Background With a progressive increase in the aging process, the challenges posed by pulmonary tuberculosis (PTB) are also increasing for the elderly population. Objective This study aimed to identify the epidemiological distribution of PTB among the elderly, forecast the achievement of the World Health Organization’s 2025 goal in this specific group, and predict further advancement of PTB in the eastern area of China. Methods All notified active PTB cases aged ≥65 years from Zhejiang Province were screened and analyzed. The general epidemiological characteristics were depicted and presented using the ArcGIS software. Further prediction of PTB was performed using R and SPSS software programs. Results Altogether 41,431 cases aged ≥65 years were identified by the surveillance system from 2015 to 2020. After excluding extrapulmonary TB cases, we identified 39,832 PTB cases, including laboratory-confirmed (23,664, 59.41%) and clinically diagnosed (16,168, 40.59%) PTB. The notified PTB incidence indicated an evident downward trend with a reduction of 30%; however, the incidence of bacteriologically positive cases was steady at approximately 60/100,000. Based on the geographical distribution, Quzhou and Jinhua Cities had a higher PTB incidence among the elderly. The delay in PTB diagnosis was identified, and a significantly prolonged treatment course was observed in the elderly. Moreover, a 50% reduction of PTB incidence by the middle of 2024 was predicted using a linear regression model. It was found that using the exponential smoothing model would be better to predict the PTB trend in the elderly than a seasonal autoregressive integrated moving average model. Conclusions More comprehensive and effective interventions such as active PTB screening combined with physical checkup and succinct health education should be implemented and strengthened in the elderly. A more systematic assessment of the PTB epidemic trend in the elderly population should be considered to incorporate more predictive factors.
Abstract Older adults living in Asia or of Asian origin have unique preferences for information that require special attention. This symposium focuses on the health information preferences and behaviors of Asian older adults. Song et al. investigated the relationship between Internet use and perceived loneliness among Older Chinese using from survey data collected in the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), a national study involving 12,400 households in Mainland China. Multiple regression results suggest that older Chinese Internet users perceived significantly less loneliness compared with their age peers who were non-Internet users. Zhang et al. investigated the role of information and communication technologies in supporting antiretroviral therapy (ART)-related knowledge seeking among older Chinese with HIV. Their cross-sectional survey data were collected from 2012 to 2013 in Guangxi, China. The results suggest that less than 5% of the participants sought HIV-related information via computers. Patients less knowledgeable about ART were more likely than those more knowledgeable to consult medical professionals about the disease via cell phones. Shiroma et al. report findings of a systematic literature review conducted in spring 2019 that examined Asian ethnic minority older adults’ preferences for end-of-Life (EOL) information seeking and decision making. The results suggest Asian ethnic minority older adults are understudied in the literature on EOL information and decision making, especially in terms of their unique cultural contexts. Du et al. examined how health information obtained from different types of social networks affect osteoporosis self-management behaviors among older White and Asian women.
The stunning empirical successes of neural networks currently lack rigorous theoretical explanation. What form would such an explanation take, in the face of existing complexity-theoretic lower bounds? A first step might be to show that data generated by neural networks with a single hidden layer, smooth activation functions and benign input distributions can be learned efficiently. We demonstrate here a comprehensive lower bound ruling out this possibility: for a wide class of activation functions (including all currently used), and inputs drawn from any logconcave distribution, there is a family of one-hidden-layer functions whose output is a sum gate, that are hard to learn in a precise sense: any statistical query algorithm (which includes all known variants of stochastic gradient descent with any loss function) needs an exponential number of queries even using tolerance inversely proportional to the input dimensionality. Moreover, this hard family of functions is realizable with a small (sublinear in dimension) number of activation units in the single hidden layer. The lower bound is also robust to small perturbations of the true weights. Systematic experiments illustrate a phase transition in the training error as predicted by the analysis.
In this paper, we present correlated logistic (CorrLog) model for multilabel image classification. CorrLog extends conventional logistic regression model into multilabel cases, via explicitly modeling the pairwise correlation between labels. In addition, we propose to learn the model parameters of CorrLog with elastic net regularization, which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification. CorrLog can be efficiently learned, though approximately, by regularized maximum pseudo likelihood estimation, and it enjoys a satisfying generalization bound that is independent of the number of labels. CorrLog performs competitively for multilabel image classification on benchmark data sets MULAN scene, MIT outdoor scene, PASCAL VOC 2007, and PASCAL VOC 2012, compared with the state-of-the-art multilabel classification algorithms.
We propose semi-random features for nonlinear function approximation. The flexibility of semi-random feature lies between the fully adjustable units in deep learning and the random features used in kernel methods. For one hidden layer models with semi-random features, we prove with no unrealistic assumptions that the model classes contain an arbitrarily good function as the width increases (universality), and despite non-convexity, we can find such a good function (optimization theory) that generalizes to unseen new data (generalization bound). For deep models, with no unrealistic assumptions, we prove universal approximation ability, a lower bound on approximation error, a partial optimization guarantee, and a generalization bound. Depending on the problems, the generalization bound of deep semi-random features can be exponentially better than the known bounds of deep ReLU nets; our generalization error bound can be independent of the depth, the number of trainable weights as well as the input dimensionality. In experiments, we show that semi-random features can match the performance of neural networks by using slightly more units, and it outperforms random features by using significantly fewer units. Moreover, we introduce a new implicit ensemble method by using semi-random features.
With the rapid development of information technology, urban planning visualization is more and more concerned. This paper describes 3D technologies associated with urban planning, analysis the feasibility of urban planning visualization, and some core techniques of 3D urban planning system based on virtual reality (VR) and geographic information system (GIS). 3D model generation, communication methods between GIS and 3D scene and system integration are also discussed in this paper. The example system shows that it is helpful for designers to achieve 2D and 3D urban information and make decisions of urban planning schemes.
This study examines the impact of the Internet on the online and offline social interactions and relationships of members of a senior-oriented computer club. Twenty semistructured, openended interviews were conducted in February 2005 to collect data. Grounded theory was used to guide data analysis. Major findings include the following: First, within this particular group of older American Internet users, there is little online interaction. The Internet is used primarily as a handy tool to obtain information rather than for developing online relationships. Second, weak tie relationships that develop as a result of face-to-face interactions in computer club meetings facilitate the exchange of information among members. Third, social interactions in the offline environment also provide rich opportunities for older adults to form and maintain companionship relationships. These findings reveal a previously ignored phenomenon: In addition to creating online social relationships, the Internet can also affect relationship formation in the physical world.
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