On the reliability of electrostatic NEMS/MEMS devices: Review of present knowledge on the dielectric charging and stiction failure mechanisms and novel characterization methodologies

This paper reviews the state of the art knowledge related to critical failure mechanisms in electrostatic micro- and nano-electromechanical systems (MEMS and NEMS) which are the dielectric charging and stiction. It describes also the recent employed nanoscale characterization techniques for these phenomena based on Kelvin probe force microscopy (KPFM) and force–distance curve measurements. The influence of relative humidity and dielectric deposition conditions on the charging/discharging processes is discussed. Moreover, different stiction mechanisms induced by electrostatic force and/or meniscus formation are analyzed. Finally, novel characterization methods are presented and used to correlate between the results from MEMS devices and metal–insulator–metal (MIM) capacitors. These methods are employed in view of application in electrostatic capacitive MEMS switches and could be easily extended to explore other NEMS/MEMS devices. The study provides an accurate understanding of the charging and stiction related failure mechanisms, presents guidelines for a proper packaging environment, and reveals precise explanations for the literature reported device level measurements of electrostatic MEMS devices.     

Physiological and Nutraceutical Perspectives of Fructan

Functional and nutraceutical foods have captured the global market owing to trends and perceptions of consumers on the natural products and diet-health linkages. Health promoting potential of such foods has been attributed to the presence of essential bioactive moieties. Wheat, being staple food in many parts of the world, gained substantial attention of researchers particularly for the extraction of various functional components. Among these, fructan oligosaccharides in nature bestow quality of baked products and provide protection against various physiological disorders. Addition of fructan in various baked products enhances softness and color, especially in bread, and also imparts textural improvement. Moreover, fructans boost mineral absorption, hypocholesterolemic, and hypoglycemic perspectives, bifidogenic nature and controlling cancer insurgence. The benefits allied with fructan are mainly dose and time dependent. In this context, its industrial applications for vulnerable groups are increasing worldwide.     

Hematological and biochemical changes in acute leukemic patients after chemotherapy

AIM: To study hematological and biochemical profiles in acute leukemic patients before and after chemotherapy. METHODS: Sera from 20 normal persons were compared with those from 40 patients of whom 20 patients were followed up after 6-8 months of treatment with cyclophosphamide, vincristine, and prednisolone. RESULTS: Hemoglobin, hematocrit and platelet count were decreased while reticulocyte count, blood sedimentation, total leukocyte count, bleeding time, bilirubin, blood coagulation time, alanine aminotransferase, lactate dehydrogenase, creatinine, and urea were increased in acute myelocytic patients compared to normal. A similar pattern was observed in acute lymphocytic patients except there was no significant increase in serum urea. CONCLUSION: In acute leukemic patients blood chemistry and hematology are useful during diagnosis and treatment. After 6-8 months of treatment 50% remission occurred.     

Energy conservation in traffic

This paper discusses the energy that is being wasted in the transport section in Amman due to three main factors: namely, the time delay at the major traffic light intersections, the warming up periods of vehicles, and the drag force acting on heavy vehicles. It was found that the fuel which is being used due to the first and second factors contribute 2.2 and 3.0% to the total annual fuel cost in Jordan. Finally, recommendations and suggestions are given to cut down the fuel consumption in Amman due to the above mentioned factors.     

Hierarchical Alignment and Full Resolution Pattern Recognition of 2D NMR Spectra: Application to Nematode Chemical Ecology

Nuclear magnetic resonance (NMR) is the most widely used nondestructive technique in analytical chemistry. In recent years, it has been applied to metabolic profiling due to its high reproducibility, capacity for relative and absolute quantification, atomic resolution, and ability to detect a broad range of compounds in an untargeted manner. While one-dimensional (1D) (1)H NMR experiments are popular in metabolic profiling due to their simplicity and fast acquisition times, two-dimensional (2D) NMR spectra offer increased spectral resolution as well as atomic correlations, which aid in the assignment of known small molecules and the structural elucidation of novel compounds. Given the small number of statistical analysis methods for 2D NMR spectra, we developed a new approach for the analysis, information recovery, and display of 2D NMR spectral data. We present a native 2D peak alignment algorithm we term HATS, for hierarchical alignment of two-dimensional spectra, enabling pattern recognition (PR) using full-resolution spectra. Principle component analysis (PCA) and partial least squares (PLS) regression of full resolution total correlation spectroscopy (TOCSY) spectra greatly aid the assignment and interpretation of statistical pattern recognition results by producing back-scaled loading plots that look like traditional TOCSY spectra but incorporate qualitative and quantitative biological information of the resonances. The HATS-PR methodology is demonstrated here using multiple 2D TOCSY spectra of the exudates from two nematode species: Pristionchus pacificus and Panagrellus redivivus. We show the utility of this integrated approach with the rapid, semiautomated assignment of small molecules differentiating the two species and the identification of spectral regions suggesting the presence of species-specific compounds. These results demonstrate that the combination of 2D NMR spectra with full-resolution statistical analysis provides a platform for chemical and biological studies in cellular biochemistry, metabolomics, and chemical ecology.     

Effect of age and parity on primary caesarean section rates

OBJECTIVE: To study the effect of maternal age and parity on the rates of primary caesarean section. METHOD: We reviewed all patients who delivered at the Princess Badeea Teaching Hospital between 1 January, 1995 and 26 November, 1995. RESULTS: There were 8,732 deliveries included in this study. The primary caesarean section rates in primiparous women less than 25, 25 to 34 and over 34 years of age were 6.1%, 11.1% and 22.2%, respectively. A similarly dramatic rise with advancing maternal age was seen in multiparous women with rates of 3.1%, 6.4% and 9.5%, respectively, in the three age groups. A strong association between maternal age and primary caesarean section exists (p < 0.05). Caesarean section rates in the primiparous women were higher in all age groups when compared with multiparous women (p < 0.0001). CONCLUSIONS: Increasing maternal age and parity are factors strongly associated with increased primary caesarean section rates.     

Automated analysis and exploration of image databases: Results,progress, and challenges

In areas as diverse as earth remote sensing, astronomy, and medical imaging, image acquisition technology has undergone tremendous improvements in recent years. The vast amounts of scientific data are potential treasure-troves for scientific investigation and analysis. Unfortunately, advances in our ability to deal with this volume of data in an effective manner have not paralleled the hardware gains. While special-purpose tools for particular applications exist, there is a dearth of useful general-purpose software tools and algorithms which can assist a scientist in exploring large scientific image databases. This paper presents our recent progress in developing interactive semi-automated image database exploration tools based on pattern recognition and machine learning technology. We first present a completed and successful application that illustrates the basic approach: the SKICAT system used for the reduction and analysis of a 3 terabyte astronomical data set. SKICAT integrates techniques from image processing, data classification, and database management. It represents a system in which machine learning played a powerful and enabling role, and solved a difficult, scientifically significant problem. We then proceed to discuss the general problem of automated image database exploration, the particular aspects of image databases which distinguish them from other databases, and how this impacts the application of off-the-shelf learning algorithms to problems of this nature. A second large image database is used to ground this discussion: Magellan's images of the surface of the planet Venus. The paper concludes with a discussion of current and future challenges.     

Hierarchical Clustering Algorithms for Document Datasets

Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularity. This paper focuses on document clustering algorithms that build such hierarchical solutions and (i) presents a comprehensive study of partitional and agglomerative algorithms that use different criterion functions and merging schemes, and (ii) presents a new class of clustering algorithms called constrained agglomerative algorithms, which combine features from both partitional and agglomerative approaches that allows them to reduce the early-stage errors made by agglomerative methods and hence improve the quality of clustering solutions. The experimental evaluation shows that, contrary to the common belief, partitional algorithms always lead to better solutions than agglomerative algorithms; making them ideal for clustering large document collections due to not only their relatively low computational requirements, but also higher clustering quality. Furthermore, the constrained agglomerative methods consistently lead to better solutions than agglomerative methods alone and for many cases they outperform partitional methods, as well.