes and lower computation requirements. However, it is unclear which binary descriptors are more appropriate for MAR. Hence, a distinctive and efficient accuracy measurement of four state-of-the-art binary descriptors, namely, BRIEF, ORB, BRISK and FREAK were performed using the Mikolajczyk dataset and ALOI dataset to identify the most appropriate descriptor for MAR in terms of computation time and robustness to brightness, scale and rotation changes. The obtained results showed that FREAK is the most appropriate descriptor for MAR application as it able to produce an application that are efficient (shortest computation time) and robust towards scale, rotation and brightness changes.
Existing structural components require strengthening after a certain period of time due to increases in service loads, errors in design, mechanical damage, and the need to extend the service period. Externally-bonded reinforcement (EBR) and near-surface mounted (NSM) reinforcement are two preferred strengthening approach. This paper presents a NSM technique incorporating NSM composites, namely steel and carbon fiber-reinforced polymer (CFRP) bars, as reinforcement. Experimental and analytical studies carried out to explore the performance of reinforced concrete (RC) members strengthened with the NSM composites. Analytical models were developed in predicting the maximum crack spacing and width, concrete cover separation failure loads, and deflection. A four-point bending test was applied on beams strengthened with different types and ratios of NSM reinforcement. The failure characteristics, yield, and ultimate capacities, deflection, strain, and cracking behavior of the beams were evaluated based on the experimental output. The test results indicate an increase in the cracking load of 69% and an increase in the ultimate load of 92% compared with the control beam. The predicted result from the analytical model shows good agreement with the experimental result, which ensures the competent implementation of the present NSM-steel and CFRP technique.
Black snail, Faunus ater is an abundant species in Malaysia yet not many research have focused on its physiological and biological activities. This research aimed to assess the condition index (CI) and reproductive status based on the gonadosomatic index (GSI) for short-term duration. Samples were collected monthly from Merchang Lagoon, from November 2018 to January 2019. Four different types of condition indices equation were applied in this study and the results revealed that there were significant differences between four equations for measuring the CI (P=0.000). However, the result for the GSI shows no significant difference between three month of sampling (P>0.05). CI based on fresh weight measurement (Fww/Tww x 100) and dry weight measurement (Fdw/Fww x 100) reached its peak when GSI decreased. The rest of the trend for CI fluctuated and CI was not affected by GSI. Overall, this study concluded that, there were trends observed in CI and GSI for the black snails. However, it is suggested that longer term observation in future research is needed have a better understanding on the black snails.
This study attempts to trace changes in the wet spells over Peninsular Malaysia based on the daily rainfall data from 32 selected rainfall stations which include four sub-regions; northwest, west, south and east, for the period of 1975 to 2004. Six wet spells indices comprising of the main characteristics (maximum, mean, standard deviation), the persistency of two consecutive wet days and the frequency of the short and long duration of wet spells will be used to identify whether or not these indices increase or decrease over Peninsular Malaysia during the monsoon seasons. The study indicates that the eastern areas of the peninsula could be considered as the wettest areas since almost all the indices of wet spells over these areas are higher than over the other regions during the northeast monsoon (NE). The Mann-Kendall (MK) trend test revealed that almost all of the stations located in the eastern areas of the peninsula exhibited a positive trend in the mean, variability and persistency of wet spells indices during the NE monsoon, while a negative trend was observed during the southwest monsoon (SW) in these areas. Moreover, these indices showed a positive trend, and at the same time a decreasing trend was observed in the frequency of the long wet spells in most stations located over the west coast of Peninsular Malaysia during the SW monsoon for the period of 1975 to 2004.
This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.
Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
Human evolution is carried out by two genetic systems based on DNA and another based on the transmission of information through the functions of the nervous system. In computational neuroscience, mathematical neural models are used to describe the biological function of the brain. Discrete-time neural models have received particular attention due to their simple analysis and low computational costs. From the concept of neuroscience, discrete fractional order neuron models incorporate the memory in a dynamic model. This paper introduces the fractional order discrete Rulkov neuron map. The presented model is analyzed dynamically and also in terms of synchronization ability. First, the Rulkov neuron map is examined in terms of phase plane, bifurcation diagram, and Lyapunov exponent. The biological behaviors of the Rulkov neuron map, such as silence, bursting, and chaotic firing, also exist in its discrete fractional-order version. The bifurcation diagrams of the proposed model are investigated under the effect of the neuron model's parameters and the fractional order. The stability regions of the system are theoretically and numerically obtained, and it is shown that increasing the order of the fractional order decreases the stable areas. Finally, the synchronization behavior of two fractional-order models is investigated. The results represent that the fractional-order systems cannot reach complete synchronization.
We aimed to investigate the association between flexor tendon degeneration and outcome of open trigger digit release. We recruited 162 trigger digits (136 patients) who had open trigger digit release from February 2017 to March 2019. Intraoperatively, six features of tendon degenerations were identified: irregular tendon surface, tendon fraying, intertendinous tear, synovial thickening, hyperaemia of sheath and tendon dryness. Longer duration of preoperative symptoms was associated with worsening tendon surface irregularity and fraying; increased number of steroid injections was associated with worsening tendon surface irregularity and dryness; higher DASH score was associated with severe tendon fraying, dryness and intertendinous tear; limited proximal interphalangeal joint (PIPJ) motion was associated with severe tendon dryness. At 1-month post-surgery, DASH score remained high in severe intertendinous tear group while PIPJ motion remained limited in severe tendon dryness group. In conclusion, the severity of various flexor tendon degenerations influenced the outcome of open trigger digit release at 1-month but did not affect the outcome at 3- and 6-months post-surgery.