An autoregressive conditional filtering process to remove intraday seasonal volatility and its application to testing the noisy rational expectations model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

The role of overseas Chinese investment in the emerging countries of Southeast and East Asia: A Confucian model of the foreign direct investment decision-making process using factors unconsidered in the West

The purpose of this study was to gain a better understanding of the foreign direct investment location decision making process through the examination of non-Western investors and their investment strategies in non-traditional markets. This was accomplished through in-depth personal interviews with 50 Overseas Chinese business owners and executives in several different industries from Hong Kong, Singapore, Taiwan, Malaysia, and Thailand about 97 separate investment projects in Southeast and East Asia, including The Philippines, Malaysia, Hong Kong, Singapore, Vietnam, India, Pakistan, South Korea, Australia, Indonesia, Cambodia, Thailand, Burma, Taiwan, and Mainland China.^ Traditional factors utilized in Western models of the foreign direct investment decision making process are reviewed, as well as literature on Asian management systems and the current state of business practices in emerging countries of Southeast and East Asia. Because of the lack of institutionalization in these markets and the strong influences of Confucian and patriarchal value systems on the Overseas Chinese, it was suspected that while some aspects of Western rational economic models of foreign direct investment are utilized, these models are insufficient in this context, and thus are not fully generalizable to the unique conditions of the Overseas Chinese business network in the region without further modification.^ Thus, other factors based on a Confucian value system need to be integrated into these models. Results from the analysis of structured interviews suggest Overseas Chinese businesses rely more heavily on their network and traditional Confucian values than rational economic factors when making their foreign direct investment location decisions in emerging countries in Asia. This effect is moderated by the firm's industry and the age of the firm's owners. ^

An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

Joint Compensation of I/Q Impairments and PA Nonlinearity for Concurrent Dual-Band Transmitters using Two-Box Model

A novel two-box model for joint compensation of nonlinear distortion introduced from both in-phase/quadrature modulator and power amplifier is proposed for concurrent dual-band wireless transmitters. Compensation of nonlinear distortion is accomplished in two phases, where phases are identified separately. It is shown that complexity of the digital predistortion is reduced. The performance of the proposed model is evaluated in terms of ACPR, EVM and NMSE improvements using 1.4 MHz LTE and WCDMA signals.

A MODEL FOR EVALUATING IMPACT OF BUSINESS PROCESS MANAGEMENT IN ORGANIZATIONAL PERFORMANCE

Business Process Management (BPM) is able to organize and frame a company focusing in the improvement or assurance of performance in order to gain competitive advantage. Although it is believed that BPM improves various aspects of organizational performance, there has been a lack of empirical evidence about this. The present study has the purpose to develop a model to show the impact of business process management in organizational performance. To accomplish that, the theoretical basis required to know the elements that configurate BPM and the measures that can evaluate the BPM success on organizational performance is built through a systematic literature review (SLR). Then, a research model is proposed according to SLR results. Empirical data will be collected from a survey of  larg and mid-sized industrial and service companies headquartered in Brazil. A quantitative analysis will be performed using structural equation modeling (SEM) to show if the direct effects among BPM and organizational performance can be considered statistically significant. At the end will discuss these results and their managerial and cientific implications.Keywords: Business process management (BPM). Organizational performance. Firm performance. Business models. Structural Equation Modeling. Systematic Literature Review.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.

Cancers of unknown primary site (CUPs) as a model of metastatic process.

Cancers of unknown primary site (CUPs) are a rare group of metastatic tumours, with a frequency of 3-5%, with an overall survival of 6-10 month. The identification of tumour primary site is usually reached by a combination of diagnostic investigations and immunohistochemical testing of the tumour tissue. In CUP patients, these investigations are inconclusive. Since international guidelines for treatment are based on primary site indication, CUP treatment requires a blind approach. As a consequence, CUPs are usually empiric treated with poorly effective. In this study, we applied a set of microRNAs using EvaGreen-based Droplet Digital PCR in a retrospective and prospective collection of formalin-fixed paraffin-embedded tissue samples. We assessed miRNA expression of 155 samples including primary tumours (N=94), metastases of known origin (N=10) and metastases of unknown origin (N=50). Then, we applied the shrunken centroids predictive algorithm to obtain the CUP’s site(s)-of-origin. The molecular test was successfully applied to all CUP samples and provided a site-of-origin identification for all samples, potentially within a one-week time frame from sample inclusion. In the second part of the study we derived two CUP cell lines, and corresponding patient-derived xenografts (PDXs). CUP cell lines and PDXs underwent histological, molecular, and genomic characterization confirming the features of the original tumour. Tissues-of-origin prediction was obtained from the tumour microRNA expression profile and confirmed by single cell RNA sequencing. Genomic testing analysis identified FGFR2 amplification in both models. Drug-screening assays were performed to test the activity of FGFR2-targeting drug and the combination treatment with the MEK inhibitor trametinib, which proved to be synergic and exceptionally active, both in vitro and in vivo. In conclusion, our study demonstrated that miRNA expression profiling could be employed as diagnostic test. Then we successfully derived two CUP models from patients, used for therapy tests, bringing personalized therapy closer to CUP patients.

A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.

Improved Selective Cholesterol Adsorption By Molecularly Imprinted Poly(methacrylic Acid)/silica (pmaa-sio₂) Hybrid Material Synthesized With Different Molar Ratios.

The present paper describes the synthesis of molecularly imprinted polymer - poly(methacrylic acid)/silica and reports its performance feasibility with desired adsorption capacity and selectivity for cholesterol extraction. Two imprinted hybrid materials were synthesized at different methacrylic acid (MAA)/tetraethoxysilane (TEOS) molar ratios (6:1 and 1:5) and characterized by FT-IR, TGA, SEM and textural data. Cholesterol adsorption on hybrid materials took place preferably in apolar solvent medium, especially in chloroform. From the kinetic data, the equilibrium time was reached quickly, being 12 and 20 min for the polymers synthesized at MAA/TEOS molar ratio of 6:1 and 1:5, respectively. The pseudo-second-order model provided the best fit for cholesterol adsorption on polymers, confirming the chemical nature of the adsorption process, while the dual-site Langmuir-Freundlich equation presented the best fit to the experimental data, suggesting the existence of two kinds of adsorption sites on both polymers. The maximum adsorption capacities obtained for the polymers synthesized at MAA/TEOS molar ratios of 6:1 and 1:5 were found to be 214.8 and 166.4 mg g(-1), respectively. The results from isotherm data also indicated higher adsorption capacity for both imprinted polymers regarding to corresponding non-imprinted polymers. Nevertheless, taking into account the retention parameters and selectivity of cholesterol in the presence of structurally analogue compounds (5-α-cholestane and 7-dehydrocholesterol), it was observed that the polymer synthesized at the MAA/TEOS molar ratio of 6:1 was much more selective for cholesterol than the one prepared at the ratio of 1:5, thus suggesting that selective binding sites ascribed to the carboxyl group from MAA play a central role in the imprinting effect created on MIP.

Prostatic Angiogenic Responses In Late Life: Antiangiogenic Therapy Influences And Relation With The Glandular Microenvironment In The Transgenic Adenocarcinoma Of Mouse Prostate (tramp) Model.

Aging is considered one of the main predisposing factors for the development of prostate malignancies. Angiogenesis is fundamental for tumor growth and its inhibition represents a promising therapeutic approach in cancer treatment. Thus, we sought to determine angiogenic responses and the effects of antiangiogenic therapy in the mouse prostate during late life, comparing these findings with the prostatic microenvironment in the Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) model. Male mice (52 week-old FVB) were submitted to treatments with SU5416 (6 mg/kg; i.p.) and/or TNP-470 (15 mg/kg; s.c.). Finasteride was administered (20 mg/kg; s.c.), alone or in association to both inhibitors. The dorsolateral prostate was collected for VEGF, HIF-1α, FGF-2 and endostatin immunohistochemical and Western Blotting analyses and for microvessel density (MVD) count. Senescence led to increased MVD and VEGF, HIF-1α and FGF-2 protein levels in the prostatic microenvironment, similarly to what was observed in TRAMP mice prostate. The angiogenic process was impaired in all the treated groups, demonstrating significantly decreased MVD. Antiangiogenic and/or finasteride treatments resulted in decreased VEGF and HIF-1α levels, especially following TNP-470 administration, either alone or associated to SU5416. The combination of these agents resulted in increased endostatin levels, regardless of the presence of finasteride. Prostatic angiogenesis stimulation during senescence favored the development of neoplastic lesions, considering the pro-angiogenic microenvironment as a common aspect also observed during cancer progression in TRAMP mice. The combined antiangiogenic therapy was more efficient, leading to enhanced imbalance towards angiogenic inhibition in the organ. Finally, finasteride administration might secondarily upregulate the expression of pro-angiogenic factors, pointing to the harmful effects of this therapy. Prostate 75: 484-499, 2015. © 2014 Wiley Periodicals, Inc.

Gut Bacteria Products Prevent Aki Induced By Ischemia-reperfusion.

Short-chain fatty acids (SCFAs) are fermentation end products produced by the intestinal microbiota and have anti-inflammatory and histone deacetylase-inhibiting properties. Recently, a dual relationship between the intestine and kidneys has been unraveled. Therefore, we evaluated the role of SCFA in an AKI model in which the inflammatory process has a detrimental role. We observed that therapy with the three main SCFAs (acetate, propionate, and butyrate) improved renal dysfunction caused by injury. This protection was associated with low levels of local and systemic inflammation, oxidative cellular stress, cell infiltration/activation, and apoptosis. However, it was also associated with an increase in autophagy. Moreover, SCFAs inhibited histone deacetylase activity and modulated the expression levels of enzymes involved in chromatin modification. In vitro analyses showed that SCFAs modulated the inflammatory process, decreasing the maturation of dendritic cells and inhibiting the capacity of these cells to induce CD4(+) and CD8(+) T cell proliferation. Furthermore, SCFAs ameliorated the effects of hypoxia in kidney epithelial cells by improving mitochondrial biogenesis. Notably, mice treated with acetate-producing bacteria also had better outcomes after AKI. Thus, we demonstrate that SCFAs improve organ function and viability after an injury through modulation of the inflammatory process, most likely via epigenetic modification.