Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of biofeedback-based stimulated recall on self-regulated online learning: A gender and cognitive taxonomy perspective

Previous studies posited the effectiveness of Stimulated Recall (SR) by exposing learners to recorded videos enhancing their personal perceptions and authentic understanding of knowledge in an interactive classroom. However, few studies explored how SR is implemented in a relatively static context, e.g., online self-directed learning, or took human factors, e.g., cognitive style and gender, into consideration in such a context. To fill this gap, the current study, based on previous psychological research findings, aims to introduce biofeedback as a stimulus for learners to engage in retrospection regarding their learning behavior. A quasi-experimental design study was carried out over a 12-week set of EFL (English as a Foreign Language) self-regulated online reading activities. The participants consisted of an experimental group (54 undergraduate students) and a control group (52 undergraduate students) at one Chinese university. Pre-post tests on reading performance and their association with a specific cognitive taxonomy were assessed through a developed scale instrument, whereas physiological signals (e.g., gazing duration, verbal fixation and brain wave) were captured via eye-tracking and electroencephalograph (EEG) technology. The results emphasized that (a) students’ reading ability and cognitive hierarchy significantly improved through biofeedback. Moreover, (b) learners in single level-one cognitive hierarchic groups had significant improvements in both cognitive abilities and reading comprehension, whereas learners in multi-level hierarchic groups had no significant enhancements. Finally, (c) the optical data results and EEG reports showed that males favor procedural feedback and females have a preference for a conclusive assessment.
High Speed Approximate Cognitive Domain Ontologies for Asset Allocation based on Isolated Spiking Neurons

Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the most time consuming and key components of CECEP is the mining of knowledge from a repository described as a Cognitive Domain Ontology (CDO). One problem that is often tasked to CDOs is asset allocation. Given the number of possible solutions in this allocation problem, determining the optimal solution via CDO can be very time consuming. In this work we show that a grid of isolated spiking neurons is capable of generating solutions to this problem very quickly, although some degree approximation is required to achieve the speedup. However, the approximate spiking approach presented in this work was able to complete all allocation simulations with greater than 98% accuracy. Given the vast increase in speed (greater than 1000 times in some cases), as well as the reduction computational requirements, the presented algorithm is ideal for moving asset allocation to low power embedded hardware.
Investigations of the T1 Temperature Dependence of Protons in Water and Contrast Agents Using a High-Tc DC-SQUID-Based ULF NMR System

The temperature dependence of proton's spin-lattice relaxation time <italic>T</italic><sub>1</sub> in water samples and Gd-based contrast agents with a measurement field of 72 <italic>μ</italic>T was systematically studied by dc-SQUID-based ultralow field (ULF) nuclear magnetic resonance (NMR) spectrometer. The temperature of samples was controlled by a thermostatic bath and the standard error of <italic>T</italic><sub>1</sub> was around 0.01 s within five different groups, the results showed that <italic>T</italic><sub>1</sub> had a similar linear temperature dependence when it was compared to conventional NMR data and varied with the purity of the water samples. Then, the relaxivity <italic>r</italic><sub>1</sub> of Gd-based metal-organic frameworks (MOFs) and Gd-DTPA (where DTPA is diethylene triamine pentaacetate) were also studied. Both of them had a much higher <italic>r</italic><sub>1 </sub> in ULF NMR than conventional NMR, which was around 45 mM<sup>−1</sup>·s<sup>−1</sup> and 98 mM<sup>−1</sup>·s<sup>−1</sup>, respectively; however, the <italic>T</italic><sub>1</sub> of Gd-based contrast agents did not follow the linear temperature dependence, and the relaxivity <italic>r</italic><sub>1 </sub> would not be influenced by temperature, which implies Gd-MOFs and Gd-DTPA are also high performance <italic>T </italic><sub>1</sub> contrast agents in ULF NMR and MRI.
A Nonuniform Clutter Intensity Estimation Algorithm for Random Finite Set Filters

In many cases, target tracking is subjected to a dense, nonuniform, and time-varying clutter background. This will seriously deteriorate the tracking performance under an unknown clutter environment. In this paper, multitarget tracking under the unknown environment is considered. First, the finite mixture distribution is used to fit the unknown clutter distributions and then Gibbs sampling and Bayesian information criterion are adopted to evaluate and estimate the clutter parameters. Besides, the unknown detection profile and clutter rate are also considered. All these issues are solved in the random finite set framework because it is a principled and top-down approach. Finally, several experiments are provided to verify the proposed algorithm.
Dam Leakage Detection by Borehole Radar: A Case-History Study

A borehole radar investigation was performed at the Sanzuodian reservoir, Chifeng, China to assess possible leakage paths located in the deep dam foundation. The key methodologies used include both single-hole reflection and cross-hole radar tomography, which make a high-resolution identification of the hydraulic connection paths between upstream and downstream sides possible. The leakage paths are characterized by direct wave loss due to high electromagnetic attenuation in the single-hole reflection profile and the nearly horizontal-banded low-velocity zone in the cross-hole velocity tomography due to possible large internal erosion. Meanwhile, some small structures inside the dam, including the core wall thickness changing point, the connecting point between asphalt and concrete walls, and the contacting interface between the dry and the water-saturated formations can be identified from the single-hole reflection profile clearly. Interpreted leakage paths are proven by the water flow measurement. Borehole radar is a useful high-resolution tool, suitable for deep leakage detection and evaluation.
A Novel Self Cooling SRM for Electric Hand Tools

Electric hand tools require high efficiency, small-size, low cost, and improving convenience. To achieve these demands, Switched Reluctance Motor (SRM) is expected for electric hand tools motor as an alternative for blushless Permanent Magnet Synchronous Motor (PMSM). SRM is suited for electric hand tools from advantages such as the low manufacturing cost, the simple and robust structure, the high speed driving and operating under high temperature condition. However, SRM has a disadvantage that torque density is lower than it of PMSM. Therefore, SRM needs to add higher current than it of PMSM to achieve the same output torque. Moreover, winding temperature increases very fast by the large copper loss. To overcome this problem, this research proposes a self cooling SRM. Self cooling SRM can realize the same output torque and the same volume with the PMSM system including fan, and it can reduce the winding temperature by the self cooling effect.
Jointly Optimizing Content Caching and Recommendations in Small Cell Networks

Caching decisions typically seek to cache content that satisfies the maximum possible demand aggregated over all users. Recommendation systems, on the contrary, focus on individual users and recommend to them appealing content in order to elicit further content consumption. In our paper, we explore how these, phenomenally conflicting, objectives can be jointly addressed. First, we formulate an optimization problem for the joint caching and recommendation decisions, aiming to maximize the cache hit ratio under minimal controllable distortion of the inherent user content preferences by the issued recommendations. Then, we prove that the problem is NP-complete and that its objective function lacks those monotonicity and submodularity properties that would guarantee its approximability. Hence, we proceed to introduce a simpler heuristic algorithm that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources. Finally, we draw on both analysis and simulations with real and synthetic datasets to evaluate the performance of the algorithm. We point out its fundamental properties, provide bounds for the achieved cache hit ratio, and study its sensitivity to its own as well as system-level parameters.
Education and Culture Affect Visualization's Effectiveness for Health Communication

In this research we focus on analyzing which contextual variables moderate the effectiveness of knowledge visualization. Through a field experiment we compare the same content expressed in a textual and in a multimodal format (here intended as composed of text and icons together, organized spatially in a meaningful way). The application context is Polycystic Ovary Syndrome which is the most common cause of infertility. We conducted a field experiment in India and found that all subjects are more engaged by the content when read it in a visual format, but that the intention to change behavior is moderated by educational level.
Hybrid Sensorless Control of an Interior Permanent Magnet Synchronous Machine using Current Derivative Measurements and a Sliding Mode Observer

The ability of current derivative based speed and position estimation of IPMSMs over full speed range has recently been demonstrated. However, at high-speed, current waveforms become distorted due to extension of the zero voltage vectors. This paper presents a hybrid position and speed estimation technique which uses current derivatives at the first active-voltage vector during each PWM switching period (the Modified Fundamental PWM Excitation – FPE-method) for enhancing the performance of a Sliding Mode Observer at low and very low speeds. This paper presents the theory and experimental evaluation of the proposed sensorless method for IPMSM over wide speed range from zero to rated speed. The experimental results have shown a significant improvement of speed and position estimation accuracy at low speeds and a significant enhancement of the current waveform over the full speed range, compared to conventional FPE methods, which utilizes current derivatives at two active-voltage vectors and two zero-voltage vectors during each PWM cycle for sensorless control.
Towards Underwater Object Recognition Based on Supervised Learning

Underwater robots play a significant role in exploring the underwater world. In recent years, underwater robots still can't recognize the underwater objects accurately. In order to find a solution to the problem of underwater robot recognition, we put forward a framework. There are three parts in our framework. First, a color correction algorithm is used to compensate color casts and produce natural color corrected images. Second, we employ Super-Resolution Generative Adversarial Network to enhance the underwater images. There are two parts in Super-Resolution Generative Adversarial Network. One is the modified generate network $G$, and the other is the discriminator network $D$. We modify the generate network $G$ on basis of ResNet. Third, we employ object recognition algorithm to process the enhanced images for detecting and recognizing the underwater object. The experimental results show that the proposed framework can achieve good results in underwater object recognition.
Implementing computer-mediated intercultural communication in English education: A critical reflection on its pedagogical challenges

Integrating Computer-Mediated Intercultural Communication (CMIC) activities into language curricula has been discussed as an innovative approach to supporting students in developing intercultural communicative competence, an essential skill in today’s internationalised society. Despite the increasing number of successful examples reporting positive outcomes of adopting CMIC activities in language learning environments, it has also been noted that there are a number of significant pedagogical challenges observed when the CMIC activities are introduced into classrooms. However, previous studies tend only to briefly mention some of those challenges and to list general solutions to them, rather than deeply engaging with the issues and their educational and social consequences upon students learning and language practices. Thus, this qualitative case study aims to investigate specifically such issues: it documents diverse pedagogical challenges faced by Korean students and a teacher in their EFL classroom, in which a series of CMIC activities were set up and conducted. The challenges analysed in this paper include: the unrealistically high implementation cost (which is not merely financial); diverse educational inequality issues created by irreducible gaps in students’ socio-economic backgrounds and perpetuated within CMIC activities; and the unresolved struggles to develop positive student subjectivities and linguistic identities. In conclusion, the article proposes three teaching and instructional principles, drawn in turn from the two closely interrelated theories: Multiliteracies Pedagogy and Critical Pedagogy. Fundamentally, the author argues that effective adoption of the CMIC activities needs a more pedagogically, socially, and culturally sensitive approach.
A Space-Vector State Dynamic Model of the Synchronous Reluctance Motor Including Self and Cross-Saturation Effects and its Parameters Estimation

This paper proposes a space-vector dynamic model of the Synchronous Reluctance Motor (SynRM) including both self-saturation and cross-saturation effects and selecting as state variables the stator currents. The proposed dynamic model is based on an original function between the stator flux and stator current components, and relies on 8 coefficients (fewer than other models in the scientific literature), presenting an interesting physical interpretation. Starting from this approach, both the static and dynamic inductances expressions of the model have been analytically developed, so that the reciprocity conditions for the cross-saturation is satisfied. This paper presents also a technique for the estimation of the parameters of the inductances expressions, which is based on stand-still tests without the need of locking the rotor. This technique is based on the minimization of a suitably defined error function which includes the difference between the measured and estimated currents The proposed parameter estimation technique has been tested in both numerical simulation and experimentally on a suitably developed test set-up.
Solution and Analysis of TDOA Localization of a Near or Distant Source in Closed Form

Point positioning and direction of arrival (DOA) localization require separate estimators, depending on whether the source is near or distant from the sensors. The use of modified polar representation (MPR) for the source location enables the integration of the two estimators together and eliminates the knowledge needed if the source is in the near-field or far-field. The previous work on MPR only provides an iterative implementation of the maximum likelihood estimator (MLE) initialized by a coarse semidefinite relaxation (SDR) solution. This paper proposes a formulation for time difference of arrival (TDOA) localization of a 2-D or 3-D source in MPR that would lead to a closed-form solution through the minimization of a quadratic function with a quadratic constraint. Two techniques, the successive unconstrained minimization (SUM) and the generalized trust region subproblem (GTRS), are applied to solve the optimization. Detailed analysis in the first order for mean-square and in the second order for estimation bias is performed for both methods. The theoretical results illustrate that the closed-form solution from each of the methods provides the Cramér–Rao lower bound performance for Gaussian noise. The GTRS solution has better accuracy than the SUM solution when the source signal is arriving at an azimuth or elevation angle close to zero, 90$^\circ$, or 180$^\circ$ or when the measurement noise level is large. They yield comparable performance with the MLE albeit having less complexity and avoiding possible divergence behavior. They outperform the closed-form solutions from the literature in angle estimation and have smaller bias.
Guess where I go?: A Mobility Predictor for Smart Vehicles

Ubiquitous computing is the growing trend of establishing a place for technology in the background, rather than the foreground, of our lives through embedding computational capability into everyday objects to make them effectively communicate and perform useful tasks. Hence, the goal of pervasive computing is to make devices "smart". Standing on this ground, the model subject of this work enhances the vehicles intelligence through enabling them to accurately predict the drivers destination without their intervention through an optimized four dimensional destination prediction model. Moreover, it takes the lead in leveraging the semantic properties of both the locations of interest and the social relations affecting the visiting activities of the drivers. In line with this, our proposed model simultaneously utilizes an optimization algorithm to blend the different dimensions and to accommodate the evolving human mobility behavior. The experimental results demonstrate that the optimized model significantly outperforms the basic model achieving an average prediction accuracy of 95% when compared to the real trace. Show the XML Only
A mechanistic classification of clinical phenotypes in neuroblastoma

A systematic look at a childhood tumor Neuroblastomas—the most common tumor type in infants—develop from fetal nerve cells, and their clinical course is highly variable. Some neuroblastomas are fatal despite treatment, whereas others respond well to treatment and some undergo spontaneous regression without treatment. Ackermann et al. sequenced more than 400 pretreatment neuroblastomas and identified molecular features that characterize the three distinct clinical outcomes. Low-risk tumors lack telomere maintenance mechanisms, intermediate-risk tumors harbor telomere maintenance mechanisms, and high-risk tumors harbor telomere maintenance mechanisms in combination with RAS and/or p53 pathway mutations. Science, this issue p. 1165 Neuroblastomas that are positive for telomere maintenance mechanisms are associated with a poorer prognosis. Neuroblastoma is a pediatric tumor of the sympathetic nervous system. Its clinical course ranges from spontaneous tumor regression to fatal progression. To investigate the molecular features of the divergent tumor subtypes, we performed genome sequencing on 416 pretreatment neuroblastomas and assessed telomere maintenance mechanisms in 208 of these tumors. We found that patients whose tumors lacked telomere maintenance mechanisms had an excellent prognosis, whereas the prognosis of patients whose tumors harbored telomere maintenance mechanisms was substantially worse. Survival rates were lowest for neuroblastoma patients whose tumors harbored telomere maintenance mechanisms in combination with RAS and/or p53 pathway mutations. Spontaneous tumor regression occurred both in the presence and absence of these mutations in patients with telomere maintenance–negative tumors. On the basis of these data, we propose a mechanistic classification of neuroblastoma that may benefit the clinical management of patients.
Functional selectivity profiling of the angiotensin II type 1 receptor using pathway-wide BRET signaling sensors

Bioluminescence-based biosensors enable comprehensive profiling of G protein–coupled receptor signaling bias. Biosensors for GPCR-biased signaling Upon binding to ligands, G protein–coupled receptors (GPCRs) stimulate heterotrimeric G proteins and recruit β-arrestin, which dampens signaling through G proteins and acts as a scaffold to activate other pathways. Whereas some ligands stimulate both G proteins and β-arrestin, others preferentially stimulate one or the other, a phenomenon known as biased agonism. For receptors engaging more than one G protein subtype, ligands may also bias downstream signaling to a particular G protein. Namkung et al. developed bioluminescence resonance energy transfer (BRET) biosensors, which they used to characterize biased signaling downstream of the angiotensin II (AngII) type 1 receptor (AT1R) in response to various ligands, as well as the bias of naturally occurring AT1R variants. This tool kit can be used to comprehensively dissect the signaling bias of other GPCR-ligand combinations, which may benefit drug development efforts. G protein–coupled receptors (GPCRs) are important therapeutic targets that exhibit functional selectivity (biased signaling), in which different ligands or receptor variants elicit distinct downstream signaling. Understanding all the signaling events and biases that contribute to both the beneficial and adverse effects of GPCR stimulation by given ligands is important for drug discovery. Here, we report the design, validation, and use of pathway-selective bioluminescence resonance energy transfer (BRET) biosensors that monitor the engagement and activation of signaling effectors downstream of G proteins, including protein kinase C (PKC), phospholipase C (PLC), p63RhoGEF, and Rho. Combined with G protein and β-arrestin BRET biosensors, our sensors enabled real-time monitoring of GPCR signaling at different levels in downstream pathways in both native and engineered cells. Profiling of the responses to 14 angiotensin II (AngII) type 1 receptor (AT1R) ligands enabled the clustering of compounds into different subfamilies of biased ligands and showed that, in addition to the previously reported functional selectivity between Gαq and β-arrestin, there are also biases among G protein subtypes. We also demonstrated that biases observed at the receptor and G protein levels propagated to downstream signaling pathways and that these biases could occur through the engagement of different G proteins to activate a common effector. We also used these tools to determine how naturally occurring AT1R variants affected signaling bias. This suite of BRET biosensors provides a useful resource for fingerprinting biased ligands and mutant receptors and for dissecting functional selectivity at various levels of GPCR signaling.