Synthesis and characterization of nanocrystalline Barium Strontium Titanate powder via sol-gel processing

Barium Strontium Titanate (BST) solid solution is a strong candidate material for application in tunable ferroelectric devices. In this research, we have synthesized and characterized nanocrystalline BST (Ba_0.7Sr_0.3TiO₃) powder with average particle-diameter of 15 nm through a simple sol-gel process, using barium acetate, strontium acetate and titanium isopropoxide as the precursors. In this process, stoichiometric proportions of barium acetate and strontium acetate were dissolved in acetic acid followed by refluxing, and addition of titanium (IV) isopropoxide to form BST gel. The gel was analyzed using Differential Scanning Calorimetry (DSC) and Thermal Gravimetric Analysis (TGA). The as-formed gel was dried at 200 °C and then calcined in the temperature range of 400 to 800 °C for crystallization. Phase evolution during calcination was studied using X-ray diffraction (XRD) technique. Particle size, morphology and the lattice fringes of the calcined powder were characterized by high-resolution transmission electron microscopy (HR-TEM). To study the effects of sintering on BST nanopowder, green ceramic specimens were prepared by uniaxial compaction and then sintered at 950–1,100 °C under atmospheric conditions. Sintered specimens were analyzed for phase composition, grain size and geometric bulk density.     

Histochemical study of magellanic penguin (Spheniscus magellanicus) minor salivary glands during postnatal growth

The histological and histochemical features of the minor salivary glands during postnatal development have been generally associated with the type of food ingested. However, recent studies support the fact that these salivary glands develop independently of the diet; in fact, minor salivary glands have similar morphological and histochemical characteristics in adult individuals of species with different diet regimens. Thus, the aim of this study was to characterize the developmental morphology of the penguin minor salivary glands and to contrast them with minor salivary glands of other species. The tongue, palatine, and mouth cavity (bottom) minor salivary glands of newborn, 1- to 20-day-old, and adult magellanic penguins were studied with hematoxylin-eosin, periodic acid-Schiff, alcian blue, toluidine blue, and lectin histochemistry. Minor salivary glands were present at all ages, although they were only moderately developed in animals less than 15 days old. After this age, glands were abundant in all age groups; in addition, cells from the glandular epithelium were functionally mature and secreted mucins. Nevertheless, in newborn to 15-day-old penguins, mucins were located only at the apical cytoplasm of mucous cells. In all ages, mucous cells displayed periodic acid-Schiff-positive, alcianophilic, and metachromatic reactions; among mucous cells, other orthochromatic cells appeared interspersed. From 15 days on, histochemical reactions became more intense until adulthood, and the cytoplasm of secretory cells was filled with glycoproteins and sulfomucins. Moreover, lectins bound to different oligosaccharides in mucous cells, depending on the stage of maturation of the glands. In conclusion, penguin minor salivary glands are already present at birth, and show progressive and quantitative increases in mucous secretion during postnatal development. These changes are necessary not only for nutrient ingestion, but also for nonimmune protection of the buccal cavity.     

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.     

A measurement framework for object-oriented software testability

Testing is an expensive activity in the development process of any software system. Measuring and assessing the testability of software would help in planning testing activities and allocating required resources. More importantly, measuring software testability early in the development process, during analysis or design stages, can yield the highest payoff as design refactoring can be used to improve testability before the implementation starts.

This paper presents a generic and extensible measurement framework for object-oriented software testability, which is based on a theory expressed as a set of operational hypotheses. We identify design attributes that have an impact on testability directly or indirectly, by having an impact on testing activities and sub-activities. We also describe the cause-effect relationships between these attributes and software testability based on thorough review of the literature and our own testing experience. Following the scientific method, we express them as operational hypotheses to be further tested. For each attribute, we provide a set of possible measures whose applicability largely depends on the level of details of the design documents and the testing techniques to be applied. The goal of this framework is twofold: (1) to provide structured guidance for practitioners trying to measure design testability, (2) to provide a theoretical framework for facilitating empirical research on testability.     

A neural circuit with transcendental energy function for solving system of linear equations

A feedback neural network based circuit to solve a system of simultaneous linear equations is presented. The circuit has an associated transcendental energy function that ensures fast convergence to the exact solution while enjoying reduction in hardware complexity over existing schemes. The proof of the energy function has been given and it is shown that the gradient network converges exactly to the solution of the system of equations. PSPICE simulation results are presented for linear systems of equations of various sizes and are found to agree exactly with the algebraic solution. Hardware implementation for small sized problems further confirm the circuit operation.     

A Time-Slotted-CDMA Architecture and Adaptive Resource Allocation Method for Connections with Diverse QoS Guarantees

We consider a time-slotted W-CDMA system for mobile stations which are connected to the wired internet. We first present an architecture for such a system that is based on a request-permission protocol incorporating power control for Best Effort transmissions on the uplink. The requesting mobiles are permitted to transmit in the next time slot with a specified power according to a schedule computed by the Base Station. To devise this scheduling method, we formulate a globally optimizing integer program that maximizes the total weighted sum of all best-effort transmissions in the entire system, keeping in view the diverse target Bit Error Rates for each one. This problem is analysed and decomposed into sub-problems that can be solved locally by each Base Station. We devise two fast heuristics to solve the Base Station's sub-problem, so that the new schedule for each successive slot can be re-computed by each Base Station in a practical time-frame. We show that one heuristic is good enough to produce optimal solutions to the sub-problem in special cases. The method is further enhanced to take account of bandwidth and delay guarantees for other connections. It is also modified to ensure fairness for best-effort code channels suffering from persistent location-dependent errors. Finally, we show that a very similar approach can be used by the Base Station for scheduling on the downlink also, leading to a unified approach to scheduling in both directions. The efficacy of the uplink method is briefly demonstrated by simulations comparing the two variants with each other, and demonstrating that one achieves a consistently higher throughput than the other.     

Verification of System Level Model Transformations

This paper presents Model Algebra (MA), a formalism for representing SoC designs at system level. We define the objects and composition rules of MA and show how system level models can be represented as expressions in this formalism. The formalism is applied to a system level design methodology, where design decisions are used to gradually transform the functional specification model of the system to a transaction level model with components and communication structure. Each transformation is represented as a manipulation of a model algebraic expression, and proven for correctness using the laws of model algebra. These laws are based on the well defined execution semantics and notion of functional equivalence for MA models. Our approach promises significant savings in the verification of system level models because only the first model needs to be verified using conventional techniques. All transformations of this model, derived using MA laws, are proven to be functionally equivalent.     

Modeling particle shape-dependent dynamics in nanomedicine

One of the major challenges in nanomedicine is to improve nanoparticle cell selectivity and adhesion efficiency through designing functionalized nanoparticles of controlled sizes, shapes, and material compositions. Recent data on cylindrically shaped filomicelles are beginning to show that non-spherical particles remarkably improved the biological properties over spherical counterpart. Despite these exciting advances, non-spherical particles have not been widely used in nanomedicine applications due to the lack of fundamental understanding of shape effect on targeting efficiency. This paper intends to investigate the shape-dependent adhesion kinetics of non-spherical nanoparticles through computational modeling. The ligand-receptor binding kinetics is coupled with Brownian dynamics to study the dynamic delivery process of nanorods under various vascular flow conditions. The influences of nanoparticle shape, ligand density, and shear rate on adhesion probability are studied. Nanorods are observed to contact and adhere to the wall much easier than their spherical counterparts under the same configuration due to their tumbling motion. The binding probability of a nanorod under a shear rate of 8 s(-1) is found to be three times higher than that of a nanosphere with the same volume. The particle binding probability decreases with increased flow shear rate and channel height. The Brownian motion is found to largely enhance nanoparticle binding. Results from this study contribute to the fundamental understanding and knowledge on how particle shape affects the transport and targeting efficiency of nanocarriers, which will provide mechanistic insights on the design of shape-specific nanomedicine for targeted drug delivery applications.