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This study explores the integration of technology platforms in online modular learning and proposes an enhanced learning continuity framework using online teaching as an alternative delivery modality. The study captures participants' experiences related to online modular education through surveys by employing a qualitative approach. The findings demonstrate that integrating technological platforms enhances learning continuity. Online teaching offers flexible and accessible learning experiences, fostering effective teaching and learning. The study emphasizes the significance of comprehensive technical infrastructure, blended learning approaches, pedagogical and learner support services, continuous assessment, and a healthy learning environment in crafting the framework. The framework emphasizes robust technological infrastructure, training programs, blended learning approaches, pedagogical support, diverse assessment strategies, and a healthy learning environment by addressing challenges like the digital divide, technical difficulties, and lack of face-to-face interaction, flexibility, adaptability, and continuous evaluation are emphasized to address specific institutional and learner needs. Implementing the framework requires collaboration among educational institutions, policymakers, and communities to create inclusive online learning environments. Efforts should be made to ensure equitable access to reliable internet connections, support disadvantaged learners, and foster creativity through interactive activities. Monitoring and evaluation play a vital role in tracking progress and making informed decisions for improvement. This study provides a foundation for institutions to develop an enhanced learning continuity framework using online teaching. By optimizing the benefits of online modular learning while addressing challenges, the framework promotes effective teaching and learning in the online education landscape. [ABSTRACT FROM AUTHOR]

In this work, we provide non-asymptotic, probabilistic guarantees for successful recovery of the common nonzero support of jointly sparse Gaussian sources in the multiple measurement vector (MMV) problem. The support recovery problem is formulated as the marginalized maximum likelihood (or type-II ML) estimation of the variance hyperparameters of a joint sparsity inducing Gaussian prior on the source signals. We derive conditions under which the resulting nonconvex constrained optimization perfectly recovers the nonzero support of a joint-sparse Gaussian source ensemble with arbitrarily high probability. The support error probability decays exponentially with the number of MMVs at a rate that depends on the smallest restricted singular value and the nonnegative null space property of the self Khatri-Rao product of the sensing matrix. Our analysis confirms that nonzero supports of size as high as $O(m^{2})$ are recoverable from $m$ measurements per sparse vector. Our derived sufficient conditions for support consistency of the proposed constrained type-II ML solution also guarantee the support consistency of any global solution of the multiple sparse Bayesian learning (M-SBL) optimization whose nonzero coefficients lie inside a bounded interval. For the case of noiseless measurements, we further show that a single MMV is sufficient for perfect recovery of the $k$ -sparse support by M-SBL, provided all subsets of $k + 1$ columns of the sensing matrix are linearly independent. [ABSTRACT FROM AUTHOR]

• A new flexible Generalized Heterogeneous Data Model (GHDM) is proposed • A non-normal factor-based approach is applied using an efficient transformation • The latent unobserved factors are allowed to be correlated with the outcomes • An application – attitudinal latent constructs correlated with outcomes • The model is applied to residential choice and bicycling frequency There is growing interest in multivariate dependent outcome models that include a mixture of different kinds of discrete and continuous variables. This may be attributed to at least two reasons. The first is the ability to generate multivariate distributions through the use of relatively flexible copula-based methods and/or effective factorization techniques for the covariance matrices. The second is the development of computationally efficient ways to estimate models based on variational methods for Bayesian inference or maximum approximate composite marginal likelihood methods for frequentist inference. However, there are two important assumptions in earlier mixed data models: (i) marginal normality of unobserved factors that generate jointness among the main outcome variables of interest, and (ii) independence between the unobserved factors and the propensity equations underlying the main outcomes of interest. In the current paper, we simultaneously relax both these assumptions and develop a flexible Generalized Heterogeneous Data Model (GHDM) for mixed data modeling. We then propose a hybrid MSL-MACML inference approach for estimation. We demonstrate an application of our proposed model in the context of individuals' high-density residential neighborhood living choice and monthly bicycling frequency. The empirical results highlight the benefits of our proposed methodology, both from a policy standpoint as well as a predictive data fit standpoint. [ABSTRACT FROM AUTHOR]

• A new multiple two-stage budgeting discrete-continuous model is proposed. • A new minLogistic distribution is formally introduced and its properties derived. • The model is applicable for finite, endogenous, and unobserved total budgets. • A vehicle fleet composition and miles driven model is estimated. In this paper, we propose a multiple discrete-continuous (MDC) model approach that (a) does not need the total budget to be observed or predetermined, (b) allows for any finite or not-so-finite budget over the entire set of inside and outside goods, and (c) preserves a strong endogenous utility-theoretic link between inside good consumptions and the budget allocated to the inside goods (that is, to the product group of interest). We show that our proposed model, including a fractional MDC model at the lower level linked up to a Tobit model for the budget allocation to the inside goods, is strictly consistent with a two-stage budgeting utility theoretic structure. As importantly, by using reverse Gumbel distributional assumptions for the stochastic terms in the model system, we derive an incredibly simple closed-form model that, to our knowledge, is a first of its kind in the econometric literature. In doing so, we formally introduce a new distribution, which we label as the minLogistic distribution, to the statistical literature, and derive the properties of the distribution that is then used in the forecasting stage of the proposed model. An application of the proposed model to investigate the household vehicle fleet composition and usage demonstrates its potential relative to an unlinked and exogenously developed budget for the inside goods. The proposed model has the potential to open up a whole new world of MDC applications in general, and particularly for those cases with an unobserved total budget over the inside and outside goods. [ABSTRACT FROM AUTHOR]

• A new two-stage budgeting multiple discrete-count model is proposed. • Alternative processes are identified for multivariate count choices. • Using reverse-Gumbel stochastic terms, a closed form is obtained. • A tourism trip generation and destination model is estimated. In this paper, we propose a new two-stage budgeting-based utility-theoretic econometric multiple discrete-count model based on the linking of a fractional split MDCEV model component with a total count model. Through the strategic specification of error distributions in the model, we derive a multiple discrete-count extreme value (MDCNTEV) model that has a closed-form probability expression and that is estimable using straightforward maximum likelihood estimation. An application of the proposed model is demonstrated in the context of individuals' multivariate count of recreational episodes to each of multiple possible tourism destination locations. The results highlight the promise of the proposed model for a variety of multivariate count consumer choice settings. The model can also serve as a base model over which random heterogeneity may be superimposed to specify more advanced models. [ABSTRACT FROM AUTHOR]

Abstract Food insecurity has broad detrimental impacts on college students, including failing grades, depression, and social disconnection. Social disconnection is concerning as students often use various support resources to manage food insecurity. Racial disparities in food insecurity are well documented in the literature. The purpose of the current study was to explore the relationship between constructs of emotional support and food insecurity among African American and White students. A validated electronic survey based on the Sense of Support Scale and the U.S. Food Security Scale was utilized for the study. Results indicated that specific constructs of emotional social support were associated with food insecurity (OR: 3.778; p = .011); (OR: −2.116; p = .036). Students who reported strong emotional ties were more likely to experience food insecurity (OR = − 3.837, p = .044). Findings implicate further campus outreach to all students. [ABSTRACT FROM AUTHOR]

This paper considers the uplink performance of a multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system with mobile users. Mobility brings two major problems to a MIMO-OFDM system: inter carrier interference (ICI) and channel aging. In practice, it is common to allot multiple contiguous subcarriers to a user as well as schedule multiple users on each subcarrier. Motivated by this, we consider a general subcarrier allocation scheme and derive expressions for the ICI power, uplink signal to interference plus noise ratio and the achievable uplink sum-rate, taking into account the ICI and the multi-user interference due to channel aging. We show that the system incurs a near-constant ICI power that depends linearly on the ratio of the number of users per subcarrier to the number of subcarriers per user, nearly independently of how the UEs distribute their power across the subcarriers. Further, we exploit the coherence bandwidth of the channel to reduce the length of the pilot sequences required for uplink channel estimation. We consider both zero-forcing and maximal-ratio combining at the receiver and compare the respective sum-rate performances. In either case, the subcarrier allocation scheme considered in this paper leads to significantly higher sum-rates compared to previous work, owing to the near-constant ICI property as well as the reduced pilot overhead. [ABSTRACT FROM AUTHOR]

In the problem of multiple support recovery, we are given access to linear measurements of multiple sparse samples in ${\mathbb {R}}^{d}$. These samples can be partitioned into $\ell$ groups, with samples having the same support belonging to the same group. For a given budget of $m$ measurements per sample, the goal is to recover the $\ell$ underlying supports, in the absence of the knowledge of group labels. We study this problem with a focus on the measurement-constrained regime where $m$ is smaller than the support size $k$ of each sample. We design a two-step procedure that estimates the union of the underlying supports first, and then uses a spectral algorithm to estimate the individual supports. Our proposed estimator can recover the supports with $m< k$ measurements per sample, from $\tilde{O}(k^{4}\ell ^{4}/m^{4})$ samples. Our guarantees hold for a general, generative model assumption on the samples and measurement matrices. We also provide results from experiments conducted on synthetic data and on the MNIST dataset. [ABSTRACT FROM AUTHOR]

Orthogonal Time Frequency Space (OTFS) modulation is a recently proposed scheme for time-varying narrowband channels in terrestrial radio-frequency communications. Underwater acoustic (UWA) and ultra-wideband (UWB) communication systems, on the other hand, confront wideband time-varying channels. Unlike narrowband channels, for which time contractions or dilations due to Doppler effect can be approximated by frequency-shifts, the Doppler effect in wideband channels results in frequency-dependent non-uniform shift of signal frequencies across the band. In this paper, we develop an OTFS-like modulation scheme – Orthogonal Delay Scale Space (ODSS) modulation – for handling wideband time-varying channels. We derive the ODSS transmission and reception schemes from first principles. In the process, we introduce the notion of $\omega$ -convolution in the delay-scale space that parallels the twisted convolution used in the time-frequency space. The preprocessing 2D transformation from the Fourier-Mellin domain to the delay-scale space in ODSS, which plays the role of inverse symplectic Fourier transform (ISFFT) in OTFS, improves the bit error rate performance compared to OTFS and Orthogonal Frequency Division Multiplexing (OFDM) in wideband time-varying channels. Furthermore, since the channel matrix is rendered near-diagonal, ODSS retains the advantage of OFDM in terms of its low-complexity receiver structure. [ABSTRACT FROM AUTHOR]

• A new model structure for multiple discrete-continuous choice is proposed. • The structure ensures that only feasible consumption patterns are considered. • A simulation study shows good performance of the proposed estimator. • The study indicates serious bias when ignoring feasibility considerations. • A time-use analysis application is undertaken. A variant of the traditional multiple discrete-continuous extreme value (MDCEV) model that obviates the need to have budget information, labeled as the Lγ -profile MDCEV model, has been proposed recently. This new model structure breaks the strong linkage between the discrete and continuous choice dimensions of decision-making. But recent studies show that this Lγ -profile model may not work well in situations when, even if the budget is unobserved, the budget is known to be finite and small in magnitude. The reason is that the formulation, while ensuring the positivity of consumptions of the inside goods (that may or may not be consumed), does not guarantee, within the model formulation and estimation itself, the positivity of the consumption of the essential outside good. In this paper, we develop a formulation based on a reverse Gumbel structure for the stochastic terms in the utility functions of alternatives that develops a closed-form probability expression, while also accommodating the positivity requirement for the outside good. The ability of our proposed Budget-based Reverse Generalized Lγ -profile model (labeled the BR- GLγ -profile model) to recover true underlying model parameters is assessed. Our results clearly point to the benefit of employing the proposed model (relative to extant linear outside utility profile models in the literature) in empirical contexts when there is reason to believe that a finite ceiling applies to the budget (even if the budget is unobserved) or if the budget is actually available. In the latter case when the budget is available, our proposed model is a serious contender to the traditional γ -profile-MDCEV model and will generally outperform the traditional γ -profile-MDCEV when the consumption share of the outside good is high. [ABSTRACT FROM AUTHOR]

• A spatial rank-ordered probit model is proposed for the first time. • A simulation study shows good performance of the proposed estimator. • The simulator study indicates serious bias when ignoring spatial effects. • A travel mode choice analysis application is undertaken. Ranking data provide important additional information related to valuation because of the implied preference sequence among all alternatives, rather than just the top choice preference. This additional information from a preference ranking can be exploited to achieve a certain desired precision in choice model estimation with a much smaller sample size, making ranked data surveys much more cost-effective than first-choice surveys. In this paper, we propose a spatial rank-ordered probit (SROP) model that accommodates both spatial lag effects as well as spatial drift effects. To our knowledge, this is the first such formulation and application of an SROP model in the econometric and transportation literature. An application of the proposed model is demonstrated in a travel mode choice ranking experiment among seven alternatives, including autonomous vehicle (AV) private ride-hailing and AV pooled ride-hailing. [ABSTRACT FROM AUTHOR]

Detachment is achieved in Magnum-PSI by increasing the neutral background pressure in the target chamber using gas puffing. The plasma is studied using the B2.5 multi fluid plasma code B2.5 coupled with Eunomia, a Monte Carlo solver for neutral species. This study focuses on the effect of increasing neutral background pressure to the plasma volumetric loss of particle, momentum and energy. The plasma particle and energy loss almost linearly scale with the increase of neutral background pressure, while the momentum loss does not scale as strongly. Plasma recombination processes include molecular activated recombination (MAR), dissociative attachment, and atomic recombination. Atomic recombination, which includes radiative and three-body recombination, is the most relevant plasma process in reducing the particle flux and, consequently, the heat flux to the target. The low temperature where atomic recombination becomes dominant is achieved by plasma cooling via elastic H+âˆ'H2 collisions. The transport of vibrationally excited H2 molecules out of the plasma serves as an additional electron cooling channel with relatively small contribution. Additionally, the transport of highly vibrational H2 has a significant impact in reducing the effective MAR and dissociative attachment collision rates and should be considered properly. The relevancy of MAR and atomic recombination occupy separate electron temperature regimes, respectively, at T e = 1.5 eV and T e = 0.3 eV, with dissociative attachment being relevant in the intermediary. Plasma cooling via elastic H+âˆ'H2 collisions is effective at T e ≤ 1 eV. [ABSTRACT FROM AUTHOR]

Data samples from ${\mathbb{R}}^ {d}$ with a common support of size $k$ are accessed through $m$ random linear projections (measurements) per sample. It is well-known that roughly $k$ measurements from a single sample are sufficient to recover the support. In the multiple sample setting, do $k$ overall measurements still suffice when only $m$ measurements per sample are allowed, with $m < k$ ? We answer this question in the negative by considering a generative model setting with independent samples drawn from a subgaussian prior. We show that $n=\Theta ((k^{2}/ m^{2})\cdot \log k(d- k))$ samples are necessary and sufficient to recover the support exactly. In turn, this shows that when $m < k$ , $k$ overall measurements are insufficient for support recovery; instead we need about $m$ measurements each from $k^{2}/ m^{2}$ samples, and therefore $k^{2}/ m$ overall measurements are necessary. [ABSTRACT FROM AUTHOR]

The temporal variations in the wireless propagation channel, referred to as channel aging, cause a mismatch between the estimated channel and the channel state at the time of data detection. This mismatch has been shown to severely impair the performance of massive MIMO systems. In this paper, we present data aided MSE-optimal channel tracking algorithms to decode the received symbols and update the channel estimates at the base station (BS) and the UEs. These algorithms combine ideas from Kalman filtering for channel tracking and deterministic equivalent analysis for symbol estimation. In the uplink case, we first develop a minimum mean squared error (MMSE) data estimator and the MSE-optimal channel predictor based on the Kalman filtering algorithm. We analytically show that the updated channel estimate obtained from the estimates of the data symbols leads to significantly larger signal to interference-plus-noise ratio (SINR), and hence achievable rate, compared to that obtained from the channel estimate based on pilot symbols. Following this, in the downlink case, we develop an algorithm to track the effective channel at the UEs and analyze its MSE, SINR and achievable rate performance. We show that tracking the effective downlink channel mitigates the effects of channel aging and leads to improved performance. However, since the beamforming matrices at the BS are not updated, downlink channel tracking is not as effective as uplink channel tracking. Finally, via Monte Carlo simulations, we validate our derived results, and demonstrate the gains achievable by tracking the time-varying channels in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Detachment experiments have been carried out in the linear plasma device Magnum-PSI by increasing the gas pressure near the target. In order to have a proper detailed analysis of the mechanism behind momentum and power loss in detachment, a quantitative match is pursued between B2.5-Eunomia solutions and experimental data. B2.5 is a multi fluid plasma code and Eunomia is a Monte Carlo solver for neutral particles, and they are coupled together to provide steady-state solution of the plasma and neutral distribution in space. B2.5-Eunomia input parameters are adjusted to produce a close replication of the plasma beam measured in the experiments without any gas puffing in the target chamber. Using this replication as an initial condition, the neutral pressure near the plasma beam target is exclusively increased during simulation, matching the pressures measured in the experiments. Reasonable agreement is found between the electron temperature of the simulation results with experimental measurements using laser Thomson scattering near the target. The simulations also reveal the effect of increased gas pressure on the plasma current, effectively reducing the current penetration from the plasma source. B2.5-Eunomia is capable of reproducing detachment characteristics, namely the loss of plasma pressure along the magnetic field and the reduction of particle and heat flux to the target. The simulation results for plasma and neutrals will allow future studies of the exact contribution of individual plasma-neutral collisions to momentum and energy loss in detachment in Magnum-PSI. [ABSTRACT FROM AUTHOR]

Walking is a mode of transport that offers many benefits. This study aims to provide insights on the emotions associated with different types of walking episodes – namely, utilitarian walking episodes that are undertaken with the purpose of fulfilling an activity at a destination and recreational walking episodes that are undertaken with no specific purpose/destination. A knowledge of the emotions associated with different types of walking episodes can help steer policies and investments in a way that would engender greater levels of walking. The paper utilizes the well-being module of the American Time Use Survey (ATUS) data set to model subjective ratings on five different emotions associated with walking, employing a total of 1583 walk episodes. The five emotions include happiness, meaningfulness, tiredness, stress, and painfulness. A multivariate ordered probit model is estimated to account for unobserved attributes that may simultaneously affect multiple emotions. Model estimation results show that a number of socio-demographic variables and walking episode attributes affect how people feel about recreational and utilitarian walking episodes. Overall, it is found that utilitarian walking episodes offer a lower level of positive emotions than recreational episodes – presumably because recreational episodes are undertaken in more pleasant environments under more relaxing conditions. The results suggest that investments in recreational walking infrastructure and green spaces may yield richer dividends in terms of engendering higher levels of walking. Targeting specific socio-demographic groups for awareness campaigns and having strategic parking policies based on the day of the week are some of the other important implications. [ABSTRACT FROM AUTHOR]

• A closed-form multiple-constraint multiple discrete-continuous model is proposed. • A new multivariate distribution is developed as part of the model. • A simulation study indicates serious bias when ignoring multiple constraints. • A time-use empirical analysis reinforces the simulation study. Traditional multiple-discrete continuous choice models that have been formulated and applied in recent years consider a single linear resource constraint, which, when combined with consumer preferences, determines the optimal consumption point. However, in reality, consumers may face multiple resource constraints, such as those associated with time, money, and storage capacity. Ignoring such multiple constraints and instead using a single constraint can, and in general will, lead to poor data fit and inconsistent preference estimation, which can then have a serious negative downstream effect on forecasting and welfare/policy analysis. Unlike earlier attempts to address this multiple constraint situation, we formulate a new multiple-constraint (MC) multiple discrete continuous extreme value (MDCEV) model (or the MC-MDCEV model) that retains a closed-form probability structure and is as simple to estimate as the MDCEV model with one constraint. We achieve this by assuming a type-I extreme value distribution for the error term in its minimization form in the baseline utility preference of each good rather than a maximization form as in Bhat's (2005; 2008) original MDCEV formulation. The statistical foundation of the proposed model is based on the fact that the difference between a minimal type-I extreme value random variable with scale σ and the weighted sum of the exponential of standardized minimal type-I extreme value random variables (scaled up by σ) leads to an apparently new multivariate distribution that has an elegant and closed-form survival distribution function. Results from a simulation experiment show that our proposed model substantially outperforms single-constraint models; the results also highlight the serious mis-estimation that is likely to occur if only a subset of active constraints is used. The proposed model is applied to a case of week-long activity participation where individuals are assumed to maximize their utility from time-use subject to time and money budgets.It is hoped that our proposed simple closed-form multi-constraint MDCEV model will contribute to a new direction of application possibilities and to new research into situations where consumers face multiple constraints within a multiple discrete-continuous choice context. [ABSTRACT FROM AUTHOR]

We consider the problem of joint channel estimation and data decoding in uplink massive multiple input multiple output systems with low resolution analog-to-digital converters (ADCs) at the base station. The nonlinearities introduced by the ADCs make the problem challenging: in particular, the existing linear detectors perform poorly. Also, the channel coding used in commercial wireless systems necessitates soft symbol detection to obtain satisfactory performance. In this paper, we present a low-complexity variational Bayesian (VB) inference procedure to jointly solve the (possibly correlated) channel estimation and soft symbol decoding problem. We present the approach in progressively more complex scenarios, including the case where even the channel statistics are not available at the receiver. Finally, we combine our proposed VB procedure with a belief propagation (BP) based channel decoder, which further enhances the performance without any additional complexity. We numerically evaluate the bit error rate (BER) and the normalized mean squared error (NMSE) in the channel estimates obtained by our algorithm as a function of various system parameters, and benchmark the performance against genie-aided and state-of-the-art receivers. The results show that VB procedure is a promising technique for the design of low-complexity advanced receivers in low resolution ADC based systems. [ABSTRACT FROM AUTHOR]

Aim: The aim of this work was to examine the efficacy of oral metronidazole in reducing posthaemorrhoidectomy pain versus placebo. Method: Forty patients were randomized to either metronidazole and standard care or placebo and standard care (21 metronidazole, 19 placebo) in a double‐blinded, randomized controlled trial. The main outcome measure was posthaemorrhoidectomy pain scores over 21 days, measured on a 10‐point Likert scale. Results: There were no significant differences between groups with regards to age, gender, smoking status, self‐reported general health or quality of life, haemorrhoid‐related pain, haemorrhoid‐related impact on quality of life, reported satisfaction with surgery, experience of surgery, median overall pain score or likelihood of recommending surgery to others. For reported median worst pain scores and defaecation‐related pain, a trend to significance was identified between groups on days 16 and 18–21, with the metronidazole group reporting less pain. However, these differences were not significant when prespecified Bonferroni correction criteria were used. Using multilevel mixed effects modelling, the impact of time on median worst pain score was identified to be highly significant (P < 0.0001) whereas treatment allocation (placebo versus metronidazole) did not significantly affect the improvement in patients' reported pain (P = 0.8837). Conclusion: Our data do not support the hypothesis that postoperative metronidazole has a clinically meaningful effect on posthaemorrhoidectomy pain. This study adds to the previous literature, and implies that it should not be routinely used as an adjunct to analgesia. [ABSTRACT FROM AUTHOR]

• A new multiple discrete-grouped extreme value model is proposed. • A linear utility profile is used for the outside good for a closed-form structure. • Identification issues with the proposed model are presented and discussed. • The model is shown to be quite robust to the grouping windows for consumption data. • The model is applied to a time-use case study and a vehicle use case study. In this paper, we propose, for the first time, a closed-form multiple discrete-grouped extreme value model that accommodates grouped observations on consumptions rather than continuous consumptions. For example, in a time-use context, respondents tend to report their activity durations in bins of time (for example, 15-minute intervals or 30-minute intervals, depending on the duration of an activity). Or when reporting annual mileages driven for each vehicle owned by a household, it is unlikely that households will be able to provide an accurate continuous mileage value, and so it is not uncommon to solicit mileages in grouped categories such as 0–4,999 miles, 5000–9,999 miles, 10,000–14,999 miles, and so on. Similarly, when reporting expenditures on different types of commodities/services, individuals may round up or down to a convenient dollar value of multiples of 10 or 100 (depending on the length of time in which expenditures are sought). In some other cases, a product itself may be available only in specific package sizes (such as say, instant coffee, which is typically packaged in fixed sizes). In this paper, we use the so-called linear outside good utility MDCEV structure of Bhat (2018) to show how the model can be used for grouped consumption observations. Of course, this is also possible because the linear outside good utility does not need a continuous budget value, and allows for unobserved budgets. We discuss an important identification issue associated with this linear outside good utility model, and proceed to demonstrate applications of the proposed model to the case of weekend time-use choices of individuals and vehicle type/use choices of households. [ABSTRACT FROM AUTHOR]

• To celebrate the 40th anniversary (1979–2019) of Transportation Research Part B (TR-Part B). • Present a systematic overview of four-decade lifetime development details of TR-Part B (1979–2019). • Analyze 2697 TR-Part B academic papers using a bibliometric technique. • Identify publication/citation structures, leading individuals/those cited TR-Part B the most. • Conduct graphical visualization analysis including co-authorship and bibliometric coupling. In 1979, Transportation Research Part B: Methodological (TR-Part B) split from its mother journal Transportation Research , which was founded by Dr. Frank A. Haight in 1967, to set off on a journey as an independent journal with a theme on methodological development of transportation science. Since then, TR-Part B has been a leading international journal in the field of transportation research. Motivated by the 40th anniversary of TR-Part B (1979–2019), this paper utilized a bibliometric analysis technique to present a lifetime overview of the development characteristics and contribution details of TR-Part B in the past 40 years. The data used in this analysis were derived from the Core Collection Database of Web of Science , producing a total of 2,697 papers that were regular articles and reviews published in TR-Part B during the study period from 1979 to 2019. A systematic bibliometric analysis was conducted using VOSviewer software around two main aspects. First, analysis was conducted on publication and citation structures, the leading authors, organizations, and regions/countries with the most number of academic publications in TR-Part B , as well as those individuals and sources citing TR-Part B the most. Second, a visualization of the bibliometric characteristics of TR-Part B was presented in a knowledge mapping network analysis of co-authorship and bibliographic coupling. This paper reviews the contributions made to the lifetime development of TR-Part B during the past four decades and reveals insightful bibliometric findings. [ABSTRACT FROM AUTHOR]

Food insecurity is a serious public health problem among college students, negatively affecting academic performance and health outcomes. The primary objective of the study involved the exploration of the potential association between food insecurity and carotenoid scan levels. Additionally, the association between food insecurity and coping strategies utilized was investigated. A Qualtrics survey and BioPhotonic Scanner ™ were utilized to conduct an exploratory cross-sectional study with students from a mid-size four-year university (n=410). The USDA food security definitions and assessment tools were used for categorization. More than 31% percent of participants were classified as food insecure, with 5.1% considered very low and 26.6% low food security. A statistically significant factor associated with food insecurity was ethnicity (OR= .19). Other factors included: skipped buying textbooks to buy food (OR=.09), avoided buying expensive foods like fruits and vegetables (OR=.24), stretched food by limiting (OR= .09), removed spoiled parts from fruits and vegetables (OR= .73). Carotenoid scan scores were not associated with food insecurity. Coping strategies are often used by food insecure students and should be further examined to pinpoint promising strategies. [ABSTRACT FROM AUTHOR]

• Imputes deadheading trips by ride-hailing vehicles using data on passenger trips. • Develops a model for predicting passenger pick-up locations. • Results provide valuable insights on deadheading trip patterns. • Model will enable travel demand forecasting models to account for deadheading trips. The mode share of app-based ride-hailing services has been growing steadily in recent years and this trend is expected to continue. Ride-hailing services generate two types of trips – passenger hauling trips and deadheading trips. Passenger hauling trips are the trips made while transporting passengers between places. Virtually all other trips made by a ride-hailing vehicle when there are no passengers in the vehicle are called deadheading trips or empty trips. Trips between the drop-off location of one passenger and the pick-up location of the next passenger could comprise a substantial share of total travel by ride-hailing vehicles, both in terms of number of trips and miles of travel. This paper aims to model the deadheading trips produced by app-based ride-hailing services at the disaggregate level of individual trips. Passenger trip data published by the app-based ride-hailing company Ride Austin is used to impute deadheading trips. The pick-up locations of passengers are then modeled using a nonlinear-in-parameters multinomial logit framework, essentially capturing the deadheading that takes place from the drop-off of one passenger to the pick-up of the next passenger. The model is sensitive to socio-demographic characteristics, as well as employment opportunities and built environment characteristics of the study area. The model results shed light on the characteristics of deadheading trips at different locations and at different time periods in a day. The paper concludes with a discussion of how transportation planners and app-based ride-hailing companies may utilize knowledge about deadheading to enact policies and pricing schemes that reduce deadheading. [ABSTRACT FROM AUTHOR]

Dictionary learning (DL) is a well-researched problem, where the goal is to learn a dictionary from a finite set of noisy training signals, such that the training data admits a sparse representation over the dictionary. While several solutions are available in the literature, relatively little is known about their convergence and optimality properties. In this paper, we make progress on this problem by analyzing a Bayesian algorithm for DL. Specifically, we cast the DL problem into the sparse Bayesian learning (SBL) framework by imposing a hierarchical Gaussian prior on the sparse vectors. This allows us to simultaneously learn the dictionary as well as the parameters of the prior on the sparse vectors using the expectation-maximization algorithm. The dictionary update step turns out to be a non-convex optimization problem, and we present two solutions, namely, an alternating minimization (AM) procedure and an Armijo line search (ALS) method. We analytically show that the ALS procedure is globally convergent, and establish the stability of the solution by characterizing its limit points. Further, we prove the convergence and stability of the overall DL-SBL algorithm, and show that the minima of the cost function of the overall algorithm are achieved at sparse solutions. As a concrete example, we consider the application of the SBL-based DL algorithm to image denoising, and demonstrate the efficacy of the algorithm relative to existing DL algorithms. [ABSTRACT FROM AUTHOR]

Rates of sexual assault and sexual violence among college-aged adults are much higher than the national rates of sexual assault and sexual violence. Therefore, reduction and prevention of sexual violence among university students is critical and is consistent with national public health priorities. Often times, messages to students focus only on sexual assault and omit larger notions of sexual health. Four focus groups with a total of 24 participants (nine men, 15 women) highlighted three main perceptions about the sexual assault programming offered at this large university: themes of resistance to traditional programming, a need for holistic sexual health programming, and a desire to have an environment, which normalizes conversations surrounding sex, sexuality, and sexual health. [ABSTRACT FROM AUTHOR]

To achieve a tolerable heat and particle flux to the divertor target of fusion reactors, the so-called plasma detachment is essential to be set up and controlled. Impurity seeding facilitates the achievement of such a regime, mostly due to the enhanced plasma radiation led by the excitation-relaxation cycle of such species. Little is known about the impurity-induced plasma chemical processes occurring in the divertor region during detachment operation. In this work, the influence of three different impurities, i.e., N2, Ar, and He, on detachment performance is studied. To do so, experimental campaigns on the linear plasma machine Magnum-PSI have been carried out. Results highlight the beneficial role of N2 + H2 seeding, decreasing the plasma pressure in front of the target, leading to a reduced heat load compared to the pure H2 seeding case. An opposite trend has been found concerning He and Ar puffing. In fact, injection of H2 + He and H2 + Ar gas mixtures led to an increased heat flux. To address the importance of different plasma-chemical reaction paths, global plasma models have been used. The resulting reduced reaction schemes for Ar + H2, He + H2, and N2 + H2 have been implemented in B2.5-Eunomia, a coupled code consisting of a Monte Carlo code treating the transport of neutrals and a fluid code solving plasma equations. Simulation results qualitatively reproduce the favorable effect of N2, while confirming the deteriorating effect of He and Ar on a detachedlike hydrogen plasma. We point the synergetic role of H2 + N2 to be due to molecular-driven ion recombination, i.e., N-molecular-assisted recombination (MAR). A direct comparison of the collision frequency between N-MAR and MAR is showed, highlighting the crucial importance of the former in reducing the ion and heat flux to the target plate. [ABSTRACT FROM AUTHOR]

• Tech-savviness is the strongest psycho-social predictor of ride-hailing adoption. • Privacy-sensitivity the main psycho-social deterrent to pooled ride-hailing adoption. • Ride-hailing commute trips are typically made alone. • Pooled ride-hailing is substituting public transit use and active travel. • Ride-hailing induced trips are often made alone and serve errand/leisure purposes. Even as ride-hailing has become ubiquitous in most urban areas, its impacts on individual travel are still unclear. This includes limited knowledge of demand characteristics (especially for pooled rides), travel modes being substituted, types of activities being accessed, as well as possible trip induction effects. The current study contributes to this knowledge gap by investigating ride-hailing experience, frequency, and trip characteristics through two multi-dimensional models estimated using data from the Dallas-Fort Worth Metropolitan Area. Ride-hailing adoption and usage are modeled as functions of unobserved lifestyle stochastic latent constructs, observed transportation-related choices, and sociodemographic variables. The results point to low residential location density and people's privacy concerns as the main deterrents to pooled ride-hailing adoption, with non-Hispanic Whites being more privacy sensitive than individuals of other ethnicities. Further, our results suggest a need for policies that discourage the substitution of short-distance "walkable" trips by ride-hailing, and a need for low cost and well-integrated multi-modal systems to avoid substitution of transit trips by this mode. [ABSTRACT FROM AUTHOR]

• Interest in using travel time productively is encouraging the use of ride-hailing. • Presence of strangers is less burdensome in a commute trip than in a leisure trip. • Delays are a greater barrier to shared rides than the actual presence of strangers. With the era of fully automated vehicles (AVs) quickly approaching, ridesharing services could have an important role in increasing vehicle occupancy, reducing vehicle miles traveled, and improving traffic conditions. However, the extent to which these potentials can be achieved depends on consumers' disposition to sharing rides. From a travel behavior perspective, two essential elements to the adoption of shared rides are individuals' acceptance of increased travel times associated with pick-up/drop-off of other passengers and their approval of strangers sharing the same vehicle. The current study develops the notion of willingness to share (WTS), which represents the money value attributed by an individual to traveling alone compared to riding with strangers, to investigate the adoption of shared rides. Using a multivariate integrated choice and latent variable approach, we examine current choices and future intentions regarding the use of shared rides and estimate individuals' WTS as well as their values of travel time for two distinct trip purposes. Results show that users are less sensitive to the presence of strangers when in a commute trip compared to a leisure-activity trip. We also observe that the travel time added to the trip to serve other passengers may be a greater barrier to the use of shared services compared to the presence of a stranger. However, the potential to use travel time productively may help overcome this barrier especially for high-income individuals. [ABSTRACT FROM AUTHOR]

The objective of this paper is to design a power control policy that maximizes the long-term time-averaged sum throughput of a Gaussian multiple access channel (MAC), where the transmitters as well as the access point (AP) are energy harvesting nodes (EHNs). In addition, the policy is required to facilitate uncoordinated operation of the network. That is, in each slot, the transmitting nodes and the AP need to independently take their actions, e.g., the amount of energy to be used for transmission or whether to turn on and receive the data. First, in order to benchmark the performance of any policy, we derive an upper bound on the throughput achievable, by analyzing a centralized genie-aided system where the nodes have infinite capacity batteries and can freely share the available energy among themselves. In addition, the genie-aided system has non-causal knowledge of the energy arrivals at all the nodes. Next, we show that, surprisingly, a simple time sharing based online policy which requires no coordination among the transmitters and uses time-dilation at the receiver achieves the upper bound asymptotically in the battery size. We also present a policy that requires an occasional one-bit feedback from the AP about its battery state, and show that it requires a smaller sized battery at the receiver compared to a policy which operates without any feedback from the AP, to achieve the same performance. We use Monte Carlo simulations to validate our theoretical results and illustrate the performance of the proposed policies. [ABSTRACT FROM AUTHOR]

Employing the projected-Hartree-Fock-Bogoliubov (PHFB) model in conjunction with four different parametrizations of pairing plus multipolar effective two-body interaction and three different parametrizations of Jastrow short-range correlations, nuclear transition matrix elements for the neutrinoless double- β decay of 9 4 , 9 6 Zr , 1 0 0 Mo , 1 1 0 Pd , 1 2 8 , 1 3 0 Te and 1 5 0 Nd isotopes are calculated within mechanisms involving light Majorana neutrino mass and right-handed current. Statistically, model specific uncertainties in sets of twelve nuclear transition matrix elements are estimated by calculating the averages along with the standard deviations. For the considered nuclei, the most stringent extracted on-axis limits on the effective light Majorana neutrino mass 〈 m ν 〉 , the effective weak coupling of right-handed leptonic current with right-handed hadronic current 〈 λ 〉 , and the effective weak coupling of right-handed leptonic current with left-handed hadronic current 〈 η 〉 from the observed limit on half-life T 1 / 2 0 ν of 1 3 0 Te isotope are 0. 3 3 eV, 4. 5 7 × 1 0 − 7 and 4. 7 2 × 1 0 − 9 , respectively. [ABSTRACT FROM AUTHOR]

In this paper, we derive mutual information based upper bounds on the number of nonadaptive group tests required to identify a given number of “non-defective” items from a large population containing a small number of “defective” items. In the asymptotic regime with the population size $N \rightarrow \infty$ , to identify $L$ nondefective items out of a population containing $K$ defective items, our results show that $\frac{C_s K}{1-o(1)} (\Phi (\alpha _0, \beta _0) + o(1))$ measurements are sufficient when the tests are reliable. Here, $C_s$ is a constant independent of $N, K$ , and $L$ , and $\Phi (\alpha _0, \beta _0)$ is a bounded function of $\alpha _0 \triangleq \lim _{N\rightarrow \infty } \frac{L}{N-K}$ and $\beta _0 \triangleq \lim _{N\rightarrow \infty } \frac{K}{N-K}$. In contrast, the necessary number of tests using the conventional approach of first identifying the $K$ defective items and picking the required number of nondefective items from the complement set grows with $N$ as $O\left(K \log N \right)$. We also derive upper bounds on the number of tests under both dilution and additive noise models. Our results are obtained under a very general sparse signal model, by virtue of which, they are also applicable to other important sparse signal based applications such as compressive sensing. [ABSTRACT FROM AUTHOR]

We present an iterative scheme for sparse channel recovery and data detection in cyclic-prefix orthogonal frequency division multiplex communication over doubly spread underwater acoustic channels. We consider the sequence of observations from partial interval demodulators (PIDs), and cast them into an observation model amenable for sparse channel recovery. We propose a two-stage iterative algorithm for channel estimation and data detection. In the first stage, we recover the channel from pilot-only observations and estimate the unknown data symbols from the postcombined PID outputs. In the second stage, we use the data symbols estimated in the first stage to reconstruct the dictionary matrix corresponding to a full interval demodulator, re-estimate the channel using the entire observations including the data subcarriers, and use it to detect the unknown data symbols from the PID outputs. Theoretically, we show that the PID outputs help in tracking the time-varying channel better by providing additional measurements to estimate the intercarrier interference due to Doppler spread compared to full interval demodulation. Also, we derive the Cramér–Rao lower bound on the mean squared error in channel estimation, and empirically show that the proposed two-stage algorithm meets the bound at high signal-to-noise ratio. Numerical studies on simulated channels and publicly available experimental channel data in Watermark show that the proposed algorithm considerably improves data detection performance, in terms of bit error rate, over that from a traditional full length demodulator output, in highly Doppler distorted scenarios. [ABSTRACT FROM AUTHOR]

Background. HIV/AIDS rates are higher in the Southern United States compared to other regions of the country. Reasons for disparities include poverty, health care access, and racism. People who inject drugs (PWID) account for 8% of HIV/AIDS incidence rates. Harm reduction can connect PWID to needed resources. AIDS United Southern REACH grantees developed the Southern Harm Reduction Coalition (SHRC) as a means to decrease HIV/AIDS and viral hepatitis rates, criminalization of drug users and sex workers, and drug overdose. Method. Investigators used an intrinsic case study design to examine the context of harm reduction in the Southern United States, successful strategies, and outcomes. Data collection included key informant interviews and coalition documents. The community coalition action theory was used to examine the data. Results. The SHRC initiated regional conferences and customized trainings. Strengths-based language and utilization of diverse strengths among coalition members were used to effect change. Coalition outcomes included syringe decriminalization legislation, syringe exchange, naloxone access, naloxone funding legislation, and 911 Good Samaritan laws, along with expanded support for PWID. Conclusions. Advocacy successes can be applied to similar organizations in the Southern United States to promote harm reduction and potentially decrease HIV/AIDS burden, viral hepatitis, criminalization, and overdose. [ABSTRACT FROM AUTHOR]

Objectives: We tested the relationships among work schedule, race, and psychological distress in impoverished working mothers. Methods: Baseline data from a longitudinal cohort study of impoverished mothers (N = 285) were analyzed. The Brief Symptom Inventory (BSI) was utilized to measure levels of psychological distress. Multivariate models including interactions between work schedule and race were used. Results: Results indicate that mothers working nonstandard schedules reported more psychological distress symptoms and overall psychological distress compared to mothers working standard schedules. Although race did not moderate the relationship between work schedule and psychological distress, we did find that black/African-American mothers reported less psychological distress compared to white participants. Conclusions: Policy implications include collaboration between employers and local agencies to address disparate mental health outcomes based on shift work. Further research is needed to determine specific individual and community supports needed for impoverished working mothers. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Decision making in the health care system – specifically with regard to diagnostic imaging investigations – occurs at multiple levels. Professional role players from various backgrounds are involved in making these decisions, from the point of referral to the outcomes of the imaging investigation. The aim of this study was to map the decision‐making processes and pathways involved when patients are referred for diagnostic imaging investigations and to explore distributed decision‐making events at the points of contact with patients within a health care system. Method: A two‐phased qualitative study was conducted in an academic public health complex with the district hospital as entry point. The first phase included case studies of 24 conveniently selected patients, and the second phase involved 12 focus group interviews with health care providers. Data analysis was based on Rapley's interpretation of decision making as being distributed across time, situations and actions, and including different role players and technologies. Results: Clinical decisions incorporating imaging investigations are distributed across the three vital points of contact or decision‐making events, namely the initial patient consultation, the diagnostic imaging investigation and the post‐investigation consultation. Each of these decision‐making events is made up of a sequence of discrete decision‐making moments based on the transfer of retrospective, current and prospective information and its transformation into knowledge. Conclusion: This paper contributes to the understanding of the microstructural processes (the ‘when’ and ‘where’) involved in the distribution of decisions related to imaging investigations. It also highlights the interdependency in decision‐making events of medical and non‐medical providers within a single medical encounter. [ABSTRACT FROM AUTHOR]

Extraction of pigments from endophytes is an uphill task. Up till now, there are no efficient methods available to extract the maximum amount of prodigiosin from Serratia marcescens. This is one of the important endophytes of Beta vulgaris L. The present work was carried out for the comparative study of six different extraction methods such as homogenization, ultrasonication, freezing and thawing, heat treatment, organic solvents and inorganic acids to evaluate the efficiency of prodigiosin yield. Our results demonstrated that highest extraction was observed in ultrasonication (98·1 ± 1·7%) while the lowest extraction by freezing and thawing (31·8 ± 3·8%) methods. However, thin layer chromatography, high-performance liquid chromatography and Fourier transform infrared data suggest that bioactive pigment in the extract was prodigiosin. To the best of our knowledge, this is the first comprehensive study of extraction methods and identification and purification of prodigiosin from cell biomass of Ser. marcescens isolated from Beta vulgaris L. [ABSTRACT FROM AUTHOR]

The columnwise Khatri-Rao product of two matrices is an important matrix type, reprising its role as a structured sensing matrix in many fundamental linear inverse problems. Robust signal recovery in such inverse problems is often contingent on proving the restricted isometry property (RIP) of a certain system matrix expressible as a Khatri-Rao product of two matrices. In this paper, we analyze the RIP of a generic columnwise Khatri-Rao product matrix by deriving two upper bounds for its kth order restricted isometry constant (k-RIC) for different values of k. The first RIC bound is computed in terms of the individual RICs of the real-valued input matrices participating in the Khatri-Rao product. The second RIC bound is probabilistic and is specified in terms of the input matrix dimensions. We show that the Khatri-Rao product of a pair of m x n sized random matrices comprising independent and identically distributed sub-Gaussian entries satisfies k-RIP with arbitrarily high probability, provided m exceeds O(√k log3/2 n). This is a substantially milder condition compared to O(k log n) rows needed to guarantee k-RIP of the input sub-Gaussian random matrices participating in the Khatri-Rao product. Our RIC bounds confirm that the Khatri-Rao product exhibits stronger restricted isometry compared to its constituent matrices for the same RIP order. The proposed RIC bounds are potentially useful in obtaining improved performance guarantees in several sparse signal recovery and tensor decomposition problems. [ABSTRACT FROM AUTHOR]

Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure (CVS). However, extracting intended meteorological cloud content from the measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work, a technique is proposed to identify and separate cloud and nonhydrometeor echoes using the radar Doppler spectral moments profile measurements. The point and volume targetbased theoretical radar sensitivity curves are used for removing the receiver noise floor and identified radar echoes are scrutinized according to the signal decorrelation period. Here, it is hypothesized that cloud echoes are observed to be temporally more coherent and homogenous and have a longer correlation period than biota. That can be checked statistically using ~4 s sliding mean and standard deviation value of reflectivity profiles. The above step helps in screen out clouds critically by filtering out the biota. The final important step strives for the retrieval of cloud height. The proposed algorithm potentially identifies cloud height solely through the systematic characterization of Z variability using the local atmospheric vertical structure knowledge besides to the theoretical, statistical and echo tracing tools. Thus, characterization of high-resolution cloud radar reflectivity profile measurements has been done with the theoretical echo sensitivity curves and observed echo statistics for the true cloud height tracking (TEST). TEST showed superior performance in screening out clouds and filtering out isolated insects. TEST constrained with polarimetric measurements was found to be more promising under high-density biota whereas TEST combined with linear depolarization ratio and spectral width perform potentially to filter out biota within the highly turbulent shallow cumulus clouds in the convective boundary layer (CBL). This TEST technique is promisingly simple in realization but powerful in performance due to the flexibility in constraining, identifying and filtering out the biota and screening out the true cloud content, especially the CBL clouds. Therefore, the TEST algorithm is superior for screening out the low-level clouds that are strongly linked to the rainmaking mechanism associated with the Indian Summer Monsoon region's CVS. [ABSTRACT FROM AUTHOR]

We present and discuss here the observations of a small long duration GOES B-class flare associated with a quiescent filament eruption, a global EUV wave and a CME on 2011 May 11. The event was well observed by the Solar Dynamics Observatory (SDO), GONG H α , STEREO and Culgoora spectrograph. As the filament erupted, ahead of the filament we observed the propagation of EIT wave fronts, as well as two flare ribbons on both sides of the polarity inversion line (PIL) on the solar surface. The observations show the co-existence of two types of EUV waves, i.e., a fast and a slow one. A type II radio burst with up to the third harmonic component was also associated with this event. The evolution of photospheric magnetic field showed flux emergence and cancellation at the filament site before its eruption. [ABSTRACT FROM AUTHOR]

Within the PHFB approach, the $ 0^{+}\rightarrow 2^{+}$ transition of two neutrino double- $ \beta$ decay of Zr, Mo, Ru, Pd, Te and Nd isotopes is studied employing wave functions generated with four different parametrizations of the pairing plus multipole type of two-nucleon interaction and the summation method. In comparison to the $ 0^{+}\rightarrow 0^{+}$ transition, the nuclear transition matrix elements $ M_{2\nu} (2^{+})$ are quite sensitive to the deformation of the yrast 2 state. Consideration of the available theoretical and experimental results suggests that the observation of the $ 0^{+}\rightarrow 2^{+}$ transition of $ 2\nu \beta^{-} \beta^{-}$ decay may be possible in Zr, Mo, Te and Nd isotopes. The effect of deformation on the $ M_{2\nu} (2^{+})$ is also studied. [ABSTRACT FROM AUTHOR]

In the classical non-adaptive group testing setup, pools of items are tested together, and the main goal of a recovery algorithm is to identify the complete defective set given the outcomes of different group tests. In contrast, the main goal of a non-defective subset recovery algorithm is to identify a subset of non-defective items given the test outcomes. In this paper, we present a suite of computationally efficient and analytically tractable non-defective subset recovery algorithms. By analyzing the probability of error of the algorithms, we obtain bounds on the number of tests required for non-defective subset recovery with arbitrarily small probability of error. Our analysis accounts for the impact of both the additive noise (false positives) and dilution noise (false negatives). By comparing with information theoretic lower bounds, we show that the upper bounds on the number of tests are orderwise tight up to a \log ^2K factor, where $K$ is the number of defective items. We also provide simulation results that compare the relative performance of the different algorithms and reveal insights into their practical utility. The proposed algorithms significantly outperform the straightforward approaches of testing items one-by-one, and of first identifying the defective set and then choosing the non-defective items from the complement set, in terms of the number of measurements required to ensure a given success rate. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of online (sequential) recovery of temporally correlated sparse vectors sharing a common support, from noisy underdetermined linear measurements. The temporal correlation of the sparse vectors is modeled using a first-order autoregressive process. The online algorithm is formulated using the sparse Bayesian learning framework and is implemented using a sequential expectation-maximization procedure. Our algorithm is noniterative in nature, and requires less computational and memory resources compared to offline processing. We analyze the convergence of the algorithm in the case when the sparse vectors are uncorrelated, using tools from stochastic approximation theory. We show that the sequence of the covariance estimates converge either to the global minimum of the offline equivalent cost function or to the all zero vector, regardless of the sparsity level of the signal. Through numerical results, we demonstrate the efficacy of the proposed online algorithm and compare it with its offline counterpart as well as with existing online sparse vector recovery algorithms. We also illustrate the performance of the algorithm in the context of sparse orthogonal frequency division multiplexing channel estimation. [ABSTRACT FROM PUBLISHER]

One of the key parameters that must be included in the analysis of atmospheric constituents (gases and particles) and clouds is the vertical structure of the atmosphere. Therefore high-resolution vertical profile observations of the atmospheric targets are required for both theoretical and practical evaluation and as inputs to increase accuracy of atmospheric models. Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure in a resourceful way. However, extracting intended meteorological cloud content from the overall measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work a technique is proposed to identify and separate cloud and non-hydrometeor returns from a cloud radar measurements. Firstly the observed cloud reflectivity profile must be evaluated against the theoretical radar sensitivity curves. This step helps to determine the range of receiver noise floor above which it can be identified as signal or an atmospheric echo. However it should be noted that the signal above the noise floor may be contaminated by the air-borne non-meteorological targets such as insects, birds, or airplanes. The second step in this analysis statistically reviews the continual radar echoes to determine the signal de-correlation period. Cloud echoes are observed to be temporally more coherent, homogenous and have a longer de-correlation period than insects and noise. This step critically helps in separating the clouds from insects and noise which show shorter de-correlation periods. The above two steps ensure the identification and removal of non-hydrometeor contributions from the cloud radar reflectivity profile which can then be used for inferring unbiased vertical cloud structure. However these two steps are insufficient for recovering the weakly echoing cloud boundaries associated with the sharp reduction in cloud droplet size and concentrations. In the final step in order to obtain intact cloud height information, identified cloud echo peak(s) needs to be backtracked along the either sides on the reflectivity profile till its value falls close to the mean noise floor. The proposed algorithm potentially identify cloud height solely through the characterization of high resolution cloud radar reflectivity measurements with the theoretical echo sensitivity curves and observed echo statistics for the cloud tracking (TEST). This technique is found to be more robust in identifying and filtering out the contributions due to insects and noise which may contaminate a cloud reflectivity profile. With this algorithm it is possible to improve monsoon tropical cloud characterization using cloud radar. [ABSTRACT FROM AUTHOR]

Persistent spectral hole burning has been observed in europium-doped sodium germanate glass. Hole-burning efficiency increased by 85% when the glasses were made in a reduced atmosphere, and hole burning was detected even at room temperature. There is evidence for photochemical as well as photophysical hole-burning mechanisms. [ABSTRACT FROM AUTHOR]

Context: Solar flares are sudden and violent releases of magnetic energy in the solar atmosphere that can be divided into two classes: eruptive flares, where plasma is ejected from the solar atmosphere resulting in a coronal mass ejection (CME), and confined flares, where no CME is associated with the flare. Aims: We present a case study showing the evolution of key topological structures, such as spines and fans, which may determine the eruptive versus non-eruptive behavior of the series of eruptive flares followed by confined flares, which all originate from the same site. Methods: To study the connectivity of the different flux domains and their evolution, we compute a potential magnetic field model of the active region. Quasi-separatrix layers are retrieved from the magnetic field extrapolation. Results: The change in behavior of the flares from one day to the next - from eruptive to confined - can be attributed to the change in orientation of the magnetic field below the fan with respect to the orientation of the overlaying spine rather than an overall change in the stability of the large-scale field. Conclusions: Flares tend to be more confined when the field that supports the filament and the overlying field gradually becomes less anti-parallel as a direct result of changes in the photospheric flux distribution, being themselves driven by continuous shearing motions of the different magnetic flux concentrations. [ABSTRACT FROM AUTHOR]

In this paper, we present a study of an M2.2 class solar flare of 26 November 2000 from NOAA AR 9236. The flare was well observed by various ground based observatories (ARIES, Learmonths Solar Observatory) and space borne instruments (SOHO, HXRS, GOES) in time interval between 02:30 UT to 04:00 UT. The flare started with long arc-shape outer flare ribbon. Afterwards the main flare starts with two main ribbons. Initially the outer ribbons start to expand with an average speed (∼20 km s − 1 ) and later it shows contraction. The flare was associated with partial halo coronal mass ejection (CMEs) which has average speed of 495 km s − 1 . The SOHO/MDI observations show that the active region was in quadrupolar magnetic configuration. The flux cancellation was observed before the flare onset close to flare site. Our analysis indicate the flare was initiated by the magnetic breakout mechanism. [ABSTRACT FROM AUTHOR]

This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal distribution for the kernel error term in traditional specifications of the spatial model. The resulting model is estimated using Bhat's (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The model is applied to an analysis of bicycling frequency, using data from the 2014 Puget Sound household travel survey undertaken in the Puget Sound region in the State of Washington in the United States. Our results underscore the important effects of demographic variables, as well as the miles of bicycle lanes in an individual's immediate residential neighborhood, on bicycling propensity. An interesting finding is that women and young individuals (18–34 years of age) in particular “warm up” to bicycling as more investment is made in bicycling infrastructure, thus leading not only to a larger pool of bicyclists due to bicycling infrastructure enhancements, but also a more diverse and inclusive one. The results highlight the importance of introducing social dependence effects and non-normal kernel error terms from a policy standpoint. Specifically, our results suggest that ignoring these effects, as has been done by all earlier bicycling studies, can underestimate the impacts of bicycling infrastructure improvements and public campaigns on bicycle use frequency, potentially leading to under-investments in bicycling infrastructure projects. [ABSTRACT FROM AUTHOR]

In 2016 Sri Lanka saw political stability and some initiatives toward ethnic reconciliation. The economy continued to grow steadily. Some progress was made toward a better institutional infrastructure to protect human rights. However, hostility between President Sirisena and loyalists to former President Rajapaksa presages political turmoil in 2017 that could imperil constitutional reform and ethnic reconciliation. [ABSTRACT FROM AUTHOR]