Miracle-dz/UniFAR-FTUs huggingface dataset:
An investigation of the structurally induced acoustic field in a car passenger compartment

The causes and characteristics of structurally induced interior noise in cars in relation to the boom problem is discussed and the relevant theory developed. The work is concerned with structural excitation of the air within the passenger compartment associated with the second order component of the engine crankshaft out of balance forces. This limits the frequency range of interest to below 200 Hz. Firstly, the acoustic modes of a Rover Metro passenger compartment are predicted using the finite element method. A new method for the experimental acoustic modal analysis of cavities is introduced and verified for a rectangular rigid walled room. The method is then applied to the untrimmed passenger compartment of a Rover Metro to determine its acoustic modes and to study the interaction of the structural modes of the vehicle body and acoustic response of the cavity. The acoustics of a Rover Metro passenger compartment are modelled using the finite element method with experimental structural FRF data from the car body as a forcing function. This model is used to predict the noise spectra associated with the second order component of engine excitation experienced by occupants for the bare body. The acoustic effects of various items of trim are added to the model as acoustic absorption coefficients to show the Significance of trim in reducing low frequency boom in car passenger compartments. The necessary absorption coefficients were measured with an impedance tube.
Journal of Graph Algorithms and Applications Scheduled Hot-potato Routing

This paper is concerned with fast, hot-potato routing, performed according to a predetermined schedule. At each time period each node selects an outgoing link, through which an incoming packet is sent. No buffers are used. We investigate first the problem of how to route a network-wide demand of packets, given the predetermined schedule. We show that certain versions of the problem have efficient solutions, while other versions are intractable. We then consider the problem of finding an optimal schedule given a network-wide demand of packets. We indicate that the problem is tractable for either a single source or single destination. However, for the multi-source multi-destination case we show that it is an NP-complete problem. We present an efficient heuristic for directed tree-networks, and adapt it to general topologies through a recursive scheme, for which an efficient performance bound is shown.
Implementation of the Combination of Lactation Management and Kangaroo Mother Care (KMC) Training for Village Midwives in the Sawit and Ngemplak Sub-districts, Boyolali, Indonesia

The incidence of low birth weight (LBW) in the Boyolali region of Indonesia in 2013 was 15.3 per 1000 live births, while the exclusive breastfeeding coverage was only 51.3%. To resolve these problems, capacity building among midwives, through a combination of lactation management and kangaroo mother care (KMC) training, was required. The purpose of this study was to describe village midwives’ experiences one month after attending the above training. This study used a quasi-experimental design with a rapid assessment procedure (RAP). Data collection was done through in-depth interviews. The informants of this study were four village midwives and pregnant and lactating women (one each). The results showed that, after one month of training, the informants had some competence in both lactation management and KMC skills. However, some informants reported some difficulties in counseling regarding breastfeeding problems. This was primarily caused by the fact that the participants did not have in-depth knowledge related to breastfeeding counseling. Breastfeeding counseling materials were not included in the above training curriculum; therefore, it is suggested that these be included in future lactation management and KMC training programs.
The Population Posterior and Bayesian Modeling on Streams

Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which require conditioning on finite data. We develop population variational Bayes, a new approach for using Bayesian modeling to analyze streams of data. It approximates a new type of distribution, the population posterior, which combines the notion of a population distribution of the data with Bayesian inference in a probabilistic model. We develop the population posterior for latent Dirichlet allocation and Dirichlet process mixtures. We study our method with several large-scale data sets.
Organic experiences: (re)shaping interactions with deformable displays

Display technology developments mean the next generation of visual output devices will extend beyond the rigid, flat surfaces with which we are familiar to those that the user or the machine can deform. These will allow users to physically push, pull, bend, fold or flex the display and facilitate a range of self-deformation to better represent on-screen content or support new modes of interaction. This workshop will provide a forum to examine, discuss and shape the three primary themes of research in this area: prototyping and implementation, interaction and experience design, and evaluation. It will bring together an interdisciplinary group of academic and industrial researchers to define the current and future challenges of crafting organic user experiences with deformable displays.
Rise in Plasma Lactate Concentrations with Psychosocial Stress: A Possible Sign of Cerebral Energy Demand

Objective: It is known that exogenous lactate given as an i.v. energy infusion is able to counteract a neuroglycopenic state that developed during psychosocial stress. It is unknown, however, whether the brain under stressful conditions can induce a rise in plasma lactate to satisfy its increased needs during stress. Since lactate is i) an alternative cerebral energy substrate to glucose and ii) its plasmatic concentration is influenced by the sympathetic nervous system, the present study aimed at investigating whether plasma lactate concentrations increase with psychosocial stress in humans. Methods: 30 healthy young men participated in two sessions (stress induced by the Trier Social Stress Test and a non-stress control session). Blood samples were frequently taken to assess plasma lactate concentrations and stress hormone profiles. Results: Plasma lactate increased 47% during psychosocial stress (from 0.9 ± 0.05 to 1.4 ± 0.1 mmol/l; interaction time × stress intervention: F = 19.7, p < 0.001). This increase in lactate concentrations during stress was associated with an increase in epinephrine (R2 = 0.221, p = 0.02) and ACTH concentrations (R2 = 0.460, p < 0.001). Conclusion: Plasma lactate concentrations increase during acute psychosocial stress in humans. This finding suggests the existence of a demand mechanism that functions to allocate an additional source of energy from the body towards the brain, which we refer to as ‘cerebral lactate demand’.
Extraction of mRNA from Soil

ABSTRACT Here, we report an efficient method for extracting high-quality mRNA from soil. Key steps in the isolation of total RNA were low-pH extraction (pH 5.0) and Q-Sepharose chromatography. The removal efficiency of humic acids was 94 to 98% for all soils tested. To enrich mRNA, subtractive hybridization of rRNA was most efficient. Subtractive hybridization may be followed by exonuclease treatment if the focus is on the analysis of unprocessed mRNA. The total extraction method can be completed within 8 h, resulting in enriched mRNA ranging from 200 bp to 4 kb in size.
Undirected Graphical Models (markov Random Fields) 19.1 Introduction 19.2 Conditional Independence Properties of Ugms 19.2.1 Key Properties

In Chapter 10, we discussed directed graphical models (DGMs), commonly known as Bayes nets. However, for some domains, being forced to choose a direction for the edges, as required by a DGM, is rather awkward. For example, consider modeling an image. We might suppose that the intensity values of neighboring pixels are correlated. We can create a DAG model with a 2d lattice topology as shown in Figure 19.1(a). This is known as a causal MRF or a Markov mesh (Abend et al. 1965). However, its conditional independence properties are rather unnatural. In particular, the Markov blanket (defined in Section 10.5) of the node X 8 in the middle is the other colored nodes (3, 4, 7, 9, 12 and 13) rather than just its 4 nearest neighbors as one might expect. An alternative is to use an undirected graphical model (UGM), also called a Markov random field (MRF) or Markov network. These do not require us to specify edge orientations, and are much more natural for some problems such as image analysis and spatial statistics. For example, an undirected 2d lattice is shown in Figure 19.1(b); now the Markov blanket of each node is just its nearest neighbors, as we show in Section 19.2. Roughly speaking, the main advantages of UGMs over DGMs are: (1) they are symmetric and therefore more " natural " for certain domains, such as spatial or relational data; and (2) discrimi-native UGMs (aka conditional random fields, or CRFs), which define conditional densities of the form p(y|x), work better than discriminative DGMs, for reasons we explain in Section 19.6. 1. The main disadvantages of UGMs compared to DGMs are: (1) the parameters are less interpretable and less modular, for reasons we explain in Section 19.3; and (2) parameter estimation is com-putationally more expensive, for reasons we explain in Section 19.5. See (Domke et al. 2008) for an empirical comparison of the two approaches for an image processing task. UGMs define CI relationships via simple graph separation as follows: for sets of nodes A, B, and C, we say x A ? G x B |x C iï¿¿ C separates A from B in the graph G. This means that, when we remove all the nodes in C, if there are no paths connecting any node in A to any node in B, then the CI property holds. This is called the global Markov property for UGMs.
A polarimetric Doppler radar time‐series simulator for biological applications

The high mobility of airborne organisms makes them inherently difficult to study, motivating the use of radars and radar networks as biological surveillance tools. While the utility of radar for ecological studies has been demonstrated, a number of challenges remain in expanding and optimizing their use for surveillance of birds, bats and insects. To explore these topics, a Lagrangian simulation scheme has been developed to synthesize realistic, polarimetric, pulsed Doppler radar baseband signals from modelled flocks of biological point scatterers. This radar simulation algorithm is described, and an application is presented using an agent‐based model of the nocturnal emergence of a cave‐dwelling colony of Brazilian free‐tailed bats (Tadarida brasiliensis). Dual‐polarization radar signals for an S‐band weather surveillance radar are synthesized and used to develop a new extension of the spectral velocity azimuth display for polarimetric roost‐ring signature analysis, demonstrating one capability of this simulation scheme. While these developments will have direct benefits for radar engineers and meteorologists, continuing investment in radar methods such as these will have cascading effects toward improving ecological models and developing new observational techniques for monitoring aerial wildlife.
Effect of Named Entities in Web Page Classification

With the rapid multiplication of World Wide Web, there is an increasing requirement for automated web page classification techniques. Web page classification is an important task in web mining and is utilized in many other areas of research as well. General practice during classification is to use lexical terms as features. In this paper we investigate the effect of considering named entities as features in web page classification. We have conducted tests in five different domains â"-baseball, football, health, politics and science â"-with web pages collected from online news providers. Our results show that incorporating named entities can result in slight gains in classifier performance for narrow domains, but is not always true for all the domains. Results also showed that classification based only on named entities can be good for certain domains (e.g., baseball) but is still lower than the lexical terms based representation.
Spatial variation of isotopic compositions of snowpack nitrate related to post-depositional processes in eastern Dronning Maud Land, East Antarctica

et al., 2009; Jacobi and Hilker, 2007; Röthlisberger et al., 2000). The relative importance of post-depositional loss processes, i.e., volatilization of nitric acid (HNO3) and photolysis of nitrate, has long been debated (Blunier et al., 2005; Röthlisberger et al., 2000). However, using the stable isotopic composition of nitrate in snow, it has recently been shown in field, laboratory and model experiments that nitrate loss is driven by UV photolysis dominates, especially under cold conditions (Berhanu et al., 2014; Erbland et al., 2013; Frey et al., 2009). HNO3 volatilization and associated isotopic fractionation may become important at warmer temperatures, greater than –20∞C (Erbland et al., 2013). To date, attention has focused on the spatial variability of photolytic NO3 – loss from surface snow in East Antarctica. This variability has been investigated along a traverse between the Dumont d’Urville station (in Adelie Land) and Vostok, passing through Dome C (Erbland et al., 2013), as well as along one from the East Antarctic coast to Dome Argus (Shi et al., 2015). Based on these studies, it is apparent that, at low accumulation sites, nitrate photolysis results in decreased nitrate concentration in the surface snow, from hundreds to tens of mg L–1 below a depth of 10 cm, and Spatial variation of isotopic compositions of snowpack nitrate related to post-depositional processes in eastern Dronning Maud Land, East Antarctica
Effect of Aerobic and Resistance Exercise Training on Liver Enzymes and Hepatic Fat in Iranian Men With Nonalcoholic Fatty Liver Disease

Background: Nonalcoholic fatty liver disease (NAFLD) has different prevalence rates in various parts of the world and is a risk factor for diabetes and cardiovascular disease that could progress to nonalcoholic steatohepatitis, cirrhosis, and liver failure. Objectives: The current study aimed to investigate the effect of Aerobic Training (AT) and resistance training (RT) on hepatic fat content and liver enzyme levels in Iranian men. Patients and Methods: In a randomized clinical trial study, 30 men with clinically defined NAFLD were allocated into three groups (aerobic, resistance and control). An aerobic group program consisted of 45 minutes of aerobic exercise at 60% - 75% maximum heart rate intensity, a resistance group performed seven resistance exercises at intensity of 50% - 70% of 1 repetition maximum (1RM ) and the control group had no exercise training program during the study. Before and after training, anthropometry, insulin sensitivity, liver enzymes and hepatic fat were elevated. Results: After training, hepatic fat content was markedly reduced, to a similar extent, in both the aerobic and resistance exercise training groups (P ≤ 0.05). In the two exercise training groups, alanine amino transferase and aspartate amino transferase serum levels were significantly decreased compared to the control group (P = 0.002) and (P = 0.02), respectively. Moreover, body fat (%), fat mass (kg), homeostasis model assessment insulin resistance (HOMI-IR) were all improved in the AT and RT. These changes in the AT group were independent of weight loss. Conclusions: This study demonstrated that RT and AT are equally effective in reducing hepatic fat content and liver enzyme levels among patients with NAFLD. However, aerobic exercise specifically improves NAFLD independent of any change in body weight.
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search

In this article, we present an efficient B+-tree based indexing method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the data based on a space- or data-partitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single dimensional value based on their similarity with respect to the reference point. This allows the points to be indexed using a B+-tree structure and KNN search to be performed using one-dimensional range search. The choice of partition and reference points adapts the index structure to the data distribution.We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness. We also present a cost model for iDistance KNN search, which can be exploited in query optimization.
A Randomized Algorithm for Pairwise Clustering

We present a stochastic clustering algorithm based on pairwise similarity of datapoints. Our method extends existing deterministic methods, including agglomerative algorithms, min-cut graph algorithms, and connected components. Thus it provides a common framework for all these methods. Our graph-based method differs from existing stochastic methods which are based on analogy to physical systems. The stochastic nature of our method makes it more robust against noise, including accidental edges and small spurious clusters. We demonstrate the superiority of our algorithm using an example with 3 spiraling bands and a lot of noise.
Utility of Homeostasis Model Assessment of β-Cell Function in Predicting Diabetes in 12,924 Healthy Koreans

OBJECTIVE It is unclear how well homeostasis model assessment of β-cell function (HOMA-β) predicts diabetes development beyond its components, especially glucose. RESEARCH DESIGN AND METHODS We identified 12,924 nondiabetic Koreans who had fasting plasma glucose and insulin concentrations measured in 2003 and again in 2008. To minimize the impact of differences in baseline glucose concentration, individuals were divided into three glucose categories: normal fasting glucose (NFG, glucose <5.6 mmol/l), impaired fasting glucose (IFG-100) (5.6–6.0 mmol/l), and IFG-110 (6.1–6.9 mmol/l). RESULTS Diabetes developed in 29% of individuals in the IFG-110 group, compared with 5% in IFG-100 and 0.3% in NFG groups. Within each glucose category, those who progressed to diabetes had higher baseline glucose concentrations (P ≤ 0.04). Baseline HOMA-β, however, was not lower but higher in individuals who developed diabetes in the NFG group (P = 0.009) and similar in the IFG-100 and IFG-110 groups. CONCLUSIONS These data question the utility of using HOMA-β to predict the development of diabetes.
Generation of Monthly Precipitation Climatologies for Costa Rica Using Irregular Rain-Gauge Observational Networks

Precipitation climatologies for the period 1961–1990 were generated for all climatic regions of Costa Rica using an irregular rain-gauge observational network comprised by 416 rain-gauge stations. Two sub-networks were defined: a high temporal resolution sub-network (HTR), including stations having at least 20 years of continuous records during the study period (157 in total); and a high spatial resolution sub-network (HSR), which includes all HTR-stations plus those stations with less than 20 years of continuous records (416 in total). Results from the kriging variance reduction efficiency (KRE) objective function between the two sub-networks, show that ordinary kriging (OK) is unable to fully explain the spatio-temporal variability of precipitation within most climatic regions if only stations from the HTR sub-network are used. Results also suggests that in most cases, it is beneficial to increase the density of the rain-gauge observational network at the expense of temporal fidelity, by including more stations even though their records may not represent the same time step. Thereafter, precipitation climatologies were generated using seven deterministic (IDW, TS2, TS2PARA, TS2LINEAR, TPS, MQS and NN) and two geostatistical (OK and KED) interpolation methods. Performance of the various interpolation methods was evaluated using cross validation technique, selecting the mean absolute error (MAE) and the root-mean square error (RMSE) as agreement metrics. Results suggest that IDW is marginally superior to OK and KED for most climatic regions. The remaining deterministic methods however, considerably deviate from IDW, which suggests that these methods are incapable of properly capturing the true-nature of spatial precipitation patterns over the considered climatic regions. The final generated IDW climatology was then validated against the Global Precipitation Climatology Centre (GPCC), Climate Research Unit (CRU) and WorldClim datasets, in which overall spatial and temporal coherence is considered satisfactory, giving assurance about the use this new climatology in the development of local climate impact studies.
Enriching music mood annotation by semantic association reasoning

Mood annotation of music is challenging as it concerns not only audio content but also extra-musical information. It is a representative research topic about how to traverse the well-known semantic gap. In this paper, we propose a new music-mood-specific ontology. Novel ontology-based semantic reasoning methods are applied to effectively bridge content-based information with web-based resources. Also, the system can automatically discover closely relevant semantics for music mood and thus a novel weighting method is proposed for mood propagation. Experiments show that the proposed method outperforms purely content-based methods and significantly enhances the mood prediction accuracy. Furthermore, evaluations show the system's accuracy could be promisingly increased with the enrichment of metadata.
Channel Estimation Under Staggered Frame Structure for Massive MIMO System

In this paper, a staggered frame structure is proposed for single-cell massive multiple-input multiple-output (MIMO) systems, and the key idea is that different users transmit training pilots at nonoverlapped time. As a result, users do not have to be synchronized strictly to send pilots and orthogonal pilots are not required. Moreover, we also propose two interference suppressed channel estimation methods, i.e., the linear minimum mean square error (LMMSE)-based and orthogonal projection based least squares (OPLS) methods for the staggered frame structure. Specifically, the LMMSE-based method minimizes the mean square error of the estimation, whereas the OPLS method only estimates the part of the user's channel response that is orthogonal to the other users. All conducted simulation results demonstrate that the massive MIMO system with the both proposed methods can achieve high average achievable rate. Furthermore, when the conjugate beamforming is employed, the massive MIMO system could obtain even higher average achievable data rate with the proposed OPLS method than that of the system with perfect channel state information. Moreover, the computational complexity of the proposed OPLS method is very low.