Low-frequency Classes Research Articles

Disposable e-cigarette use: Factors, frequency and cigarette smoking among United States high school students.

Disposable e-cigarette use has increased among United States (US)high school students in recent years. However, there is limited research on the profile of these users, how often they use these products, and whether they displace cigarette smoking. This study aimed to measure how disposable e-cigarette use among US youth varies according to demographic characteristics and whether there is any association between e-cigarette use and reduced use of traditional cigarettes. We used cross-sectional data from the 2022 National Youth Tobacco Survey and conducted a multinomial logistic regression to examine factors associated with the types of e-cigarette devices used in the prior 30 days, adjusting for sex, sexual identity, grade level and race/ethnicity. We also used a finite mixture model to account for unobserved differences among users and identify e-cigarette use patterns in different subgroups of users. United States. High school students in grades 9-12 (n = 14 389). Survey participants self-reported the type of e-cigarette device used, the frequency of e-cigarettes used and cigarettes smoked over the past 30 days. Disposable e-cigarettes were the most popular e-cigarette type. Sex, sexual orientation, grade level and race/ethnicity were associated with disposable e-cigarette use. The odds of disposable e-cigarette use were lower in male students than in female students (odds ratio [OR] = 0.78, 95% confidence interval [CI] = [0.64-0.96]), and higher in students who identified as gay or lesbian (OR = 1.70, 95% CI = [1.11-2.61]) or bisexual (OR = 1.52, 95% CI = [1.16-1.99]) than in heterosexual students. The odds of disposable use were higher among students in higher grades (10th, 11th and 12th) than in 9th graders (OR = 1.71, 2.24 and 2.52, respectively). Disposable e-cigarette users had a lower frequency of traditional cigarette use than other e-cigarette users, both in the low-frequency class (incidence rate ratio [IRR] = 0.33, 95% CI = [0.12-0.92]) and the high-frequency class (IRR = 0.27, 95% CI = [0.08-0.92]). Disposable e-cigarette use appears to be higher among United States high school students who are female, older and/or identify as gay, lesbian or bisexual. Disposable e-cigarettes appear to be associated with reduced traditional cigarette use.

Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition

Load forecasting has significant implications on optimizing the operation of air conditioning systems for industrial mushroom houses and energy saving. This research paper presents a novel approach for short-term load forecasting in mushroom houses, which face challenges in accurately modeling cold and heat loads due to the complex interplay of various factors, including climatic conditions, mushroom growth, and equipment operation. The proposed method combines empirical wavelet transform (EWT), hybrid autoregressive integrated moving average (ARIMA), convolutional neural network (CNN), and bi-directional long short-term memory (BiLSTM) with an attention mechanism (CNN-BiLSTM-Attention) to address these challenges. The first step of this method was to select input features via the Boruta algorithm. Then, the EWT method was used to decompose the load data of mushroom houses into four modal components. Subsequently, the Lempel–Ziv method was introduced to classify the modal components into high-frequency and low-frequency classes. CNN-BiLSTM-Attention and ARIMA prediction models were constructed for these two classes, respectively. Finally, the predictions from both classes were combined and reconstructed to obtain the final load forecasting value. The experimental results show that the Boruta algorithm selects key influential feature factors effectively. Compared to the Spearman and Pearson correlation coefficient methods, the mean absolute error (MAE) of the prediction results is reduced by 14.72% and 3.75%, respectively. Compared to the ensemble empirical mode decomposition (EEMD) method, the EWT method can reduce the decomposition reconstruction error by an order of magnitude of 103, effectively improving the accuracy of the prediction model. The proposed model in this paper exhibits significant advantages in prediction performance compared to the single neural network model, with the MAE, root mean square error (RMSE), and mean absolute percentage error (MAPE) of the prediction results reduced by 31.06%, 26.52%, and 39.27%, respectively.

WA-ResUNet: A Focused Tail Class MRI Medical Image Segmentation Algorithm.

Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%.

The Status of Dry Evergreen Afromontane Forest Patches in Amhara National Regional State, Ethiopia

Ethiopian dry evergreen afromontane forests are primary biodiversity priority areas including many forest patches of Amhara Region. Natural vegetation of the region is now almost exclusively limited to sacred places, very few protected and less accessible areas. Despite different studies on the various dry evergreen afromontane forest patches of the region, there was not a review work on these forest patches at a regional level. Lack of comprehensive review work creates ambiguity to the understanding of the current status of the forest patches. Scientific information on these forest patches is important for future managerial intervention and sustainable utilization. Thus, the review was aimed to evaluate the status of dry evergreen afromontane forest patches of the Region. Each forest patch contained 15–156 plant species with a total of 525 species under 328 genera and 112 families. Asteraceae was the most species richest family followed by Fabaceae. Most patches harbored considerable number of endemic plant species with a total of 46 species within 44 genera and 26 families. Asteraceae was the most endemic species rich family. Most forest patches had high diversity. Diameter at breast height (DBH) and height class analyses of most forest patches revealed an inverted J-shape pattern. Mean basal area of the forest patches ranged from 1 m2h−1 to 115.36 m2h−1. In most forest patches many number of species were put in low frequency classes. The forests had more seedlings and/or saplings than mature individuals. Although each patch had a rich source of biodiversity, it is influenced by severe anthropogenic disturbances. Therefore, appropriate managerial interventions are required.

A Cost-Sensitive Deep Learning-Based Approach for Network Traffic Classification

Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each type of misclassification because the cost matrix is specifically generated for each partition. To determine its utility, we implement the proposed cost-sensitive learning method in two deep learning classifiers, namely: stacked autoencoder and convolution neural networks. Our experiments on the ISCX VPN-nonVPN dataset show that the proposed approach can obtain higher classification performance on low-frequency classes, in comparison to three other NTC methods.

Label matrix normalization for semisupervised learning from imbalanced Data

Manually labeled data-sets are vital to graph-based semisupervised learning. However, in the real world, labeled data-sets are often heavily imbalanced, and the classifiers trained on such skewed data tend to show poor performance for low-frequency classes. In this paper, we deal with an imbalanced data case of semisupervised learning and propose a novel label matrix normalization solution called LMN to tackle the general imbalance problem. Experiments over different data-sets reveal the effectiveness of the devised algorithm.

Glacier microseismicity

Research Article| April 01, 2010 Glacier microseismicity Michael E. West; Michael E. West 1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA Search for other works by this author on: GSW Google Scholar Christopher F. Larsen; Christopher F. Larsen 1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA Search for other works by this author on: GSW Google Scholar Martin Truffer; Martin Truffer 1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA Search for other works by this author on: GSW Google Scholar Shad O'Neel; Shad O'Neel 2Water Resources Division, U.S. Geological Survey, Anchorage, Alaska 99508, USA Search for other works by this author on: GSW Google Scholar Laura LeBlanc Laura LeBlanc 1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA Search for other works by this author on: GSW Google Scholar Author and Article Information Michael E. West 1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA Christopher F. Larsen 1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA Martin Truffer 1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA Shad O'Neel 2Water Resources Division, U.S. Geological Survey, Anchorage, Alaska 99508, USA Laura LeBlanc 1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA Publisher: Geological Society of America Received: 10 Aug 2009 Revision Received: 25 Oct 2009 Accepted: 27 Oct 2009 First Online: 09 Mar 2017 Online ISSN: 1943-2682 Print ISSN: 0091-7613 © 2010 Geological Society of America Geology (2010) 38 (4): 319–322. https://doi.org/10.1130/G30606.1 Article history Received: 10 Aug 2009 Revision Received: 25 Oct 2009 Accepted: 27 Oct 2009 First Online: 09 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Michael E. West, Christopher F. Larsen, Martin Truffer, Shad O'Neel, Laura LeBlanc; Glacier microseismicity. Geology 2010;; 38 (4): 319–322. doi: https://doi.org/10.1130/G30606.1 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGeology Search Advanced Search Abstract We present a framework for interpreting small glacier seismic events based on data collected near the center of Bering Glacier, Alaska, in spring 2007. We find extremely high microseismicity rates (as many as tens of events per minute) occurring largely within a few kilometers of the receivers. A high-frequency class of seismicity is distinguished by dominant frequencies of 20–35 Hz and impulsive arrivals. A low-frequency class has dominant frequencies of 6–15 Hz, emergent onsets, and longer, more monotonic codas. A bimodal distribution of 160,000 seismic events over two months demonstrates that the classes represent two distinct populations. This is further supported by the presence of hybrid waveforms that contain elements of both event types. The high-low-hybrid paradigm is well established in volcano seismology and is demonstrated by a comparison to earthquakes from Augustine Volcano. We build on these parallels to suggest that fluid-induced resonance is likely responsible for the low-frequency glacier events and that the hybrid glacier events may be caused by the rush of water into newly opening pathways. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Using granular computing model to induce scheduling knowledge in dynamic manufacturing environments

Scheduling environments are usually dynamic and vary with time. It is necessary that the scheduling method is flexible enough for modifications or changes during production, without interrupting actual operations. Recent researches indicate that applying inductive learning technologies is one of the useful ways to solve these kinds of problems. However, when learning from imbalanced data (almost all the examples are labelled as one class while far fewer objects are labelled as the other class), these methods have poor predictive ability to identify minority instances. This is because most inductive learning algorithms assume that maximizing accuracy on a full range of cases is the goal, and this results in very poor performance for cases associated with the low-frequency class. In this study, we introduce a novel knowledge acquisition algorithm called ‘granular computing model’ for imbalanced data and integrate this method into a scheduler within a simulated flexible manufacturing system (FMS) environment. Compared with costs adjusting, cluster-based sampling techniques and decision tree (C 4.5), the experimental results indicate that our approach can dramatically increase the predictive ability of minority examples while improving classification performances.

Towards Link Characterization From Content: Recovering Distributions From Classifier Output

In processing large volumes of speech and language data, we are often interested in the distribution of languages, speakers, topics, etc. For large data sets, these distributions are typically estimated at a given point in time using pattern classification technology. It is well known that such estimates can be highly biased, especially for rare classes. While these biases have been addressed in some applications, they have thus far been ignored in the speech and language literature. This neglect causes significant error for low-frequency classes. Correcting this biased distribution involves exploiting uncertain knowledge of the classifier error patterns. We describe a numerical method, the Metropolis-Hastings (M-H) algorithm, which provides a Bayes estimator for the distribution. We experimentally evaluate this algorithm for a speaker recognition task, demonstrating a fivefold reduction in root mean squared error.

Frequency effects and properties of plasma deposited fluorinated silicon nitride

The properties of low-hydrogen, fluorinated plasma-enhanced chemical vapor deposition (PECVD) silicon nitride films grown using NF3/SiH4/N2 feed mixtures in 200 kHz and 14 MHz discharges were compared. High-energy ion bombardment at 200 kHz is expected to enhance surface diffusion and chemical reconstruction. Compared to fluorinated silicon nitride deposited at 14 MHz under otherwise comparable conditions, the 200 kHz films had a lower Si–H bond concentration (≲1×1021 cm−3), lower total hydrogen content (5–8×1021 cm−3), better resistance to oxidation, lower compressive stress (−0.7 to −1.5 Gdyne/cm), and higher density (3.1 g/cm3). The dielectric constant of better low-frequency Class I films was constant to 500 MHz, while that of high-frequency films fell up to 15% between 100 Hz and 10 MHz. The absorption edges of low-frequency PECVD fluorinated silicon nitride films were between 5.0 and 6.1 eV, which compare with 4.4 to 5.6 eV for the high-excitation frequency fluorinated material and 3 to 4 eV for conventional PECVD nitride. However high-frequency films may have fewer trap centers and a lower dielectric constant. 14 MHz p-SiN:F films grown with NH3 as an auxiliary nitrogen source showed absorption edges similar to low-frequency material grown from NF3/SiH4/N2, but they have substantially more N–H bonding. The dielectric constant and absorption edge of these films were comparable to those of low-frequency p-SiN:F from NF3/SiH4/N2.