Miracle-dz/UniFAR-FTUs huggingface dataset:
ON THE ESTIMATION AND TESTING OF TIME VARYING CONSTRAINTS IN ECONOMETRIC MODELS

In this paper, we propose a new nonparametric method to impose time varying restrictions in econometric models. The method relies on the assumption that the structural parameters evolve smoothly over time and they are required to satisfy some pre-specied constraints. Smoothing techniques are employed in order to estimate the parameter sequences. In particular, we propose a local constrained least squares method. The asymptotic properties are derived and a test proce- dure for the validity of the time varying constraints is derived. The methodology is applied to estimate a time varying demand system and a simulation study is performed to illustrate the estimation method as well as the test procedure.
High-Latitude Stratospheric Sensitivity to QBO Width in a Chemistry-Climate Model with Parameterized Ozone Chemistry

In a pair of idealized simulations with a simplified chemistry-climate model, the sensitivity of the wintertime Arctic stratosphere to variability in the width of the quasi-biennial oscillation (QBO) is assessed. The width of the QBO appears to have equal influence on the Arctic stratosphere as does the phase (i.e. the Holton-Tan mechanism). In the model, a wider QBO acts like a preferential shift toward the easterly phase of the QBO, where zonal winds at 60 N tend to be relatively weaker, while 50 hPa geopotential heights and polar ozone values tend to be higher.
Centre Emile Bernheim

: The paper assess the relative importance of trade and foreign exchange liberalization, infrastructure availability and economic and political stability in increasing Middle East and North African (MENA) countries attractiveness with respect to FDI. The analysis is conducted for total FDI and for FDI in manufacturing. The results show that trade and foreign exchange liberalization, infrastructure availability and sound economic and political conditions increase FDI inflows. Their effects are much higher for FDI in the manufacturing sector than for total FDI. This result is robust to alternative indicators of trade and foreign exchange liberalization, and to change in the specification. The message to MENA’s policy makers is twofold. First, efforts toward trade and foreign exchange liberalization should be initiated or further increased in order to make the region attractive to foreign investors. Second improvements in other aspects of the investment climate are important complements to liberalization and result in additional and sensitive increase of FDI inflows. of the article: 20 pages Abstract: The paper assess the relative importance of trade and foreign exchange liberalization, infrastructure availability and economic and political stability in increasing Middle East and North African (MENA) countries attractiveness with respect to FDI. The analysis is conducted for total FDI and for FDI in manufacturing. The results show that trade and foreign exchange liberalization, infrastructure availability and sound economic and political conditions increase FDI inflows. Their effects are much higher for FDI in the manufacturing sector than for total FDI. This result is robust to alternative indicators of trade and foreign exchange liberalization, and to change in the specification. The message to MENA’s policy makers is twofold. First, efforts toward trade and foreign exchange liberalization should be initiated or further increased in order to make the region attractive to foreign investors. Second improvements in other aspects of the investment climate are important complements to liberalization and result in additional and sensitive increase of FDI inflows.
Integrating Backcasting and Eco‐Design for the Circular Economy: The BECE Framework

The circular economy (CE) is essential for decoupling economic growth from resource consumption and environmental impacts. However, effective implementation requires a systemic change across supply chains, involving both technological and nontechnological innovations. Frameworks are beginning to emerge to foster CE thinking in organizations. However, literature review carried out as part of this research has revealed gaps in their ability to fulfil CE requirements. Furthermore, few frameworks provide support on how CE requirements may be implemented. To address these issues, this article presents a new framework, BECE (backcasting and eco‐design for the circular economy), to ensure that businesses can implement CE requirements more readily. BECE empowers organizations to tackle the CE holistically by embedding the concept into corporate decision making and by bringing operational and systems thinking together, thus increasing the likelihood of successful implementation. The potential of the BECE framework was tested through a pilot workshop focusing on the development of a CE business model through redesign of products and supply chains. Using vacuum cleaners as an illustrative case study, several product design and supply‐chain alternatives were identified, including the development of scenarios and action plans for their implementation at the business level. Although the case study focuses on a particular product, the BECE framework is generic and applicable across different products and business sectors.
Term Premium Variability and Monetary Policy

This chapter integrates two traditional explanations for the mean and variability of the term premium into a medium-scale DSGE model: (i) time-varying risk premiums on long bonds and (ii) segmented markets between short and long bond markets. Two sources of business cycle variability, shocks to total factor productivity (TFP) and the marginal efficiency of investment (MEI), are included. The authors characterize the ability of each approach to match both the mean and the variability of the term premium when either shock is the driving source of variation in the model. The risk approach fails with a negative average term premium in response to MEI shocks and trivially small variability in the term premium overall. In contrast, the segmented markets model can easily match both moments. Market segmentation reflects a real distortion, thus smoothing the term premium is typically welfare-improving, although there are potential difficulties with such a policy.
Bayesian Inference of a Multivariate Regression Model

We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
No 1402 DECISION SUPPORT FOR NEGOTIATION PROTOCOL SELECTION : A MACHINE LEARNING APPROACH BASED ON ARTIFICIAL NEURAL NETWORKS

Decision making in operational planning is increasingly affected by conflicting interests of different stakeholders such as subcontractors, customers, or strategic partners. Addressing this, automated negotiation is a well-suited mechanism to mediate between stakeholders and search for jointly beneficial agreements. However, the outcome of a negotiation is strongly dependent on the applied negotiation protocol defining the rules of encounter. Although protocol design is well discussed in literature, the question on which protocol should be selected for a given scenario is little regarded so far. Since negotiation problems and protocols are very diverse, the protocol choice itself is a challenging task. In this study, we propose a decision support system for negotiation protocol selection (DSS-NPS) that is based on a machine learning approach – an artificial neural network (ANN). Besides presenting and discussing the system, we, furthermore, evaluate the design artifact in elaborate computational experiments that take place in an intercompany machine scheduling environment. Our findings indicate that the proposed decision support system is able to improve the outcome of negotiations by finding adequate protocols dynamically on the basis of the underlying negotiation problem characteristics.
Energy loss spectra of doped graphene and armchair carbon nanotubes at finite temperature

We have studied the dynamical polarizability associated to energy loss spectra of armchair carbon nanotubes in the context of tight binding model Hamiltonian including long range electronic interaction. Linear response theory in the context of random phase approximation has been implemented to obtain charge response function via calculating correlation function of density operators. The effects of both temperature and electron doping as well as the diameter on the frequency behaviour of charge response have been investigated. The sharp peak in the energy loss spectra of the armchair carbon nanotube disappears upon raising the temperature. This is not the case of non interacting case where temperature has no considerable effect on the dynamical susceptibility. Also, the similar calculations have been performed for graphene sheet.
Development and Implementation of a Monitoring System for STRC with the Aid of Head Detection

The Development and Implementation of a Monitoring System for STRC with the Aid of Head Detection aims to automate the research room operations to increase its efficiency. It consists of a head detection system, website managed database and a mobile application controlled room access. The website and mobile application cater to two types of users namely, the administrator and the students. The website exclusively features announcements and management of equipment inventory both handled by the administrator. The mobile application features an IN/OUT button which manages the users access on the five (5) research rooms in the building. The head detection system detects, tracks and counts each person that enters a research room. The count noted by the system is then compared to the count of students that used the mobile application for room access. The e-mail and SMS notification system observes the comparison of the two counts and sends the appropriate notification to the administrator when there is a violation in the conditions set for the two systems. The database serves as the central point as it holds every information needed by each element in order to communicate with the whole system.
ASSOCIATION FOR CONSUMER RESEARCH

Three experiments demonstrate a power-matching effect. High power increases communicators’ generation of, and recipients’ responsiveness to, competent arguments. In contrast, low power increases communicators’ generation of, and recipients’ responsiveness to, warm arguments. Consequently, messages from powerful versus powerless communicators are more effective against recipients at the same level of power.
Soil Properties in Old-Growth Douglas-Fir Forest Gaps in the Western Cascade Mountains of Oregon

Abstract This was a study of vegetation and soil properties in tree-fall gaps in a coniferous forest of the Pacific Northwest. It had three objectives: (1) to determine if there are correlations between above-ground vegetation and below-ground soil properties within large 50 m diameter gaps, (2) to determine how large gaps influence forest soils compared with non-gap soils, and (3) to measure the effects of differently sized gaps on gap soils. To address these objectives, circular canopy gaps were created in old-growth Douglas-fir forests of the H. J. Andrews Experimental Forest in the western Oregon Cascade Mountains. To address the first objective, within-gap soil spatial patterns were compared with above ground distributions of both vegetation and large woody debris in two large gaps. Spatial and Pearson correlation analyses showed no consistent correlations between soil characteristics and above ground vegetation and coarse woody debris. With reference to the second objective, statistically significant differences between gap and non-gap soil characteristics were observed. Soil moisture, temperature and denitrification potentials were all elevated in forest 50 m diameter gaps and litter depth, labile C, soil respiration, &bgr;-gluosidase activity, and ectomytcorrhizal mat concentrations were all reduced. Comparisons between north and south gap soils, showed significant differences in soil characteristics in one but not the other 50 m gap. The third objective was addressed by documenting gap size effects on differences between gap and non gap soil characteristics in two gaps each of 10, 20, 30, and 50 m diameter. Differences between gap and nongap soil moisture, litter depth and ectomycorrhizal mat coverages were essentially the same regardless of gap size. Soil respiration rates and soil organic matter concentrations were similar in 10 m gaps but both lower in gaps 20 m and larger.
Quantification of Drug-Induced mRNA in Human Whole Blood ex vivo

Apoptosis was induced in heparinized human whole blood by 3 different ways (radiation, bleomycin, or etoposide), and various mRNA were quantified using the method we reported (Clin. Chem. 2006; 52:634-642). We found that cyclin-dependent kinase inhibitor 1A (p21) and p53 upregulated modulator of apoptosis (PUMA) were the most sensitive and universal mRNA markers of apoptosis in leukocytes. In order to define positive and negative responses, a synthetic RNA was spiked into the lysis buffer and the fold increase was calculated. As a result, 837/880 (95.1%) of data points stayed between 0.75 and 1.5 fold increase, and 874/880 (99.3%) were within 0.5-2.0 fold increase. When blood samples from 40 healthy adults were stimulated with 22 different drugs, more than 75% of the samples responded to bleomycin (1 μM), idarubicin (2 μM), vincristine (1 μM), daunorubicin (2 μM), cytarabine (10 μM), to induce p21 and/or PUMA mRNA, and approximately 25% showed no induction. Significant correlation was found between p21 and PUMA mRNA responses, and between daunorubicin and cytarabine, idarubicin, and vincristine for both p21 and PUMA. The quantification of drug-induced mRNA in whole blood will be considered as ex vivo, and is a suitable platform for biomarker screening as well as a model system for drug sensitivity tests in future.
Antibacterial properties of wild edible and non-edible mushrooms found in Zimbabwe

Mushrooms have been used extensively in traditional medicine as antimicrobial, antiviral and antitumor agents. Infectious diseases remain a major threat to human health, due to global antimicrobial resistance. This has led to an increase in the search for new and potent antimicrobial substances. The aim of the present study was to investigate the antimicrobial activity of the aqueous (cold and hot) and organic solvents (methanol, ethanol and acetone) extracts of ten mushroom species collected from the woodlands in Zimbabwe against common local bacterial isolates Escherichia coli, Salmonella typhi, Staphylococcus aureus and Streptococcus pneumoniae using agar disc diffusion method. The crude extracts of the mushrooms exhibited antibacterial properties to all the bacteria tested. Extracts obtained from ethanol were the most effective tested against bacteria (36.5%), followed by methanol (30.8%) and acetone (30.8%). Aqueous extracts exhibited the lowest effect on bacterial growth inhibition (1.9%), despite including the extract with the highest inhibitory activity (14 mm). The acetone extract of Cantharellus symoensii had the second highest inhibitory value of 11.5 mm followed by the methanol extract from Cantharellus miomboensis and the ethanol extracts of Ganoderma lucidum and C. symoensii with values 11.0, 10.67 and 10.0 mm, respectively. Cantharellus heinemannianus and C. symoensii had the highest effect on inhibition of bacteria as indicated by the different extracts showing high inhibitory properties ranging from 8-14 mm [15.4% (8) each] followed by G. lucidum [13.5% (7)], while Boletus edulis, Coprinus sp. and Trametes strumosa had the least [5.8% (3) each]. The positive results of screening local mushrooms for antibacterial activity forms the basis for further phytochemical studies and development of antimicrobial agents against common human bacterial and fungal infections. Key words: Antibacterial activity, Cantharellus species, Salmonella typhi, organic extracts, aqueous extracts.
The Effects of Prison-Based Educational Programming on Recidivism and Employment

This study evaluated the effectiveness of prison-based educational programming by examining the effects of obtaining secondary and post-secondary degrees on recidivism and post-release employment outcomes among offenders released from Minnesota prisons between 2007 and 2008. Obtaining a secondary degree in prison significantly increased the odds of securing post-release employment but did not have a significant effect on recidivism or other employment measures such as hourly wage, total hours worked, or total wages earned. Earning a post-secondary degree in prison, however, was associated with greater number of hours worked, higher overall wages, and less recidivism.
An Improved SIFT Algorithm Based on Color Vector

SIFT is a well-known algorithm on image retrieval with good precision and robustness, but its high computation time makes it difficult in practical projects. A fast SIFT algorithm based on color vector (CV-SIFT) is proposed to enhance the time performance and keep precision. A feature extracted method based on region-averaged color vector is introduced to achieve color descriptors, which is a new feature descriptor in CV-SIFT. At the matching stage, a new matching policy is used to reduce false matches by combining the threshold and ratio of similarity. Experiments demonstrate that the time performance of CV-SIFT is boosted greatly, and the precision is retained fairly.
EM based algorithms for Malaria diagnose via crowdsourcing

We live in a world in which medicine and technology are more united than ever. That is why in the last few years, lots of research groups initially dedicated to the development of technologies, have started to investigate in a field in which progresses are needed in order to protect the humanity against diseases, an this field is the medicine one. This work, following this trend, is focused on one of the diseases that affects the bast majority of tropical countries, Malaria. Along this final Degree Thesis, this disease will be the center of the work, firstly trying to sensitize the reader about its importance and after that, once the objectives have been defined, develop signal processing techniques and algorithms that in the end will count and detect malaria parasites via crowdsourcing, system that is explained in the Introduction chapter.
Automatic classification of municipal call data for quantitative urban drainage system analysis

Flooding in urban areas can be caused by heavy rainfall, improper planning or component failures. Quantification of these various causes to urban flood probability supports prioritisation of flood risk reduction measures. In many cases, a lack of data on flooding incidents impedes quantification of probability and risk. In the proposed paper we use municipal call data that describe citizens’ observations of urban flooding incidents to quantify flood probabilities in a quantitative fault tree analysis. Given the unstructured nature of call information, calls are first assigned to classes that correspond with causes of flooding as represented in the fault tree. Since manual classification of calls is very timeconsuming, pattern recognition routines are used to automatically classify the call data. The aim of this study is to assess whether by automatic classification of citizen’s calls, sufficient accuracy can be obtained to allow for use of the results in quantitative risk analysis. This is illustrated by application of automatic classification results in a quantitative fault tree analysis for urban flooding, for two cases with datasets of approximately 6000 calls. The results show that straightforward automatic classification routines can reach error rates below 20%. Largest classification errors occur for small classes, where few data are available to train the classifiers. Automatic classification errors lead to small deviations in the outcomes of quantitative fault tree analysis. Still, conclusions about the ranking of contributions to urban flooding that are to be drawn from fault tree analysis, remain intact.
Improved Differential Evolution Algorithm Based On Elite Group

By introduce the information entropy and the average-distance-amongst-points to analysis the population distribution in the process of evolution, and figured out the cause of the DE/best/* premature convergence is the control function of the current optimal individual to decrease the population diversity of the algorithm. Based on the number of base vectors, improved the DE algorithm by setting up the elite group, the elite differential evolution algorithm is proposed. Finally, several typical test functions are used to test the performance. The results show that the elite differential evolution algorithm has a good performance in the search success rate and the global search capability.
Interaction of magnetization and heat dynamics for pulsed domain wall movement with Joule heating

Pulsed domain wall movement is studied here in Ni80Fe20 nanowires on SiO2, using a fully integrated electrostatic, thermoelectric, and micromagnetics solver based on the Landau-Lifshitz-Bloch equation, including Joule heating, anisotropic magneto-resistance, and Oersted field contributions. During the applied pulse, the anisotropic magneto-resistance of the domain wall generates a dynamic heat gradient, which increases the current-driven velocity by up to 15%. Using a temperature-dependent conductivity, significant differences are found between the constant voltage-pulsed and constant current-pulsed domain wall movement: constant voltage pulses are shown to be more efficient at displacing domain walls whilst minimizing the increase in temperature, with the total domain wall displacement achieved over a fixed pulse duration having a maximum with respect to the driving pulse strength.
Switching criteria for hybrid rarefied gas flow solvers

Switching criteria for hybrid hydrodynamic/molecular gas flow solvers are developed, and are demonstrated to be more appropriate than conventional criteria for identifying thermodynamic non-equilibrium. For switching from a molecular/kinetic solver to a hydrodynamic (continuum-fluid) solver, the criterion is based on the difference between the hydrodynamic near-equilibrium fluxes (i.e. the Navier–Stokes stress and Fourier heat flux) and the actual values of stress and heat flux as computed from the molecular solver. For switching from hydrodynamics to molecular/kinetic, a similar criterion is used but the values of stress and heat flux are approximated through higher order constitutive relations; in this case, we use the R13 equations. The efficacy of our proposed switching criteria is tested within an illustrative hybrid kinetic/Navier–Stokes solver. For the test cases investigated, the results from the hybrid procedure compare very well with the full kinetic solution, and are obtained at a fraction of the computational cost.