Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews

Mobile app reviews by users contain a wealth of information on the issues that users are experiencing. For example, a review might contain a feature request, a bug report, and/or a privacy complaint. Developers, users and app store owners (e.g. Apple, Blackberry, Google, Microsoft) can benefit from a better understanding of these issues – developers can better understand users’ concerns, app store owners can spot anomalous apps, and users can compare similar apps to decide which ones to download or purchase. However, user reviews are not labelled, e.g. we do not know which types of issues are raised in a review. Hence, one must sift through potentially thousands of reviews with slang and abbreviations to understand the various types of issues. Moreover, the unstructured and informal nature of reviews complicates the automated labelling of such reviews. In this paper, we study the multi-labelled nature of reviews from 20 mobile apps in the Google Play Store and Apple App Store. We find that up to 30 % of the reviews raise various types of issues in a single review (e.g. a review might contain a feature request and a bug report). We then propose an approach that can automatically assign multiple labels to reviews based on the raised issues with a precision of 66 % and recall of 65 %. Finally, we apply our approach to address three proof-of-concept analytics use case scenarios: (i) we compare competing apps to assist developers and users, (ii) we provide an overview of 601,221 reviews from 12,000 apps in the Google Play Store to assist app store owners and developers and (iii) we detect anomalous apps in the Google Play Store to assist app store owners and users.     

Silicene Nanoribbons on an Insulating Thin Film

Silicene, a new 2D material has attracted intense research because of the ubiquitous use of silicon in modern technology. However, producing free-standing silicene has proved to be a huge challenge. Until now, silicene could be synthesized only on metal surfaces where it naturally forms strong interactions with the metal substrate that modify its electronic properties. Here, the authors report the first experimental evidence of silicene nanoribbons on an insulating NaCl thin film. This work represents a major breakthrough, for the study of the intrinsic properties of silicene, and by extension to other 2D materials that have so far only been grown on metal surfaces.     

Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages

The evolution of nanotechnology brings materials with novel performance and during last year’s much attempt has been established to include nanoparticles especially nano-silica (NS) into the concrete to improve performance and develop concrete with enhanced characteristics. Generally, NS is incorporated into the self-compacting concrete (SCC) aiming to positively influence the fresh, mechanical, microstructure, and durability properties of the composite. The most important mechanical property for all types of concrete composites is compressive strength. Therefore, developing reliable models for predicting the compressive strength of SCC is crucial regarding saving time, energy, and cost-effectiveness. Moreover, it gives valuable information for scheduling the construction work and provides information about the correct time for removing the formwork. In this study, three different models including the linear relationship model (LR), nonlinear model (NLR), and multi-logistic model (MLR) were proposed to predict the compressive strength of SCC mixtures made with or without NS. In this regard, a comprehensive data set that consists of 450 samples were collected and analyzed to develop the models. In the modeling process, the most important variables affecting the compressive strength such as NS content, cement content, water to binder ratio, curing time from 1 to 180 days, superplasticizer content, fine aggregate content, and coarse aggregate content were considered as input variables. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R²) were used to evaluate the performance of the proposed models. The results indicated that the MLR model performed better for forecasting the compression strength of SCC mixtures modified with NS compared to other models. The SI and OBJ values of the MLR model were 18.8% and 16.7% lower than the NLR model, indicating the superior performance of the MLR model. Moreover, the sensitivity analysis demonstrated that the curing time is the most affecting variable for forecasting the compressive strength of SCC modified with NS.

     

Fluorescent Polyimide Films Produced with Diatomite and Mesoporous Silica as Promising High-Tech Material

Journal of Inorganic and Organometallic Polymers and Materials - Due to their excellent properties, polymides (PIs) result promising as high-performance materials in different technological fields....     

Unraveling robustness of deep face anti-spoofing models against pixel attacks

Multimedia Tools and Applications - In the last few decades, deep-learning-based face verification and recognition systems have had enormous success in solving complex security problems. However,...     

Self-activated ‘green’ carbon nanoparticles for symmetric solid-state supercapacitors

Tuning of porosity and surface properties of nanoparticles especially on carbon-based nanomaterials, adopting a ‘greener’ or self-activation synthesis technique for electrical charge storage, is progressing. Herein, we report the self-activation of Teak wood sawdust in a nitrogen atmosphere at different activation temperatures to synthesize carbon nanoparticles. The activated carbon nanoparticles synthesized at 900 °C exhibits a maximum?~?360 m² g^?1 surface area with?~?2 nm average pore size diameter. Five electrolytes viz. KOH, KCl, Na₂SO₄, NaCl, and H₃PO₄ are used for studying the supercapacitance nature of the activated carbon nanoparticles in a 3-electrode configuration. A maximum specific capacitance of?~?208 F g^?1 @ 0.25 A g^?1 is obtained in 1 M KOH as the electrolyte. Two symmetric supercapacitors, aqueous (1 M KOH) and solid-state (PVA/KOH), are fabricated, and their performance difference is compiled. The solid-state symmetric supercapacitor performs in a wider voltage window (1.7 V) with a superior energy density of 27.1 Wh kg^?1 at a power density of 178 W kg^?1.

Graphical abstract

     

Wells turbine for wave energy conversion: a review

In the past 20 years, the use of wave energy systems has significantly increased, generally depending on the oscillating water column concept. Wells turbine is one of the most efficient oscillating water column technologies. This article provides an updated and a comprehensive account of the state‐of‐the‐art research on Wells turbine. Hence, it draws a roadmap for the contemporary challenges, which may hinder future reliance on such systems in the renewable energy sector. In particular, the article is concerned with the research directions and methodologies, which aim at enhancing the performance and efficiency of Wells turbine. The article also provides a thorough discussion of the use of CFD for performance modeling and design optimization of Wells turbine. It is found that a numerical model using the CFD code can be employed successfully to calculate the performance characteristics of W‐T as well as other experimental and analytical methods. The increase of research papers about CFD, especially in the last 5 years, indicates that there is a trend that considerably depends on the CFD method. Copyright © 2016 John Wiley & Sons, Ltd.     

Physico-chemical properties of multi-floral honey from the West Bank,Palestine

Honey is a nutritious product that is produced by honey bees. Its nutritional value and genuine quality is of great importance to consumers. The present study aimed at evaluating the physico-chemical properties of Palestinian multi-floral honey from the West Bank. For this purpose, a total of 33 honey samples were collected directly from honey beekeepers in different geographic regions of the West Bank and analyzed for pH, moisture, relative density, total sugars, reducing sugars, sucrose, fructose, proteins, ash, hydroxymethylfurfural, and mineral content. The mean physico-chemical contents were: pH = 3.44, moisture = 16.53%, relative density = 1.424, reducing sugars = 78.86%, sucrose = 4.10%, fructose = 38.29%, proteins = 0.33%, ash = 0.14%, and hydroxymethylfurfural = 12.32 mg/kg. Cd and Pb were below detection limits in all samples analyzed. Honey samples were rich in K, Na, Ca, Pb, and Mg with concentrations ranging between 183.86 mg/kg (K) and 22.74 mg/kg (Mg). Other trace elements (Fe, Li, B, Mn, Ba, Zn, Cu, Cr, and Ni) were found in much smaller concentrations between 5.21 mg/kg (Fe) and 0.08 mg/kg (Ni). In conclusion, the results of physico-chemical analysis of Palestinian multi-floral honey indicated purity and good quality that meet the international honey standards.