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The purpose of the study was to develop a powder shampoo with antioxidant attributes.

Dry shampoo compositions were formulated containing alpha olefin sulfonate (AOS), sodium cocoyl isethionate (SCI), microcrystalline cellulose, mannitol, carboxymethyl cellulose, maltodextrin and sodium benzoate with or without extract of Cinnamomum zeylanicum bark. Cinnamon extract was chosen for this study owing to its ubiquitously known antioxidant attributes. The formulations were tested for detergency action and antioxidant potential in vitro.

Cinnamomum zeylanicum extract exhibited noticeable antioxidant activity in vitro. The authors observed that addition of the bark extract to the shampoo formulation was associated with remarkable increase in total phenolic content, total antioxidant activity and radical scavenging activity without any effect on detergency action.

This preliminary study provides a powder shampoo formulation which exhibits antioxidant attributes as a result of incorporation of cinnamon bark extract. Clinical efficacy of the formulation remains to be tested.

Owing to the powder format of the shampoo, the formulation can be manufactured with ease and economically. Functionalizing the formulation with enhancement of antioxidant activity by incorporation of cinnamon bark extract may be associated with beneficial clinical outcomes, which remains to be tested.

The proposed formulation may be stored and sold in eco-friendly packing material, thus could pave the way for reducing the burden of plastic consumption by the shampoo industry.

The present work demonstrates that incorporation of cinnamon bark extract to a powder shampoo formulation, containing AOS and SCI as principle surfactants, significantly enhances its antioxidant attributes.

Within the framework of the valorization of natural resources, a characterization of the biochemical composition of the edible parts of Adansonia Digitata is applied. The antibacterial effect against bacteria is also realized and compared to some synthetic antibiotics.

The biochemical characterization is carried out according to the norms of the French Association of Normalization, methods of Association of Official Analytical Chemists (AOAC International) and gas chromatography (GC). The antibacterial activity is tested by disk diffusion on a solid medium. Parametric tests are used to compare the differences between groups and heat maps to show the expression of the mean inhibitions according to the studied parameters. Multivariate logistic modeling is applied to study the effect of extracts and antibiotics on bacteria.

Biochemical characterization showed a variable importance of proteins, fibers and total sugars, with the presence of highly desired fatty acids such as palmitic, oleic, stearic, linoleic and a-linolenic acids. This gives the tested parts important energy values, especially in the seeds very rich in fatty acids. Methanol proved to be a better extraction solvent than dichloromethane. Antibacterial activity showed that pulp and leaves extracted with methanol had quite similar inhibitory activities against Enterococcus faecalis ATCC29212 and that this effect was better than some antibiotics. Multivariate analysis showed that the leaves had a similar effect to antibiotics, and a significant effect against Staphylococcus aureus ATCC29213.

This important activity and the attractive nutritional value of this plant could justify its extensive use in the traditional pharmacopoeia.

Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details. Therefore, in this paper, the basics of ICA are provided to show how it works to serve as a comprehensive source for researchers who are interested in this field. This paper starts by introducing the definition and underlying principles of ICA. Additionally, different numerical examples in a step-by-step approach are demonstrated to explain the preprocessing steps of ICA and the mixing and unmixing processes in ICA. Moreover, different ICA algorithms, challenges, and applications are presented.

This study aims to evaluate the effect of Piper betle leaf extract towards the acute-lethal toxicity, LC50 of red Nile tilapia juveniles (Oreochromis niloticus).

Ten red Nile tilapia juveniles per tank (in triplicate) were used as an experimental fish for the LC50 bioassay. Five different concentrations of P. betle extract; 80 ppm, 90 ppm, 100 ppm, 110 ppm and 120 ppm, were tested on the red Nile tilapia juveniles and one tank was acting as a control. The progress of the LC50 and lethal time of fish mortality were observed and recorded within the random interval of 96 h. The value for LC50 was determined as 100 ppm of P. betle leaf extract. Higher number of fish mortalities was observed when concentration higher than 100 ppm was tested on to the red Nile tilapia juveniles.

Data obtained shows that the P. betle concentration of 120 ppm accelerated the fish mortality period.

However, adaption of P. betle extract occurred after 50 h, as there was no fish mortality observed within the time.

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

This paper aims to extract the comment targets in Chinese online shopping platform.

The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment.

The extracting comment target method the authors proposed in this paper is effective.

First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information.

Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients.

The extracting comment target method the authors proposed in this paper is effective.

Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.

In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.

The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.

Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.

This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.

The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.

Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

Purpose – The aim of this study is to determine the differences in the antimicrobial activity of katuk leaf (Sauropus androgynus (L.) Merr) against Escherichia coli.

Design/Methodology/Approach – The method used in this study was experimental posttest using a control group design. Analysis of the effect of katuk leaf was performed in the dilution method with 20%, 40%, 60%, 80%, and 100% concentration. The data were analyzed using one-way ANOVA test (α = 0.05) and was then tested using the least significant difference (LSD) test.

Findings – Bacterial colony counting that used total plant count found the average of E. coli amount at 20% of concentration (526.820 CFU/ml), 40% of concentration (449.380 CFU/ml), concentration of 60% (255.710 CFU/ml), concentration of 80% (194.110 CFU/ml), and at concentration 100% (168.600 CFU/ml). This study concluded that the katuk leaf extract at 20%, 40%, 60%, 80%, and 100% of concentration had antimicroba effect with significant influence. The 100% of concentration had the most significant effect compared with the other concentrations.

Research Limitations/Implications – Katuk leaf could be used as one of the alternative herbal choices that has a compound antimicrobial effect.

Originality/Value – This study increases the theoretical understanding of the difference of antimicrobial effectivity of katuk leaf extract (S. Androgynus (L.) Merr.) concentration against E. coli

Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method.

To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method.

Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle.

This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.