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Thus, the results revealed a viable contribution to the design of functional-active materials based on a natural polymer such as chitosan. This proposal may be considered as a potential tool to inhibit the propagation and dissemination of enveloped viruses, including SARS-CoV-2.

The online version contains supplementary material available at 10.1007/s10904-021-02192-x.

The online version contains supplementary material available at 10.1007/s10904-021-02192-x.Alcohol consumption and sleep disorders are both prevalent and relevant problems among college students, but the relationship between these conditions is unclear. This study aimed to analyze the association between alcohol-related problems and sleep in first-year college students from Brazil, Chile, and Spain. Cross-sectional analyses were performed with data from three independent studies with first-year college students from each country. The risk of alcohol-related problems (RARP) and sleep quality and duration were self-reported using mixed methods. Pooled odds ratios (p-OR) and 95% confidence intervals (95% CI) of suboptimal sleep quality and of short (

The online version contains supplementary material available at 10.1007/s11469-022-00763-8.

The online version contains supplementary material available at 10.1007/s11469-022-00763-8.In order to understand the characteristic data of athletes' training load, a method based on nine-axis sensor was proposed. Twenty-seven male college athletes were tested twice with a time interval of more than 48 hours. In part 1, participants take the 1 Repetition Maximum (1RM) test. The results show that maximum strength is one of the basic factors to develop the output power of athletes. In the process of skeletal muscle contraction, the curve of speed, force, and power is closely related. When the external load is 10%∼70%, the average power increases with the increase in the average force, it increases with the decrease in the average speed, and at 70%1RM, the average power reaches the peak and then decreases at an inflection point. It is proved that the accurate weight ratio of strength training is the basis of winning athletes, the focus of high level physical coach, and the premise of scientific sports training.A newly emerged coronavirus disease affects the social and economical life of the world. This virus mainly infects the respiratory system and spreads with airborne communication. Several countries witness the serious consequences of the COVID-19 pandemic. Early detection of COVID-19 infection is the critical step to survive a patient from death. The chest radiography examination is the fast and cost-effective way for COVID-19 detection. Several researchers have been motivated to automate COVID-19 detection and diagnosis process using chest x-ray images. However, existing models employ deep networks and are suffering from high training time. This work presents transfer learning and residual separable convolution block for COVID-19 detection. The proposed model utilizes pre-trained MobileNet for binary image classification. The proposed residual separable convolution block has improved the performance of basic MobileNet. Two publicly available datasets COVID5K, and COVIDRD have considered for the evaluation of the proposed model. Our proposed model exhibits superior performance than existing state-of-art and pre-trained models with 99% accuracy on both datasets. We have achieved similar performance on noisy datasets. Moreover, the proposed model outperforms existing pre-trained models with less training time and competitive performance than basic MobileNet. Further, our model is suitable for mobile applications as it uses fewer parameters and lesser training time.The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words' importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.The COVID-19 pandemic increase the use of distance learning while studies have shown that there is insufficient digital knowledge among students in distance leaning as they do not adequately use technology as a digital citizenship indicator, while the awareness and knowledge of digital citizenship among teachers and students remains a key criterion for improving distance learning that mainly depends on information technology. Therefore, this study comes up to examine the awareness and knowledge of students and faculty of digital citizenship in distance environment by focusing on two different higher academic institutions, namely the Al-Quds Open University (QOU) in the Palestinian territories and the University of Kyrenia (KU) in the Turkish Republic of Northern Cyprus in 2020, using interview, descriptive analysis, and Z-test Technique. The results revealed that students and faculty in both institutions were aware of the digital citizenship concepts, but lacked the in-depth knowledge and understanding of concepts such as digital rights, digital security, and digital ethics. Furthermore, the awareness and knowledge of digital citizenship among KU students are higher than QOU students. Faculty in both institutions agreed with the importance of integrating digital citizenship practices such as digital rights, digital security, and digital ethics into elearning curriculum.

The Skvortsov Herbarium of the Tsitsin Main Botanical Garden, Russian Academy of Sciences (MHA) in the 1945-1980s dealt with vascular plants and only scattered occasional collections of bryophytes and lichens were accumulated there without special arrangement. Since the late 1980s, the bryophyte studies in the MHA Herbarium became permanent and several projects were started since then, including the currently conducted "Moss Flora of Russia". There are many white spots on the map of bryophyte exploration of Russia, but one of the most conspicuous was Yakutia, the largest administrative unit of Russia, covering 3,081,000 km

. Yana-Indigirka Region, originally defined as a floristic region, includes Verkhoyansky Range and some smaller adjacent mountain areas. It is the largest amongst the bryofloristic regions in Russia, but exploration of its territory, which is difficult to access, remains far from complete.

Several expeditions of the Institute for Biological Problems of Cryolithozone, Siberian Branch of Russian Academy of Sciences, and the Main Botanical Garden, Russian Academy of Sciences in 2000-2018 yielded in many bryophyte specimens, partly published in a number of papers. This dataset comprehensively represents the diversity of mosses of the Region. It includes 7,738 records of moss specimens preserved in the MHA Herbarium.

Several expeditions of the Institute for Biological Problems of Cryolithozone, Siberian Branch of Russian Academy of Sciences, and the Main Botanical Garden, Russian Academy of Sciences in 2000-2018 yielded in many bryophyte specimens, partly published in a number of papers. This dataset comprehensively represents the diversity of mosses of the Region. It includes 7,738 records of moss specimens preserved in the MHA Herbarium.Pogostemonhainanensis, a new species of Lamiaceae from Hainan Island, China, is described. The phylogenetic position of the new species within Pogostemon was investigated based on analyses of the nuclear ribosomal internal transcribed spacer (nrITS) and five plastid markers (viz. matK, psbA-trnH, rbcL, rsp16, trnL-F). The results show that P.hainanensis is supported to be a member of subgenus Pogostemon and is sister to P.parviflorus, a species widely distributed from Eastern Himalaya, through the Indo-China peninsula to China. Morphologically, the new species can be distinguished from all the other taxa of subgenus Pogostemon in having long petioles usually 4.5‒11.5 cm in length, and the calyx teeth 2/3 to subequal as long as the calyx tube. The new species differs from P.parviflorus further by its obviously double serrate leaf margin, spikes of inflorescence usually 2.5-8.0 cm long, calyx 4‒5 mm long and corolla 6-7 mm long.A new species, Achnanthidiumbratanense, is described from Lake Bratan, located on the island of Bali (Indonesia). The morphology of this species was analyzed with light (LM) and scanning electron microscopy (SEM). A.bratanense is characterized by linear-elliptic to nearly elliptic valves with convex margins and rounded, broadly subcapitate apices. The striae of this species are hardly discernable under LM; they are weakly radiate throughout the valve and composed of one to four large transapically elongated areolae of different length and shape. The most similar taxon to A.bratanense is A.macrocephalum, a species described from Sumatra, another Indonesian island. The differences of A.bratanense from similar taxa are discussed.The genus Coryphantha includes plants with globose to cylindrical stems bearing furrowed tubercles, flowers arising at the apex, and seeds with flattened testa cells. Coryphantha is the second richest genus in the tribe Cacteae. Nevertheless, the genus lacks a phylogenetic framework. The limits of Coryphantha with its sister genus Escobaria and the infrageneric classification of Coryphantha have not been evaluated in a phylogenetic study. In this study we analyzed five chloroplast regions (matK, rbcL, psbA-trnH, rpl16, and trnL-F) using Bayesian phylogenetic analysis. We included 44 species of Coryphantha and 43 additional species of the tribe Cacteae. Our results support the monophyly of Coryphantha by excluding C.macromeris. Escobaria + Pelecyphora + C.macromeris are corroborated as the sister group of Coryphantha. Within Coryphantha our phylogenetic analyses recovered two main clades containing seven subclades, and we propose to recognize those as two subgenera and seven sections, respectively. Also, a new delimitation of Pelecyphora including C.macromeris and all species previously included in Escobaria is proposed. To accommodate this new delimitation 25 new combinations are proposed. The seven subclades recovered within Coryphantha are morphologically and geographically congruent, and partially agree with the traditional classification of this genus.