Prediction of postpartum depression using machine learning techniques from social media text

Early screening of mental disorders plays a crucial role in diagnosis and treatment. This study explores how data‐driven methods can leverage the information available on social media platforms to predict postpartum depression (PPD). A generalized approach is proposed where linguistic features are extracted from user‐generated textual posts on social media and categorized as general, depressive, and PPD representative using multiple machine learning techniques. We find that techniques used in our study exhibit strong predictive capabilities for PPD content. Holdout validation showed that multilayer perceptron outperformed other techniques such as support vector machine and logistic regression used in this study with 91.7% accuracy for depressive content identification and up to 86.9% accuracy for PPD content prediction. This work adopts a hierarchical approach to predict PPD. Therefore, the reported PPD accuracy represents the performance of the model to correctly classify PPD content from non‐PPD depressive content.     

Ground plane impact on quadrifilar helix antenna performance with respect to deployment heights

Quadrifilar helix antenna (QHA) has its applications in satellite communications. This paper presents the performance optimisation of input and radiation characteristics of QHA in the presence of infinite and finite metallic ground planes. For the infinite ground plane, it has been observed that input parameters such as impedance and voltage standing wave ratio (VSWR) are stable, and the antenna has broader half power beamwidth (HPBW). Smaller metallic platforms that act as finite ground planes produce better 3‐dB axial ratio beamwidth and boresight axial ratio. Deployment of QHA on smaller metallic platforms such as nanosatellites and CubeSats enhances the circularly polarised beamwidth of the antenna with improved boresight axial ratio. However, on large low earth orbit (LEO) satellites, stable input characteristics and broader HPBW have been achieved at the cost of narrow circularly polarised beamwidth and degraded boresight axial ratio.     

Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics, namely lack of prior information (p (θ)), and reduced parameters (θ). Therefore, this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction. Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion. Finally, the results are extended to evaluate detection, accuracy and false alarm rate of the model against the subject matter expert model, Support Vector Machine (SVM), k nearest neighbor (k-NN) using simulated and ground-truth dataset. The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario. Results have shown that the proposed model consistently outperformed other models.     

On increasing the sensitivity of moving average control chart using auxiliary variable

In the statistical process control, the most useful tool to monitor the manufacturing processes in the industries is the control chart. Quality practitioners always desire the charting structure that identifies sustainable changes in the monitoring processes. The sensitivity of the control chart is improved when additional correlated auxiliary information about the study variable is introduced. The regression estimate in the form of auxiliary and supporting variables presents an unbiased and efficient statistic of the mean of the process variable. In this study, auxiliary information-based moving average (AB-MA) control chart is designed for efficient monitoring of shifts in the process location parameter. The performance of the AB-MA control chart is evaluated and compared with existing charts using average run length and other run length characteristics. The comparison reveals that the AB-MA control chart outperforms the competitors in detecting the small and medium changes in the process location parameter. The application of the proposal is also provided to implement it in real situation.     

Climate change and the UN-2030 agenda: Do mitigation technologies represent a driving factor? New evidence from OECD economies

Clean Technologies and Environmental Policy - The United Nations 2030 agenda states various sustainable development goals (SDGs) to counter climate change affecting individuals' health and...     

Photocatalytic activity and radiation-attenuation ability of copper ions surface-doped dysprosium oxide

Journal of Materials Science: Materials in Electronics - Herein, Cu ions surface-doped Dy2O3 photocatalyst (Cu–Dy2O3) is synthesized utilizing hydrothermal technique to degrade methyl violet...     

An enhanced approach for the progressive mean control charts

To maintain and improve the quality of the processes, control charts play an important role for reduction of variation. To detect large shifts in the process parameters, Shewhart control charts are commonly applied but for small shifts, exponentially weighted moving averages (EWMA), cumulative sum (CUSUM), double exponentially weighted moving average (DEWMA), double CUSUM, moving average (MA), double moving average (DMA), and progressive mean (PM) control charts, are used. This study proposes double progressive mean (DPM) and optimal DPM control charts to enhance the performance of the PM chart. As the proposed DPM control charts use information sequentially, hence their performance is compared with natural competitors EWMA, CUSUM, DEWMA, double CUSUM, MA, DMA, and PM control charts. Run length and its different properties are evaluated to compare the performance of the proposed charts and counterparts. Results reveal that proposed optimal DPM outperforms the other charts. An example related to voltage on fixed capacitance level is also provided to illustrate the proposed charts.     

Development of Optical Code Division Multiple Access Based System Using Spectral Amplitude Coding via Fiber Bragg Gratings (FBGs)

Wireless Personal Communications - The multiple user’s based transmission systems are developed in the past using various multiple access techniques for fibre optic communications. Among...     

Preparation of pyrazolines from methyl 4-Oxooctadec-2(e)-Enoate and their antimicrobial activity

Reactions of methyl 4-oxooctadec-2(E)-enoate (I) with hydrazine, phenyl hydrazine and tolylhydrazine provided pyrazoline derivatives (IIa-c) in excellent yields. The structures of pyrazolines (IIa-c) were established by IR, NMR and mass spectral analysis. These pyrazoline derivatives showed antifungal and antibacterial activities.     

Interfacial performance and durability of textile reinforced concrete

The aim of this work is to understand the bonding characteristics between various yarn structures and cement matrix which was studied by yarn pull out test. In this work, the effect of accelerated ageing under alkaline environment for different types of fibers i.e. Basalt, Polypropylene, Polyester, and Jute is studied in order to comparatively evaluate their weight loss and reduction of tensile strength. SEM images are also used for studying the morphology of alkali degraded fibers. Material characterization can also be done by FTIR, XRD, ICP, Zeta potential, and UV spectrometer.