Operating Model of Knowledge Quantum Engineering for Decision-Making in Conditions of Indeterminacy

The operating model of knowledge quantum engineering for identification and prognostic decision- making in conditions of α-indeterminacy is suggested in the article. The synthesized operating model solves three basic tasks: Аt-task to formalize tk-knowledge; Вt-task to recognize (identify) objects according to observed results; Сt-task to extrapolate (prognosticate) the observed results. Operating derivation of identification and prognostic decisions using authentic different-level algorithmic knowledge quantum (using tRAKZ-method) assumes synthesis of authentic knowledge quantum database (BtkZ) using induction operator as a system of implicative laws, and then using deduction operator according to the observed tk-knowledge and BtkZ a derivation of identification or prognostic decisions in a form of new tk-knowledge.

Leveraging Existing Plasma Simulation Codes

This paper describes the process of wrapping existing scientific codes in the domain of plasma physics simulations through the use of the Sun’s Java Native Interface. We have created a Java front-end for a particular functionality, offered by legacy native libraries, in order to achieve reusability and interoperability without having to rewrite these libraries. The technique, introduced in this paper, includes two approaches – the one-to-one mapping for wrapping a number of native functions, and using peer classes for wrapping native data structures.

Research on reliability evaluation of reconfigurable manufacturing system

It is a crucial task to evaluate the reliability of manufacturing process in product development process. Process reliability is a measurement of production ability of reconfigurable manufacturing system (RMS), which serves as an integrated performance indicator of the production process under specified technical constraints, including time, cost and quality. An integration framework of manufacturing process reliability evaluation is presented together with product development process. A mathematical model and algorithm based on universal generating function (UGF) is developed for calculating the reliability of manufacturing process with respect to task intensity and process capacity, which are both independent random variables. The rework strategies of RMS are analyzed under different task intensity based on process reliability is presented, and the optimization of rework strategies based on process reliability is discussed afterwards.

Numerical solution of the Dirichlet initial boundary value problem for the heat equation in exterior 3-dimensional domains using integral equations

A numerical method for the Dirichlet initial boundary value problem for the heat equation in the exterior and unbounded region of a smooth closed simply connected 3-dimensional domain is proposed and investigated. This method is based on a combination of a Laguerre transformation with respect to the time variable and an integral equation approach in the spatial variables. Using the Laguerre transformation in time reduces the parabolic problem to a sequence of stationary elliptic problems which are solved by a boundary layer approach giving a sequence of boundary integral equations of the first kind to solve. Under the assumption that the boundary surface of the solution domain has a one-to-one mapping onto the unit sphere, these integral equations are transformed and rewritten over this sphere. The numerical discretisation and solution are obtained by a discrete projection method involving spherical harmonic functions. Numerical results are included.

Discovering knowledge structures in mind maps of mental health risks

This thesis addressed the problem of risk analysis in mental healthcare, with respect to the GRiST project at Aston University. That project provides a risk-screening tool based on the knowledge of 46 experts, captured as mind maps that describe relationships between risks and patterns of behavioural cues. Mind mapping, though, fails to impose control over content, and is not considered to formally represent knowledge. In contrast, this thesis treated GRiSTs mind maps as a rich knowledge base in need of refinement; that process drew on existing techniques for designing databases and knowledge bases. Identifying well-defined mind map concepts, though, was hindered by spelling mistakes, and by ambiguity and lack of coverage in the tools used for researching words. A novel use of the Edit Distance overcame those problems, by assessing similarities between mind map texts, and between spelling mistakes and suggested corrections. That algorithm further identified stems, the shortest text string found in related word-forms. As opposed to existing approaches’ reliance on built-in linguistic knowledge, this thesis devised a novel, more flexible text-based technique. An additional tool, Correspondence Analysis, found patterns in word usage that allowed machines to determine likely intended meanings for ambiguous words. Correspondence Analysis further produced clusters of related concepts, which in turn drove the automatic generation of novel mind maps. Such maps underpinned adjuncts to the mind mapping software used by GRiST; one such new facility generated novel mind maps, to reflect the collected expert knowledge on any specified concept. Mind maps from GRiST are stored as XML, which suggested storing them in an XML database. In fact, the entire approach here is ”XML-centric”, in that all stages rely on XML as far as possible. A XML-based query language allows user to retrieve information from the mind map knowledge base. The approach, it was concluded, will prove valuable to mind mapping in general, and to detecting patterns in any type of digital information.

Application of Oribatid mites as indicators

This review discusses the connection between quantitative changes of environmental factors and oribatid communities. With the overview of available studies, it can be clearly explored how various characteristics of Oribatid communities are modified due to changes in moisture, temperature, heavy metal concentration, organic matter content and level of disturbance. The most important question concerning the application of Oribatids as indicators is to clarify what kind of information content does natural Oribatid coenological patterns possess from the aspect of bioindication. Most of the variables listed above can be directly measured, since rapid methods are available to quantify parameters of the soil. Responses of Oribatids are worth to study in a more complex approach. Even now we have an expansive knowledge on how communities change due to modifications of different factors. These pieces of information necessitate the elaboration of such methods which render Oribatid communities suitable for the task to prognosticate what extent the given site can be considered near-natural or degraded, based on the Oribatid composition of a single sample taken from the given area. Answering this problem needs extensive and coordinated work.

Space division multiple access for wireless sensor networks

This dissertation proposed a self-organizing medium access control protocol (MAC) for wireless sensor networks (WSNs). The proposed MAC protocol, space division multiple access (SDMA), relies on sensor node position information and provides sensor nodes access to the wireless channel based on their spatial locations. SDMA divides a geographical area into space divisions, where there is one-to-one map between the space divisions and the time slots. Therefore, the MAC protocol requirement is the sensor node information of its position and a prior knowledge of the one-to-one mapping function. The scheme is scalable, self-maintaining, and self-starting. It provides collision-free access to the wireless channel for the sensor nodes thereby, guarantees delay-bounded communication in real time for delay sensitive applications. This work was divided into two parts: the first part involved the design of the mapping function to map the space divisions to the time slots. The mapping function is based on a uniform Latin square. A Uniform Latin square of order k = m 2 is an k x k square matrix that consists of k symbols from 0 to k-1 such that no symbol appears more than once in any row, in any column, or in any m x in area of main subsquares. The uniqueness of each symbol in the main subsquares presents very attractive characteristic in applying a uniform Latin square to time slot allocation problem in WSNs. The second part of this research involved designing a GPS free positioning system for position information. The system is called time and power based localization scheme (TPLS). TPLS is based on time difference of arrival (TDoA) and received signal strength (RSS) using radio frequency and ultrasonic signals to measure and detect the range differences from a sensor node to three anchor nodes. TPLS requires low computation overhead and no time synchronization, as the location estimation algorithm involved only a simple algebraic operation.

Fluorescence-enhanced optical imaging on three-dimensional phantoms using a hand-held probe based frequency-domain intensified charge coupled device (ICCD) optical imager

Fluorescence-enhanced optical imaging is an emerging non-invasive and non-ionizing modality towards breast cancer diagnosis. Various optical imaging systems are currently available, although most of them are limited by bulky instrumentation, or their inability to flexibly image different tissue volumes and shapes. Hand-held based optical imaging systems are a recent development for its improved portability, but are currently limited only to surface mapping. Herein, a novel optical imager, consisting primarily of a hand-held probe and a gain-modulated intensified charge coupled device (ICCD) detector, is developed towards both surface and tomographic breast imaging. The unique features of this hand-held probe based optical imager are its ability to; (i) image large tissue areas (5×10 sq. cm) in a single scan, (ii) reduce overall imaging time using a unique measurement geometry, and (iii) perform tomographic imaging for tumor three-dimensional (3-D) localization. Frequency-domain based experimental phantom studies have been performed on slab geometries (650 ml) under different target depths (1-2.5 cm), target volumes (0.45, 0.23 and 0.10 cc), fluorescence absorption contrast ratios (1:0, 1000:1 to 5:1), and number of targets (up to 3), using Indocyanine Green (ICG) as fluorescence contrast agents. An approximate extended Kalman filter based inverse algorithm has been adapted towards 3-D tomographic reconstructions. Single fluorescence target(s) was reconstructed when located: (i) up to 2.5 cm deep (at 1:0 contrast ratio) and 1.5 cm deep (up to 10:1 contrast ratio) for 0.45 cc-target; and (ii) 1.5 cm deep for target as small as 0.10 cc at 1:0 contrast ratio. In the case of multiple targets, two targets as close as 0.7 cm were tomographically resolved when located 1.5 cm deep. It was observed that performing multi-projection (here dual) based tomographic imaging using a priori target information from surface images, improved the target depth recovery over using single projection based imaging. From a total of 98 experimental phantom studies, the sensitivity and specificity of the imager was estimated as 81-86% and 43-50%, respectively. With 3-D tomographic imaging successfully demonstrated for the first time using a hand-held based optical imager, the clinical translation of this technology is promising upon further experimental validation from in-vitro and in-vivo studies.

An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

Competencies of a Recreational Food Service Manager

Recreational food service is a newly-identified industry segment. It represents over 3 percent of the total industry and has its own specific competency requirements, in addition to core food service management competency needs. The education, training, and development needs for professionals in this industry segment have not as yet been ascertained. This article is an effort to establish a benchmark for future research in this area.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

Geochemistry on manganese nodules from the central Pacific

During Cruise VA 04/2 of the R. V. Valdivia in 1972, numerous samples were collected of manganese nodules, sodiments, and the pore water of the Sediments in the area betwoen 6° N and 11° N and between 149° W and 154° W in the Central Pacific. This paper reports on the geochemical study of 70 manganese nodules from 12 stations (box-core and dredge samples). The nodules Irom a single Station vary considerably in diemical composition. These variations are of the same magnitude as the variations in nodule composition over the entire survey area. The ratios of manganese to nickel, copper, and zinc show good positive correlation as do the ratios of iron to lead and, to a lesser extent, of iron to cobalt. No correlation was found between the environment of the manganese nodules and their metal content. Three internal zones, the outer. intermediate, and core zones, of some nodules were studied in detail. Trends of differences of Chemical composition from surface to core were found for numerous elements and elemental ratios.

Estimates of annual mean surface temperatures in the late Quaternary of the tropical Atlantic

At least two modes of glacial-interglacial climate change have existed within the tropical Atlantic Ocean during the last 20,000 years. The first mode (defined by cold glacial and warm interglacial conditions) occurred symmetrically north and south of the equator and dominated the eastern boundary currents and tropical upwelling areas. This pattern suggests that mode 1 is driven by a glacial modification of surface winds in both hemispheres. The second mode of oceanic climate change, defined by temperature extremes centered on the deglaciation, was hemispherically asymmetrical, with the northern tropical Atlantic relatively cold and the southern tropical Atlantic relatively warm during deglaciation. A likely cause for this pattern of variation is a reduction of the presently northward cross-equatorial heat flux during deglaciation. No single mechanism accounts for all the data. Potential contributors to oceanic climate changes are linkage to high-latitude climates, modification of monsoonal winds by ice sheet and/or insolation changes, atmospheric CO2 and greenhouse effects, indirect effects of glacial meltwater, and variations in thermohaline overturn of the oceans.

Non-auditory Influences on the Auditory Periphery

Once thought to be predominantly the domain of cortex, multisensory integration has now been found at numerous sub-cortical locations in the auditory pathway. Prominent ascending and descending connection within the pathway suggest that the system may utilize non-auditory activity to help filter incoming sounds as they first enter the ear. Active mechanisms in the periphery, particularly the outer hair cells (OHCs) of the cochlea and middle ear muscles (MEMs), are capable of modulating the sensitivity of other peripheral mechanisms involved in the transduction of sound into the system. Through indirect mechanical coupling of the OHCs and MEMs to the eardrum, motion of these mechanisms can be recorded as acoustic signals in the ear canal. Here, we utilize this recording technique to describe three different experiments that demonstrate novel multisensory interactions occurring at the level of the eardrum. 1) In the first experiment, measurements in humans and monkeys performing a saccadic eye movement task to visual targets indicate that the eardrum oscillates in conjunction with eye movements. The amplitude and phase of the eardrum movement, which we dub the Oscillatory Saccadic Eardrum Associated Response or OSEAR, depended on the direction and horizontal amplitude of the saccade and occurred in the absence of any externally delivered sounds. 2) For the second experiment, we use an audiovisual cueing task to demonstrate a dynamic change to pressure levels in the ear when a sound is expected versus when one is not. Specifically, we observe a drop in frequency power and variability from 0.1 to 4kHz around the time when the sound is expected to occur in contract to a slight increase in power at both lower and higher frequencies. 3) For the third experiment, we show that seeing a speaker say a syllable that is incongruent with the accompanying audio can alter the response patterns of the auditory periphery, particularly during the most relevant moments in the speech stream. These visually influenced changes may contribute to the altered percept of the speech sound. Collectively, we presume that these findings represent the combined effect of OHCs and MEMs acting in tandem in response to various non-auditory signals in order to manipulate the receptive properties of the auditory system. These influences may have a profound, and previously unrecognized, impact on how the auditory system processes sounds from initial sensory transduction all the way to perception and behavior. Moreover, we demonstrate that the entire auditory system is, fundamentally, a multisensory system.

Putting together phylogenetic and ontogenetic perspectives on empathy.

The ontogeny of human empathy is better understood with reference to the evolutionary history of the social brain. Empathy has deep evolutionary, biochemical, and neurological underpinnings. Even the most advanced forms of empathy in humans are built on more basic forms and remain connected to core mechanisms associated with affective communication, social attachment, and parental care. In this paper, we argue that it is essential to consider empathy within a neurodevelopmental framework that recognizes both the continuities and changes in socioemotional understanding from infancy to adulthood. We bring together neuroevolutionary and developmental perspectives on the information processing and neural mechanisms underlying empathy and caring, and show that they are grounded in multiple interacting systems and processes. Moreover, empathy in humans is assisted by other abstract and domain-general high-level cognitive abilities such as executive functions, mentalizing and language, as well as the ability to differentiate another's mental states from one's own, which expand the range of behaviors that can be driven by empathy.