Miracle-dz/UniFAR-FTUs huggingface dataset:
Marketing Decision-Information Systems: An Emerging View

Marketing decision-information systems are composed of four key elements: a data bank, a model bank, a measurement-statistics bank, and a communications capability. If full advantage is to be taken of the new information technology, a coordinated, balanced growth of the system components must be achieved. The authors, extend their initial information systems proposal to include further design concepts and practical examples.
Fostering Gender Equity in Schools through Reflective Professional Development: A Critical Analysis of Teacher Perspectives.

The continued persistence of sexism and institutional gender bias in schools is well documented. The empirical research in this area has uncovered a host of negative outcomes associated with gender inequity for all children. Research suggests that schools provide an excellent forum in which issues of gender inequities may be examined and challenged. The literature further suggests that teachers can be critical allies in schools’ struggle to achieve gender equity. Drawing on three years of data from an evaluation of an in-service professional development program aimed at raising teachers’ awareness of and responses to gender inequities in their schools, this paper explores teachers’ perceptions of school climate with regard to gender inequity and teachers’ perceptions of their own efforts to challenge these inequities. Findings suggest that teachers who participated in the program made significant inroads in their thinking related to gender equity. Findings also suggest that considerable challenges, especially around personal growth, awareness, and change persist and that teachers continue to grapple with gender bias, both within themselves and in their schools.
Optimal power allocation for an energy harvesting estimation system

Optimal transmit power allocation strategies are proposed for an energy harvesting estimation system, where energy can be harvested from the environment and buffered in a battery for future use. With the aim of minimizing the mean squared error at the receiver, two types of side information (SI) available to the transmitter are considered: causal SI (energy harvested in the past) and non-causal SI (energy harvested in the past, present and future). For the case where non-causal SI is available and battery storage is unlimited, it is shown that the optimal power allocation can be attained by a simple water-filling-like procedure, where the water level follows a non-decreasing staircase function. Dynamic programming is used to optimize the allocation policy when causal SI is available. The issue of unknown transmit power at the receiver is also addressed.
Automatic generation of geographical networks for maritime traffic surveillance

In this paper, an algorithm is proposed to automatically produce hierarchical graph-based representations of maritime shipping lanes extrapolated from historical vessel positioning data. Each shipping lane is generated based on the detection of the vessel behavioural changes and represented in a compact synthetic route composed of the network nodes and route segments. The outcome of the knowledge discovery process is a geographical maritime network that can be used in Maritime Situational Awareness (MSA) applications such as track reconstruction from missing information, situation/destination prediction, and detection of anomalous behaviour. Experimental results are presented, testing the algorithm in a specific scenario of interest, the Dover Strait.
Cellular Phenotype-Dependent and -Independent Effects of Vitamin C on the Renewal and Gene Expression of Mouse Embryonic Fibroblasts

Vitamin C has been shown to delay the cellular senescence and was considered a candidate for chemoprevention and cancer therapy. To understand the reported contrasting roles of vitamin C: growth-promoting in the primary cells and growth-inhibiting in cancer cells, primary mouse embryonic fibroblasts (MEF) and their isogenic spontaneously immortalized fibroblasts with unlimited cell division potential were used as the model pair. We used microarray gene expression profiling to show that the immortalized MEF possess human cancer gene expression fingerprints including a pattern of up-regulation of inflammatory response-related genes. Using the MEF model, we found that a physiological treatment level of vitamin C (10−5 M), but not other unrelated antioxidants, enhanced cell growth. The growth-promoting effect was associated with a pattern of enhanced expression of cell cycle- and cell division-related genes in both primary and immortalized cells. In the immortalized MEF, physiological treatment levels of vitamin C also enhanced the expression of immortalization-associated genes including a down-regulation of genes in the extracellular matrix functional category. In contrast, confocal immunofluorescence imaging of the primary MEF suggested an increase in collagen IV protein upon vitamin C treatment. Similar to the cancer cells, the growth-inhibitory effect of the redox-active form of vitamin C was preferentially observed in immortalized MEF. All effects of vitamin C required its intracellular presence since the transporter-deficient SVCT2−/− MEF did not respond to vitamin C. SVCT2−/− MEF divided and became immortalized readily indicating little dependence on vitamin C for the cell division. Immortalized SVCT2−/− MEF required higher concentration of vitamin C for the growth inhibition compared to the immortalized wildtype MEF suggesting an intracellular vitamin C toxicity. The relevance of our observation in aging and human cancer prevention was discussed.
A ROBUST GA/KNN BASED HYPOTHESIS VERIFICATION SYSTEM FOR VEHICLE DETECTION

Vehicle detection is an important issue in driver assistance systems and self-guided vehicles that includes two stages of hypothesis generation and verification. In the first stage, potential vehicles are hypothesized and in the second stage, all hypothesis are verified. The focus of this work is on the second stage. We extract Pyramid Histograms of Oriented Gradients (PHOG) features from a traffic image as candidates of feature vectors to detect vehicles. Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are applied to these PHOG feature vectors as dimension reduction and feature selection tools parallelly. After feature fusion, we use Genetic Algorithm (GA) and cosine similarity-based K Nearest Neighbor (KNN) classification to improve the performance and generalization of the features. Our tests show good classification accuracy of more than 97% correct classification on realistic on-road vehicle images.
Clusterability Detection and Initial Seed Selection inLarge Data

The need for a preliminary assessment of the clustering tendency or clusterability of massive data sets is known. A good clusterability detection method should serve to innuence a decision as to whether to cluster at all, as well as provide useful seed input to a chosen clustering algorithm. We present a framework for the deenition of the clusterability of a data set from a distance-based perspective. We discuss a graph-based system for detecting clusterability and generating seed information including an estimate of the value of k { the number of clusters in the data set, an input parameter to many distance-based clustering methods. The output of our method is tunable to accommodate a wide variety of clustering methods. We have conducted a number of experiments using our methodology with stock market data and with the well-known BIRCH data sets, in two as well as higher dimensions. Based on our experiments and results we nd that our methodology can serve as the basis for much future work in this area. We report our results and discuss promising future directions.
Robust invariant automatic image mosaicing and super resolution for UAV mapping

The aim of this paper is to develop efficient, robust and automated image frame fusion algorithms for mosaicing and super-resolution. Image registration is the key step in combining multiple independent low-resolution images to give one large mosaic image with high resolution. Our research is based on Scale Invariant Features Transform (SIFT detector/descriptor) for geometric and photometric images registration for mosaicing. The proposed super-resolution technique is also based on SIFT features and integrated to the image mosaicing in order to construct a full high resolution UAV map that is insensitive to the order, orientation, scale and illumination of the images. Real data validations show the effectiveness of our proposed techniques in comparison with other classical techniques.
Theoretical Perspective of Complexity of Evolutionary Algorithms Adopting Optimal Mixing

The optimal mixing evolutionary algorithms (OMEAs) have recently drawn much attention for their robustness, small size of required population, and efficiency in terms of number of function evaluations (NFE). In this paper, the performances and behaviors of OMEAs are studied by investigating the mechanism of optimal mixing (OM), the variation operator in OMEAs, under two scenarios—one-layer and two-layer masks. For the case of one-layer masks, the required population size is derived from the viewpoint of initial supply, while the convergence time is derived by analyzing the progress of sub-solution growth. NFE is then asymptotically bounded with rational probability by estimating the probability of performing evaluations. For the case of two-layer masks, empirical results indicate that the required population size is proportional to both the degree of cross competition and the results from the one-layer-mask case. The derived models also indicate that population sizing is decided by initial supply when disjoint masks are adopted, that the high selection pressure imposed by OM makes the composition of sub-problems impact little on NFE, and that the population size requirement for two-layer masks increases with the reverse-growth probability.
The development and evaluation of a survey to measure user engagement

Facilitating engaging user experiences is essential in the design of interactive systems. To accomplish this, it is necessary to understand the composition of this construct and how to evaluate it. Building on previous work that posited a theory of engagement and identified a core set of attributes that operationalized this construct, we constructed and evaluated a multidimensional scale to measure user engagement. In this paper we describe the development of the scale, as well as two large-scale studies (N=440 and N=802) that were undertaken to assess its reliability and validity in online shopping environments. In the first we used Reliability Analysis and Exploratory Factor Analysis to identify six attributes of engagement: Perceived Usability, Aesthetics, Focused Attention, Felt Involvement, Novelty, and Endurability. In the second we tested the validity of and relationships among those attributes using Structural Equation Modeling. The result of this research is a multidimensional scale that may be used to test the engagement of software applications. In addition, findings indicate that attributes of engagement are highly intertwined, a complex interplay of user-system interaction variables. Notably, Perceived Usability played a mediating role in the relationship between Endurability and Novelty, Aesthetics, Felt Involvement, and Focused Attention.
Understanding Fit and Appropriation Effects in Group Support Systems via Meta-Analysis

Many previous papers have lamented the fact that the findings of past GSS research have been inconsistent. This paper develops a new model for interpreting GSS effects on performance (a Fit-Appropriation Model), which argues that GSS performance is affected by two factors. The first is the fit between the task and the GSS structures selected for use (i.e., communication support and information processing support). The second is the appropriation support the group receives in the form of training, facilitation, and software restrictiveness to help them effectively incorporate the selected GSS structures into their meeting process. A meta-analysis using this model to organize and classify past research found that when used appropriately (i.e., there is a fit between the GSS structures and the task, and the group receives appropriation support), GSS use increased the number of ideas generated, took less time, and led to more satisfied participants than if the group worked without the GSS. Fitting the GSS to the task had the most impact on outcome effectiveness (decision quality and ideas), while appropriation support had the most impact on the process (time required and process satisfaction). We conclude that when using this theoretical lens, the results of GSS research do not appear inconsistent.
Annotated bibliography on the economic effects of global climate change on fisheries

S OF PAPERS PRESENTED AT THE WORKSHOP ON ECONOMIC EFFECTS OF CLIMATE CHANGE ON FISHERIES, 20-21 JUNE 2005, BERGEN, NORWAY. THESE ABSTRACTS AND THE PAPERS THEY REPRESENT ARE IN DRAFT FORM AND ARE NOT YET FOR CITATION. Arnason, R. Climate change and fisheries: assessing the economic impact in Iceland and Greenland. Climate changes in the 21st century are expected to significantly increase ocean temperatures and modify other oceanographic conditions in the North Atlantic. Fisheries biological predictions suggest that the impacts on the commercially most important fish stocks in the Icelandic-Greenland ecosystem may well be quite substantial. Obviously, there will be a commensurate impact on the economies of these two countries. However, the timing, extent and biological impact of global warming is quite uncertain. As a result, the economic impact is similarly uncertain. This paper attempts to provide estimates of this impact on the Icelandic and Greenland economies. The approach is one of stochastic simulations, which involves essentially three steps: The first step is to obtain predictions of the impact of global warming on fish stocks and, even more importantly, a probability distribution for that prediction. The second step is to estimate the role of the fisheries sector in the two economies. This is done with the help of modern econometric techniques based on standard economic growth theory and historical data. Obviously these estimates are also subject to stochastic errors and uncertainty. The third step is to carry out Monte Carlo simulations on the basis of the above model and the associated uncertainties. The result of the Monte Carlo simulations consists of a set of dynamic paths for gross domestic product (GDP) over time with some expected value and distribution in each future year. On this basis, it is possible to calculate confidence intervals for the most likely path of GDP over time. Preliminary results indicate that the fisheries impact of global warming on the Icelandic GDP is more likely to be positive rather than negative and unlikely to be substantial compared to historically experienced growth rates and fluctuations. The uncertainty of that prediction, however, is large. For Greenland, the impact on fish stocks and the GDP is highly likely to be positive and quite substantial relative to the current GDP. Due to less knowledge of the relationship between the fisheries sector and the Greenland economy, however, the confidence interval of this prediction is even wider than in the case of Iceland.
Performance optimization of media distribution in overlay networks using OpenFlow

Throughout the last decade, overlay multicast is a hot issue for media distribution services. High bandwidth consumption and multi-source multicasting in widely distributed networks is a critical issue for a wide range of applications including video conferencing, multi-party games, content distribution etc. A number of research projects have explored to use multicast as an efficient and scalable mechanism to deal with group oriented communications. However, there are still some limitations, such as long latency, duplicated packet transmission at certain points and lack of network equipment functionality support. With the appearance of Software Defined Networking, represented by OpenFlow devices, network control and management becomes possible to remote administrators. With the support of OpenFlow, it is possible to do some optimizations of existing media distribution mechanisms in overlay networks. In this paper, we proposed a novel method to improve the traditional overlay multicast relay networks with available limited number of OpenFlow devices, to cut down the bandwidth expenditures and perform a good control and management of media distribution services.
Viabilidad del sistema de pensiones y reformas por el lado de los ingresos: una aproximacion intergeneracional

This paper examines, using the Generational Accounting method, the long run viability and the intergenerational income redistribution effects of the Spanish public pension system. Given the huge intertemporal debt accumulated by the current pension system, which is allocated entirely to generations not yet born, we explore the impact of alternative revenue policy reforms as a way to mitigate the strong demographic dependency of Social Security financing resources. Our findings indicate that, due to a rapid demographic ageing, these type of reform measures are not clearly enough to restore the intergenerational balance.
Effect of Dietary Fat Supplementation during Late Pregnancy and First Six Months of Lactation on Maternal and Infant Vitamin A Status in Rural Bangladesh

Dietary fat intake is extremely low in most communities with vitamin A deficiency. However, its role in vitamin A status of pregnant and lactating women is poorly understood. The aim of the study was to examine the effect of supplementing women with fat from mid-/late pregnancy until six months postpartum on their vitamin A status and that of their infants. Women recruited at 5-7 months of gestation were supplemented daily with 20 mL of soybean-oil (n=248) until six months postpartum or received no supplement (n=251). Dietary fat intake was assessed by 24-hour dietary recall at enrollment and at 1, 3 and 6 months postpartum. Concentrations of maternal plasma retinol, β-carotene, and lutein were measured at enrollment and at 1, 3 and 6 months postpartum, and those of infants at six months postpartum. Concentration of breastmilk retinol was measured at 1, 3 and 6 months postpartum. The change in concentration of plasma retinol at three months postpartum compared to pregnancy was significantly higher in the supplemented compared to the control women (+0.04 vs -0.07 μmol/L respectively; p<0.05). Concentrations of plasma β-carotene and lutein declined in both the groups during the postpartum period but the decline was significantly less in the supplemented than in the control women at one month (β-carotene -0.07 vs -0.13 μmol/L, p<0.05); lutein -0.26 vs -0.49 μmol/L, p<0.05) and three months (β-carotene -0.04 vs -0.08 μmol/L, p<0.05; lutein -0.31 vs -0.47 μmol/L, p<0.05). Concentration of breastmilk retinol was also significantly greater in the supplemented group at three months postpartum than in the controls (0.68±0.35 vs 0.55±0.34 μmol/L respectively, p<0.03). Concentrations of infants’ plasma retinol, β-carotene, and lutein, measured at six months of age, did not differ between the groups. Fat supplementation during pregnancy and lactation in women with a very low intake of dietary fat has beneficial effects on maternal postpartum vitamin A status.
Chloride Cotransporters as a Molecular Mechanism underlying Spreading Depolarization-Induced Dendritic Beading

Spreading depolarizations (SDs) are waves of sustained neuronal and glial depolarization that propagate massive disruptions of ion gradients through the brain. SD is associated with migraine aura and recently recognized as a novel mechanism of injury in stroke and brain trauma patients. SD leads to neuronal swelling as assessed in real time with two-photon laser scanning microscopy (2PLSM). Pyramidal neurons do not express aquaporins and thus display low inherent water permeability, yet SD rapidly induces focal swelling (beading) along the dendritic shaft by unidentified molecular mechanisms. To address this issue, we induced SD in murine hippocampal slices by focal KCl microinjection and visualized the ensuing beading of dendrites expressing EGFP by 2PLSM. We confirmed that dendritic beading failed to arise during large (100 mOsm) hyposmotic challenges, underscoring that neuronal swelling does not occur as a simple osmotic event. SD-induced dendritic beading was not prevented by pharmacological interference with the cytoskeleton, supporting the notion that dendritic beading may result entirely from excessive water influx. Dendritic beading was strictly dependent on the presence of Cl−, and, accordingly, combined blockade of Cl−-coupled transporters led to a significant reduction in dendritic beading without interfering with SD. Furthermore, our in vivo data showed a strong inhibition of dendritic beading during pharmacological blockage of these cotransporters. We propose that SD-induced dendritic beading takes place as a consequence of the altered driving forces and thus activity for these cotransporters, which by transport of water during their translocation mechanism may generate dendritic beading independently of osmotic forces. SIGNIFICANCE STATEMENT Spreading depolarization occurs during pathological conditions such as stroke, brain injury, and migraine and is characterized as a wave of massive ion translocation between intracellular and extracellular space in association with recurrent transient focal swelling (beading) of dendrites. Numerous ion channels have been demonstrated to be involved in generation and propagation of spreading depolarization, but the molecular machinery responsible for the dendritic beading has remained elusive. Using real-time in vitro and in vivo two-photon laser scanning microscopy, we have identified the transport mechanisms involved in the detrimental focal swelling of dendrites. These findings have clear clinical significance because they may point to a new class of pharmacological targets for prevention of neuronal swelling that consequently will serve as neuroprotective agents.
Study of TCM clinical records based on LSA and LDA SHTDT model

Description of syndromes and symptoms in traditional Chinese medicine are extremely complicated. The method utilized to diagnose a patient's syndrome more efficiently is the primary aim of clinical health care workers. In the present study, two models were presented concerning this issue. The first is the latent semantic analysis (LSA)-based semantic classification model, which is employed when the classification and words used to depict these classfications have been confirmed. The second is the symptom-herb-therapies-diagnosis topic (SHTDT), which is employed when the classification has not been confirmed or described. The experimental results showed that this method was successful, and symptoms can be diagnosed to a certain extent. The experimental results indicated that the topic feature reflected patient characteristics and the topic structure was obtained, which was clinically significant. The experimental results showed that when provided with a patient's symptoms, the model can be used to predict the theme and diagnose the disease, and administer appropriate drugs and treatments. Additionally, the SHTDT model prediction results did not yield completely accurate results because this prediction is equivalent to multi-label prediction, whereby the drugs, treatment and diagnosis are considered as labels. In conclusion, diagnosis, and the drug and treatment administered are based on human factors.