Miracle-dz/UniFAR-FTUs huggingface dataset:
Anatomical , Chemical , and Molecular Genetic Studies of Ephedra distachya

Ephedrae Herba (ma-huang in Chinese), the herbal stems of Ephedra sinica Stapf, which belongs to the Ephedraceae family, is an important crude drug in traditional Chinese medicine. Some researchers think that this species is identical to E. distachya L. , which mainly grows in Europe. Thus, in this study, we carried out anatomical, chemical, and molecular genetic studies on E. distachya plants collected in Switzerland, France, and Turkey to consider whether E. distachya could be used to produce Ephedrae Herba. As a result, we found that the morphology of the subepidermal fiber bundles in cross sections of the herbal stems of E. distachya and the frequencies of ephedrine alkaloids in E. distachya were quite different from those observed in E. sinica and that the DNA sequence of the ITS1 region of E. distachya contains 11 base changes compared with that of E. sinica. Based on these facts, we concluded that E. distachya and E. sinica should be treated as different taxa and that, according to the prescriptions of the current Japanese and Chinese pharmacopoeias, E. distachya is not suitable for producing Ephedrae Herba.
DEBUGGING TRAINED MACHINE LEARNING MODELS USING FLIP POINTS

We study and debug a trained machine learning model by interpreting it as a mathematical function and investigating its flip points. A flip point is any point that lies on the boundary between two output classes: e.g. for a model with a binary yes/no output, a flip point is any input that generates equal scores for “yes” and “no”. The flip point closest to a given input is of particular importance, and this point is the solution to a well-posed optimization problem. The flip point tells us the least change in the data that changes the prediction of the model and thus can identify model biases. We use flip points to identify flaws in the model and to debug the model by generating synthetic training data to correct the flaws. We use the Adult Income dataset as an example. Although we demonstrate our method using neural networks, the use of flip points is model agnostic, and we expect them to be useful for other machine learning models, too.
Porter ’ s model : applicability to the Indian IT-ITeS sector

http://dx.doi. org/10.1016/j.iimb. 201 0970-3896 a 2015 Indian Institute of M Abstract The paper investigates the factors instrumental in imparting a competitive edge to the Indian IT-ITeS sector using Porter’s diamond model. The paper ascertains the relative superiority of the model in explaining India’s prominence in the IT-ITeS sector. The study carries out a “complex” application of the model that is “instrumental” in its attitude by identifying certain distinct factors under the realms of the diamond, the interplay of which catapulted India into the most favoured outsourcing destination. The study posits that the sector is likely to retain its technological prominence in the foreseeable future. a 2015 Indian Institute of Management Bangalore. Production and hosting by Elsevier Ltd. All rights reserved.
SAT-Hard: A Learning-Based Hardware SAT-Solver

Within the last decades, tremendous research work has been carried out on the development of software-based algorithms to solve the Boolean Satisfiability Problem. These SAT-solvers have then been heavily orchestrated for addressing complex computational tasks like the verification of circuits. In this field, most of the applied techniques focused only on the design phase of the circuit. Due to this fact, new approaches have been published in the literature solely focusing on online verification as well as self-verification. These kind of solutions strictly require Hardware (HW) SAT-solvers that can be integrated into a system while introducing only low hardware overhead and still providing high flexibility. By following these observations, this work presents SAT-Hard: In contrast to the state-of-the-art, SAT-Hard takes advantage of learning techniques to support features like clause learning and non-chronological backtracking, and combines them within a lightweight and standalone HW device. By this, a run-time speed-up of 2,000x can be achieved. Furthermore, the experimental evaluation clearly demonstrates that those complex problems can be solved in less than 20 seconds. Particularly due to its compactness, SAT-Hard is suitable for self-verification that enables the continuous verification of an integrated system during its lifetime.
The Quality of Boards Decisions and Corporate Governance

The objective of many corporate governance reforms related to the board of directors is to provide greater transparency and control over the actions of management, strengthening shareholder rights and firm performance. However, the main challenge is how to measure board’s performance and evaluate the effectiveness of these governance mechanisms. Existing literature identifies well-functioning boards based on the extent to which corporations comply with good governance recommendations. In contrast to these measures of board performance that are mostly static in nature, this paper introduces a top-down approach for evaluating the board’s effectiveness in a more dynamic context focusing on empirical outcomes of the decisions they make. A Principal Component Analysis is employed to constructs an index for evaluating the quality of boards’ performance. Consistent with much empirical research on the relationship between firm performance and corporate governance, we find that after controlling for endogeneity, our governance performance measure does not show any significant relationship with standard best practice board characteristics . *Corresponding author e-mail: nava.ramezanian@auckland.ac. nz
Long Term Development and Short Term Dynamics of DOC in Runoff from a Forested Watershed

Dissolved organic carbon (DOC) is transported in considerable amounts from terrestrial to aquatic systems and plays an important role in the global carbon cycle. The objective of this study was to analyze the long term trend and the short term dynamics of DOC in a forested catchment, characterized by a large proportion of Histosols. The long term data set on discharge and runoff chemistry from the Lehstenbach catchment, Germany, from 1987 to 2009 is based on biweekly sampling and daily average discharge. In addition, DOC in runoff was measured during several discharge events in 2010 and 2011 in high temporal resolution (15 min) by a spectrometric device. Simultaneously, the groundwater table was monitored by pressure sensors in a fen site close to the watershed outlet. The long term data revealed that DOC concentrations in runoff increased from about 5 mg DOC L in 1987 to 9 mg DOC L in 2009. DOC concentrations were highly variable at biweekly time scales. Runoff did not change significantly in the given period of time, while sulfate concentrations decreased significantly. Following rain events the discharge increases within a few hours. Measurements with high temporal resolution showed that DOC increased with discharge and maximum concentrations were observed shortly after maximum discharge. The relation of discharge and DOC is characterized by a hysteresis with higher concentrations at falling discharge as compared to rising discharge. Furthermore, the relation is temperature dependent with largest response during the summer period. High concentrations of DOC in runoff coincide with near surface groundwater table, indicating that the peaty riparian zones are the major source of DOC in runoff. In the Lehstenbach catchment, the long term trend in DOC seems to be due to decreased sulphate deposition. The short term temporal variation of DOC in runoff is hydrologically driven and overlapped by temperature. Changes in the climatic regime, e.g. by changing precipitation pattern and changing temperature will influence DOC export from the watershed.
Imaging Moving Targets for a Forward-Scanning Automotive SAR

In this paper, we extend the forward-scanning synthetic aperture radar (SAR) methodology to reconstruct images of the moving targets, for a forward-looking automotive radar. We adapt a matrix decomposition approach to forward-scanning SAR in order to separate moving targets from clutter/stationary objects. To solve our optimization problem, we propose an iterative solution based on augmented Lagrangian method. Image focusing, over the synthetic aperture, is achieved through spatial segmentation and cross-correlation maximization. Our proposed method results in well-focused imaging of the moving targets with enhanced angular resolution. Experimental results from simulation as well as real-data corroborate our proposed methodology.
PREVALENCE OF BACTERIAL INFECTION IN PATIENTS WITH DIABETIC FOOT LESIONS

Diabetic foot infections are the most common problems in persons with diabetes. Among the 50 samples, 43 (86%) showed positive results of bacterial infection. Diabetic foot lesions are divided into six grades based on the depth of the wound and extent of the tissue necrosis. Incidences of bacteria were recorded as Staphylococcus aureus (31.37%) followed by Proteus mirabilis (21.05%), Pseudomonas aeruginosa (15.79%), Streptococcus pyogenes (14.04%), Escherichia coli (7.02%), Clostridium botulinum (5.26%), Peptococcus spp. (3.50%) and Salmonella typhimurium (1.75). The prevalence of diabetic foot infections varies according to sex, age, sugar level and economic status. Males were more susceptible to infection than females because of higher outdoor activities. Age groups of 40-50 years and fasting sugar levels of 100-150 mg/dl showed maximum incidence of bacterial infection in diabetic foot lesions. Maximum incidences of bacterial infection were found in patients of poor economic status followed by those of middle and high economic status respectively, due to lack of education about the disease and unhygienic surroundings. Except Peptococcus spp. the remaining isolates exhibited Multiple Drug Resistance (MDR). The selection of empiric antibiotic therapy depends on various factors such as infection severity, over all patient condition, medication allergies, previous antibiotic treatment, antibiotic activity, toxicity, excretion and glycemic control. Proper identification of causative agents, appropriate antibiotic therapy and management of complications of diabetic foot infections remain essential to the achievement of a successful outcome.
EURASIAN JOURNAL OF BUSINESS AND MANAGEMENT

The main objective of the current study is to investigate whether ethical sensitivity and selfesteem affect the academic performance of business students in financial accounting course. Also, the relationships among self-esteem ethical sensitivity cynicism were examined. The data was gathered from students in undergraduate business program of one public university in Turkey via questionnaire. Factor, reliability and regression analyses were conducted to analyze the data and to test hypotheses of the study. The results showed that ethical sensitivity and selfesteem have positive effect on academic performance. Self–esteem has positive impact on ethical sensitivity. In addition, results displayed that cynicism negatively affects the ethical sensitivity of students. It was also found that ethical sensitivity has mediation effect on the relationship between self-esteem and academic performance.
econstor Make Your Publications Visible . A Service of zbw

Preferences over jobs depend on wages and non-wage aspects. Variation in wealth may change the importance of income as a motivation for working. Higher wealth levels may make good non-wage characteristics relatively more important. This hypothesis is tested empirically using a reduced form search model in which differential job leaving rates identify willingness to pay for non-wage aspects of jobs. Marginal willingness to pay for non-wage aspects (measured by “job satisfaction for work in itself”) is found to increase significantly after large windfall wealth gains in British panel data. Thus, wealth influences more than just the hours worked. JEL: J21,J28,J32,J64
Towards building a district development model for india using census data

Models for socio-economic development are useful for planners to build appropriate policies. Such models are ideally constructed based on empirical data, and we take up the problem of working towards a district development model for India by using two waves of census data. India has almost six hundred districts, diverse in terms of their social and economic development, and hence presents a unique natural experiment to understand how social and economic factors interplay with one another. In this paper, we present some interesting observations we are able to make from the analysis of census data from the years 2001 and 2011, and also raise some questions calling for the need for additional ethnographic and other surveys to be able to understand the underlying mechanisms that would have led to the observed patterns.
Economic News and Stock Market Linkages: Evidence from the U.S., U.K., and Japan

This paper examines the cross-market equity return and volatility linkages for the U.S., U.K., and Japan. We investigate the extent to which these linkages can be explained by macroeconomic news announcements in the three countries, including money supply, industrial production, price inflation, unemployment rate, and trade deficit during the 1985-1996 period. Our results indicate that these news announcements account for little of the direct intermarket return spillovers. However, we find that these announcements can affect the size of intermarket return spillovers. We also find these news announcements appear to explain, at least partially, the volatility spillovers among the three markets.
Computation of masses of heavy hybrid mesons from hybrid static potentials

In this work, masses of heavy hybrid mesons are calculated from hybrid static potentials. For this purpose, a parametrisation is fitted to lattice data for the hybrid static potentials Πu and Σu . Applying the Born-Oppenheimer approximation, a Schrödinger equation for the heavy quark-antiquark pair is derived. By using the fitted hybrid static potential as an input in the Schrödinger equation, binding energies for a system of two bottom quarks are computed numerically. From this, we obtain heavy hybrid meson masses and discuss corresponding meson quantum numbers.
GEOLOGICAL AND GEOPHYSICAL MODEL OF THE QUATERNARY LAYERS BASED ON IN SITU MEASUREMENTS IN BUCHAREST , ROMANIA

The local surface geology, the soil behaviour as well as the local hydrogeological factors have a well-known influence upon the characteristics of the earthquake ground motion and the corresponding site effects. The geological and hydrogeological setting in Bucharest, the capital of Romania, as well as relative short recurrence intervals of strong intermediate-depth earthquakes in the Vrancea area, make necessary to study the geological cause of observed seismic site effects. In the case of Bucharest, the source geometry of the intermediate-depth Vrancea earthquakes remains almost the same in respect to the city area, the hypocentral distances being in the range of 160–220 km, while the epicentral distances for Bucharest are in the range of 100–130 km. Compared to these distances the spatial extend of Bucharest – of about 20 km is small. Influences due to source directivity and travel path effects can be assumed to be constant over the entire city area. In the latest moderate earthquakes recorded peak ground accelerations varied over the city area by a factor of up to 4. The high ground motions recorded across Bucharest during strong earthquakes in the past result from sitespecific amplification within the city, obviously due to lateral variation of local soil and hydrogeologic near-surface conditions. To study this correlation it was necessary to know the geotechnical and dynamic properties of the involved geological layers.
Performance information in politics: How framing, format, and rhetoric matter to politicians’ preferences

Performance information research has grown rapidly over the last decade with much research emphasizing the importance of how information is framed, presented, and communicated by using a distinct rhetorical appeal. In this study, we examine how the framing, format, and rhetoric of performance information influences preferences among elected politicians. We study the direct effects of how information is presented. We also argue that performance information is always a mixture of different frames, formats, and rhetorical appeals and that it is therefore important to account for interaction effects. Using a large-scale survey experiment with responses from 1,406 Italian local politicians, we find that framing and ethos-based rhetoric affect politicians’ responses to performance information. We also find that the format of presentation is important in several ways. Thus, politicians are more likely to support the status quo when information is presented graphically rather than textually, and a graphical format furthermore reduces the impact of ethos-based rhetoric and – to a lesser extent – the impact of equivalence framing.
Chapter 1 DAG Mining in Procedural Abstraction

We present DAGMA, a new graph mining algorithm for Directed A cyclic Graphs (DAGs). DAG mining is important in general, but our work is in pired by DAGs that appear in code generation, especially in code compaction. C de–size optimization is crucial for embedded systems as cost and energy consumpti on depend on the size of the built–in memory. Moreover, the smaller the code is, th e more functionality fits into the memory. Procedural Abstraction (PA) reduces assembly code size by d etecting frequent code fragments, extracting them into new procedures and sub stituting them with call/jump instructions. Traditionally, text–matching, e . g. suffix trees [6], is used for candidate detection. The obvious disadvantage is that to de tect r peated instructions they must occur in the exact same order in the program. Drewek et al. have shown in [7] that a graph–based PA outperforms the code shrinking r esults of a purely textual approach. Graph–based PA transforms the instruction s equences of basic blocks into data flow graphs (DFGs). A basic block is a code sequence t hat has exactly one entry point (i.e. only the first instruction may be the target of a jump instruction) and one exit point (i.e. only the last instruction may be a jump in struction). A DFG is a DAG that represents the instructions as nodes and the data d epen encies between these instructions as directed edges. In general, from a sin gle DFG several differently ordered instruction sequences can be generated that h ave t e same semantics but cannot be detected by textual approaches. A graph–based approach, i.e. mining for frequent DFG fragments, can therefore find more opportun ities for PA.
Separating and quantifying the distinct impacts of El Niño and sudden stratospheric warmings on North Atlantic and Eurasian wintertime climate

Sudden stratospheric warmings (SSWs) significantly influence Eurasian wintertime climate. The El Niño phase of the El Niño–Southern Oscillation (ENSO) also affects climate in that region through tropospheric and stratospheric pathways, including increased SSW frequency. However, most SSWs are unrelated to El Niño, and their importance compared to other El Niño pathways remains to be quantified. We here contrast these two sources of variability using two 200‐member ensembles of 1‐year integrations of the Whole Atmosphere Community Climate Model, one ensemble with prescribed El Niño sea surface temperatures (SSTs) and one with neutral‐ENSO SSTs. We form composites of wintertime climate anomalies, with and without SSWs, in each ensemble and contrast them to a basic state represented by neutral‐ENSO winters without SSWs. We find that El Niño and SSWs both result in negative North Atlantic Oscillation anomalies and have comparable impacts on European precipitation, but SSWs cause larger Eurasian cooling. Our results have implications for predictability of wintertime Eurasian climate.
Corporate Social Responsibility Accounting and the Effect of Donations on Profitability of Oil and Gas Companies in Nigeria

There is a crying need for an in-depth study into the quality, extent of corporate social responsibility (CSR) disclosure and identification of areas for future improvement so that transparency can be ensured, especially in developing countries like Nigeria where CSR practices are limited. Based on this we carried out this study on corporate social responsibility accounting and the effect of donations on profitability of oil and gas companies in Nigeria. The study used a cross sectional survey design to carry out the research. The population of the study was all the oil and gas companies in Nigeria. Data collected were analyzed through the Simple Regression Analysis and Partial Correlation. Results of the study revealed that: Donation significantly affects Return on Equity and Donation significantly affects Net Profit of the oil and gas companies in Nigeria, Hence, from the findings of this work the research concludes that there is a strong positive relationship between CSRA and profitability. And that; Donation significantly affects the Return on Assets of the oil and gas companies in Nigeria; Donation significantly affects the return on equity of the oil and gas companies in Nigeria; Donation significantly affects the Net Profit of the oil and gas companies in Nigeria. Therefore all the null hypotheses have been rejected and the alternative hypotheses accepted. Based on the findings of the study, the following recommendations were made; Companies should carry out operational impact evaluation. This is in order to evaluate the effect of their operation on the community, the environment and the people as this will be able to audit and control their CSR practices. It will help them check unwholesome practices; Companies should report regularly to its stakeholders their corporate social responsibility practices. The companies are too secretive and do not allow the people have insight of most of their activities. Forums should be created where these are reported to the people; Government should consider allowable all arm’s length expenditure on corporate social responsibility for tax purposes to enable the companies become completely socially responsible.
Metrics selection for group type identification in Enterprise Social Network (ESN) analytics

With the proliferation of Enterprise Social Networks (ESN) measurement of ESN activity becomes increasingly relevant. ESN analytics aims to develop metrics and models to measure and classify ESN user activity to support organisational goals and outcomes. In this paper we focus on a neglected area of ESN analytics, metrics for understanding activity in ESN groups. We engage in explorative research, utilising actual ESN meta data describing activity in 350 groups across three organisations, to identify a set of metrics that divides the group sample into distinct user types. By employing clustering techniques we derive a set of four group types: broadcast streams, information forums, communities of practice and project teams. For ESN analytics research we contribute a set of metrics and group types. For practice we envision a method that enables group managers to compare aspirations for their groups to embody a certain group type, with actual activity patterns.