Single mask fabrication process for movable MEMS devices

The current work reports on the realization of movable micromachining devices using self-aligned single-mask fabrication process. Only dry etching process utilizing inductively coupled plasma reactive ion etching was used to release 3D micro structures from single crystal silicon substrate. No wet etching process is required to release the structures as is the case with silicon on insulator (SOI) wafers. Also the developed process does not require an SOI substrate and accordingly dispensing with the application of a wet etching step, thus yielding uniform structures without stiction. The optimized process was applied to realize thermally actuated microgrippers. The article presents the development of the fabrication process and demonstrates the operation of the fabricated device. The optimized process provides an avenue for low cost fabrication of movable micromachining devices without the use of complicated wet etching steps typically associated with SOI substrates.     

Silhouette-based human action recognition using SAX-Shapes

Human action recognition is an important problem in Computer Vision. Although most of the existing solutions provide good accuracy results, the methods are often overly complex and computationally expensive, hindering practical applications. In this regard, we introduce the combination of time-series representation for the silhouette and Symbolic Aggregate approXimation (SAX), which we refer to as SAX-Shapes, to address the problem of human action recognition. Given an action sequence, the extracted silhouettes of an actor from every frame are transformed into time series. Each of these time series is then efficiently converted into the symbolic vector: SAX. The set of all these SAX vectors (SAX-Shape) represents the action. We propose a rotation invariant distance function to be used by a random forest algorithm to perform the human action recognition. Requiring only silhouettes of actors, the proposed method is validated on two public datasets. It has an accuracy comparable to the related works and it performs well even in varying rotation.     

A cross-layer approach for partition detection at overlay layer for structured P2P in MANETs

Due to limited radio range and mobility of nodes in mobile ad hoc networks (MANETs), the network partitioning and merging could occur frequently. When structured peer-to-peer (P2P) overlays are running over MANETs, then network partition in the physical network can also cause network partition at the overlay layer. Existing approaches for structured P2P overlay over MANETs do not detect network partition at the overlay layer. This paper proposes a cross-layer approach to detect network partition at the overlay layer for structured P2P overlay over MANETs. Simulation results show that the proposed approach is highly effective and efficient in terms of routing overhead, success ratio and false-negative ratio.     

Silicone oil emulsions: strategies to improve their stability and applications in hair care products

Silicone oils have wide range of applications in personal care products due to their unique properties of high lubricity, non‐toxicity, excessive spreading and film formation. They are usually employed in the form of emulsions due to their inert nature. Until now, different conventional emulsification techniques have been developed and applied to prepare silicone oil emulsions. The size and uniformity of emulsions showed important influence on stability of droplets, which further affect the application performance. Therefore, various strategies were developed to improve the stability as well as application performance of silicone oil emulsions. In this review, we highlight different factors influencing the stability of silicone oil emulsions and explain various strategies to overcome the stability problems. In addition, the silicone deposition on the surface of hair substrates and different approaches to increase their deposition are also discussed in detail.     

Nutritive value of maize silage in relation to dairy cow performance and milk quality

Maize silage has become the major forage component in the ration of dairy cows over the last few decades. This review provides information on the mean content and variability in chemical composition, fatty acid (FA) profile and ensiling quality of maize silages, and discusses the major factors which cause these variations. In addition, the effect of the broad range in chemical composition of maize silages on the total tract digestibility of dietary nutrients, milk production and milk composition of dairy cows is quantified and discussed. Finally, the optimum inclusion level of maize silage in the ration of dairy cows for milk production and composition is reviewed. The data showed that the nutritive value of maize silages is highly variable and that most of this variation is caused by large differences in maturity at harvest. Maize silages ensiled at a very early stage (dry matter (DM) < 250 g kg^?1) were particularly low in starch content and starch/neutral detergent fibre (NDF) ratio, and resulted in a lower DM intake (DMI), milk yield and milk protein content. The DMI, milk yield and milk protein content increased with advancing maturity, reaching an optimum level for maize silages ensiled at DM contents of 300–350 g kg^?1, and then declined slightly at further maturity beyond 350 g kg^?1. The increases in milk (R² = 0.599) and protein (R² = 0.605) yields with maturity of maize silages were positively related to the increase in starch/NDF ratio of the maize silages. On average, the inclusion of maize silage in grass silage‐based diets improved the forage DMI by 2 kg d^?1, milk yield by 1.9 kg d^?1 and milk protein content by 1.2 g kg^?1. Further comparisons showed that, in terms of milk and milk constituent yields, the optimum grass/maize silage ratio depends on the quality of both the grass and maize silages. Replacement of grass silage with maize silage in the ration, as well as an increasing maturity of the maize silages, altered the milk FA profile of the dairy cows, notably, the concentration of the cis‐unsaturated FAs, C18:3n‐3 and n‐3/n‐6 ratio decreased in milk fat. Despite variation in nutritive value, maize silage is rich in metabolizable energy and supports higher DMI and milk yield. Harvesting maize silages at a DM content between 300 and 350 g kg^?1 and feeding in combination with grass silage results in a higher milk yield of dairy cows. © 2014 Society of Chemical Industry     

Evaporated Thin Films of CdS and CdTe: Optimization for Photovoltaic Applications

Thin films of cadmium sulphide and cadmium telluride have been prepared by thermal evaporation under various conditions of deposition. These films have been characterized optically. electrically and for structure determination. The results of these characterizations along with the initial results of all thin film CdS/CdTe solar cells are presented in this paper     

On the in-plane shear response of the high bond strength concrete masonry walls

Conventional unreinforced masonry walls subject to in-plane shear loading fail due to exceedance of shear and tensile bond strengths. This paper examines whether or not the in-plane shear capacity of masonry walls would increase with the increase in the bond strengths through experimental and numerical investigations. For these investigations, shear walls were built with high bond strength polymer cement mortar; they were applied in thin layers of 2 mm thickness each. Material tests were carried out to characterise the bond and the compressive strengths of the high bond strength thin layer mortared masonry; the bond strengths were found approximately double that of the conventional 10 mm thick cement mortars. The shear walls, however, exhibited significantly lower capacity (contrasting the expectation) and displayed base course sliding mode of failure. To ascertain the validity of the experimental results, a combined surface contact—interface element micro finite element (FE) modelling technique was formulated; the results adequately reproduced the experimental datasets. The validated FE model was then applied to examine the effect of the aspect ratios and pre-compression levels to the failure modes, deformation and strength of the high bond strength shear walls and is shown that once the pre-compression exceeds 15% of the masonry compressive strength, the base sliding failure mode changes to the diagonal cracking mode with corresponding increase in in-plane shear capacity. Therefore, it is concluded that the increase the bond strength without regard to pre-compression could adversely affect the safety of the high bond strength unreinforced masonry shear walls.     

Graphene‐based ternary composites for supercapacitors

The research on electrode materials for supercapacitor application continues to evolve as the request of high‐energy storage system has increased globally due to the demand for energy consumption. Over the past decades, various types of carbon‐based materials have been employed as electrode materials for high‐performance supercapacitor application. Among them, graphene is 1 of the most widely used carbon‐based materials due to its excellent properties including high surface area and excellent conductivity. To exploit more of its interesting properties, graphene is tailored to produce graphene oxide and reduced graphene oxide to improve the dispersibility in water and easy to be incorporated with other materials to form binary composites or even ternary composites. Nowadays, ternary composites have attracted enormous interest as 2 materials (binary composites) cannot satisfy the requirement of the high‐performance supercapacitor. Thus, many approaches have been employed to fabricate ternary composites by combining 3 different types of electroactive materials for high‐performance supercapacitor application. This review focuses on the supercapacitive performance of graphene‐based ternary composites with different types of active materials, ie, conducting polymers, metal oxide, and other carbon‐based materials.     

Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review

Money laundering has been affecting the global economy for many years. Large sums of money are laundered every year, posing a threat to the global economy and its security. Money laundering encompasses illegal activities that are used to make illegally acquired funds appear legal and legitimate. This paper aims to provide a comprehensive survey of machine learning algorithms and methods applied to detect suspicious transactions. In particular, solutions of anti-money laundering typologies, link analysis, behavioural modelling, risk scoring, anomaly detection, and geographic capability have been identified and analysed. Key steps of data preparation, data transformation, and data analytics techniques have been discussed; existing machine learning algorithms and methods described in the literature have been categorised, summarised, and compared. Finally, what techniques were lacking or under-addressed in the existing research has been elaborated with the purpose of pinpointing future research directions.