Facile preparation of glycine-based mesoporous graphitic carbons with embedded cobalt nanoparticles

Journal of Materials Science - A simple route has been developed for the preparation of mesoporous graphitic carbons with embedded cobalt nanoparticles just using glycine as a nitrogen source,...     

Nano and Microtechnologies for the Study of Magnetotactic Bacteria

Magnetotactic bacteria (MTB) naturally synthesize magnetic nanoparticles that are wrapped in lipid membranes. These membrane‐bound particles, which are known as magnetosomes, are characterized by their narrow size distribution, high colloidal stability, and homogenous magnetic properties. These characteristics of magnetosomes confer them with significant value as materials for biomedical and industrial applications. MTB are also a model system to study key biological questions relating to formation of bacterial organelles, metal homeostasis, biomineralization, and magnetoaerotaxis. The similar size scale of nano and microfluidic systems to MTB and ease of coupling to local magnetic fields make them especially useful to study and analyze MTB. In this Review, a summary of nano‐ and microtechnologies that are developed for purposes such as MTB sorting, genetic engineering, and motility assays is provided. The use of existing platforms that can be adapted for large‐scale MTB processing including microfluidic bioreactors is also described. As this is a relatively new field, future synergistic research directions coupling MTB, and nano‐ and microfluidics are also suggested. It is hoped that this Review could start to bridge scientific communities and jump‐start new ideas in MTB research that can be made possible with nano‐ and microfluidic technologies.     

Photon Upconversion Hydrogels for 3D Optogenetics

The ability to optically induce biological responses in 3D has been dwarfed by the physical limitations of visible light penetration to trigger photochemical processes. However, many biological systems are relatively transparent to low-energy light, which does not provide sufficient energy to induce photochemistry in 3D. To overcome this challenge, hydrogels that are capable of converting red or near-IR (NIR) light into blue light within the cell-laden 3D scaffolds are developed. The upconverted light can then excite optically active proteins in cells to trigger a photochemical response. The hydrogels operate by triplet–triplet annihilation upconversion. As proof-of-principle, it is found that the hydrogels trigger an optogenetic response by red/NIR irradiation of HeLa cells that have been engineered to express the blue-light sensitive protein Cry2olig. While it is remarkable to photoinduce the clustering of Cry2olig with blanket NIR irradiation in 3D, it is also demonstrated how the hydrogels trigger clustering within a single cell with great specificity and spatiotemporal control. In principle, these hydrogels may allow for photochemical control of cell function within 3D scaffolds, which can lead to a wealth of fundamental studies and biochemical applications.     

A systematic graph-based method for the kinematic synthesis of non-anthropomorphic wearable robots for the lower limbs

The choice of non-anthropomorphic kinematic solutions for wearable robots is motivated both by the necessity of improving the ergonomics of physical Human-Robot Interaction and by the chance of exploiting the intrinsic dynamical properties of the robotic structure so to improve its performances. Under these aspects, this new class of robotic solutions is potentially advantageous over the one of anthropomorphic robotic orthoses. However, the process of kinematic synthesis of non-anthropomorphic wearable robots can be too complex to be solved uniquely by relying on conventional synthesis methods, due to the large number of open design parameters. A systematic approach can be useful for this purpose, since it allows to obtain the complete list of independent kinematic solutions with desired properties. In this perspective, this paper presents a method, which allows to generalize the problem of kinematic synthesis of a non-anthropomorphic wearable robot for the assistance of a specified set of contiguous body segments. The methodology also includes two novel tests, specifically devised to solve the problem of enumeration of kinematic structures of wearable robots: the HR-isomorphism and the HR-degeneracy tests. This method has been implemented to derive the atlas of independent kinematic solutions suitable to be used for the kinematic design of a planar wearable robot for the lower limbs.     

The Use of Macros in Atomic Force Microscopy Image Analysis and Image Processing

The public domain image analysis program NIH Image (http://rsb.info.nih.gov/nih-image) has been modified and extended to produce Image SXM (http://reg.ssci.liv.ac.uk), a program that supports many of the image file formats used by various scanning probe microscope manufacturers. This article discusses the different approaches to software customization and describes the use of two sets of macros in the analysis and processing of atomic force microscopy images.     

User Interfaces for Complex Engineering Software: Application of Virtual Reality

IT systems for engineers are becoming more numerous and complex and will further proliferate in the future. It is infeasible to expect users to be trained to work with a plethora of systems, especially as many of them will only see occasional use. Therefore, in the near future, systems will need user interfaces that allow intuitive access and usage. This paper examines a new style of user interface based on virtual reality.     

Stream water temperature prediction based on Gaussian process regression

The prediction of stream water temperature presents an interesting topic since the water temperature has a significant ecological and economical role, such as in species distribution, fishery, industry and agriculture water exploitation. The prediction of stream water temperature is usually based on appropriate mathematical model and measurements of different atmospheric factors. In this paper, a probabilistic approach to daily mean water temperature prediction is proposed. The resulting model is a combination of two Gaussian process regression models where the first model describes the long-term component of water temperature and the other model describes the short-term variations in water temperature. The proposed approach is developed even further by modeling the short-term variations with multiple Gaussian process regression models instead with a single one. Apart from that, variable selection procedure based on mutual information is presented which is suitable for input variable selection when nonlinear models for stream water prediction are developed. The proposed approach is compared with traditional modeling approaches on the measurements obtained on the Drava river in Croatia. The presented methodology can be used as a basis of the predictive tools for water resource managers.     

Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach

This paper presents an intelligent failure prediction system for oil and gas pipeline using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach. Since the past decade, the incidents of oil and gas pipeline leaks and failures which happened around the world are becoming more frequent and have caused loss of life, properties and irreversible environmental damages. This situation is due to the lack of a full-proof method of inspection on the condition of oil and gas pipelines. Onset of corrosion and other defects are undetected which cause unplanned shutdowns and disruption of energy supplies to consumers. Existing failure prediction systems for pipeline which use non-destructive testing (NDTs) methods are accurate, but they are deployed at pre-determined intervals which can be several months apart. Hence, a full-proof and reliable inspection method is required to continuously monitor the condition of oil and gas pipeline in order to provide sufficient information and time to oil and gas operators to plan and organize shutdowns before failures occur. Permanently installed long range ultrasonic transducers (LRUTs) offer a solution to this problem by providing an inspection platform that continuously monitor critical pipeline sections. Data are acquired in real-time and processed to make decision based on the condition of the pipe. The continuous nature of the data requires an automatic decision making software rather than manual inspection by operators. Support Vector Machines (SVMs) classification approach has been increasingly used in a multitude of domains including LRUT and has shown better performance than other classification algorithms. SVM is heavily dependent on the choice of kernel functions as well as fine tuning of the kernel and soft margin parameters. Hence it is unsuitable to be used in continuous monitoring of pipeline data where constant modifications of kernels and parameters are not unrealistic. This paper proposes a novel classification technique, namely Euclidean-Support Vector Machines (Euclidean-SVM), to make a decision on the integrity of the pipeline in a continuous monitoring environment. The results show that the classification accuracy of the Euclidean-SVM approach is not dependent on the choice of the kernel function and parameters when classifying data from pipes with simulated defects. Irrespective of the kernel function and parameters chosen, classification accuracy of the Euclidean-SVM is comparable and also higher in some cases than using conventional SVM. Hence, the Euclidean-SVM approach is ideally suited for classifying data from the oil and gas pipelines which are continuously monitored using LRUT.     

200 ns pulse high-voltage supply for terahertz field emission

We present a method of generating 200 ns high-voltage (up to 40 kV) pulses operating at repetition rates of up to 100 kHz, which may be synchronized with laser pulses. These supplies are simple to make and were developed for ultrafast terahertz pulse generation from GaAs photoconductive antennas using a high-repetition-rate regeneratively amplified laser. We also show an improvement in signal-to-noise ratio over a continuous dc bias field and application of the supply to terahertz pulse generation.