Superior Thermoelectric Performance of Textured Ca3Co4−xO9+δ Ceramic Nanoribbons

Calcium cobaltite Ca₃Co_4−xO_9+δ (CCO) is a promising p-type thermoelectric (TE) material for high-temperature applications in air. The grains of the material exhibit strong anisotropic properties, making texturing and nanostructuring mostly favored to improve thermoelectric performance. On the one hand multitude of interfaces are needed within the bulk material to create reflecting surfaces that can lower the thermal conductivity. On the other hand, low residual porosity is needed to improve the contact between grains and raise the electrical conductivity. In this study, CCO fibers with 100% flat cross sections in a stacked, compact form are electrospun. Then the grains within the nanoribbons in the plane of the fibers are grown. Finally, the nanoribbons are electrospun into a textured ceramic that features simultaneously a high electrical conductivity of 177 S cm⁻¹ and an immensely enhanced Seebeck coefficient of 200 µV K⁻¹ at 1073 K are assembled. The power factor of 4.68 µW cm⁻¹ K⁻² at 1073 K in air surpasses all previous CCO TE performances of nanofiber ceramics by a factor of two. Given the relatively high power factor combined with low thermal conductivity, a relatively large figure-of-merit of 0.3 at 873 K in the air for the textured nanoribbon ceramic is obtained.     

Attenuation of cocaine kindling by 7-nitroindazole, an inhibitor of brain nitric oxide synthase

Recent studies suggest the involvement of the N-methyl-D-aspartate (NMDA) type of glutamate receptors and nitric oxide synthase (NOS) in the process of increased sensitivity to the convulsive effect of cocaine ("cocaine kindling"). The present study was undertaken to analyze the various behavioral stages in the development of cocaine kindling and to investigate the effect of 7-nitroindazole (7-NI), a relatively selective inhibitor of the neuronal NOS isoform, on the induction and expression of sensitization to the convulsive effect of cocaine. Also, the effect of 7-NI on responses produced by acute systemic administration of cocaine or N-methyl-D,L-aspartate (NMDLA) was investigated. Cocaine kindling was assessed on a five-stage scale following the administration of a sub-convulsant dose of the drug (35 mg/kg/day; i.p.) to Swiss Webster mice for 10 days. Stage 5 seizures developed following the 9th day of cocaine administration. Pre-treatment with 7-NI (25 mg/kg/day; i.p.) 15 min before cocaine for 10 days completely prevented the appearance of stage 4 and 5 seizures, and it significantly attenuated stage 3 behavior in response to a challenge cocaine dose (35 mg/kg) given either 24 hr or 10 days after 7-NI/cocaine administration was stopped. A single injection of 7-NI (25 mg/kg; i.p.) completely prevented the expression of cocaine kindled seizures. Whereas 7-NI had no effect on the responses elicited by acute cocaine administration (60 mg/kg; i.p.), this agent partially attenuated the effects induced by systemic administration of the NMDA receptor agonist NMDLA (250 mg/kg; i.p.). The present study indicates that 7-NI attenuates both the induction and expression of sensitization to the convulsive effect of cocaine. The findings that 7-NI attenuated cocaine kindling and partially blocked the effects produced by activation of the NMDA receptor, but not the effects induced by acute cocaine administration, support the role of the NMDA receptor and brain NOS in the development of cocaine kindling rather than in the acute effects of the drug.     

Use of center of gravity with the common vector approach in isolated word recognition

In this paper, the subspace based classifier, common vector approach (CVA), with the center of gravity (COG) method is used for isolated word recognition. Since the CVA classifier is sensitive to shifts through the time axis, endpoint detection becomes extremely important for the recognition of isolated words. The COG method eliminates the need for endpoint detection. The effects of the COG method and a classical endpoint detection algorithm on the recognition rates of isolated words are investigated. The experimental results show that the COG method yields slightly higher recognition rates than the endpoint detection method in the TI-digit database when CVA is used.     

Combinatorial Optimization and Performance Analysis of a Multi-arm Cartesian Robotic Fruit Harvester—Extensions of Graph Coloring

A mobile melon robotic harvester consisting of multiple Cartesian manipulators, each with three degrees of freedom, is being developed. In order to design an optimal robot in terms of number of arms, manipulator capabilities, and robot speed, a method of allocating the fruits to be picked by each manipulator in a way that yields the maximum harvest has been developed. Such a method has already been devised for a multi-arm robot with 2DOF each. The maximum robotic harvesting problem was shown there to be an example of the maximum k-colorable subgraph problem (MKCSP) on an interval graph. However, for manipulators with 3DOF, the additional longitudinal motion results in variable intervals. To overcome this issue, we devise a new model based on the color-dependent interval graph (CDIG). This enables the harvest by multiple robotic arms to be modeled as a modified version of the MKCSP. Based on previous research, we develop a greedy algorithm that solves the problem in polynomial time, and prove its optimality using induction. As with the multi-arm 2DOF robot, when simulated numerous times on a field of randomly distributed fruits, the algorithm yields a nearly identical percentage of fruit harvested for given robot parameters. The results of the probabilistic analysis developed for the 2DOF robot was modified to yield a formula for the expected harvest ratio of the 3DOF robot. The significance of this method is that it enables selecting the most efficient actuators, number of manipulators, and robot forward velocity for maximal robotic fruit harvest.     

Workload, control, and social support effects on serum lipids: A longitudinal study among apparently healthy employed adults.

The authors investigated the direct and interactive effects of the job demand– control–support (JDC-S) model’s components on subsequent changes in high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides (TRI) separately for male and female employees. In contrast to all 14 past studies on these relationships, the authors used a longitudinal design. Study participants (N = 1,137, 66% men) were all apparently healthy employees who underwent a routine health check at 2 points in time (Time 1 and Time 2) about 22 months apart. In these analyses, the authors controlled for the Time 1 level of each criterion and for other confounders. Most of the direct and moderating effects found did not support the predictions of the JDC-S model; this finding is in agreement with the majority of past cross-sectional studies. The authors did not find any evidence supporting the existence of a reverse causation for either of the components of the JDC-S model. The authors suggest that serum lipids may not be a physiological mechanism mediating the effects of the JDC-S model on atherosclerotic diseases. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Decision support system for multiproject scheduling of resources in blood collection programmes

A model of a repetitive multiproject management is introduced that includes uncertainties in activity durations, and the corresponding quantities of resources needed. Following the convention, a project is viewed here as a network of related activities aimed at the accomplishment of a predetermined objective at a given deadline. A project has a starting point in time and a preplanned completion point and so does each activity within it. The structural and operational characteristics described here are drawn from organization and management theories and used to describe project organization. This is demonstrated by means of field data gathered from a sample of ‘bloodmobile projects’ - an organized effort for the collection of blood donations in support of medical needs.     

Boron nitride coatings of steel and graphite produced with a low pressure r.f. plasma

Boron nitride (BN) coatings were deposited onto die steel and graphite substrates with a low pressure r.f. plasma. The coatings were deposited onto substrates at temperatures of 550–620 °C from a gas mixture of argon, NH₃ and BCl₃. X-ray diffraction and scanning electron microscopy were employed to identify and to characterize the coatings. The coatings are mostly amorphous; however, the existence of small amounts of hexagonal BN was identified. The influence on the growth rate of the deposition time and the pressure in the reactor is described.     

The kernel common vector method: a novel nonlinear subspace classifier for pattern recognition.

The common vector (CV) method is a linear subspace classifier method which allows one to discriminate between classes of data sets, such as those arising in image and word recognition. This method utilizes subspaces that represent classes during classification. Each subspace is modeled such that common features of all samples in the corresponding class are extracted. To accomplish this goal, the method eliminates features that are in the direction of the eigenvectors corresponding to the nonzero eigenvalues of the covariance matrix of each class. In this paper, we introduce a variation of the CV method, which will be referred to as the modified CV (MCV) method. Then, a novel approach is proposed to apply the MCV method in a nonlinearly mapped higher dimensional feature space. In this approach, all samples are mapped into a higher dimensional feature space using a kernel mapping function, and then, the MCV method is applied in the mapped space. Under certain conditions, each class gives rise to a unique CV, and the method guarantees a 100% recognition rate with respect to the training set data. Moreover, experiments with several test cases also show that the generalization performance of the proposed kernel method is comparable to the generalization performances of other linear subspace classifier methods as well as the kernel-based nonlinear subspace method. While both the MCV method and its kernel counterpart did not outperform the support vector machine (SVM) classifier in most of the reported experiments, the application of our proposed methods is simpler than that of the multiclass SVM classifier. In addition, it is not necessary to adjust any parameters in our approach.     

Defending the beauty of the Invariance Principle

Customary stability analysis methods for nonlinear nonautonomous systems seem to require a strict condition of uniform continuity. Although extensions of LaSalle’s Invariance Principle to nonautonomous systems that mitigate this condition have been available for a long time, they have remained surprisingly unknown or open to misinterpretations. The large scope of the Principle might have misled the prospective users and its application to Control problems has been received with amazing yet clear uneasiness. Counterexamples have been used in order to claim that the Invariance Principle cannot be applied to nonlinear nonautonomous systems. Because the original formulation of the Invariance Principle still imposes conditions that are not necessarily needed, this paper presents a new Invariance Principle that further mitigates previous conditions and thus further expands the scope of stability analysis. A brief comparative review of various alternatives to stability analysis of nonautonomous nonlinear systems and their implications is also presented in order to illustrate that thorough analysis of same examples may actually confirm the efficiency of the Invariance Principle approach when dealing with stability of nonautonomous nonlinear systems problems that may look difficult or even unsolvable otherwise.