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Supplemental Material for Caregiver Experiences Participating in a Home-Based Primary Care Program: A Pragmatic Evaluation Including Qualitative Interviews and Quantitative Surveys by Samantha M. R. Kling, Anna Sophia Lessios, Laura M. Holdsworth, Maria Yefimova, Siqi Wu, Marina Martin, Meera Sheffrin, and Marcy Winget in Journal of Applied Gerontology

The outbreak of Covid-19 accelerated the practice of digital survival, and the "health code" launched based on the needs of epidemic prevention and control has become the representative of digital survival media, which is jointly built by science and technology enterprises and government departments. it has realized the full-state use in China, and accumulated long-term digital survival experience for the country, enterprises and individuals. At the same time, there are some media ethical problems in the use of Health Code, such as distinguishing users, leaking information, imprisoning the body and leading to the lack of subjects. In order to resolve the risk, we should re-examine the relationship between people and the media from the perspective of the subject, treat "health code" as a digital projection of personal health, and regain the service principle of digital technology. Humanize the "health code" and other digital media.

The ideological asymmetries have been recently observed in contested online spaces, where conservative voices seem to be relatively more pronounced even though liberals are known to have the population advantage on digital platforms. Most prior research, however, focused on either one single platform or one single political topic. Whether an ideological group garners more attention across platforms and/or topics, and how the attention dynamics evolve over time, have not been explored. In this work, we present a quantitative study that links collective attention across two social platforms -- YouTube and Twitter, centered on online activities surrounding popular videos of three controversial political topics including Abortion, Gun control, and Black Lives Matter over 16 months. We propose several sets of video-centric metrics to characterize how online attention is accumulated for different ideological groups. We find that neither side is on a winning streak: left-leaning videos are overall more viewed, more engaging, but less tweeted than right-leaning videos. The attention time series unfold quicker for left-leaning videos, but span a longer time for right-leaning videos. Network analysis on the early adopters and tweet cascades show that the information diffusion for left-leaning videos tends to involve centralized actors; while that for right-leaning videos starts earlier in the attention lifecycle. In sum, our findings go beyond the static picture of ideological asymmetries in digital spaces and provide a set of methods to quantify attention dynamics across different social platforms., Accepted into ICWSM 2022. 11-page main paper and 11-page appendix

Ecotoxicology and environmental safety : EES 241, 113742 (2022). doi:10.1016/j.ecoenv.2022.113742, Published by Elsevier, Amsterdam

Supplemental Digital Content is available in the text, Objective: To determine whether delayed or canceled elective procedures due to COVID-19 resulted in higher rates of ED utilization and/or increased mortality. Summary of Background Data: On March 15, 2020, the VA issued a nationwide order to temporarily pause elective cases due to COVID-19. The effects of this disruption on patient outcomes are not yet known. Methods: This retrospective cohort study used data from the VA Corporate Data Warehouse. Surgical procedures canceled due to COVID-19 in 2020 (n = 3326) were matched to similar completed procedures in 2018 (n = 151,863) and 2019 (n = 146,582). Outcome measures included 30- and 90-day VA ED use and mortality in the period following the completed or canceled procedure. We used exact matching on surgical procedure category and nearest neighbor matching on patient characteristics, procedure year, and facility. Results: Patients with elective surgical procedures canceled due to COVID-19 were no more likely to have an ED visit in the 30- [Difference: –4.3% pts; 95% confidence interval (CI): –0.078, –0.007] and 90 days (–0.9% pts; 95% CI: –0.068, 0.05) following the expected case date. Patients with cancellations had no difference in 30- (Difference: 0.1% pts; 95% CI: –0.008, 0.01) and 90-day (Difference: –0.4% pts; 95% CI: –0.016, 0.009) mortality rates when compared to similar patients with similar procedures that were completed in previous years. Conclusions: The pause in elective surgical cases was not associated with short-term adverse outcomes in VA hospitals, suggesting appropriate surgical case triage and management. Further study will be essential to determine if the delayed cases were associated with longer-term effects.

ObjectiveThe objective of this study was to examine the structure of sibling relationships among preschool children in China and develop a questionnaire.MethodsThe concept of sibling relationships among preschool children in China was established through literature review, open interviews, and expert review, and the initial project was designed. Using the questionnaire survey method, with 651 mothers of preschool children as the research objects, we performed item analysis, exploratory factor analysis, confirmatory factor analysis, and reliability and validity tests on the initial questionnaire.ResultsThe questionnaire for the sibling relationships among preschool children in China had 18 questions covering dimensions of sibling interaction, sibling acceptance, sibling warmth, and sibling rivalry. The questionnaire fitting indexes were better (χ2/df = 1.829, CFI = 0.930, TLI = 0.917, RMSEA = 0.055, SRMR = 0.053). The internal consistency coefficient between the total questionnaire and each dimension was 0.759–0.8548, and the total questionnaire significantly correlated with each dimension (r = 0.229–0.792) and the total score of parental self-efficacy (r = –0.106 to 0.338).ConclusionThe Sibling Relationship Questionnaire for Chinese Preschool Children (Parental Version) exhibited good reliability and validity, making it an effective tool for the evaluation of sibling relationships among preschool children.

Cilj je ovog istraživanja ispitati učinak dviju kategorija potresnih događaja na krivulju oštetljivosti čelične okvirne konstrukcije zgrade (građevine s različitim brojem katova) razmatranjem relativnog bočnog pomaka kao kriterija oštećenosti. Kategorije za opisivanje promjene relativnog bočnog položaja bile su blaga, umjerena, značajna i potpuna. Kako se povećava seizmički zahtjev, veća je vjerojatnost prekoračenja. Drugim riječima, ako maksimalno ubrzanje potresa raste, povećava se vjerojatnost prekoračenja (eng. probability of exceeding - PoE). Povećanja vršnog ubrzanja tla (PGA) povećat će PoE trokatne konstrukcije pri značajnom oštećenju. Krivulje oštetljivosti druge kategorije pokazuju pomak iz stanja mirovanja (postupni porast) u stanje stajanja (nagli porast) u usporedbi s potresima prve klase kod modela s pet katova. Sve veći broj katova utječe na povećanje PoE pri značajnom i teškom oštećenju i povećava vjerojatnost prekoračenja. Model sa sedam katova u značajnom načinu ima 10 i 15,5 % PoE veći od modela s pet i tri kata. Međutim, u stanju potpune oštećenosti, PoE u modelu s pet katova je 6 i 7 puta veća nego u modelu sa sedam i tri kata. Stoga se može zaključiti da je s povećanjem broja katova porasala PoE, što se može bolje uočiti pri značajnom stupnju oštenja., The aim of this study is to investigate the effect of two categories of earthquake events on the fragility curves of steel building construction (structures with different number of stories) by considering relative lateral displacement as a damage criterion. The categories used to describe the change in the relative lateral position were chosen to be slight, moderate, extensive, and complete. Increased seismic demand increases the probability of exceeding. In other words, the greater the maximum earthquake acceleration, the higher is the probability of exceeding (PoE). In a 3-story structure, the increase in PGA increases the PoE of the structure at extensive failure levels. The fragility curves for the 2nd category earthquakes show a shift from the sleeping mode (gradual increase) to the standing state (rapid increase) compared to the 1st-class earthquakes in the 5-story model. Increasing the number of stories increases the PoE of extensive and large failures. The PoE of the extensive mode in the 7-story model was 10 and 15.5 % higher than that in the 5- and 3-story models, respectively. However, for the complete damage state, the PoE in the 5-story model was 6 and 7 % more than that in the 7- and 3-story models, respectively. Therefore, it can be concluded that increasing the number of stories increases the PoE, but this increase is more evident for the extensive failure level.

Additional file 1: Table S1. Coordinates of sampling sites. Table S2. Information of 21 organochlorine pesticides. Table S3. Information of 34 organophosphorus pesticides. Table S4. Information of 3 estrogenic substances.

We study the problem of globally recovering a dictionary from a set of signals via $\ell_1$-minimization. We assume that the signals are generated as i.i.d. random linear combinations of the $K$ atoms from a complete reference dictionary $D^*\in \mathbb R^{K\times K}$, where the linear combination coefficients are from either a Bernoulli type model or exact sparse model. First, we obtain a necessary and sufficient norm condition for the reference dictionary $D^*$ to be a sharp local minimum of the expected $\ell_1$ objective function. Our result substantially extends that of Wu and Yu (2015) and allows the combination coefficient to be non-negative. Secondly, we obtain an explicit bound on the region within which the objective value of the reference dictionary is minimal. Thirdly, we show that the reference dictionary is the unique sharp local minimum, thus establishing the first known global property of $\ell_1$-minimization dictionary learning. Motivated by the theoretical results, we introduce a perturbation-based test to determine whether a dictionary is a sharp local minimum of the objective function. In addition, we also propose a new dictionary learning algorithm based on Block Coordinate Descent, called DL-BCD, which is guaranteed to have monotonic convergence. Simulation studies show that DL-BCD has competitive performance in terms of recovery rate compared to many state-of-the-art dictionary learning algorithms.

Spatial gene expression data enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. In the first part of the thesis, we developed staNMF, a method that combines a dictionary learning algorithm called nonnegative matrix factorization (NMF), with a new stability-driven criterion to select the number of dictionary atoms. When applied to a set of {\em Drosophila} early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified a dictionary with 21 atoms, which we call {\em principal patterns} (PP). Providing a compact yet biologically interpretable representation of {\em Drosophila} expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Furthermore, we used the PP to generate local transcription factor (TF) regulatory networks. Spatially local correlation networks (SLCN) were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5\% cut-off on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. The performance of PP with the {\em Drosophila} data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.The biological interpretability of the NMF-derived dictionary motivated us to understand why dictionary learning works analytically. In particular, if the observed data are generated from a ground truth dictionary, under what conditions can dictionary learning recovers the true dictionary? In the second part of the thesis, we studied the local correctness, or {\em local identifiability}, of a particular dictionary learning formulation with the $l_1$-norm objective function. Suppose we observe $N$ data points $\x_i\in \mathbb R^K$ for $i=1,...,N$, where $\x_i$'s are $i.i.d.$ random linear combinations of the $K$ columns from a square and invertible dictionary $\D_0 \in \mathbb R^{K\times K}$. We assumed that the random linear coefficients are generated from either the $s$-sparse Gaussian model or the Bernoulli-Gaussian model. For the population case, we established a sufficient and almost necessary condition for $\D_0$ to be locally identifiable, i.e., a local minimum of the expected $l_1$-norm objective function. Our condition covers both sparse and dense cases of the random linear coefficients and significantly improves the sufficient condition in Gribonval and Schnass (2010). Moreover, we demonstrated that for a complete $\mu$-coherent reference dictionary, i.e., a dictionary with absolute pairwise column inner-product at most $\coh\in[0,1)$, local identifiability holds even when the random linear coefficient vector has up to $O(\mu^{-2})$ nonzeros on average. Finally, it was shown that our local identifiability results translate to the finite sample case with high probability provided $N = O(K\log K)$.

We study the theoretical properties of learning a dictionary from $N$ signals $\mathbf x_i\in \mathbb R^K$ for $i=1,...,N$ via $l_1$-minimization. We assume that $\mathbf x_i$'s are $i.i.d.$ random linear combinations of the $K$ columns from a complete (i.e., square and invertible) reference dictionary $\mathbf D_0 \in \mathbb R^{K\times K}$. Here, the random linear coefficients are generated from either the $s$-sparse Gaussian model or the Bernoulli-Gaussian model. First, for the population case, we establish a sufficient and almost necessary condition for the reference dictionary $\mathbf D_0$ to be locally identifiable, i.e., a local minimum of the expected $l_1$-norm objective function. Our condition covers both sparse and dense cases of the random linear coefficients and significantly improves the sufficient condition by Gribonval and Schnass (2010). In addition, we show that for a complete $\mu$-coherent reference dictionary, i.e., a dictionary with absolute pairwise column inner-product at most $\mu\in[0,1)$, local identifiability holds even when the random linear coefficient vector has up to $O(\mu^{-2})$ nonzeros on average. Moreover, our local identifiability results also translate to the finite sample case with high probability provided that the number of signals $N$ scales as $O(K\log K)$.

In this paper, a novel space-time-frequency code scheme - bit-interleaved space-time-frequency block coding (BI-STFBC) - being fit for OFDM system is considered for frequency-selective Rayleigh fading channels. Incorporating subcarrier grouping and choosing appropriate system parameters, we convert out system into a set of group STF (GSTF) system, within which STF coding is considered. Code bits are bit-wise interleaved across every grouping subcarrier before being grouped and mapped to space-time-frequency code, followed the unitary constellation-rotating precodes. The spectral efficiency and pairwise error probability (PEP) are also considered. Comparing with the existing alternatives, the proposed scheme can attain maximum diversity with low decoding flexibility. Simulation results confirmed the effectiveness of the proposed scheme.