Preparation and adsorbing sodium borohydride of porous hollow capsules

AbstractSodium borohydride, a solid material, has great attractive for its hydrogen storage and preparation properties. The solubility of sodium borohydride in water is up to 35% at room temperature. It can react with water for generating hydrogen. The merits of this reaction include high purity, mild reaction conditions and the high theoretical density of hydrogen generation. Complete hydrolysis of sodium borohydride can produce hydrogen and sodium metaborate, which can be recovered by advanced technology for sodium borohydride recycling. Porous hollow capsules containing nickel boride were prepared and used as storage and reaction space for sodium borohydride. The influences of the concentration of polymer solution, the ratio of the coagulation bath, the concentration and temperature on the porous structure of hollow capsule were investigated. The adsorption of porous hollow capsule was influenced and optimized by soaking time, adsorption conditions, the drying temperature and time. The best conditions of preparation of porous hollow capsule are: 15 wt% PVDF into capsule system configuration, with adding 15wt % attapulgite or 5 wt% PVP. The adsorption amount is up to 36%. The preparation method of porous hollow capsule is simple and easy to operate, low energy consumption, simple process only including dissolving, mixing, molding, adsorption and drying. The structure of porous hollow is stable and easy to storage and use. Hydrogen can be simple to release when mixed the adsorbed capsules with water

Overview of the Canadian pediatric end-stage renal disease database

<p>Abstract</p> <p>Background</p> <p>Performing clinical research among pediatric end-stage renal disease patients is challenging. Barriers to successful initiation and completion of clinical research projects include small sample sizes and resultant limited statistical power and lack of longitudinal follow-up for hard clinical end-points in most single center studies.</p> <p>Description</p> <p>Existing longitudinal organ failure disease registry and administrative health datasets available within a universal access health care system can be used to study outcomes of end-stage renal disease among pediatric patients in Canada. To construct the Canadian Pediatric End-Stage Renal Disease database, registry data were linked to administrative health data through deterministic linkage techniques creating a research database which consists of socio-demographic variables, clinical variables, all-cause hospitalizations, and relevant outcomes (death and renal allograft loss) for this patient population. The research database also allows study of major cardiovascular events using previously validated administrative data definitions.</p> <p>Conclusion</p> <p>Organ failure registry linked to health administrative data can be a powerful tool to perform longitudinal studies in pediatric end-stage renal disease patients. The rich clinical and demographic information found in this database will facilitate study of important medical and non-medical risk factors for death, graft loss and cardiovascular disease among pediatric end-stage renal disease patients.</p

Blind Identification and Equalization of FIR MIMO Channels by BIDS

This paper presents an algorithm of blind identification and equalization of finite-impulse-response and multipleinput and multiple-output (FIR MIMO) channels driven by colored signals. This algorithm is an improved realization of a concept referred to as blind identification via decorrelating subchannels (BIDS). This BIDS algorithm first constructs a set of decorrelators which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators, and finally recovers the input signals using the estimated channel matrix. This BIDS algorithm in general assumes that the channel matrix is irreducible and the input signals are mutually uncorrelated and of sufficiently diverse power spectra. However, for channel matrix identification, this BIDS algorithm only requires the channel matrix to be nonsingular (i.e., full rank almost everywhere as opposed to everywhere) and column-wise coprime. Such a channel matrix may have zeros and be of non-minimum phase

Blind identification of FIR MIMO channels by decorrelating subchannels

We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel to be identifiable up to a scaling and permutation using the second-order statistics of the channel output. This condition is that the channel matrix is irreducible (but not necessarily column-reduced), and the input signals are mutually uncorrelated and of distinct power spectra. We also show that this condition is necessary in the sense that no single part of the condition can be further weakened without another part being strengthened. While the above condition is a strong result that sets a fundamental limit of blind identification, there does not yet exist a working algorithm under that condition. In the second part of this paper, we show that a method called blind identification via decorrelating subchannels (BIDS) can uniquely identify an FIR MIMO channel if a) the channel matrix is nonsingular (almost everywhere) and column-wise coprime and b) the input signals are mutually uncorrelated and of sufficiently diverse power spectra. The BIDS method requires a weaker condition on the channel matrix than that required by most existing methods for the same problem

Classification Method of Teaching Resources Based on Improved KNN Algorithm

In order to effectively utilize the network teaching resources, a teaching resource classification method based on the improved KNN (K-Nearest Neighbor) algorithm was proposed. Taking the text class primary and secondary school teaching resources as the research object, combined with the domain characteristics, the KNN algorithm was improved. By measuring the sample space density, the text of the high-density area was found. Different clipping methods were proposed for both intra-class and inter-class regions. The problem of cropping in the space of multiple class boundaries was considered. Results showed that the method ensured uniform distribution of samples and reduced the time of classification. Therefore, under the Weka platform, the improved KNN algorithm is effective

Blind identification of FIR MIMO channels by decorrelating subchannels

We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel to be identifiable up to a scaling and permutation using the second-order statistics of the channel output. This condition is that the channel matrix is irreducible (but not necessarily column-reduced), and the input signals are mutually uncorrelated and of distinct power spectra. We also show that this condition is necessary in the sense that no single part of the condition can be further weakened without another part being strengthened. While the above condition is a strong result that sets a fundamental limit of blind identification, there does not yet exist a working algorithm under that condition. In the second part of this paper, we show that a method called blind identification via decorrelating subchannels (BIDS) can uniquely identify an FIR MIMO channel if a) the channel matrix is nonsingular (almost everywhere) and column-wise coprime and b) the input signals are mutually uncorrelated and of sufficiently diverse power spectra. The BIDS method requires a weaker condition on the channel matrix than that required by most existing methods for the same problem.<br /