CHA: Categorical Hierarchy-based Attention for Next POI Recommendation

Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.

Circulating Blood-Based Biomarkers in Pulmonary Hypertension

Pulmonary hypertension (PH) is a serious hemodynamic condition, characterized by increased pulmonary vascular resistance (PVR), leading to right heart failure (HF) and death when not properly treated. The prognosis of PH depends on etiology, hemodynamic and biochemical parameters, as well as on response to specific treatment. Biomarkers appear to be useful noninvasive tools, providing information about the disease severity, treatment response, and prognosis. However, given the complexity of PH, it is impossible for a single biomarker to be adequate for the broad assessment of patients with different types of PH. The search for novel emerging biomarkers is still ongoing, resulting in a few potential biomarkers mirroring numerous pathophysiological courses. In this review, markers related to HF, myocardial remodeling, inflammation, hypoxia and tissue damage, and endothelial and pulmonary smooth muscle cell dysfunction are discussed in terms of diagnosis and prognosis. Extracellular vesicles and other markers with complex backgrounds are also reviewed. In conclusion, although many promising biomarkers have been identified and studied in recent years, there are still insufficient data on the application of multimarker strategies for monitoring and risk stratification in PH patients.

Socioeconomic profile of producers and dairy technological of farms in the southern mesoregion of Santa Catarina

This study characterized the socioeconomic profile of milk producers and dairy technological of farms in the southern mesoregion of Santa Catarina. Following a 6.5% sampling of total farms by municipality, 95% confidence level, 5% sampling error, and group heterogeneity, data were collected from 308 farms, 22 of which were excluded due to insufficient data. The farmers were selected randomly, and information extracted from an electronic form, addressing social, economic, technical and technological issues. The data were analyzed using factor, cluster, and discriminant analysis. As farms with the main activity of dairy farming, they have an average area of 20.79 hectares and a production of 12.18 liters per animal per day. I n the factor analysis, the first factor was related to the area and productivity of farms and the second factor to sanitary control and the feed variety of the animals. The cluster analysis formed three clusters; the first and the second were composed of larger farms and areas, and the other consisted of smaller farms, the latter involving more producers. As farms are characterized by low production, there is clearly a deficiency in technical support for producers.

HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification

Cardiovascular disease is now one of the leading causes of morbidity and mortality in humans. Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular system. Currently, there has been a lot of focus on accurately categorizing heartbeats. There is a high demand on automatic ECG classification systems to assist medical professionals. In this paper we proposed a new deep learning method called HeartNet for developing an automatic ECG classifier. The proposed deep learning method is compressed by multi-head attention mechanism on top of CNN model. The main challenge of insufficient data label is solved by adversarial data synthesis adopting generative adversarial network (GAN) with generating additional training samples. It drastically improves the overall performance of the proposed method by 5-10% on each insufficient data label category. We evaluated our proposed method utilizing MIT-BIH dataset. Our proposed method has shown 99.67 ± 0.11 accuracy and 89.24 ± 1.71 MCC trained with adversarial data synthesized dataset. However, we have also utilized two individual datasets such as Atrial Fibrillation Detection Database and PTB Diagnostic Database to see the performance of our proposed model on ECG classification. The effectiveness and robustness of proposed method are validated by extensive experiments, comparison and analysis. Later on, we also highlighted some limitations of this work.

Insights into Acute Pancreatitis Associated COVID-19: Literature Review

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) primarily affects the lungs, causing respiratory symptoms. However, the infection clearly affects all organ systems including the gastrointestinal system. Acute pancreatitis associated with coronavirus disease 2019 (COVID-19) has been widely reported Recent studies have discussed pancreatic compromise incidentally in asymptomatic patients, or in a form of clinical symptoms such as abdominal pain, nausea, or vomiting, which is further reflected in some cases with abnormal serum lipase and amylase levels It was suggested that upregulation of angiotensin-converting enzyme II cell receptors or inflammatory cytokines play a major role in predisposing pancreatic injury in SARS-CoV-2 positive patients To date, there is insufficient data to establish the causality of acute pancreatitis in SARS-CoV-2 infected cases. In this paper, we organize recent studies conducted to observe the frequency of acute pancreatitis associated with COVID-19 cases while highlighting present hypotheses, predisposing factors, and their effect on the outcome, and point to gaps in our knowledge.

Contemporary State of Preservation of the Lichen Biota in the Eastern Part of the “Paprocie Serpentynitowe w Masywie Ślęży” Ecological Area

The purpose of this study was to assess the contemporary conditions of the lichen biota in the eastern part of the “Paprocie Serpentynitowe w Masywie Ślęży” ecological area in the Sudetes Foothills. The studies were carried out at three localities selected from 10 sites that were designed to protect the Asplenium fern genus. A total of 47 lichen species were found in the three localities. For each of the recorded species, the state of preservation in the Ślęża Massif area was assessed according to three factors: frequency at all three localities, general viability of the thalli, and stability in occurrence on the habitat. Stability was defined based on contemporary and historical data. From the analysis, most of the species (17) were classified as taxa with “moderate preservation status” in the study area. However, the preservation status of the 18 newly recorded taxa was defined as “insufficient data” due to the lack of previous records. Other species received a “very good preservation status” (three taxa) and “good preservation status” (nine taxa). The analyses allowed us to document the current number of taxa, determine their health condition, and indicate trends in the transformation within the lichen biota of the serpentinite rocks in the study area. Moreover, some actions were proposed to actively protect the valuable lichens in the ecological area of “Paprocie Serpentynitowe w Masywie Ślęży.”

Violence Against Women in Russia and Brazil: International and Domestic Responses

The problem of domestic violence and violence against women, despite being an age-old phenomenon, came to the fore of public debate relatively late. It entered the agenda of intergovernmental organizations in the 1990s, but became the subject of international litigation only in the 2000s and 2010s. While this belated response of the international community can be associated with the inadequate conceptualization of the problem and insufficient data, it also has to do with the ongoing public/private dichotomy that became especially pronounced in the recent years when various conservative groups increasingly question the necessity of specific laws and policies aimed at eliminating this kind of crime. In this article, I briefly trace the developments concerning women’s rights, and, particularly, domestic violence and violence against women in international law. Then, based on the analysis of international and regional court decisions, I try to see how and whether these decisions contributed to the domestic developments in the field of combatting this phenomenon in Russia and Brazil. It is also important to examine how COVID-19 pandemic impacted the narratives of violence and how the international community should respond to the challenge of protecting the most vulnerable members of the society in the conditions of health emergency.

The Effect of Aromatherapy Alone or in Combination with Massage on Dysmenorrhea: A Systematic Review and Meta-analysis

Objective The aim of the present systematic review meta-analysis is to assess the effect of olfactory stimulation on reducing dysmenorrhea. Methods Systematic search was conducted in several databases, such as PubMed, Web of Science, Cochrane, and Scopus, to identify relevant research up to October 26, 2019. The identified studies were evaluated based on a modified Jadad scale. The intervention involves aromatherapy alone or in combination with essential oils. There was no restriction for the control group such as a placebo group or other common treatments. The Comprehensive Meta-Analysis Version 2 (Bio stat, Englewood, NJ, USA) was used for meta-analysis. Cochran's Q and I2 tests were utilized. Results The findings of our meta-analysis, which contained 13 trials (15 data), showed that dysmenorrhea decreased significantly in the group receiving aromatherapy with herbal compared with the control group (standardized mean difference [SMD] = -0.795; 95% confidence interval [CI]: -0.922 to- 0.667; 17 trials O < 0.001); heterogeneity; I2 = 19.47%; p = 0.236). In addition, four studies with insufficient data were not included in our meta-analysis. The results of all studies suggested that aromatherapy with herbal medicine group compared with control group is effective. Conclusion Aromatherapy with herbal medicine decreased dysmenorrhea. This treatment was particularly effective when aroma oil was combined with massage or when a mixture of aroma oil was used for the treatment of dysmenorrhea.

Evaluating the effectiveness of vegetation conservation on a sacred mountain in western China

Sacred natural sites, as probably the oldest form of habitat reserve for religious or cultural causes worldwide, are suggested to have an important role in conserving vegetation; however, there are insufficient data supporting the detailed implications of such sites for vegetation conservation. Thus, we evaluated the effectiveness of vegetation conservation on a Tibetan sacred mountain in Yajiang County, Sichuan, China, by investigating species richness and the structural attributes of higher vascular plant communities on and around the sacred mountain from April to June 2009. The results showed that the number of tree species on the sacred mountain was significantly higher than that in the surrounding area, but there were no notable differences in the numbers of shrub and grass species between the two sites. The sacred mountain harbored a greater number of small, short trees compared with the surrounding area, wherein the low-shrub and grass understory was relatively dense. We conclude that the sacred mountain has a positive impact on indigenous vegetation protection, but disparities in the management of the allowed uses of such sites could reduce their conservation effectiveness.

Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net

Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient’s infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.