Miracle-dz/UniFAR-FTUs huggingface dataset:
Disturbance Effect on the Harbour Porpoise Population in the North Sea ( DEPONS ) : Status Report on Model Development – February 2015

The goal of this report is to provide a status update of an individual-based model (IBM) that is being developed to evaluate the impact of pile-driving noise from offshore wind farm construction on the harbour porpoise (Phocoena phocoena) population in the North Sea (DEPONS model). We considered five different porpoise movement and dispersal strategies in combination with three hypothetical piledriving scenarios ranging from no noise to a realistic but worst case piling scenario. We compared the average simulated porpoise population sizes and dynamics across movement/dispersal models and pile-driving scenarios. Although the results should be considered preliminary, the patterns generated by the current version of the DEPONS model did not suggest any clear, long-lasting effects of pile-driving noise on the average porpoise population size and dynamics in the North Sea. We discuss several model components that need to be investigated in more detail so as to replace certain underlying assumptions in the model with a direct data driven parameterization approach.
Learning Semantics of Movement

The basic scientific question behind this presentation is how to model computationally the interrelated processes of understanding natural language and perceiving and producing movement in real world contexts. Rather than relying on manually defined representations, we approach this problem by analyzing the statistical regularities in contexts of language use. A central aspect is that the learning of internal representations is based on large amounts of fully or mostly unlabeled training data [1, 2]. A promising methodological approach towards this is that data from different sources can be brought together through multiview/transfer/multi-task learning (see e.g. [2, 3, 4, 5]). Another promising approach is learning relations. Relations are powerful abstractions because they allow reasoning not just about objects, but about their combinations. Recent work suggests that at least limited classes of relations can be learned from data [6]. We have earlier conducted research in which text contexts have been used to learn semantic similarities (see e.g. [7, 8]. However, to reach a more human-level understanding, we have to take into account that language is fully understood only through its use in its multimodal and embodied contexts including linguistic, visual, auditory, tactile and kinestetic dimensions. In general, patterns and signals are natural representations for multimodal contexts. These differ considerably from the discrete representations of symbols and expressions in symbolic languages. For instance, images are typically represented as numerical matrices. We have earlier conducted research related to the combination of image and language data (see e.g. [9, 10]).
Relationship between Human Resource Practices and Employee Turnover Intention in Hospitality Industry

Employee turnover is an endemic issue in hospitality industry, worldwide. Employee turnover intention acts as a proxy for actual employee turnover. One of the challenging issues faced by the hospitality organizations today is to manage the changing employment relationship. Being in customer service business, hospitality industry capitalizes heavily on its human resources in order to achieve its competitive advantage. Choi and Dickson (2009) emphasized that the hospitality industry is a highly guest service–oriented business where encounters between employees and guests determine the success of the business. GJMBR-A Classification : JEL Code: M00, M19. Relationship between Human Resource Practices and Employee Turnover Intention in Hospitality Industry Strictly as per the compliance and regulations of: : A Relationship between Human Resource Practices and Employee Turnover Intention in Hospitality Industry S. Nivethitha , Lata Dyaram σ & T.J. Kamalanabhan ρ
BICM-ID for Relay System Allowing Intra-link Errors and a Similarity Constellation to ARQ Schemes

In this paper, we propose an accumulator-assisted (ACC) relay system with bitinterleaved coded modulation using iterative decoding (BICM-ID) technique and apply the network topology to an Automatic Repeat Request (ARQ) scheme. Our code design is based on the analysis of the extrinsic information transfer (EXIT) chart. The ACC enables the convergence tunnel of the EXIT curves opening until almost the (1, 1) point of the mutual information, which avoids the error floor. The most advantageous point of the proposed technique is that even though errors may happen in the intra-link (source-relay), they can be corrected at the destination by exploiting the correlation knowledge between the source and the relay nodes. This technique significantly reduces the complexity of the relay where the source bits are simply extracted, even though errors may occur due to the imperfect channel. The error rate of the intra-link can be estimated at the destination node by using the a-posteriori Log likelihood Ratios (LLRs) of the two decoders. Then, it can be further utilised in the iterative processing. Since the relay location directly influences the quality of the intra-link, we change the relay locations, and provide the analysis of the performances corresponding to different relay locations. The theoretical background of this technique is the Slepian-Wolf/Shannon theorem for correlated source coding. The simulation results show that the bit-error-rate (BER) performances of the proposed system are very close to theoretical limits supported by the Slepian-Wolf/Shannon theorem. In this paper, it is also shown that if the intra-link is error free, the topology of the relay network is equivalent to an ARQ scheme that exploits Shannon’s random coding theorem by utilising an interleaver in the framework of ARQ. Based on this observation, results of simulations conducted to evaluate the throughput of an ARQ scheme are presented.
Identification of a Soluble NADPH Oxidoreductase (BmNOX) with Antiviral Activites in the Gut Juice of Bombyx mori

Silkworms show high variability in silk quality and disease resistance. Attempts are on to combine the disease tolerance of multivoltine races and the silk quality of bivoltine races to generate new races with desirable phenotypic traits. We report the identification of a 26.5-kDa protein that is overexpressed in the gut juice of disease-resistant multivoltine races and that has anti-BmNPV activity. We have characterized this protein as a soluble NADH-oxidoreductase-like protein (BmNOX). Treatment of live BmNPV particles with BmNOX inhibited the capability of the viral particles to infect BmN cells in vitro.
GeneSIS: Continuous Orchestration and Deployment of Smart IoT Systems

Multiple tools have emerged to support the development as well as the continuous deployment of cloud-based software systems. However, currently, there is a lack of proper tool support for the continuous orchestration and deployment of software systems spanning across the IoT, edge, and cloud space. In particular, there is a lack of languages and abstractions that can support the orchestration and deployment of software services across vastly heterogeneous IoT infrastructures. In this paper, we present a tool supported framework for the continuous orchestration and deployment of IoT systems, named GeneSIS. In particular, GeneSIS enables to cope with the heterogeneity at each of the IoT, edge, and cloud levels and allows to control the orchestration and continuous deployment of software systems that executes across IoT, edge, and cloud infrastructures.
Low-Level Automation as a Pathway to Appropriate Trust in the PED Enterprise : Design of a Collaborative Work Environment

In Military Intelligence, Processing, Exploitation, and Dissemination (PED) functions are critical to success, providing capabilities that support intelligence request lifecycles. PED capabilities are becoming increasingly available to smaller, more centralized teams that support multiple battlespace operators. Lessons learned reveal that while existing PED processes provide detailed intelligence, operators are burdened with continual coordination, communication, and interpretation tasks that, coupled with the data volume generated by multi-INT ISR platforms, cause breakdowns in coordination and communication. These breakdowns result in failures to share, attend to, and interpret information critical to mission success. Key contributors to these breakdowns are the lack of automated support and cognitive incongruence between existing automated solutions and the support required by analysts, which reduce or destroy trust. Developers must recognize that system interactions that establish a human-machine dialogue are necessary to develop the trust required for automation’s successful adoption and employment. To overcome these challenges, we present a Collaborative Work Environment for PED operations. It includes decisioncentered analytics automation and low-level task automation designed to establish the human-machine dialogue that engenders trust. This approach facilitated appropriate attitudes of trust, leading analysts to request that we develop and deploy higher-level, planned automation capabilities to further offload PED tasks.
Multi-scale sparse denoising model based on non-separable wavelet

For the issue of image denoising, in order to avoid the traditional multi-scale sparse representation methods, which used blocks of different sizes as a base function to represent image, the non-separable wavelets were taken. Their advantages included revealing the multi-scale structure, depicting the texture structure under different scales, and separating different directions and different types of singularity structure in a certain extent. Based on non-separable wavelets, a multi-scale sparse denoising model in the wavelet domain was we established, and then a collaboration sparse model for the sub-bands contained similar structures was designed to enhance the stability and accuracy of the sparse representation. The results show that the denoising effect based on new approach is obvious superior to the K-SVD algorithm.
Immunomodulatory agents for the treatment of cachexia

Purpose of reviewIn patients with advanced cancer, AIDS and end-stage organ diseases, symptoms of cachexia syndrome include decrease in appetite, weight loss, decreased performance status, and an increase in the systemic inflammatory response. Inflammatory cytokines and other immune interactions affect the lean tissue mass and body fat. It is hopeful that modulation of these inflammatory interactions may contribute to the delay in the development and treatment of cachexia. This review summarizes the current state of the art. Recent findingsThis article covers the role of inflammatory response in cachexia, measurement of inflammatory response, mechanism and measurement of cachexia, immunomodulation in cancer, drugs targeting inflammatory cytokines, effect of exercise in cachexia, and treatment of cancer cachexia using immunomodulatory agents. SummaryUnderstanding the immune response associated with cachexia may improve future pharmacological modification of the cytokines. In addition, the multifactorial contributions to the mechanisms of cachexia indicate that a multimodal approach may be necessary to treat cachexia and its associated symptoms.
Depressive symptoms moderate the influence of the APOE epsilon 4 allele on cognitive decline in a sample of community dwelling older adults

Objectives—The APOE ε4 allele and a history of depression are each separate risk factors for cognitive decline (CD). However, little research has investigated whether a history of depression influences the relationship between APOE ε4 and CD. The present study examined whether depressive symptoms had greater influence on subsequent CD among participants with APOE ε4 than those without the allele. Design—Prospective six year longitudinal study. Setting—Community in-home interviews. Participants—A biracial sample of community dwelling older adults (N=1992) from the Duke Established Populations for Epidemiologic Studies of the Elderly (EPESE). Measurements—Data were drawn from Waves 1 and 3 of the EPESE, which were conducted 6 years apart. Cognitive functioning and depressive symptoms were assessed at both waves, and APOE genotyping was completed during the Wave 3 assessment. Results—Regression analyses revealed that depressive symptoms and the APOE ε4 allele independently predicted CD. Importantly, the influence of depressive symptoms on CD was greater for individuals with the APOE ε4 allele compared to those without the allele. Conclusion—Depressive symptoms and the APOE ε4 allele are independent contributors to CD. Moreover, the influence of depressive symptoms on CD is greater among individuals with the APOE ε4 allele. Depression and the APOE ε4 allele may act together in disrupting neurological functioning, which may in turn lower an individual's cognitive reserve capacity. Given the efficacious treatments currently available for depression, future research should investigate the extent to which interventions for depression may reduce the risk for subsequent CD.
Construction of reference data for classification of elasticity images of arterial wall

We have developed a phased tracking method (Kanai, H. et al., IEEE Trans. Ultrason. Ferroelect. Freq. Contr. vol.44, no.2, p.791-810, 1996) for measuring the minute change in thickness during one heartbeat and the elasticity of the arterial wall. By comparing pathological findings with the elasticity distribution measured with ultrasound, statistical parameters for respective tissues in the arterial wall were determined. We have already measured the statistical parameters for lipids and a mixture of smooth muscle and collagen fiber (Kanai et al., Circulation, vol.107, p.3019-21, 2003). The paper reports the measurement of elasticity distributions for blood clot and calcified tissue. We discuss whether the measured elasticity distributions can be used as reference data for classifying cross-sectional elasticity images. We also investigated the measurement of elasticity distribution and the correlation between collagen content and elasticity for fibrous tissue. Collagen is supposed to contribute mainly to the stability of the fibrous cap of atherosclerotic plaque. Therefore, such a correlation between elasticity and a tissue element of the wall may provide useful information for the diagnosis of plaque vulnerability.
A strongly fault-secure and strongly code-disjoint realization of combinational circuits

Strongly fault-secure (SFS) circuits are known to achieve the totally self-checking (TSC) goal of producing a noncodeword as the first erroneous output due to a fault. Strongly code-disjoint (SCD) circuits are known to always map noncodeword inputs to noncodeword outputs, even in the presence of faults, as long as the faults remain undetected. The authors present a general design method for SFS and SCD combinational circuits for the previously proposed fault model that covers the broad classes of likely faults in VLSI. In the design, the input and output of a combinational circuit are encoded in systematic unordered codes whose check part is obtained by adding two extra bits to the check part of any known systematic unordered code. Thanks to the uniform input/output encoding and the SCD property for the proposed combinational circuits, a number of the circuits can be interconnected in cascade to construct a larger SFS combinational circuit if each interface is sufficiently exercised. <<ETX>>
What Are the Determinants of Long-Term Nominal Interest Rates?

The determinants of long-term nominal interest rates have not yet been fully explained by either economic theory or empirical studies. Since long-term nominal interest rates are the sum of long-term real interest rates and inflation expectations, any macroeconomic factor that impacts expected inflation, real rates or both should affect long-term nominal interest rates. The objective of this paper is to examine how the dynamics of nominal bond yields is related to macroeconomic fundamentals. We consider a structural VECM where identification is achieved by imposing longrun restrictions. A technical innovation of the paper is the identification of structural stochastic trends in a VECM including exogenous variables, which enables us to address the special features of a small-open economy like Canada. We then assess the impact of various shocks monetary, fiscal and supply shocks on nominal bond yields. Three main results emerge from our analysis. First, an unexpected permanent fiscal deterioration results in large increases in long-term nominal interest rates. Second, in the long-run, supply shocks have no significant impact on longterm nominal rates. Finally, a permanent shock to inflation results in higher nominal long rates. JEL Classification: E43
Predictive Ef fi ciency Optimization for DC-DC Converters with Digital Electronic Loads

This work presents a novel technique and system for increasing the efficiency of dc-dc converters that supply electronic loads, such as modern audio and video equipment and other devices whose power consumption largely depends on the digital data they process. The optimization does not require a current measurement circuit and is well-suited to portable applications. It is based on a real-time prediction of the dc-dc converter output current from easily accessible digital data streams present in the targeted loads. The result of the prediction is then used for dynamic adjustment of the size of the power stage transistors and/or for switching into pulse-frequency-mode (PFM) of output voltage regulation, in order to maximize the instantaneous converter efficiency on-the-fly. The use of a segmented power stage allows the effective power transistor size to be changed on-the-fly, and the trade-off between the gate-drive and RMS conduction losses is continuously optimized over the full range of operation. The effectiveness of the optimization is demonstrated on an experimental system including a 1 W digitally controlled 4 MHz, 3.6V-to-1.8V buck converter with an integrated segmented power stage and a digital high-fidelity classD audio amplifier acting as the digital load. The results show a good agreement between the digitally predicted and actual dc-dc converter load current, as well as a reduction in total energy consumption of up to 38 %.
Workshop on the Interior Delta Flows And Related Stressors Panel Summary Report

The Delta Science Program is pleased to present the enclosed report titled, “Workshop on the Interior Delta Flows and Related Stressors, Panel Summary Report. ” The transmittal of this report fulfills a request from the State Water Resources Control Board (State Water Board) to conduct workshops to help resolve some of the key scientific uncertainties and disagreements associated with its comprehensive review of the 2006 Water Quality Control Plan for the BayDelta (Bay-Delta Plan).
Authorship Segmentation for Retrieving Source Documents in Plagiarism Detection

Plagiarism is an act of using another person’s words or ideas without giving credit to that person. Plagiarism detection is a task to determine whether an input document was made through plagiarism and present one or more source documents as evidence. Because a plagiarist often uses more than one source document by different authors to produce a single document, it is useful to segment the input document based on the authorship attribution. We propose a text segmentation method that uses a number of authorship attribution features, such as distributions of characters, words, and parts of speech, and divides the input document where the writing style is substantially different. We experimentally show the accuracy of our segmentation method and its effectiveness for retrieving source documents.
Characteristics of Inverse Delayed Model for Neural Computation

Many models of neural cells and networks have been proposed and investigated. The typical cell models are, for examples, Hodgkin-Huxley model [1] based on physiological assumptions, FitzHugh’s BVP model [2] which is a projection of the Hodgkin-Huxley model on a phase plane, McCulloch-Pitts [3] and Caianiello [4] models for analyses of network functions, the continuous-valued units model [5], [6], Aihara model [7] for chaotic behaviors, etc. And there are many studies for layer and full connection structures, learning functions, statistical methods, etc. [8] The most of existing neural network models interpret the biological behaviours in terms of time averaging techniques, rather than directly emulating them. It is a resonable simplification to handle macroscopically the collective computational properties of the networks. On the other hand, the direct emulations are main concerns to the single or microscopic neuron models. It seems there are some gaps between these macroscopic and microscopic models. Particularly, the ability of excitation which is an important characteristic for neurons is not fully included in the macroscopic models. A normal neuron, which, however, seldom shows spontaneous excitations, is ordinarily in a resting state. And it fires depending on inputs from other neurons and external stimulations. It is a fundamental condition for the neurons which are in the critical state to be able to fire. The condition is also considered to have many rolls for the functions of neural networks. It is an elementary assumption to have two states resting and excitation for neurons in the past network models where the output of neuron units corresponds to a short-term average firing rate. And hence, it seems few discussions have been presented what rolls the neuron in the critical state or the ability of excitation has on the network actions. In this paper, we present the novel model (Inverse Delayed Model) to represent networks with neurons in the critical state, and discuss the fundamental characteristics of the ID model including stimulus responses and an energy function of the the network. The ID model has a simple structure, however, it can clear up the relation between the macroscopic and the microscopic models, because it includes both of the BVP and the Hopfield models.
Reflections on Social Support for HIV prevention among Youth in Mufindi and Mbulu , Tanzania

Youth get involved in high risk sexual encounters and they become infected to HIV at an early age. This paper aims to assess social support for HIV prevention among youth in Mbulu and Mufindi in Tanzania. The sample consisted of 232 youth both in and out of school aged 15-35 years. Descriptive statistics was used to determine frequency, percentages and the mean scores for social support for HIV prevention among the youth. The findings show that social support provided for HIV prevention was inadequate and it differed among female and male youth in Mbulu and Mufindi respectively. There is the need to evaluate and improve the ways on which social support for HIV prevention is provided specifically among female.
Salt Stress Alleviation of Chamomile Plant by Mycorrhizal Fungi and Salicylic Acid

Chamomile (Matricaria chamomilla, L) plant, belonging to Asteraceae family, has been cultivated in arid and semi-arid regions (Renuka, 1992). Chamomile medicinal compounds make it one of the highest consuming medicinal plants that have been largely recognized (Farkoosh et al., 2011). The main constituents of chamomile volatile oil are chamazulen and bisabolol that are used widely in pharmaceutical and flavoring industries (Glambosi and Holm, 1991). Chamomile volatile oil has been reported to be used as a carminative, antiseptic, sedative and anti-inflammatory (Avallone et al., 2000). Salinity is the major problem in different counties in Arab lands (Ruiz-Lozano et al., 2001) and hence the sustainable production in many areas is at risk due to soil salinization (Rengasamy, 2006). The adverse effects of salinity not only observed on the growth and development but also decrease the productivity (Giri et al., 2003).