Miracle-dz/UniFAR-FTUs huggingface dataset:
Forest Monitoring Application Systems Based on Wireless Sensor Networks

This paper illustrates a kind of forest monitoring system solution of wireless sensor networks based on ZigBee in order to improve the insufficiency of the existing monitoring ways and introduces wireless sensor networks technology. Through the comprehensive analysis of three respects of network topology, energy saving as well as being stable and reliable, this paper brings forward the hardware design of sensor network node and network protocols suitable for forest monitoring. The designed system can conduct effective monitoring of forest environment.
Title Overview of permanent-magnet brushless drives for electric andhybrid electric vehicles

With ever-increasing concerns on our environment, there is a fast growing interest in electric vehicles (EVs) and hybrid EVs (HEVs) from automakers, governments, and customers. As electric drives are the core of both EVs and HEVs, it is a pressing need for researchers to develop advanced electric-drive systems. In this paper, an overview of permanent-magnet (PM) brushless (BL) drives for EVs and HEVs is presented, with emphasis on machine topologies, drive operations, and control strategies. Then, three major research directions of the PM BL drive systems are elaborated, namely, the magnetic-geared outer-rotor PM BL drive system, the PM BL integrated starter-generator system, and the PM BL electric variable-transmission system.
Identification of Two Mutations (F758W and F758Y) in the N-methyl-D-aspartate Receptor Glycine-binding Site that Selectively Prevent Competitive Inhibition by Xenon without Affecting Glycine Binding

Background: Xenon is a general anesthetic with neuroprotective properties. Xenon inhibition at the glycine-binding site of the N-Methyl-D-aspartate (NMDA) receptor mediates xenon neuroprotection against ischemic injury in vitro. Here we identify specific amino acids important for xenon binding to the NMDA receptor, with the aim of finding silent mutations that eliminate xenon binding but leave normal receptor function intact. Methods: Site-directed mutagenesis was used to mutate specific amino-acids in the GluN1 subunit of rat NMDA receptors. Mutant GluN1/GluN2A receptors were expressed in HEK 293 cells and were assessed functionally using patch-clamp electrophysiology. The responses of the mutant receptors to glycine and anesthetics were determined. Results: Mutation of phenylalanine 758 to an aromatic tryptophan or tyrosine left glycine affinity unchanged, but eliminated xenon binding without affecting the binding of sevoflurane or isoflurane. Conclusions: These findings confirm xenon binds to the glycine site of the GluN1 subunit of the NMDA receptor and indicate that interactions between xenon and the aromatic ring of the phenylalanine 758 residue are important for xenon binding. Our most important finding is that we have identified two mutations, F758W and F758Y, that eliminate xenon binding to the NMDA receptor glycine site without changing the glycine affinity of the receptor or the binding of volatile anesthetics. The identification of these selective mutations will allow knock-in animals to be used to dissect the mechanism(s) of xenon's neuroprotective and anesthetic properties in vivo.
Challenges in Combining Projections from Multiple Climate Models

Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal— for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate ‘‘good’’ and ‘‘bad’’ models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.
QOC Design Rationale Retrieval: A Cognitive Task Analysis and Design Implications

The study reported here addresses issues associated with the retrieval of semiformal, argu,mentation-based DR, taking the QOC notation as an example. If QOC-based DR is to be accessible, what requirements does this place on its representation? What are the cognitive tasks in retrieving relevant QOC in response to a query? How suited are graphical structures as a medium for QOC retrieval, and how do users manage multiple representations of a QOC when answering queries? Seven subjects answered queries about the design of two systems by formulating requests to an imaginary hypertext tool to display specific QOC information. Subjects had available to them – for each system, in both textual and graphical forms, and at different levels of detail – representations of the QOC, plus a hierarchical “Criterion tree” showing inter-Criterion relationships. The key results of this work were as follows: • A cognitive task model of the QOC retrieval process is developed as an analytic framework; • Errors in specifying QOC browsers are traced in the task model to different sources; • Multiple representations of the QOC are necessary to support different kinds of queries; tools could compute virtual structures to map relations between them; • Criterion trees which make inter-Criterion relationships explicit could be extended to compare the use of Criteria between different QOC DRs; • The general conclusion is that the coherence of a QOC’s structure is critical to its reusability, but that this requirement is in tension with its semiformality; this is particularly the case with DR approaches which focus on representing the deliberation process , rather than the structure of its content. This work stems from doctoral research (Shum, 1991) funded by Rank Xerox EuroPARC, Esprit Basic Research Action 3066 (AMODEUS Project), and SERC CASE Award 88504176. This technical report is an updated version of the report presented in the dissertation.
Obesity, inflammatory markers, and endometrial cancer risk: a prospective case–control study

Obesity, a major risk factor for endometrial cancer, is a low-grade inflammatory state characterized by elevated concentrations of cytokines and acute phase reactants. The current study had two aims: first to investigate the associations of C-reactive protein (CRP), interleukin 6 (IL6), and IL1 receptor antagonist (IL1Ra) with endometrial cancer risk and second to examine to which extent these markers can influence the association between obesity and endometrial cancer. We conducted a case–control study, nested within the European Prospective Investigation into Cancer and Nutrition, which comprised 305 incident cases of endometrial cancer and 574 matched controls. CRP, IL6, and IL1Ra were measured in prospectively collected blood specimens by immunoassays. Data were analyzed using conditional logistic regression. All statistical tests were two-sided, and P values <0.05 were considered statistically significant. We observed a significant increase in risk of endometrial cancer with elevated levels of CRP (odds ratio (OR) for top versus bottom quartile: 1.58, 95% confidence interval (CI): 1.03–2.41, Ptrend=0.02), IL6 (OR for top versus bottom quartile: 1.66, 95% CI: 1.08–2.54, Ptrend=0.008), and IL1Ra (OR for top versus bottom quartile: 1.82, 95% CI: 1.22–2.73, Ptrend=0.004). After adjustment for body mass index (BMI), the estimates were strongly reduced and became non-significant. The association between BMI and endometrial cancer was also substantially attenuated (∼10–20%) after adjustment for inflammatory markers, even when the effects of C-peptide or estrone had already been taken into account. We provided epidemiological evidence that chronic inflammation might mediate the association between obesity and endometrial cancer and that endometrial carcinogenesis could be promoted by an inflammatory milieu.
Analysis of Wireless Communications with Finite Blocklength and Imperfect Channel Knowledge

With the rise of critical machine-to-machine (M2M) applications, next generation wireless communication systems must be designed with strict constraints on the latency and the reliability. A key enabler for designing low-latency systems is the availability of accurate and tractable analytic models. Unfortunately, many performance models do not account for the effects of channel coding at finite blocklength and imperfect channel state information (CSI) due to time-limited channel training. These models are therefore inaccurate for low-latency systems. In this work, we derive a closed-form approximation for the transmission error probability while considering both of these effects. The approximation provides an inverse mapping from the error probability to the achievable rate. Using this approximation, we analyze the queuing delay of the system through stochastic network calculus. Our results show that even though systems with rate adaptation must spend resources on channel training, these systems perform better than systems operating at fixed rate. The enhancement becomes even greater under stricter delay constraints. Moreover, we find that adapting both the training sequence length and the code rate to the delay constraints is crucial for achieving high reliability.
DNA melting structures in the generalized Poland-Scheraga model

The Poland-Scheraga model for DNA denaturation, besides playing a central role in applications, has been widely studied in the physical and mathematical literature over the past decades. More recently a natural generalization has been introduced in the biophysics literature to overcome the limits of the original model, namely to allow an excess of bases -- i.e. a different length of the two single stranded DNA chains -- and to allow slippages in the chain pairing. The increased complexity of the model is reflected in the appearance of configurational transitions when the DNA is in double stranded form. In a previous work of two of the authors the generalized Poland-Scheraga model has been analyzed thanks to a representation in terms of a bivariate renewal process. In this work we exploit this representation farther and fully characterize the path properties of the system, making therefore explicit the geometric structures -- and the configurational transitions -- that are observed when the polymer is in the double stranded form. What we prove is that, when the excess of bases is not absorbed in a homogeneous fashion along the double stranded chain, then it either condensates in a single macroscopic loop or it accumulates into an unbound single strand free end.
Touching transport - a case study on visualizing metropolitan public transit on interactive tabletops

Due to recent technical developments, urban systems generate large and complex data sets. While visualizations have been used to make these accessible, often they are tailored to one specific group of users, typically the public or expert users. We present Touching Transport, an application that allows a diverse group of users to visually explore public transit data on a multi-touch tabletop. It provides multiple perspectives of the data and consists of three visualization modes conveying tempo-spatial patterns as map, time-series, and arc view. We exhibited our system publicly, and evaluated it in a lab study with three distinct user groups: citizens with knowledge of the local environment, experts in the domain of public transport, and non-experts with neither local nor domain knowledge. Our observations and evaluation results show we achieved our goals of both attracting visitors to explore the data while enabling gathering insights for both citizens and experts. We discuss the design considerations in developing our system, and describe our lessons learned in designing engaging tabletop visualizations.
Enabling program comprehension through a visual object-focused development environment

Integrated development environments (IDEs) include many tools that provide the means to construct programs. Coincidentally, the very same IDEs are a primary vehicle for program comprehension. We claim that IDEs may be an impediment for program comprehension because they treat software elements as text, which may be counterproductive in the context of program understanding-where abstracting from the source text to the level of structural entities and relationships is the key. We are currently building Gaucho, a visual object-focused environment that allows developers to write programs by creating and manipulating lightweight and intuitive depictions of object-oriented constructs. The research question we investigate here is how such an environment compares with traditional IDEs when it comes to performing program comprehension tasks. To answer our question, we conducted a preliminary controlled experiment with eight subjects, comparing Gaucho against a traditional IDE. We found that Gaucho outperforms the IDE regarding the correctness of the tasks, while it is slower with respect to the completion time. Our preliminary results suggest that alternative-visual-IDEs may be superior to traditional IDEs as program comprehension aids.
Acoustic Fingerprinting Revisited: Generate Stable Device ID Stealthily with Inaudible Sound

The popularity of mobile devices has made people's lives more convenient, but threatened people's privacy at the same time. As end users are becoming more and more concerned on the protection of their private information, it is even harder for hackers to track a specific user by using conventional technologies. For example, cookies might be cleared by users regularly. Besides, OS designers have developed a series of measures to cope with tracker. Apple has stopped apps accessing UDIDs, and Android phones use some special permissions to protect IMEI code. However, some recent studies showed that attackers are able to find new ways to get around those limitations, even though these new methods should be improved in order to be practically deployed in large scale. For example, attackers can trace smart phones by using the hardware features resulting from the imperfect manufacturing process of accelerometers. In this paper, we will present another new and more practical method for the adversaries to generate stable and unique device ID stealthily for the smartphone by exploiting the frequency response of the speaker. With carefully selected audio frequencies and special sound wave patterns, we can reduce the impact of non-linear effects and noises, and keep our feature extraction process un-noticeable to phone owners. The extracted feature is not only very stable for a given smart phone, but also unique to that phone. The feature contains rich information, which is even enough to differentiate millions of smart phones of the same model. We have built a prototype to evaluate our method, and the results show that the generated device ID can be used to track users practically.
A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Trust and reputation models for distributed, collaborative systems have been studied and applied in several domains, in order to stimulate cooperation while preventing selfish and malicious behaviors. Nonetheless, such models have received less attention in the process of specifying and analyzing formally the functionalities of the systems mentioned above. The objective of this paper is to define a process algebraic framework for the modeling of systems that use (i) trust and reputation to govern the interactions among nodes, and (ii) communication models characterized by a high level of adaptiveness and flexibility. Hence, we propose a formalism for verifying, through model checking techniques, the robustness of these systems with respect to the typical attacks conducted against webs of trust.
Mechanically verified proof obligations for linearizability

Concurrent objects are inherently complex to verify. In the late 80s and early 90s, Herlihy and Wing proposed linearizability as a correctness condition for concurrent objects, which, once proven, allows us to reason about concurrent objects using pre- and postconditions only. A concurrent object is linearizable if all of its operations appear to take effect instantaneously some time between their invocation and return. In this article we define simulation-based proof conditions for linearizability and apply them to two concurrent implementations, a lock-free stack and a set with lock-coupling. Similar to other approaches, we employ a theorem prover (here, KIV) to mechanize our proofs. Contrary to other approaches, we also use the prover to mechanically check that our proof obligations actually guarantee linearizability. This check employs the original ideas of Herlihy and Wing of verifying linearizability via possibilities.
Cosmic microwave background power spectrum estimation with the destriping technique

Extraction of the cosmic microwave background (CMB) angular power spectrum is a challenging task for current and future CMB experiments due to the large data sets involved. Here we describe an implementation of Monte Carlo apodized spherical transform estimator (MASTER) described in Hivon et al., which exploits the destriping technique as a map-making method. In this method a noise estimate based on destriped noise-only Monte Carlo (MC) simulations is subtracted from the pseudo-angular power spectrum. As a working case we use realistic simulations of the Planck low-frequency instrument (LFI). We found that the effect of destriping on a pure sky signal is minimal and requires no correction. Instead we found an effect related to the distribution of detector pointings, which affects the high-� part of the power spectrum. We correct for this by subtracting a ‘signal bias’ estimated by MC simulations. We also give analytical estimates for this signal bias. Our method is fast and accurate enough (the estimator is unbiased and errors are close to theoretical expectations for maximal accuracy) to estimate the CMB angular power spectra for current and future CMB space missions. This study is related to Planck LFI activities. Ke yw ords: methods: data analysis ‐ cosmic microwave background.
An empirical comparison of topology-based hypertext similarity measures

The ability to accurately estimate the semantic distance between hypertext documents is an essential component in many approaches to overcoming the well-known problem of information overload. For example, an author trying to compile a definitive “hub” index on a particular topic needs to know the similarity between the pages that have already been incorporated into the index, and other pages from across the Web. Over the past years, a number of link-based approaches to computing similarity have been proposed. Motivated by previous bibliometrics research, we empirically compare five alternative link-based similarity measures, and demonstrate that comparing common descendants yields more accurate estimates than comparing common ancestors, which agrees with the intuition that, due to the decentralized nature of the Web, a page’s outlinks are a more reliable indicator of its true semantic content than its inlinks.
Incidence and predictors of heart failure hospitalization and death in permanent pacemaker patients: a single-centre experience over medium-term follow-up.

AIM The aim of this study was to assess the incidence and clinical predictors of the development of new-onset heart failure (HF) over medium-term follow-up, in patients treated with permanent pacing in daily clinical practice. METHODS AND RESULTS We retrospectively enrolled all consecutive patients who underwent single- or dual-chamber pacemaker implantation at the study centre. Patients with a left ventricular ejection fraction (LVEF) ≤35% or a prior diagnosis of HF were excluded. Ventricular leads were routinely implanted in the right apex. Pacemakers were implanted in 490 patients with a standard pacemaker indication and LVEF >35%. Left bundle-branch block (LBBB) was reported in 30 (8%) patients, and an LVEF <50% in 64 (13%) patients. During a follow-up of 27 ± 21 months, 32 (7%) patients reached the combined endpoint of HF death or hospitalization. On multivariate analysis, LBBB (HR, 3.50; 95% CI, 1.1-11.1; P = 0.033) and LVEF <50% (HR, 5.1; 95% CI, 1.9-14.2; P = 0.002) were confirmed as independent predictors of HF death or hospitalization. Patients with LVEF <50% and/or LBBB displayed significantly higher rates of HF death or hospitalization (log-rank test, all P<0.001). CONCLUSION The majority of patients with a standard indication for permanent pacing and normal LV function remained in a clinically stable condition after pacemaker implantation. However, ∼7% of patients developed new-onset HF over a period of follow-up of 27 months, and the presence of LBBB and LVEF <50% at the baseline predicted HF death or hospitalization.
Text Mining and Expert Systems Applied in Labor Laws

Based on the technological evolution of data processing, mainly the capacity of processing and storage, a new research field has emerged, called Knowledge Discovery in Database (KDD). Data mining (DM) is the essential and important step in the process of KDD with the goal of extracting high-level knowledge from low-level data. Unstructured DM or Text Mining is an essential part of discovering previously unknown patterns useful for particular purposes from textual data bases. The objective of this paper is to present a Labor- related Judicial application of Unstructured Data Mining techniques using probabilistic methodologies, linear regression and Rule Induction in Text Categorization, as well as an Expert System.