Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery Planning and Management Control System in Company Crisis

The main goal of this paper is to analyze situations of financial crisis in Italian companies. The analysis has also been focused on the company’s attitude to overcome the financial crisis. Secondly, the authors have analyzed if the recovery project has required the introduction of a new management control system or changes to the existing management control system. The methodology is composed of several steps. First of all, the authors have analyzed the existing literature. Secondly, the authors have referred to a sample of 98 Italian recovery projects formalized by Italian private companies. The analysis of the literature and the recovery projects permits to achieve some provisional results, which have been tested through surveys. The methodology approach is inspired by the grounded theory. The findings have been several: (1) The crisis is a usual event in the lifecycle of a firm, due to a deterioration of its strength and a change of the context; (2) The timing factor has played a fundamental role in managing the crisis situation, because a late identification of the symptoms or a minimization of them can generate an irreversible situation; and (3) In addition, a recovery project includes the introduction of a new management control system or changes to the existing management control system. These new or modified management control systems use “feed-forward” mechanisms, which make early predictions about the changes in the characteristics of the competitive framework.
MMSE-Based Block Implementation of Constant Modulus Algorithm

This paper introduces a new parallel scheme of Godard's constant modulus algorithm which can be easily implemented by hardware. With the criterion of Godard's constant modulus, stochastic gradient decency and block minimum mean square error, it realizes a parallel equalizer by adaptively adjusting the tap-weight vector. From numeric analysis and simulation, it is manifested that our scheme for quadrature phase-shift keying (QPSK) communication systems can achieve fast parallel equalization at the cost of less than 3 dB BER performance loss than the theoretic BER performance under low Eb/No (Eb/No is less than 10 dB) when the channel condition is rather severe. Therefore, it can be used to overcome the speed limitation of hardware for designing and implementing parallel blind equalizers especially in digital modems with high data rate.
Adaptive Algorithms for Free-Surface Flow Problems 1

This paper provides a review of some of the most widely-used computational techniques for the solution of free-surface flow problems using adaptive numerical methods. Approaches that are discussed in detail include front-tracking methods with moving grids, auxiliary variable techniques such as level-set and phase-field methods, and techniques based upon thin-film approximations. Other approaches such as the marker and cell and volume of fluid methods are also described. In recent years a significant amount of research into the development and application of adaptive algorithms for a wide variety of free-surface flow problems has been undertaken by the author and his co-workers at the University of Leeds and so numerous illustrative examples are included from this body of work.
No changes in neocortical cell volumes or glial cell numbers in chronic alcoholic subjects compared to control subjects.

AIMS To study if the total glial cell population in the neocortex is intact in subjects with a history of severe alcohol abuse compared to control subjects. Further, to investigate whether the cortical nerve cell nuclei and nerve cell perikarya volumes are the same in chronic alcoholic subjects as in the control subjects. METHODS Using the stereological method, the optical rotator in a vertical design, the perikaryon cell volume and nuclei cell volume in the neocortex and its four subdivisions were studied in 11 alcoholics and 10 control subjects. Using the Cavalieri estimator of volumes and the optical disector for cell counting, we estimated the total number of glial cells in the neocortex and compared previous stereological results for chronic alcoholic subjects. RESULTS We found the mean neuronal cell volumes to be unaffected by severe alcohol abuse (p = 0.84) and a normal total number of glial cells (p = 0.39) in chronic alcoholic subjects compared to control subjects. CONCLUSION Only glial cells and dendritic/synaptic changes have so far been reported in stereological studies of the brains of alcoholic subjects. We thus have increasing evidence that it may be possible for some individuals to return to their previous cognitive abilities after cessation of alcohol which may give hope and encouragement for chronic alcoholic subjects to stop the abuse.
A new LMS/Newton algorithm for robust adaptive filtering in impulsive noise

This paper proposes a new LMS/Newton algorithm for robust adaptive filtering in impulse noise. The new algorithm is obtained by applying the non-linear filtering technique and the robust statistic approach to the conventional fast LMS/Newton method. A robust method for estimating the required threshold parameters for impulse suppression is also given. Simulation results show that aside from retaining the advantages of the LMS/Newton algorithm such as low complexity and numerical stability, the new algorithm is more robust and effective in suppressing the adverse effects of the impulses.
A Distributed Anytime Algorithm for Dynamic Task Allocation in Multi-Agent Systems

We introduce a novel distributed algorithm for multi-agent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fast-max-sum), and give it significant new capa- bilities: namely, an online pruning procedure that simplifies the problem, and a branch-and-bound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents. We empirically evaluate our algorithm against established benchmarks and find that, even in such large environments, a solution is found up to 31% faster, and with up to 23% more utility, than state-of-the-art approximation algorithms. In addition, our algorithm sends up to 30% fewer messages than current approaches when the set of agents or tasks changes.
Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice.

OBJECTIVE Selective serotonin reuptake inhibitors (SSRIs) are widely used to treat depression, but the rates, timing, and baseline predictors of remission in "real world" patients are not established. The authors' primary objectives in this study were to evaluate the effectiveness of citalopram, an SSRI, using measurement-based care in actual practice, and to identify predictors of symptom remission in outpatients with major depressive disorder. METHOD This clinical study included outpatients with major depressive disorder who were treated in 23 psychiatric and 18 primary care "real world" settings. The patients received flexible doses of citalopram prescribed by clinicians for up to 14 weeks. The clinicians were assisted by a clinical research coordinator in the application of measurement-based care, which included the routine measurement of symptoms and side effects at each treatment visit and the use of a treatment manual that described when and how to modify medication doses based on these measures. Remission was defined as an exit score of <or=7 on the 17-item Hamilton Depression Rating Scale (HAM-D) (primary outcome) or a score of <or=5 on the 16-item Quick Inventory of Depressive Symptomatology, Self-Report (QIDS-SR) (secondary outcome). Response was defined as a reduction of >or=50% in baseline QIDS-SR score. RESULTS Nearly 80% of the 2,876 outpatients in the analyzed sample had chronic or recurrent major depression; most also had a number of comorbid general medical and psychiatric conditions. The mean exit citalopram dose was 41.8 mg/day. Remission rates were 28% (HAM-D) and 33% (QIDS-SR). The response rate was 47% (QIDS-SR). Patients in primary and psychiatric care settings did not differ in remission or response rates. A substantial portion of participants who achieved either response or remission at study exit did so at or after 8 weeks of treatment. Participants who were Caucasian, female, employed, or had higher levels of education or income had higher HAM-D remission rates; longer index episodes, more concurrent psychiatric disorders (especially anxiety disorders or drug abuse), more general medical disorders, and lower baseline function and quality of life were associated with lower HAM-D remission rates. CONCLUSIONS The response and remission rates in this highly generalizable sample with substantial axis I and axis III comorbidity closely resemble those seen in 8-week efficacy trials. The systematic use of easily implemented measurement-based care procedures may have assisted in achieving these results.
Reliable and secure regions for the Gaussian wiretap channel using LDPC codes with granular HARQ

This paper investigates reliable and secure transmissions over the Gaussian wiretap channel. A physical layer coding scheme based on Low-Density-Parity-Check (LDPC) codes with granular Hybrid Automatic Repeat reQuest(HARQ) protocol is presented. Increasing the HARQ granularity aims at sending coded data at the minimum rate required for legitimate successful decoding. This minimizes the information leakage that may benefit to eavesdropping. It will be shown that the granularity increases the frame error rate of the eavesdropper. Since the secrecy level can be assessed through the bit error rate (BER) at the unintended receiver, intraframe and interframe error contamination is employed to convert the loss of only few packets into much higher BERs. After obtaining the BERs at the legitimate and illegitimate receivers the reliable and secret regions can be determined. It is observed that with granular HARQ and interframe error contamination, regions that are reliable and secure at the same time are expanded.
Chemical Library Screening and Structure-Function Relationship Studies Identify Bisacodyl as a Potent and Selective Cytotoxic Agent Towards Quiescent Human Glioblastoma Tumor Stem-Like Cells

Cancer stem-like cells reside in hypoxic and slightly acidic tumor niches. Such microenvironments favor more aggressive undifferentiated phenotypes and a slow growing "quiescent state" which preserves them from chemotherapeutic agents that essentially target proliferating cells. Our objective was to identify compounds active on glioblastoma stem-like cells, including under conditions that mimick those found in vivo within this most severe and incurable form of brain malignancy. We screened the Prestwick Library to identify cytotoxic compounds towards glioblastoma stem-like cells, either in a proliferating state or in more slow-growing "quiescent" phenotype resulting from non-renewal of the culture medium in vitro. Compound effects were assessed by ATP-level determination using a cell-based assay. Twenty active molecules belonging to different pharmacological classes have thus been identified. Among those, the stimulant laxative drug bisacodyl was the sole to inhibit in a potent and specific manner the survival of quiescent glioblastoma stem-like cells. Subsequent structure-function relationship studies led to identification of 4,4'-dihydroxydiphenyl-2-pyridyl-methane (DDPM), the deacetylated form of bisacodyl, as the pharmacophore. To our knowledge, bisacodyl is currently the only known compound targeting glioblastoma cancer stem-like cells in their quiescent, more resistant state. Due to its known non-toxicity in humans, bisacodyl appears as a new potential anti-tumor agent that may, in association with classical chemotherapeutic compounds, participate in tumor eradication.
A new in-network data reduction mechanism to gather data for mining wireless sensor networks

Recently, association rules for sensors have received a great deal of attention due to their importance in capturing the temporal relations between sensor nodes in wireless sensor networks (WSNs). Because of this capability, these rules can be used to improve the Quality of Service (QoS) of wireless sensor networks by participating in the resource management process. To mine sensor association rules, behavioral data that describes the sensors' activities over time must be extracted and accumulated at the central node (the Sink) for further analysis. Given the limited resources of sensor nodes, a well designed data gathering algorithm is required for gathering the behavioral data efficiently. In this paper, an in-network data reduction technique is proposed to reduce the amount of data that needs to be routed to the Sink by exploiting the redundancy between sensors' activities. The in-network reduction technique will be implemented on top of a data gathering tree that we refer to as the Minimum Nodes Data Gathering Tree (MNDGT), which consists of the nodes that will participate in formulating the sensor rules and those that are necessary for maintaining the minimum distance to the Sink. To report on the performance of the reduction mechanism, a comparison analysis with other two gathering schemas is introduced. Indeed, the results show that the in-network reduction technique is able to reduce the number of messages by a factor ranging from 10% to 70% compared to the other techniques.
Infant cry classification: Time frequency analysis

Acoustic analysis of infant cry has been the subject of a number of researchers since half decades ago. This paper addresses a simple time-frequency analysis based signal processing technique using short-time Fourier transform (STFT) for the investigation and classification of infant cry signals. A cluster of statistical features are derived from the time-frequency plots of infant cry signals. The extracted feature vectors are used to model and train two types of radial basis neural network namely Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) in classification phases. Three classes of infant cry signals are considered such as normal cry signals cry signals from deaf infants and infants with asphyxia. Promising classification results above 99% reveals that the proposed features and classification technique can effectively classify different infant cries.
Evaluation of Internet addiction and the quality of life of Brazilian adolescents from public and private schools

Abstract This study evaluated the profile of public and private high school students (N = 254, Mage = 15.1, SD = 1.3) in the city of São Paulo in relation to their Internet use patterns and quality of life. We used the Internet Addiction Test and the Pediatric Quality of Life Inventory. About 70% of the students had moderate Internet addiction. Those students from public school showed higher scores of Internet addiction (p < 0.001) than students from private schools. Regarding the quality of life, those from private school showed better scores regarding academic performance (p < 0.01) and worse scores in social aspect (p < 0.05). The data showed a strong association between Internet addiction and poor levels of quality of life, as observed in studies from other countries.
Toward glaucoma classification with moment methods

This paper presents a series of experiments testing the feasibility of employing image-processing techniques for the feature extraction stage in the implementation of a basic optic nerve image classifier. Such a scheme completely removes the need for manually identifying the edge of the optic nerve. In this work, Zernike moments are extracted from Confocal Scanning Laser Tomography images of optic discs for the purposes of classifying the disc as healthy or damaged using a linear discriminant function derived from a linear perceptron. Our preliminary results, when compared with the performance of conventional feature sets, demonstrate the appropriateness of this approach.
Crossover Digital Predistorter for the Compensation of Crosstalk and Nonlinearity in MIMO Transmitters

This paper proposes a novel crossover digital predistorter (CO-DPD) model to compensate for crosstalk and nonlinearity in multiple-input multiple-output (MIMO) radio systems. Crosstalk can take place before or after the power amplifiers, designated herein as nonlinear and linear crosstalk, respectively. This paper demonstrates that, contrary to linear crosstalk, nonlinear crosstalk significantly affects the performance of the digital predistortion algorithm; and, it cannot be embedded and compensated for by the conventional channel matrix inversion algorithm at the receiver side of MIMO links. Based on a parametric study of system-level simulations and measurements, it was found that for a -20-dB nonlinear crosstalk level, the use of a memory multibranch polynomial predistorter, along with the channel matrix inversion algorithm, bounds the adjacent channel power ratio (ACPR) for a wideband code division multiple access (WCDMA) signal to -46 dBc and the error vector magnitude (EVM) for a world interoperability for microwave access (WiMAX) signal to -43 dB in MIMO links. The proposed CO-DPD was investigated and analyzed for the MIMO transmitter with N = 2, where N is the number of RF front-ends. Its performance was evaluated through measurements, the experimental results obtained show that, in the presence of -20-dB nonlinear crosstalk, the proposed CO-DPD improve the ACPR of the WCDMA signal by 13 dB to -56.81 dBc from those obtained using a conventional digital predistorter. The same improvement was observed in the EVM measurement of the WiMAX signal, where the EVM decreases from -21.22 dB for the conventional DPD to -49.71 dB for the proposed CO-DPD.
Not gone, but forgotten: Helping users re-find web pages by identifying those which are most likely to be lost

We describe LostRank, a project in its formative stage which aims to produce a way to rank results in re-nding search engines according to the likelihood of their being lost to the user. To this end, we have explored a number of ideas, including applying users’ temporal document access patterns to determine the documents that are both important and have not been recently accessed (indicating greater potential for loss), understanding users’ topical access patterns to determine the topics that are more unfamiliar and hence more dicult to re-nd documents within, and assessing users’ diculties in originally nding documents in order to predict future diculties in re-nding them. As a position paper, we use this as an opportunity to describe early work, invite collaboration with others, and further the case for the use of temporal access patterns as a source for assisting users’ re-nding of personal documents.
MULTI-HUMPED PULSES INTERACTION RESULTING IN TEMPORAL SPREADING

The main goal of our paper is to analyse the propagation of soliton pulses. We analyse the possibility of using soliton to weaken undesirable effect for variable nonlinearity and group velocity dispersion. In analysis of the pulse propagation in optical fiber of a new nonlinear effect, solitons pass through localized fibers and the effect of non-linearity and dispersion of the pulse propagation causes temporal spreading of pulse and it can be compensated by nonlinear effect using different types of pulse including Gaussian pulses and hyperbolic secant pulses. Numerical simulations of the suitable GNLSE using these solutions as input showed that they are stable, however, they may be related with the analysed bound pairs and they propagate steadily for transpacific link compared to the observed ones.
SQLAS: Tool To Detect And Prevent Attacks In Php Web Applications

Web applications become an important part of our daily lives. Many other activities are relay on the functionality and security of these applications. Web application injection attacks, such as SQL injection (SQLIA), Cross-Site Scripting (XSS) and Cross-Site Request Forgery (XSRF) are major threats to the security of the Web Applications. Most of the methods are focused on detection and prevention from these web application vulnerabilities at Run Time, which need manual monitoring efforts. Main goal of our work is different in the way it aims to create new systems that are safe against injection attacks to begin with, thus allowing developers the freedom to write and execute code without having to worry about these attacks. In this paper we present SQL Attack Scanner (SQLAS) a Tool which can detect & prevent SQL injection Attack in web applications. We analyzed the performance of our proposed tool SQLAS with various PHP web applications and its results clearly determines the effectiveness of detection and prevention of our proposed tool. SQLAS scans web applications offline, it reduces time and manual effort due to less overhead of runtime monitoring because it only focus on fragments that are vulnerable for attacks. We use XAMPP for client server environment and developed a TESTBED on JAVA for evaluation of our proposed tool SQLAS.
Practical examples of GPU computing optimization principles

In this paper, we provide examples to optimize signal processing or visual computing algorithms written for SIMT-based GPU architectures. These implementations demonstrate the optimizations for CUDA or its successors OpenCL and DirectCompute. We discuss the effect and optimization principles of memory coalescing, bandwidth reduction, processor occupancy, bank conflict reduction, local memory elimination and instruction optimization. The effect of the optimization steps are illustrated by state-of-the-art examples. A comparison with optimized and unoptimized algorithms is provided. A first example discusses the construction of joint histograms using shared memory, where optimizations lead to a significant speedup compared to the original implementation. A second example presents convolution and the acquired results.
Coset Construction for Duals of Non-relativistic CFTs

We systematically analyze backgrounds that are holographic duals to non-relativistic CFTs, by constructing them as cosets of the Schrodinger group and variants thereof. These cosets G/H are generically non-reductive and we discuss in generality how a metric on such spaces can be determined from a non-degenerate H-invariant symmetric two-form. Applying this to the d = 2 Schrodinger algebra, we reproduce the five-dimensional backgrounds proposed as duals of fermions at unitarity, and under reasonable physical assumptions, we demonstrate uniqueness of this background. The proposed gravity dual of the Lifshitz fixed-point, for which Galileian symmetry is absent, also fits into this organizational scheme and uniqueness of this background can also be shown.
Adaptive optimal control for parallel grid-connected inverters with LCL filters

With the increasing penetration of renewable energy sources into modern power system especially in a microgrid form, power inverters are commonly employed as the interface to the utility grid. LCL-filters are commonly used in interfacing inverters, in order to reduce the harmonics around the switching frequency and its multiples. However, unlike the single grid-connected inverter system where the resonance frequency is mainly fixed by the inverter output LCL filter parameters, the parallel-inverter-based grid-interactive power system presents a more challenging picture where inverter interactions will excite complex resonances at various frequencies. In this paper, an adaptive optimal feedback control is proposed to address system uncertain multi-resonance problem in the parallel-connected inverter system with LCL filters. The proposed adaptive control will force the inverter by modifying state-feedback optimal control law adaptively to follow the desired system response, thus significantly improve the grid current quality under various grid conditions. Experimental results have verified the performance of the proposed adaptive control.