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Using Mawhin's continuation theorem, we prove the existence of solutions for a class of nonlinear second-order differential equations in Rn associated with a three-point boundary conditions at resonance. [ABSTRACT FROM AUTHOR]

Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics interpretation, but could also be informative for classification tasks. In this paper, we describe the Topic Model Kernel (TMK), a topicbased kernel for Support Vector Machine classification on data being processed by probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks with real world datasets. TMK outperforms existing kernels on the distributional features and give comparative results on nonprobabilistic data types. [ABSTRACT FROM AUTHOR]

Simple Summary: Pig production leads to high levels of ammonia emissions, which in turn causes environmental pollution. This paper, therefore, looks at the possibilities for reducing ammonia emissions. Reducing ammonia precursors in diets and acidifying the urine and manure pH by acidifying diets make it possible to reduce the ammonia emissions from pig production facilities. The present study tested the impact of decreased crude protein or acidifying diets on urine and manure composition and ammonia emissions from growing and finishing pig houses. We found that decreasing dietary crude protein levels reduced the ammonia emissions from the floor as well as from the pig houses, whereas acidifying diets failed to reduce ammonia emissions from the floor and the pig houses. Reducing dietary crude protein is, therefore, a positive solution to reduce ammonia emissions from pig houses. This study aimed to investigate the impact of decreased crude protein (CP) levels (by 2% units) or acidifying diets (by adding 10 g benzoic acid/kg diet in combination with replacing a part of CaCO3 by about 10 g Ca-formate/kg diet) on urine, feces and manure composition and ammonia emissions from growing and finishing pig houses. Yorkshire x F1(Landrace x Yorkshire) pigs (n = 576) with an initial body weight of 24.9 ± 3.4 kg were randomly allocated to four treatments of (i) a control diet with normal protein content and no acidifying components added; (ii) a diet with 2% units CP reduction; (iii) a diet with an acidifying effect on the manure; (iv) or a diet consisting of a combination of diet (ii) and (iii). Pigs were housed in four mechanically ventilated and temperature-controlled rooms. Results showed that decreasing the dietary CP levels by 2% units reduced the ammonia emission from the floor by 46% (p = 0.06) and from the pig house by 31% (p = 0.08). Decreased CP diets reduced the total N in feces and in manure and NH4-N in the manure, as well as the ammonia concentration at 1 cm and 10 cm above the manure surface (p < 0.05). However, acidifying diets failed to reduce ammonia emissions from the floor and the pig house (p > 0.05). Reducing dietary crude protein is, therefore, a solution to reducing ammonia emissions from pig houses. [ABSTRACT FROM AUTHOR]

4-month-old twins were diagnosed with X-pert MTB/RIF® confirmed tuberculosis (TB). The mother was treated for TB after delivery, but no household contact evaluation was performed or preventive therapy offered. This report illustrates the vulnerability of young children to develop TB, poor implementation of preventive therapy strategies, and the need for meticulous TB exposure assessment in children not responding to pneumonia treatment. [ABSTRACT FROM AUTHOR]

Background: To date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1-6 month risk. Methods: 7,399 patients undergoing suicide risk assessment were followed up for 180 days. The dataset was divided into a derivation and validation cohorts of 4,911 and 2,488 respectively. Clinicians used an 18-point checklist of known risk factors to divide patients into low, medium, or high risk. Their predictive ability was compared with a risk stratification model derived from the EMR data. The model was based on the continuation-ratio ordinal regression method coupled with lasso (which stands for least absolute shrinkage and selection operator). Results: In the year prior to suicide assessment, 66.8% of patients attended the emergency department (ED) and 41.8% had at least one hospital admission. Administrative and demographic data, along with information on prior self-harm episodes, as well as mental and physical health diagnoses were predictive of high-risk suicidal behaviour. Clinicians using the 18-point checklist were relatively poor in predicting patients at high-risk in 3 months (AUC 0.58, 95% CIs: 0.50 - 0.66). The model derived EMR was superior (AUC 0.79, 95% CIs: 0.72 - 0.84). At specificity of 0.72 (95% CIs: 0.70-0.73) the EMR model had sensitivity of 0.70 (95% CIs: 0.56-0.83). Conclusion: Predictive models applied to data from the EMR could improve risk stratification of patients presenting with potential suicidal behaviour. The predictive factors include known risks for suicide, but also other information relating to general health and health service utilisation. [ABSTRACT FROM AUTHOR]

Background One hundred days after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Vietnam on 23 January, 270 cases were confirmed, with no deaths. We describe the control measures used by the government and their relationship with imported and domestically acquired case numbers, with the aim of identifying the measures associated with successful SARS-CoV-2 control. Methods Clinical and demographic data on the first 270 SARS-CoV-2 infected cases and the timing and nature of government control measures, including numbers of tests and quarantined individuals, were analyzed. Apple and Google mobility data provided proxies for population movement. Serial intervals were calculated from 33 infector-infectee pairs and used to estimate the proportion of presymptomatic transmission events and time-varying reproduction numbers. Results A national lockdown was implemented between 1 and 22 April. Around 200 000 people were quarantined and 266 122 reverse transcription polymerase chain reaction (RT-PCR) tests conducted. Population mobility decreased progressively before lockdown. In total, 60% (163/270) of cases were imported; 43% (89/208) of resolved infections remained asymptomatic for the duration of infection. The serial interval was 3.24 days, and 27.5% (95% confidence interval [CI], 15.7%-40.0%) of transmissions occurred presymptomatically. Limited transmission amounted to a maximum reproduction number of 1.15 (95% CI,.·37–2.·36). No community transmission has been detected since 15 April. Conclusions Vietnam has controlled SARS-CoV-2 spread through the early introduction of mass communication, meticulous contact tracing with strict quarantine, and international travel restrictions. The value of these interventions is supported by the high proportion of asymptomatic and imported cases, and evidence for substantial presymptomatic transmission. [ABSTRACT FROM AUTHOR]

We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data. Our method involves a joint optimization formulation over several spaces of discrete probability measures, which are endowed with Wasserstein distance metrics. We propose several variants of this problem, which admit fast optimization algorithms, by exploiting the connection to the problem of finding Wasserstein barycenters. Consistency properties are established for the estimates of both local and global clusters. Finally, experimental results with both synthetic and real data are presented to demonstrate the flexibility and scalability of the proposed approach. [ABSTRACT FROM AUTHOR]

To assess the role of in-flight transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we investigated a cluster of cases among passengers on a 10-hour commercial flight. Affected persons were passengers, crew, and their close contacts. We traced 217 passengers and crew to their final destinations and interviewed, tested, and quarantined them. Among the 16 persons in whom SARS-CoV-2 infection was detected, 12 (75%) were passengers seated in business class along with the only symptomatic person (attack rate 62%). Seating proximity was strongly associated with increased infection risk (risk ratio 7.3, 95% CI 1.2-46.2). We found no strong evidence supporting alternative transmission scenarios. In-flight transmission that probably originated from 1 symptomatic passenger caused a large cluster of cases during a long flight. Guidelines for preventing SARS-CoV-2 infection among air passengers should consider individual passengers' risk for infection, the number of passengers traveling, and flight duration. [ABSTRACT FROM AUTHOR]

Production of bioenergy by the fermentation reaction is gaining attraction due to its easy operation and the wide feedstock selection. Anaerobic fermentation of organic waste materials is generally considered a cost-effective and proven technology, allowing simultaneous waste management and energy production. Small-scale biogas plants are widely and increasingly used to transform waste into gas through anaerobic fermentation of organic materials in the developing world. In this research, the quality of biogas produced in small-scale biogas plants was evaluated, as it has a direct effect on its use (as fuel for biogas cookers), as well as being able to influence a decision making process over purchasing such technology. Biogas composition was measured with a multifunctional portable gas analyser at 107 small-scale biogas plants. Complementary data at household level were collected via the questionnaire survey with the owners of biogas plants (n = 107). The average daily biogas production equals 0.499 m3, not covering the demand of rural households which are using other sources of energy as well. Related to the biogas composition, the mean content of methane (CH4) was 65.44% and carbon dioxide (CO2) was 29.31% in the case of biogas plants younger than five years; and CH4 was 64.57% and CO2 was 29.93% for biogas plants older than five years. Focusing on the age of small-scale biogas plants there are no, or only minor, differences among tested values. In conclusion, the small-scale biogas plants are sustaining a stable level of biogas quality during their life-span. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in kernel online learning is to bound the model size and to promote model sparsity. Sparse models not only improve computation and memory usage, but also enhance the generalization capacity -- a principle that concurs with the law of parsimony. However, inappropriate sparsity modeling may also significantly degrade the performance. In this paper, we propose Approximation Vector Machine (AVM), a model that can simultaneously encourage sparsity and safeguard its risk in compromising the per- formance. In an online setting context, when an incoming instance arrives, we approximate this instance by one of its neighbors whose distance to it is less than a predefined threshold. Our key intuition is that since the newly seen instance is expressed by its nearby neigh- bor the optimal performance can be analytically formulated and maintained. We develop theoretical foundations to support this intuition and further establish an analysis for the common loss functions including Hinge, smooth Hinge, and Logistic (i.e., for the classification task) and &# 8467;1, &# 8467;2, and "ε-insensitive (i.e., for the regression task) to characterize the gap between the approximation and optimal solutions. This gap crucially depends on two key factors including the frequency of approximation (i.e., how frequent the approximation operation takes place) and the predefined threshold. We conducted extensive experiments for classification and regression tasks in batch and online modes using several benchmark datasets. The quantitative results show that our proposed AVM obtained comparable pre- dictive performances with current state-of-the-art methods while simultaneously achieving significant computational speed-up due to the ability of the proposed AVM in maintaining the model size. [ABSTRACT FROM AUTHOR]

Background The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors. Methods Web activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r. Results For 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81; 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96; 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93. Conclusions The high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts. [ABSTRACT FROM AUTHOR]