Search

This paper studies the statistical correlation in mechanical characteristics of class F fly ash based geopolymer concrete (CFGPC). Experimentally measured values of the compressive strength, elastic modulus and indirect tensile strength of CFGPC specimens made from class F fly ash (CFA) were presented and analyzed. The results were compared with those of corresponding ordinary Portland cement concrete (OPCC) using statistical hypothesis tests. Results illustrated that when possessing similar compressive and tensile strength, the elastic modulus for CFGPC is significantly lower than that of OPCC. The corresponding expressions recommended by standards for the case of OPCC is proved to be inaccurate when applied in the case of CFGPC. Statistical regression was used to identify tendencies and correlations within the mechanical characteristics of CFGPC, as well as the empirical equations for predicting tensile strength and elastic modulus of CFGPC from its compressive strength values. In conclusion, CFGPC and OPCC has significant differences in terms of the correlations between mechanical properties. The empirical equations obtained in this study could provide relatively accurate predictions on the mechanical behavior of CFGPC. [ABSTRACT FROM AUTHOR]

Taking advantage of the possibility of fuzzy test statistic falling in the rejection region, a statistical hypothesis testing approach for fuzzy data is proposed in this study. In contrast to classical statistical testing, which yields a binary decision to reject or to accept a null hypothesis, the proposed approach is to determine the possibility of accepting a null hypothesis (or alternative hypothesis). When data are crisp, the proposed approach reduces to the classical hypothesis testing approach. [ABSTRACT FROM AUTHOR]

On magnetic resonance (MR) imaging, Modic type 1 (MT1) endplate changes and infectious spondylodiscitis share similar findings. Therefore, this study investigated vertebral bone marrow and endplate changes to enable their differentiation. The lumbar spine MR examinations of 91 adult patients were retrospectively included: 39 with MT1; 19 with early spondylodiscitis without abscess; and 33 with advanced spondylodiscitis with abscess. The assessment included percentage of bone marrow edema on sagittal short tau inversion recovery images, and the signal ratio of edema to unaffected bone and endplate contour (normal; irregular, yet intact; blurred; destructive) on sagittal unenhanced T1-weighted images. Differences were tested for statistical significance by Chi-square test and mixed model analysis of variance. The MR diagnostic accuracy in differentiating MT1 and spondylodiscitis was assessed by cross-tabulation and receiver-operating characteristic analysis. The endplate contours, edema extents, and T1-signal ratios of MT1 (extent, 31.96%; ratio, 0.83) were significantly different (p < 0.001) from early spondylodiscitis (56.42%; 0.60), and advanced spondylodiscitis (91.84%; 0.61). The highest diagnostic accuracy (sensitivity, 94.87%; specificity, 94.23%; accuracy, 94.51%) in identifying MT1 was provided by an irregular, yet intact endplate contour. This may be a useful MR feature for the differentiation between MT1 and spondylodiscitis, particularly in its early stage. [ABSTRACT FROM AUTHOR]

Increasing gabapentinoid use has raised concerns of misuse and abuse in the United States (US). Little is known about the characteristics of gabapentinoid use in general clinical practice over time. This cross-sectional study used data from the National Ambulatory Medical Care Survey. We examined the trends of patient and prescriber characteristics and the diagnoses associated with US ambulatory care visits involving gabapentinoids for adult visits from 2003 to 2016. Using multivariable logistic regression, we estimated the adjusted proportion of gabapentinoid-involved visits among all visits and tested for trend significance. Among the weighted estimate of 260.1 million gabapentinoid-involved visits (aged 18–64 years: 61.8%; female: 61.9%; white: 85.5%), the adjusted annual proportion of gabapentinoid-involved visits nearly quadrupled from 2003 to 2016 (9.1 to 34.9 per 1000 visits; Ptrend < 0.0001), driven mainly by gabapentin. Nearly half had concurrent use with opioids (32.9%) or benzodiazepines (15.3%). Primary care physicians (45.8%), neurologists (8.2%), surgeons (6.2%), and psychiatrists (4.8%) prescribed two-thirds of the gabapentinoids. Most (96.6%) of the gabapentinoid visits did not have an approved indication for gabapentinoids among the first three diagnoses. Among US ambulatory care visits from 2003 to 2016, gabapentinoid use increased substantially, commonly prescribed by primary care physicians. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the problem of multi-label streaming feature selection with no prior knowledge of the feature space. However, the algorithms proposed to handle this problem seldom consider the group structure of streaming features. Another shortcoming arises from the fact that few studies have addressed atomic feature models, and particularly, few have measured the attraction and repulsion between features. To remedy these shortcomings, we develop the streaming feature selection algorithm with dynamic sliding windows and feature repulsion loss (SF-DSW-FRL). This algorithm is essentially carried out in three consecutive steps. Firstly, within dynamic sliding windows, candidate streaming features that are strongly related to the labels in different feature groups are selected and stored in a fixed sliding window. Then, the interaction between features is measured by a loss function inspired by the mutual repulsion and attraction between atoms in physics. Specifically, one feature attraction term and two feature repulsion terms are constructed and combined to create the feature repulsion loss function. Finally, for the fixed sliding window, the best feature subset is selected according to this loss function. The effectiveness of the proposed algorithm is demonstrated through experiments on several multi-label datasets, statistical hypothesis testing, and stability analysis. [ABSTRACT FROM AUTHOR]

Biodiesel is an alternative fuel for diesel engine. Considering the differences between diesel and biodiesel fuels, the engine condition should be modified based on the fuel or fuel blends to achieve optimum performance. This study presented a performance analysis of a direct-injected (DI) diesel engine with a dynamometer fueled with diesel-tomato seed biodiesel (TSOB) blends employing ANOVA and universal nonlinear model based on ANN. The experiments were carried out under conditions of some independent variables including different engine loads (0, 50, 100%) and speed (1800, 2150, and 2500 rpm) for four diesel-biodiesel combinations (B0, B5, B10, and B20). In this research, the effect of these factors on dependent variables including power, torque, SFC, FC, and Exhaust Gas Temperature (EGT) are investigated. Duncan′s multi-domain test at a significance level of R < 0.01 shows that the highest and lowest of the torque and power are produced from B5 and B20, respectively. These results show that the lowest EGT of 613 K is related to B20 and the highest EGT is related to B5 and B10. The regression models showed that the torque decreases with increasing the engine speed and biodiesel percentage. These results also show that the highest and the lowest SFC is related to B0 and B20, respectively. The ANN model shows high capability of predicting the engine performance parameters and emissions, without running costly and time-consuming experiments with the histogram error of 0.004 and R = 0.96. It also proved that ANN is a non-linear model of choice to deal with these data, instead of multivariate linear regression employed for preliminary analysis. [ABSTRACT FROM AUTHOR]

Urban areas involve different functions that attract individuals and fit personal needs. Understanding the distribution and combination of these functions in a specific district is significant for urban development in cities. Many researchers have already studied the methods of identifying the dominant functions in a district. However, the degree of collection and the representativeness of a function in a district are controlled not only by its number in the district but also by the number outside this district and a number of other functions. Thus, this study proposed a quantitative method to identify urban functions, using Fisher's exact test and point of interest (POI) data, applied in determining the urban districts within the Sixth Ring Road in Beijing. To begin with, we defined a functional score based on three statistical features: the p-value, odds-ratio, and the frequency of each POI tag. The p-value and odds-ratio resulted from a statistical significance test, the Fisher's exact test. Next, we ran a k-modes clustering algorithm to classify all urban districts in accordance with the score of each function and their combination in one district, and then we detected four different groups, namely, Work and Tourism Mixed-developed district, Mixed-developed Residential district, Developing Greenland district, and Mixed Recreation district. Compared with the other identifying methods, our method had good performance in identifying functions, except for transportation. In addition, the Coincidence Degree was used to evaluate the accuracy of classification. In our study, the total accuracy of identifying urban districts was 83.7%. Overall, the proposed identifying method provides an additional method to the various methods used to identify functions. Additionally, analyzing urban spatial structure can be simpler, which has certain theoretical and practical value for urban geospatial planning. [ABSTRACT FROM AUTHOR]

Manual Lymphatic Drainage (MLD) appears to stimulate lymphatic contraction, aid in the development of secondary derivation pathways, and stimulate the appearance of collateral pathways that could function as the main drainage routes of the limb in case of lymphedema. Through stretching, call up maneuvers are used to stimulate lymphangion reflex contraction and, therefore, lymphatic function. The aim was to describe the presence of areas and pathways of collateral lymphatic drainage under basal conditions and to determine, using Indocyanine Green (ICG) lymphography, whether an increase in these pathways occurs after 30 min of manual lymphatic stimulation with only call up maneuvers according to the Leduc Method®®. In this prospective analytical study (pretest–posttest), the frequency of presentation of areas and collateral lymphatic pathways was analyzed in 19 patients with secondary lymphedema of the upper limb after breast cancer using an infrared camera. Analyses were completed at three time points: after ICG injection, at baseline (pretest), and after the application of MLD (post-test). The Leduc Method maneuvers were applied to the supraclavicular and axillary nodes, chest, back, Mascagni, and Caplan pathways. The areas visualized in the pretest continued to be visible in the posttest. Additional pathways and fluorescent areas were observed after the maneuvers. The McNemar test showed statistical significance (p = 0.008), the odds ratio was infinite, and the Cohen's g value was equal to 0.5. Manual stimulation by call up maneuvers increased the observation frequency of areas and collateral lymphatic pathways. Therefore, ICG lymphography appears to be a useful tool for bringing out the routes of collateral bypass in secondary lymphoedema after cancer treatment. [ABSTRACT FROM AUTHOR]

Companies operating in the energy sector are under pressure to boost the level of green energy production. The use of renewable energy sources will have a positive impact on the environment, but the basic question is whether power plants that produce electricity using renewable energy sources are in better financial condition than those that use only traditional energy sources. We address the latter using a new hybrid approach that extends prior research by combining three analyses: ratio analysis with a large set of indicators, the Altman model and cluster analysis. To test the statistical significance of differences between groups, Student's t-test is applied. The sample concerns companies from the Baltic States and Central Europe in the years 2008–2017. The results indicate that in most cases there is no statistical difference in the financial standing of companies that use renewable energy sources and those that generate only fossil fuel-based energy. [ABSTRACT FROM AUTHOR]

Just like any physical system, projects have entropy that must be managed by spending energy. The entropy is the project's tendency to move to a state of disorder (schedule delays, cost overruns), and the energy process is an inherent part of any project management methodology. In order to manage the inherent uncertainty of these projects, accurate estimates (for durations, costs, resources, ...) are crucial to make informed decisions. Without these estimates, managers have to fall back to their own intuition and experience, which are undoubtedly crucial for making decisions, but are are often subject to biases and hard to quantify. This paper builds further on two published calibration methods that aim to extract data from real projects and calibrate them to better estimate the parameters for the probability distributions of activity durations. Both methods rely on the lognormal distribution model to estimate uncertainty in activity durations and perform a sequence of statistical hypothesis tests that take the possible presence of two human biases into account. Based on these two existing methods, a new so-called statistical partitioning heuristic is presented that integrates the best elements of the two methods to further improve the accuracy of estimating the distribution of activity duration uncertainty. A computational experiment has been carried out on an empirical database of 83 empirical projects. The experiment shows that the new statistical partitioning method performs at least as good as, and often better than, the two existing calibration methods. The improvement will allow a better quantification of the activity duration uncertainty, which will eventually lead to a better prediction of the project schedule and more realistic expectations about the project outcomes. Consequently, the project manager will be able to better cope with the inherent uncertainty (entropy) of projects with a minimum managerial effort (energy). [ABSTRACT FROM AUTHOR]

This paper introduces pragmatic hypotheses and relates this concept to the spiral of scientific evolution. Previous works determined a characterization of logically consistent statistical hypothesis tests and showed that the modal operators obtained from this test can be represented in the hexagon of oppositions. However, despite the importance of precise hypothesis in science, they cannot be accepted by logically consistent tests. Here, we show that this dilemma can be overcome by the use of pragmatic versions of precise hypotheses. These pragmatic versions allow a level of imprecision in the hypothesis that is small relative to other experimental conditions. The introduction of pragmatic hypotheses allows the evolution of scientific theories based on statistical hypothesis testing to be interpreted using the narratological structure of hexagonal spirals, as defined by Pierre Gallais. [ABSTRACT FROM AUTHOR]

The daily fields from three reanalysis datasets are utilized to explore the subseasonal influence of teleconnection patterns on the surface air temperature (SAT) over southern China. Due to the similarity of the results from the different datasets, the ensemble mean is then used in this study. After applying the false discovery rate to the significance test, the composite results reveal that positive Western Pacific (WP) events, East Atlantic (EA) events, Scandinavian (SCA) events, and Eastern Atlantic/Western Russia (EAWR) events are the teleconnection events that have an influence on SAT anomalies over southern China. The timing of inducing significant SAT anomalies over southern China is similar among positive WPevents, EA events and EAWR events, i.e., approximately the first 5-day period after their peak day. In contrast, SCA events exert a lagged significant influence on SAT, i.e., during approximately the second 6-day period after their peak day. Therefore, considering that significant circulation anomalies generally begin to appear at least 4 days before the peak day, these teleconnection events could be used as subseasonal predictors for SAT anomalies over southern China. [ABSTRACT FROM AUTHOR]

In the field of quantitative remote sensing of forest biomass, a prominent phenomenon is the increasing number of explanatory variables. Then how to effectively select explanatory variables has become an important issue. Linear regression model is one of the commonly used remote sensing models. In the process of establishing the linear regression model, a vital step is to select explanatory variables. Focusing on variable selection and model stability, this paper conducts a comparative study on the performance of eight linear regression parameter estimation methods (Stepwise Regression Method (SR), Criterions Based on The Bayes Method (BIC), Criterions Based on The Bayes Method (AIC), Criterions Based on Prediction Error (Cp), Least Absolute Shrinkage and Selection Operator (Lasso), Adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Non-negative garrote (NNG)) in the subtropical forest biomass remote sensing model development. For the purpose of comparison, OLS and RR, are commonly used as methods with no variable selection ability, and are also compared and discussed. The performance of five aspects are evaluated in this paper: (i) Determination coefficient, prediction error, model error, etc., (ii) significance test about the difference between determination coefficients, (iii) parameter stability, (iv) variable selection stability and (v) variable selection ability of the methods. All the results are obtained through a five ten-fold CV. Some evaluation indexes are calculated with or without degrees of freedom. The results show that BIC performs best in comprehensive evaluation, while NNG, Cp and AIC perform poorly as a whole. Other methods show a great difference in the performance on each index. SR has a strong capability in variable selection, although it is poor in commonly used indexes. The short-wave infrared band and the texture features derived from it are selected most frequently by various methods, indicating that these variables play an important role in forest biomass estimation. Some of the conclusions in this paper are likely to change as the study object changes. The ultimate goal of this paper is to introduce various model establishment methods with variable selection capability, so that we can have more choices when establishing similar models, and we can know how to select the most appropriate and effective method for specific problems. [ABSTRACT FROM AUTHOR]

Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher's Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes. [ABSTRACT FROM AUTHOR]

The device-independent approach to physics is one where conclusions about physical systems (and hence of Nature) are drawn directly and solely from the observed correlations between measurement outcomes. This operational approach to physics arose as a byproduct of Bell's seminal work to distinguish, via a Bell test, quantum correlations from the set of correlations allowed by local-hidden-variable theories. In practice, since one can only perform a finite number of experimental trials, deciding whether an empirical observation is compatible with some class of physical theories will have to be carried out via the task of hypothesis testing. In this paper, we show that the prediction-based-ratio method—initially developed for performing a hypothesis test of local-hidden-variable theories—can equally well be applied to test many other classes of physical theories, such as those constrained only by the nonsignaling principle, and those that are constrained to produce any of the outer approximation to the quantum set of correlations due to Navascués-Pironio-Acín. We numerically simulate Bell tests using hypothetical nonlocal sources of correlations to illustrate the applicability of the method in both the independent and identically distributed (i.i.d.) scenario and the non-i.i.d. scenario. As a further application, we demonstrate how this method allows us to unveil an apparent violation of the nonsignaling conditions in certain experimental data collected in a Bell test. This, in turn, highlights the importance of the randomization of measurement settings, as well as a consistency check of the nonsignaling conditions in a Bell test. [ABSTRACT FROM AUTHOR]

We study a binary hypothesis testing problem in which a defender must decide whether a test sequence has been drawn from a given memoryless source P 0 , while an attacker strives to impede the correct detection. With respect to previous works, the adversarial setup addressed in this paper considers an attacker who is active under both hypotheses, namely, a fully active attacker, as opposed to a partially active attacker who is active under one hypothesis only. In the fully active setup, the attacker distorts sequences drawn both from P 0 and from an alternative memoryless source P 1 , up to a certain distortion level, which is possibly different under the two hypotheses, to maximize the confusion in distinguishing between the two sources, i.e., to induce both false positive and false negative errors at the detector, also referred to as the defender. We model the defender–attacker interaction as a game and study two versions of this game, the Neyman–Pearson game and the Bayesian game. Our main result is in the characterization of an attack strategy that is asymptotically both dominant (i.e., optimal no matter what the defender's strategy is) and universal, i.e., independent of P 0 and P 1 . From the analysis of the equilibrium payoff, we also derive the best achievable performance of the defender, by relaxing the requirement on the exponential decay rate of the false positive error probability in the Neyman–Pearson setup and the tradeoff between the error exponents in the Bayesian setup. Such analysis permits characterizing the conditions for the distinguishability of the two sources given the distortion levels. [ABSTRACT FROM AUTHOR]

In this article, we address the common criticisms of the Visible Learning research and offer a long-term view of the potential presented with this body of knowledge. We contextualize our view with some experiences in a high school that is focused on improving student learning. [ABSTRACT FROM AUTHOR]

A strong earthquake of magnitude M w 6.8 struck Western Greece on 25 October 2018 with an epicenter at 37.515 ∘ N 20.564 ∘ E. It was preceded by an anomalous geolectric signal that was recorded on 2 October 2018 at a measuring station 70 km away from the epicenter. Upon analyzing this signal in natural time, we find that it conforms to the conditions suggested for its identification as precursory Seismic Electric Signal (SES) activity. Notably, the observed lead time of 23 days lies within the range of values that has been very recently identified as being statistically significant for the precursory variations of the electric field of the Earth. Moreover, the analysis in natural time of the seismicity subsequent to the SES activity in the area candidate to suffer this strong earthquake reveals that the criticality conditions were obeyed early in the morning of 18 October 2018, i.e., almost a week before the strong earthquake occurrence, in agreement with earlier findings. Finally, when employing the recent method of nowcasting earthquakes, which is based on natural time, we find an earthquake potential score around 80%. [ABSTRACT FROM AUTHOR]

As previous research indicates, a multiple-scanning methodology for discretization of numerical datasets, based on entropy, is very competitive. Discretization is a process of converting numerical values of the data records into discrete values associated with numerical intervals defined over the domains of the data records. In multiple-scanning discretization, the last step is the merging of neighboring intervals in discretized datasets as a kind of postprocessing. Our objective is to check how the error rate, measured by tenfold cross validation within the C4.5 system, is affected by such merging. We conducted experiments on 17 numerical datasets, using the same setup of multiple scanning, with three different options for merging: no merging at all, merging based on the smallest entropy, and merging based on the biggest entropy. As a result of the Friedman rank sum test (5% significance level) we concluded that the differences between all three approaches are statistically insignificant. There is no universally best approach. Then, we repeated all experiments 30 times, recording averages and standard deviations. The test of the difference between averages shows that, for a comparison of no merging with merging based on the smallest entropy, there are statistically highly significant differences (with a 1% significance level). In some cases, the smaller error rate is associated with no merging, in some cases the smaller error rate is associated with merging based on the smallest entropy. A comparison of no merging with merging based on the biggest entropy showed similar results. So, our final conclusion was that there are highly significant differences between no merging and merging, depending on the dataset. The best approach should be chosen by trying all three approaches. [ABSTRACT FROM AUTHOR]

A method is presented to detect earthquake precursors from time series data on earthquakes in a target region. The Regional Entropy of Seismic Information (RESI) is an index that represents the average influence of an earthquake in a target region on the diversity of clusters to which earthquake foci are distributed. Based on a simple qualitative model of the dynamics of land crust, it is hypothesized that the saturation that occurs after an increase in RESI precedes the activation of earthquakes. This hypothesis is validated by the earthquake catalog. This temporal change was found to correlate with the activation of earthquakes in Japanese regions one to two years ahead of the real activation, more reliably than the compared baseline methods. [ABSTRACT FROM AUTHOR]

Testing of evidence in criminal cases can be limited by temporal or financial constraints or by the fact that certain tests may be mutually exclusive, so choosing the tests that will have maximal impact on the final result is essential. In this paper, we assume that a main hypothesis, evidence for it and possible tests for existence of this evidence are represented in the form of a Bayesian network, and use three different methods to measure the impact of a test on the main hypothesis. We illustrate the methods by applying them to an actual digital crime case provided by the Hong Kong police. We conclude that the Kullback–Leibler divergence is the optimal method for selecting the tests with the highest impact. [ABSTRACT FROM AUTHOR]

In recent years, analytical tools of network theory have provided strong empirical support to the well-known hypothesis that regions develop through the local learning of capabilities (tacit productive knowledge). In this paper, we compare two indexes of competitiveness (or accumulated capabilities) for a subnational database of 32 Mexican states in the period 2004–2014. We find that Endogenous Fitness (i.e., region fitness and product complexity are derived jointly using only a Mexican exports database) has a better performance than Exogenous Fitness (i.e., product complexity comes from a world exports database and fitness is the sum of the complexity scores for the region's competitive products). The performance criterion is established with the indicator's capacity to meet a requirement of growth predictability: the existence of at least one laminar (ordered) regime in the fitness–income plane. In the Mexican data, Endogenous Fitness is a reliable predictor of per capita GDP in two distinct areas of the plane: one of continuous progress and opportunities, and another of stagnation and deteriorating fitness. The predictive capacity of this indicator becomes clear only when the metrics' calculations are filtered by removing raw petroleum or oil-dependent states, while such capacity is robust to the inclusion of tourism—another important industry of the Mexican economy. [ABSTRACT FROM AUTHOR]

In this study, we performed uni- and multivariate data analysis on the extended binding curves of several affinity pairs: immobilized acetylcholinesterase (AChE)/bioconjugates of aflatoxin B1(AFB1) and immobilized anti-AFB1 monoclonal antibody/AFB1-protein carriers. The binding curves were recorded on three mass sensitive cells operating in batch configurations: one commercial surface plasmon resonance (SPR) sensor and two custom-made Love wave surface-acoustic wave (LW-SAW) sensors. We obtained 3D plots depicting the time-evolution of the sensor response as a function of analyte concentration using real-time SPR binding sensograms. These "calibration" surfaces exploited the transient periods of the extended kinetic curves, prior to equilibrium, creating a "fingerprint" for each analyte, in considerably shortened time frames compared to the conventional 2D calibration plots. The custom-made SAW sensors operating in different experimental conditions allowed the detection of AFB1-protein carrier in the nanomolar range. Subsequent statistical significance tests were performed on unpaired data sets to validate the custom-made LW-SAW sensors. [ABSTRACT FROM AUTHOR]

Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models); (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities; and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data. Such methods are discussed and evaluated on real-life data, collected within several EU-funded projects. Overall, the presented methods may prove very useful to build effective monitoring services, suitable for practical exploitation in caregiving activities, complementing conventional telemedicine techniques. [ABSTRACT FROM AUTHOR]

The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system. [ABSTRACT FROM AUTHOR]

Regarding the notion of putative “best” practices in social neuroscience and science in general, we contend that following established procedures has advantages, but prescriptive uniformity in methodology can obscure flaws, bias thinking, stifle creativity, and restrict exploration. Generating hypotheses is at least as important as testing hypotheses. To illustrate this process, we describe the following exploratory study. Psychiatric patients have difficulties with social functioning that affect their quality of life adversely. To investigate these impediments, we compared the performances of patients with schizophrenia and those with bipolar disorder to healthy controls on a task that involved matching photographs of facial expressions to a faceless protagonist in each of a series of drawn cartoon emotion-related situations. These scenarios involved either a single character (Nonsocial) or multiple characters (Social). The Social scenarios were also Congruent, with everyone in the cartoon displaying the same emotion, or Noncongruent (with everyone displaying a different emotion than the protagonist should). In this preliminary study, both patient groups produced lower scores than controls (p < 0.001), but did not perform differently from each other. All groups performed best on the social-congruent items and worst on the social-noncongruent items (p < 0.001). Performance varied inversely with illness duration, but not symptom severity. Complete emotional, social, cognitive, or perceptual inability is unlikely because these patient groups could still do this task. Nevertheless, the differences we saw could be meaningful functionally and clinically significant and deserve further exploration. Therefore, we stress the need to continue developing novel, alternative ways to explore social cognition in patients with psychiatric disorders and to clarify which elements of the multidimensional process contribute to difficulties in daily functioning. [ABSTRACT FROM AUTHOR]