Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Music Genres Based on Social Networks and Statistical Methods

The study of musical influence is important for understanding the creative laws of musical works and musicians, as well as for examining the social culture of a particular era, whereas the sociological study of music is more intuitive and not accurate enough because it does not apply many mathematical methods. The aim of this paper is to develop a series of mathematical methods to measure the social impact of music and enrich the research tools of musicology. In this paper, an influence network and a similarity network are created on nodes representing artists. In the influence network, the PageRank algorithm is used to measure the influence of the artist. In the similarity network, weighted Euclidean distance is used to measure the similarity between any pair of music, where entropy weighting method is used to establish the weights in the Euclidean distance. Finally, community detection is performed in the similarity network using Louvain's algorithm, and a fit metric is invented to examine whether genres are reflected in the community. The above mathematical methods give results that find their counterparts within the sociology of music, proving that the methods developed in this paper are valuable.
Implantable Biosensor for Brain Dopamine using Microwire-Based Electrodes

This paper systematically demonstrates the feasibility of wirelessly monitoring dopamine concentration in the brain with an implantable biosensor. The biosensor was realized using microwires, and then, the dopamine concentration was measured in-vitro ranging from 0.3 $\mu$M to 2 $\mu$M, corresponding to the physio-pathological concentration range in human brain. The obtained results were used to design and optimise a full-custom CMOS sensor interface for in-vivo dopamine monitoring. The key component of this interface is a potentiostat with a maximum power consumption of 10.24 $\mu$W in a 10kHz sampling frequency. The CMOS interface automatically subtracts the background current up to 2.34 $\mu$A. The obtained sensitivity in dopamine detection has been evaluated in 150$\mu$A/$\mu$M, with a Limit of Detection (LoD) of 33 nM, thus being suitable for dopamine monitoring in human brain.
Infiltration and runoff water quality response to silvicultural and grazing treatments on a longleaf pine forest.

The impacts of intensive vs. extensive silviculture, and moderate continuous livestock grazing vs. no livestock grazing as they relate to infiltration and runoff water quality were evaluated using rainfall simulation. Study sites were located in the Vernon District of the Kisatchie National Forest, Louisiana. Infiltration was greater, and interrill erosion, suspension-solution phase total nitrogen concentrations, and suspension-solution phase total phosphate concentrations were less from areas under extensive silviculture and no livestock grazing than from areas under intensive silviculture and livestock grazing, respectively. Intensive silviculture exposed more bare soil than extensive treatments. Litter cover and litter biomass were significantly reduced by the intensive silvicultural treatment. Livestock grazing also exposed more bare soil mainly resulting from a removal of grass cover and biomass.
Fake News Detection: Hybrid Deep Supervised Learning Approach

Social interaction on media is a daily activity. In addition, various information can be quickly obtained on social media. However, not all information conveyed on social media is in accordance with factual conditions so it can mislead readers. Therefore, we need a system that can detect news that contains disinformation. Machine learning and deep learning can be used to detect and classify news containing disinformation. In this study, Hybrid classification is used on Twitter datasets related to political news in Indonesia, namely Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), with Support Vector Machine (SVM). With pre-trained Word2Vec that changes words into numerical vectors, the hybrid model will extract and learn the features. The hybrid model is compared with the individual LSTM or CNN to see the best outcome given by those methods. The comparison is based on the SVM and Sigmoid classification results. The results of the study show that the proposed hybrid model, namely the CNN-LSTM-SVM, shows a better result than any other single model, which gives the hybrid model with 82.55% average score in 5-fold and the other hand, the LSTM-CNN-SVM model gives 82.20% average score which has a slight difference on the first hybrid model.
Acquisition of earthquake damage information basedon remote sensing technology:history,current situation and trend

For damage information acquisition methods based on remote sensing, there have not been any general (overview.) In order to exhibit some overall perspective of the approaches, this paper reviews the development history of earthquake damage information acquisition methods based on remote sensing and summarizes earthquake damage identification techniques in every development stage of remote sensing. The earthquake damage identification approaches are classified and the criteria and principles of the classification are presented. The features of every kind of earthquake damage identification approach are analyzed and the merit and shortcoming are pointed out. Based on the characteristics about the identification approach,remote sensing development and earthquake damage,the development trend for earthquake damage information acquisition technology is discussed.
An Investigation of the Effect of the Prostaglandin Ep 2 Receptor Agonist, Butaprost, on the Human Isolated Myometrium from Pregnant and Non-pregnant Women

The aim of this study was to compare the effect of two known spasmogens, oxytocin and the stable thromboxane receptor mimetic, U46619, on human myometrium treated with the prostaglandin E receptor (EP 2) agonist, butaprost (selective for the EP 2 receptor). Strips of myometrium from pregnant and non-pregnant donors were set up in a superfusion apparatus. Butaprost was administered as a bolus dose and via infusion. During the infusion of 10 6 M butaprost, spasmogens were administered as bolus doses. Butaprost caused dose-related inhibition of myometrial activity when administered as a bolus dose (3–100 nmol) and concentration-dependent inhibition during infusion studies (10 8 –10 5 M). Butaprost (10 6 M) attenuated the response to U46619 (0·l–10 nmol) in pregnant myo-metrium, but this difference was not statistically significant. Responses of pregnant myometrium to oxytocin (0·001–0·l nmol) were significantly attenuated (P<0·05) in the presence of butaprost (10 6 M). Butaprost physiologically antagonised the oxytocin response, possibly by increasing intracellular cAMP levels. This antagonism was much more marked than that seen with butaprost and U46619. It is unclear why these two types of antagonism differ and this effect is currently being investigated further using other prostanoid and non-prostanoid agents.
Remote sensing of soil moisture with microwave radiometers

Microwave radiometry has been used for the remote sensing of soil moisture in a series of aircraft flights over an agricultural test area in the vicinity of Phoenix, Arizona, The radiometers covered the wavelength range 0.8–21 cm. Ground truth in the form of gravimetric measurements of the soil moisture in the top 15 cm were obtained for 200 fields at this site. The results indicate that it is possible to monitor soil moisture variations with airborne radiometers. The emission is a function of the radiometer wavelength and the distribution of the moisture in the soil. At a wavelength of 1.55 cm there is little or no variation in the emission for soil moisture values below 10 or 15% moisture content by weight. Above this value there is a linear decrease in the emission with a slope of approximately 3°K for each percentage point increase in soil moisture. On the other hand, the emission was observed to be a linear function of soil moisture over the range 0–35% at a wavelength of 21 cm. Results of calculations using a layered model are presented that indicate the importance of the surface moisture in determining the emission from the soil.
Evaluation of the In�uenza Sentinel Surveillance System in the Democratic Republic of Congo, 2012-2015

Background The World Health Organization recommends periodic evaluations of in�uenza surveillance systems to identify areas for improvement and provide evidence of data reliability for policymaking. However, data about the performance of established in�uenza surveillance systems are limited in Africa, including in the Democratic Republic of Congo (DRC). Methods We used the Centers for Disease Control and Prevention guidelines to evaluate the performance of the in�uenza sentinel surveillance system (ISSS) in DRC during 2012-2015. The performance of the system was evaluated using eight surveillance attributes: (i) data quality and completeness for key variables, (ii) timeliness, (iii) representativeness, (iv) �exibility, (v) simplicity, (vi) acceptability, (vii) stability and (viii) utility. For each attribute, speci�c indicators were developed and described using quantitative and qualitative methods. Scores for each indicator were as follows: <60% weak performance; 60-79% moderate performance; ≥ 80% good performance. Results During 2012-2015, we enrolled and tested 4,339 patients with inuenza-like illness (ILI) and 2,869 patients with severe acute respiratory illness (SARI) from 11 sentinel sites situated in 5 of 11 provinces. In�uenza viruses were detected in 446 (10.3%) samples from patients with ILI and in 151 (5.5%) samples from patients with SARI with higher detection during December-May. Data quality and completeness was >90% for all evaluated indicators. Other strengths of the system were timeliness, simplicity, stability and utility that scored >70% each. Representativeness, �exibility and acceptability had moderate performance. It was reported that the ISSS contributed to: (i) a better understanding of the epidemiology, circulating patterns and proportional contribution of in�uenza virus among patients with ILI or SARI; (ii) acquisition of new key competences related to in�uenza surveillance and diagnosis; and (iii) continuous education of surveillance staff and clinicians at sentinel sites about in�uenza. However, due to limited resources no actions were undertaken to mitigate the impact of seasonal in�uenza epidemics. Conclusions The system performed overall satisfactorily and provided reliable and timely data about in�uenza circulation in DRC. The simplicity of the system contributed to
MIT Open Access

We present a method for studying synaptic transmission in mass cultures of dissociated hippocampal neurons based on patch clamp recording combined with laser stimulation of neurons expressing Channelrhodopsin-2 (ChR2). Our goal was to use the high spatial resolution of laser illumination to come as close as possible to the ideal of identifying monosynaptically coupled pairs of neurons, which is conventionally done using microisland rather than mass cultures. Using recombinant adeno-associated virus (rAAV) to deliver the ChR2 gene, we focused on the time period between 14 and 20 days in vitro , during which expression levels are high, and spontaneous bursting activity has not yet started. Stimulation by wide-field illumination is sufficient to make the majority of ChR2-expressing neurons spike. Stimulation with a laser spot at least 10 μ m in diameter also produces action potentials, but in a reduced fraction of neurons. We studied synaptic transmission by voltage-clamping a neuron with low expression of ChR2 and scanning a 40 μ m laser spot at surrounding locations. Responses were observed to stimulation at a subset of locations in the culture, indicating spatial localization of stimulation. Pharmacological means were used to identify responses that were synaptic. Many responses were of smaller amplitude than those typically found in microisland cultures. We were unable to find an entirely reliable criterion for distinguishing between monosynaptic and polysynaptic
Monetary Policy , Asset Prices and Misspecification The robust approach to bubbles with model uncertainty

The period from 1995:Q1 to 2000:Q2 was an unusual one for the U.S. economy. Labor productivity growth, which had averaged 1-1/4 percent per year over the previous 20 years, nearly doubled. Over the same boom period, the federal funds rate was remarkably stable--perhaps in response to core inflation rates that mostly fell. From 1952 to 1994, stock-market capitalization fluctuated between 30 and 100 percent of nominal GDP. From there, it rocketed to a peak of 185 percent of GDP in 2000:Q1. Over the past two years, however, the stock market has retraced a significant portion of its previous gains, the economy has slid into recession and the subsequent recovery has been a halting one. The question arises as to the role of the apparent stock-market bubble in bringing about the recession and whether there was more that the Fed could have done to forestall that outcome. Bernanke and Gertler (1999) argue the laissez-faire view that the quiescence of monetary policy was the correct response, that monetary policy should respond only to the projected effects of stock-market movements on inflation and perhaps output, but not to perceived stock-market bubblesper se. Cecchettiet al. (2000) disagree, advancing the interventionist view that, in the words of Mussa (2002) central banks “can, should and do” respond (directly) to bubbles. Both Bernanke and Gertler (1999) and Cecchetti et al. (2000) rely primarily onad hoc augmentations of Taylor-type policy rules to examine model properties in response to shocks that are carefully constructed to isolate bubble phenomena. This obliges them to only loosely infer the implications of being wrong about the existence, nature, persistence and implications of a bubble. In this paper, we use a variant of the Bernanke-Gertler-Gilchrist model to reassess the case for responding to bubbles. The paper makes three contributions. First, we embellish the BGG model to see if the optimistic conclusion offered by BG is sensitive to changes in specification. Second, we add a bit more rigor regarding what is an optimal policy given the beliefs of the monetary authority. And third, we consider robust responses by the policy maker to uncertainty about aspects of the bubble process. Monetary Policy, Asset Prices and Misspecification 2
Cycle de l'azote à l'interface eau-sédiment dans le bassin d'Arcachon : rôle des bactéries dans les processus de perte en azote (nitrification-dénitrification)

Les processus microbiens de nitrification et de denitrification ont ete etudies dans les sediments du bassin d'Arcachon, une lagune macrotidale situee dans le sud-ouest de la cote atlantique francaise colonisee par des phanerogames marines (Z. noltii). Les densites de populations des bacteries nitrifiantes ont ete estimees par la methode du NPP. Les densites sont faibles. comprises entre 102 et 104 bacteries par ml de sediment, et montrent une legere augmentation durant la periode hivernale. L'activite nitrifiante a ete evaluee par la methode d'accumulation d'ammonium apres blocage a la N-serve. Les activites sont generalement faibles allant de 0 a 90 µmol m-2 h-1. Pour les sediments intertidaux, la nitrification represente 120 a 140 mmol m-2 an·1. La denitrification a ete evaluee sur l'ensemble du bassin par l'utilisation de nitrates marques a l'azote 15 (15N isotope pairing technique). Les activites moyennes sont faibles : de 10 a 20 µmol m-2 h-1 avec un maximum de 75 µmol m-2 h-1. Generalement, l'activite denitrifiante est soutenue l' ete par l' activite nitrifiante couplee. Au contraire, l' hiver ce sont les nitrates diffusant de la colonne d'eau qui sont principalement denitrifies. Le bilan des processus internes bacteriens du cycle sedimentaire de l'azote montre que la nitrification represente de 3 a 14 %, la denitrification de 12 a 24% et la fixation d'azote de 3 a 16% des apports en sels azotes dissous dans la lagune. L'impact des processus bacteriens sur le cycle global de l' azote du bassin est donc faible comparativement aux apports externes azotes et a l'assimilation d'azote par les phanerogames marines.
Exploring the dietary niche of Atlantolacerta andreanskyi (Lacertidae) using DNA metabarcoding

Determining the dietary niche is an essential part of any conservation strategy, and for modeling the community responses to climate change. DNA metabarcoding methods are revolutionizing such approaches, allowing higher taxonomic resolution than typically possible using microscopy. However, few studies have compared directly the approaches to evaluate the differences in methodology. Here we assess the dietary niches of two genetically diverse populations of Atlantolacerta andreanskyi , a lizard endemic to the Atlas Mountains, Morocco, using DNA metabarcoding of faecal samples, and for one of these we compared the results to two previously published assessments of diets obtained using microscopy of pellets and stomach contents respectively. While results at the Order level were similar, the higher taxonomic resolution obtained in this study provided new insights into the dietary niche of this species. Comparisons between the two populations further highlighted how ecologically distinct these are.
Machine Intelligence Accelerated Design of Conductive MXene Aerogels with Programmable Properties

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative optimization experiments, which are time-consuming when exploring a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable electrical and mechanical properties. First, an automated pipetting robot is operated to prepare 264 mixtures using four building blocks at different ratios/loadings (including Ti 3 C 2 T x MXene, cellulose nanofibers, gelatin, glutaraldehyde). After freeze-drying, the structural integrity of conductive aerogels is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 kinds of conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model can conduct two-way design tasks: ( 1 ) predicting the physicochemical properties of conductive aerogels from fabrication parameters and ( 2 ) automating the inverse design of conductive aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and its compressive strength. The model-suggested conductive aerogels with high electrical conductivity, customized compression resilience, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management. The fusion of robotics-accelerated experimentation, machine intelligence, and simulation tools expedites the tailored design and scalable production of conductive aerogels.
Becker Digital

Objective: To characterize interactivity during resident and nurse handoffs by investigating listening and question-asking behaviors during conversations. Materials and Methods: Resident ( n ¼ 149) and nurse ( n ¼ 126) handoffs in an inpatient medicine unit were audio-recorded. Handoffs were coded based on listening behaviors (active and passive), question types (patient status, coordination of care, clinical reasoning, and framing and alignment), and question responses. Comparisons between residents and nurses for listening and question-asking behaviors were performed us-ing the Wilcoxon rank-sum tests. A Poisson regression model was used to investigate differences in the question-asking behaviors between residents and nurses, and the association between listening and question-asking behaviors. Results: There were no signiﬁcant differences between residents and nurses in their active (18% resident vs 39% nurse handoffs) or passive (88% resident vs 81% nurse handoffs) listening behaviors. Question-asking was common in resident and nurse handoffs (87% vs 98%) and focused primarily on patient status, co-ordination, and framing and alignment. Nurses asked signiﬁcantly more questions than residents ( M resident ¼ 2.06 and M nurse ¼ 5.52) by a factor of 1.76 ( P < 0.001). Unit increase in listening behaviors was associated with an increase in the number of questions during resident and nurse handoffs by 7% and 12%, respectively. Discussion and Conclusion: As suggested by the Joint Commission, question-asking behaviors were common across resident and nurse handoffs, playing a critical role in supporting resilience in communication and collaborative cross-checks during conversations. The role of listening in initiating question-asking behaviors is discussed.
EMI Mitigation Using a Learning-Based Frequency Modulation Carrier in PWM Inverters

Pulse-width-modulation (PWM) inverters generate electromagnetic interference (EMI) due to large dv/dt and di/dt of switching operations. When a periodic carrier is used to generate the PWM control signal, the corresponding EMI spectrum contains strong harmonic components of the carrier frequency. Research has shown that the carrier harmonic spectrum can be mitigated by modulating the carrier frequency. In this paper, we proposed a learning-based method to design the carrier frequency modulation of the inverter by solving an optimization problem, given EMI spectra of the PWM inverter under different carriers as learning data. As a result, a desired EMI spectrum envelop can be achieved according to the EMC regulation. Synthesized EMI results show that our learning-based method generalize the frequency modulation carrier design for EMI mitigation. Experiments provide promising results on EMI reduction.
H OW S EMANTICS D ICTATES THE S YNTACTIC V OCABULARY

In this paper I argue that the set of formal features that can head a functional projection is not given by UG but derived through L1 acquisition. I formulate a hypothesis that says that initially every functional category F is realised as a semantic feature [F]; whenever there is an overt doubling effect in the L1 input with respect to F, this semantic feature [F] is reanalysed as a formal feature [i/uF]. In the first part of the paper I provide a theoretical motivation for this hypothesis, in the second part I test this proposal for a case-study, namely the cross-linguistic distribution of Negative Concord (NC). I demonstrate that in NC languages negation has been reanalysed as a formal feature [i/uNEG], whereas in Double Negation languages this feature remains a semantic feature [NEG] (always interpreted as a negative operator), thus paving the way for an explanation of NC in terms of syntactic agreement. In the third part I discuss that the application of the hypothesis to the phenomenon of negation yields two predictions that can be tested empirically. First I demonstrate that negative markers X° can be available only in NC languages; second, independent change of the syntactic status of negative markers, can invoke a change with respect to the exhibition of NC in a particular language. Both predictions are proven to be correct. I finally argue what the consequences of the proposal presented in this paper are for both the syntactic structure of the clause and second for the way parameters are associated to lexical items.