Miracle-dz/UniFAR-FTUs huggingface dataset:
Polymorphisms in the XPC gene and gastric cancer susceptibility in a Southern Chinese population

Previous studies have reported that XPC gene polymorphisms may modify the individual susceptibility to gastric cancer. In this case–control study with a total of 1,142 cases and 1,173 controls, four potentially functional polymorphisms were genotyped in the XPC gene (rs2228001 A>C, rs2228000 C>T, rs2607775 C>G, and rs1870134 G>C) by Taqman assays and their associations were analyzed with the risk of gastric cancer in a Southern Chinese population. No significant association between any of XPC polymorphisms and gastric cancer risk was detected except for a borderline association with the rs2228000 CT/TT genotype (crude odds ratio =0.86, 95% confidence interval =0.73–1.02, P=0.088) when compared to the rs2228000 CC genotype. Further stratified analysis revealed that the protective effect of rs2228000 CT/TT on the risk of gastric cancer was only significant among subjects older than 58 years. In summary, results indicated that genetic variations in XPC gene may play a weak effect on gastric cancer susceptibility in Southern Chinese population, which warrants further confirmation in larger prospective studies with different ethnic populations.
The Maison Verte, a transitional space: introducing the work of Françoise Dolto in the UK

This paper looks at Dolto’s contribution to preventative interventions with the foundation of the Maison Verte in 1979. The author’s intention is to show that this project brings with it an interesting development of psychoanalysis in the community. The author was part of a team setting up the first Maison Verte in the UK. Maison Verte-UK was established in 2013 as an independent non-profit organisation, financed mainly by public grant funds. The centre provides a free, three half-days weekly service, for pre-school children and their families. It offers a welcoming, non-structured and non-bureaucratic space for conviviality. This openness generates an unusual quality of encounter between children and their adults, through the mediation of the staff.
A Novel Frequency Offset Estimation Scheme for OFDM Systems

In this paper, we propose a novel frequency offset estimation scheme for orthogonal frequency division multiplexing (OFDM) systems. By correlating the OFDM signals within the coherence phase bandwidth and employing a threshold in the frequency offset estimation process, the proposed scheme is not only robust to the timing offset but also has a reduced complexity compared with that of the conventional scheme. Moreover, a timing offset estimation scheme is also proposed as the next stage of the proposed frequency offset estimation. Numerical results show that the proposed scheme can estimate frequency offset with lower computational complexity and does not require additional memory while maintaining the same level of estimation performance. Keywords—OFDM, frequency offset estimation, threshold.
Performance comparison of a multiple-detection probabilistic data association filter with PCRLB

Most target tracking algorithms assume that at most one measurement is generated by a target in a scan. However, there are tracking problems where this assumption is not valid. For example, multiple detections from a target can arise due to multipath propagation where different signals scattered from a target arrive at the sensor via different paths. With multiple target-originated measurements, most multitarget trackers will fail or become ineffective due to the violation of the one-to-one assumption. For example, the joint probabilistic data association (JPDA) filter is capable of using multiple measurements for a single target through weighted measurement-to-track association, but its fundamental assumption is still one-to-one. In order to rectify this shortcoming, we developed a new algorithm in our previous work, the multiple-detection probabilistic data association (MD-PDA) filter, which is capable of handling multiple detections from a target in a scan, in the presence of false alarm and probability of detection less than unity. In this paper, the performance of this MD-PDA filter is compared with the posterior Cramér-Rao lower bound (PCRLB), which is explicitly derived for the multiple-detection scenario. Furthermore, experimental results show multiple-detection pattern based probabilistic data association improves the state estimation accuracy and reduces the total number of false tracks.
University Students' Awareness of Their Metacognitive Competence

The awareness of one's own performance (metacognitive judgments) is important for the successful regulation of learning activities in higher education. According to studies about self-regulated learning, achievement and metacognition are related to each other, but metacognitive judgments usually are not completely precise in relation to achievement. The investigation of the awareness of metacognitive judgments (meta-metacognition) also sheds light on the generation of metacognitive judgments. The aim of the present study is to investigate the underlying processes while students provide meta-metacognitive judgments and to what extent such judgments rely on the preceding metacognitive judgment, the accuracy of this judgment, or on the preceding performance. We analyzed data from N = 116 university students in a multilevel approach. The final model revealed effects of the metacognitive judgment, its accuracy, and also of performance, with the largest effect of the metacognitive judgment. Implications for the successful regulation of learning activities will be discussed. Theoretical Framework Learning in the higher education context is characterized by a high level of self-regulation compared to learning in the school context, where students are strongly guided by their teachers. For the successful regulation of learning activities, university students need to monitor their learning processes and to make decisions about which contents they already understand and which contents they need to study further. Metacognitive judgments about achievement and their respective awareness give insight into students' monitoring of their learning processes. For example, it could be shown that especially low-performing learners overestimate their achievement (unskilled and unaware effect, Kruger & Dunning, 1999). Recent studies went further and investigated the unskilled and unaware effect with meta-metacognitive judgments; that means, students were also asked to provide judgments about their metacognitive judgments. The analysis of these judgments revealed that low-performing learners are aware that their metacognitive judgments might be less reliable in comparison to the other students in the sample (Händel & Fritzsche, 2015; Miller & Geraci, 2011). Thus, it might be more difficult for low-performers to effectively regulate their learning processes. To gain more insight into students' metacognitive processes, finer-grained analyses of (meta-)metacognitive judgments with university students are necessary. The population of students in higher education seems to be very suitable for research in this area, as for these students self-regulation is especially important compared to school students. As already stated, previous research revealed that metacognitive judgments are not necessarily appropriate. Whether metacognitive judgments are appropriate can be computed with measures of …
Cognitive and electrophysiological responses to dopaminergic manipulations

Methylphenidate (MPH), a stimulant drug with dopamine and noradrenaline reuptake inhibition properties is mainly prescribed in attention deficit hyperactivity disorder, is increasingly used by the general population, intending to enhance their cognitive function. We aim to answer whether this is effective and present a novel way to determine the extent to which MPH enhances cognitive performance in a certain domain. Namely, we quantify this by a percentage that reflects the number of studies showing positive effects of MPH. To evaluate whether the dose-response relationship follows an inverted-U-shaped curve, MPH effects on cognition are also quantified for low, medium and high doses respectively. The studies reviewed here show that MPH improves cognitive performance in the healthy population in the domains of working memory (68% of included studies) and speed of processing (61%), and to a lesser extent may also improve verbal learning and memory (34%), reasoning and problem solving (18%) and attention and vigilance (16%), but does not have an effect on visual learning and memory. MPH effects are dose dependent and the dose response relationship differs between cognitive domains. In conclusion, healthy individuals should be very careful when considering using MPH for cognition enhancing purposes; effects may be disappointing to abusers.
Intertemporal Dynamics: Convergence Speed of Current Account across Countries

Empirical data shows that current account has the property of mean-reversion and the speed of current account convergence is heterogenous across countries. In order to explore the factors which affect the current account adjustment speed of a small country, this paper builds up a model to study how current account would be affected by factors such as labor supply and monetary policies. We found that labor market rigidity may have a significant effect, but monetary policies only have a small impact on current account adjustment.
Real-time Electromagnetic and Transient Stability Simulations for Active Distribution Networks

This paper presents the solutions of a real-time parallel-processing based simulator, eMEGAsim, to perform electromagnetic transient and transient stability simulations for distribution power networks. The electromagnetic transient simulation can be distributed into several processors without adding an artificial delay, and the transient stability simulation tool can model three-phase unbalance systems. The simulator provides a flexible environment to interface discrete-time and phasor domains to create a hybrid simulation. HE distribution power systems were traditionally considered as passive networks that only involved end- users and loads. The simple topology of these systems and the utilization of cables instead of overhead lines made it less sensitive to transient stability phenomena. Distribution systems are usually coupled in a common bus with a strong transmission system. Therefore, disturbances such as short circuits in distribution networks were considered as localized phenomena without significant impact on the stability of the overall power system. However, the integration of several types of energy resources has transformed the nature of distribution systems from passive to active. Currently, a large number of wind turbines, micro-turbine generators, and photovoltaic panels are connected to distribution networks. The massive penetration of small but geographically distributed generators (DG) has changed the operation of distribution networks during the steady and dynamic states. From the dynamic state viewpoint, the disturbance in the advanced distribution systems cannot be considered anymore an insignificant local event. First, each type of DG unit presents a different behavior during transients in the system. Second, a disturbance in the transmission system can easily lead to the tripping of the DG units in the
IGBT module technology with high partial discharge resistance

The high operating voltages of 6.5 kV IGBT modules place additional demands on the insulation and partial-discharge resistance. The most important component affected here is the metallized aluminum nitride ceramic which is embedded in a silicone gel. A high electric field strength can cause a local electric discharge in the silicone gel known as partial discharge, leading ultimately to electric insulation failure and reducing the reliability of the IGBT module. For a 6.5 kV IGBT module, the insulation test must be performed up to a voltage of 10.5 kV rms. Technological steps have been carried out to reduce the maximum electric field strength along the edge of the copper metallization. The edge of the ceramic was coated with a high-impedance layer of doped amorphous silicon. The electric current along the edge of the ceramic homogenizes the electric field strength. The partial discharge was determined up to 11 kV and a considerable reduction was observed compared to standard modules. Without an a-Si:H coating, the partial discharge already increases strongly at low voltages of 3-4 kV. At high voltages, the interface between the silicone gel and the substrate is a major source of partial discharge. The a-Si:H coating reduces electric field peaks and the partial discharge does not exceed 10 pC up to a voltage of 10 kV.
Sensing Touch Force using Active Acoustic Sensing

We present a lightweight technique with which creators can prototype force-sensitive objects by attaching a pair of piezoelectric elements: one a vibration speaker and one a contact microphone. The key idea behind our technique is that touch force, in addition to the way the object is touched, can also be observed as different resonant frequency spectra. We also show that recognition of a touch and estimation of the touch force can be implemented by using the combination of support vector classification (SVC) and support vector regression (SVR). An experiment with an additional pressure sensor revealed that our technique might perform well in estimating touch force. We also show a tool for machine learning based on our technique that uses an animated guide, allowing creators to give the system both the training data and the labels for training machine learning needed for dealing with continuous-valued output such as SVR.
Dynamic Interactions and Intersubjectivity: Challenges to Causal Modeling in Studies of College Student Debt

Loans are a central component of college finance, yet research has generated a dearth of strong evidence of their effect on student choices. This article examines challenges to causal modeling regarding the effects of borrowing and the prospects of indebtedness on students’ college-going behaviors. Statistical estimates of causal effects are confounded by dynamic interactions between the decision to borrow and the characteristics of borrowers (endogeneity), their degree and earnings expectations (self-selection bias), and cumulative debt (temporal and threshold effects). Furthermore, interpretive research illustrates that college counseling is highly intersubjective, with application and financial aid advice predicated on perceptions of student socioeconomic class and degree prospects. These studies indicate the need for an interdisciplinary research agenda more inclusive of sociopsychological perspectives.
A Complex Matrix Factorization Approach to Joint Modeling of Magnitude and Phase for Source Separation

Conventional NMF methods for source separation factorize the matrix of spectral magnitudes. Spectral Phase is not included in the decomposition process of these methods. However, phase of the speech mixture is generally used in reconstructing the target speech signal. This results in undesired traces of interfering sources in the target signal. In this paper the spectral phase is incorporated in the decomposition process itself. Additionally, the complex matrix factorization problem is reduced to an NMF problem using simple transformations. This results in effective separation of speech mixtures since both magnitude and phase are utilized jointly in the separation process. Improvement in source separation results are demonstrated using objective quality evaluations on the GRID corpus. As a side result, we have also investigated the intelligibility improvement aspect of target speaker in the presence of interfering speaker.
Strategic hedging

For a Cournot duopoly with a foreign firm exporting to the home firm’s market hedging against unfavorable shifts in the stochastic spot exchange rate is analyzed. In a two-stage setting with product market and hedging decisions we show that hedging can be used as a strategic device. Under constant and decreasing absolute risk aversion an increase in hedging volume by the foreign firm promotes its exports and lowers the equilibrium output of the home firm. In contrast to the well-known full-hedging result in a perfectly competitive environment, we find that the foreign firm will overhedge for strategic reasons. Furthermore, the separation result from the literature on hedging under perfect competition no longer holds in the duopoly framework, i.e., equilibrium output levels depend on the preference of the foreign firm and the probability distribution of the spot exchange rate.
Formal verification of an extension of a secure , compatible UNIX file system

We specify and formally verify security properties of an extension of a UNIX file system. Extensions include a multi-level security model, ACLs, separate MAC and DAC administration, and others. The security properties we verified are: simple security and confinement as defined in the Bell and LaPadula security model [3, 4], the standard DAC policy for ACLs, and a security policy for the administration of security attributes. Both, formalization and verification were done using Coq.
Models and Algorithms for Network Immunization

Recently, there has been significant research activity in the algorithmic analysis of complex networks, such as social networks, or information networks. A problem of great practical importance is that of network immunization against virus spread. Given a network, a virus-propagation model, and an immunization cost function, we are interested in containing the spread of the virus while minimizing the immunization cost. In this paper, we consider two viruspropagation models and we propose immunization algorithms for each model. The experimental evaluation shows that our algorithms perform well on both synthetic and real graphs. Furthermore, it reveals the following interesting facts (a) the simple heuristic of immunizing the nodes with the highest degree is not optimal, and (b) our algorithms perform significantly better in small-world networks.
Power system harmonics estimation using sliding window based LMS

The widespread use of power electronics devises and nonlinear loads in power system grids is increasing in the last decades leads to rise of harmonic in power system signals. Great damage to power system gird can happen due to harmonics. Thus it is important to precisely estimate the harmonics components that may help to avoid its harmful effect of the electrical grid performance. The more common algorithm that has been used to estimate the harmonic component is the Fast Fourier Transform (FFT), however FFT has few limitations, furthermore, modern power system network getting complex and noisy. Therefore, fast and accurate harmonic estimation in the presence of noise is needed. Sliding window based least mean square (LMS) algorithm is introduced in this paper to estimate the harmonic components in noisy environment. The result shows that the sliding window method able to give a good estimation to the harmonic component even when the signal to noise ratio (SNR) is 0 dB.
15 Planar Antenna Technology for mm-Wave Automotive Radar , Sensing

Planar antennas are common components in sensing applications due to their low cost, low profile and simple integration with systems. They can be used commonly at frequencies as high as 77 GHz and above, for example in automotive sensing. Also, new ultra-wideband communication systems operating around 60 GHz will heavily rely on planar antennas due to their unique properties. One special advantage of planar elements is that they can easily form array structures combining very simple elements, like microstrip patches. Also phased and conformal arrays can be built. Due to all these characteristics, planar antennas are very good candidates for building front ends for mm-wave applications in radar, sensing or communications. For some applications, which are either established today or which will become commercial in a few years, general requirements can be given. In addition to automotive radar, this mainly applies to 60 GHz ultra-broadband wireless indoor communications. Microwave imaging at 94 GHz and above is still much under research, as well as other sensing applications in the millimeter-wave region above 100 GHz. Prominent frequency bands are in the 122 and 140 GHz range1. Typical antenna requirements are multi-beam or scanning capability, high gain up to 30.36 dBi and moderate to low sidelobes. In monopulse radar systems, the sidelobes may be included in the angle determination scheme, then the sidelobe level requirements are rather relaxed. Loss is generally an important issue. Comparing a planar approach to a commercially available dielectric lens, the planar antenna exhibits significantly higher losses, especially if the beamforming device is included. The antenna efficiency of 77 GHz planar array columns is roughly 50%. These losses are caused by dielectric losses and conductor losses. To attain reliable predictions of the losses in full-wave simulations, care has to be taken in the
Model-driven engineering of industrial process control applications

Software is an important part of industrial process control systems. However, the state-of-the-practice for developing industrial process control software still has several key challenges that need to be addressed (e.g., migration to platforms of different vendors, lack of automation). This paper introduces a model-driven engineering approach to the development of industrial process control software, which is based on the ProcGraph domain-specific modeling language. The paper discusses and offers solutions to several of the development challenges that have not been addressed by existing techniques in the process controls domain. The contributions of the paper are a model-driven engineering approach for the industrial process control domain and a supporting tool infrastructure. The approach is demonstrated by a case study focused on the development of a control system for a TiO2 (titanium dioxide) pigment production subprocess.
CATEGORIZATION AND ADAPTIVE BEHAVIOR : THE ROLE OF ASSOCIATIVE PROCESSES IN SYMBOLIC CONCEPT LEARNING

A new tendency is emerging in cognitive science that pays attention to the possible commonalities between elementary processes of learning (such as classical or instrumental conditioning) and more complex inductive processes (like categorization or causality judgements) (see, e.g., Estes, 1985; Gluck & Bower, 1988; Holland et al., 1986; Pearce, 1988; Shanks & Dickinson, 1987; Schlimmer & Granger, 1986a,b; Waldmann and Holyoak, 1990).