Lakewide and Nearshore Microbial Water Quality Modelling in Lake St. Clair

Lake St. Clair is a freshwater lake in the Lake Huron to Erie corridor in the Great Lakes Basin. Millions of people in Canada and the United States rely on that water source for drinking, fishing and recreational purposes. Lake St. Clair’s watershed is heavily impacted by human activity, which can result in contamination of its waters by fecal matter of human or animal origin containing waterborne pathogens, and thus pose a direct threat to human health. Common sources of such pollution include combined sewer overflows, wastewater treatment plant bypasses, and agricultural application of manure derived from animal fecal waste. Several such sources are present in Windsor Essex County (WEC), Ontario, Canada, which is located along the southern edge of Lake St. Clair. Two popular public beaches and drinking water intakes are located in the nearshore region adjacent to the southern edge. Fecal microbial pollution is currently monitoring using fecal indicator bacteria (FIB), such as Escherichia coli (E. coli). Monitoring methods have several limitations including their inability to predict water quality in real-time or in advance, or to identify potential sources of contamination for more effective management. Mathematical models are tools that can be very effective and complementary to monitoring in overcoming its limitations. Model predictions can be real-time or near real-time and also help to identify or exonerate potential sources of microbial pollution. In the current study, two types of modelling approaches that are commonly being used in the assessment of microbial contamination in beach waters and lakes were investigated: statistical modelling based on multiple linear regression (MLR) and hydrodynamic-ecological modelling. The statistical MLR models developed for Sandpoint Beach in Lake St. Clair showed higher accuracy in the range 64-78%, for predicting both exceedance and non-exceedance of the applicable standard, as compared to 54% accuracy obtained using the current method based on E. coli measurements. Amongst the MLR models developed, an increase of about 5-14% in model performance was observed when qualitative sky weather condition was included. Results with mechanistic structured grid high-resolution AEM3D model developed for Lake St. Clair showed that four major tributaries (Thames, Sydenham, St. Clair and Clinton River) are unlikely to be responsible for the E. coli exceedances of provincial guideline observed at Sandpoint Beach. Amongst the major tributaries, predicted E. coli concentrations were dominated by the contribution of St. Clair River for most of Lake St. Clair. The maximum predicted E. coli concentration from the combined input of the major tributaries was less than 100 CFU/100 ml for most of the lake and less than 10 CFU/100 ml at Sandpoint Beach. Predicted E. coli were significantly affected by varying water temperature and sunlight result in the temporal and diurnal dynamics of microbial water quality in Lake St. Clair. About 12–148% differences in predicted E. coli concentrations were observed at six drinking water intakes located in Lake St. Clair when time-variable decay rates were used instead of a constant decay rate. Also, on average nighttime E. coli predictions were 21–68% higher at these water intakes, as compared to daytime levels. Results from the AEM3D model showed that while the flow contribution of eight smaller tributaries in Windsor Essex Region to the lake is insignificant (less than 0.2%), their contribution to the adjacent nearshore region along the southern edge of Lake St. Clair could be quite significant. Within about 1 km from the shoreline of this nearshore region, flow contributions from the small tributaries were estimated in the range between 18-35%, while their contribution to E. coli concentration was estimated to be more than 80%. Results with mechanistic unstructured grid TUFLOW-FV/AED2+ lakewide model and with a finer mesh nested model over a 2 km region surrounding Belle River showed differences of up to a factor of four in predicted E. coli concentrations at adjacent Lakeview Park West Beach (LP Beach). The differences reduced to a factor of up to 1.3 at nearby Lakeshore WTP intake located about one km away from shore. While the average contribution of the Belle River to E. coli concentrations at Lakeshore WTP intake was predicted to be \u3c20%, the contribution increased to \u3e80% when higher concentrations (10-35 CFU/ 100 ml) were predicted. The results also indicate that the construction of the marina may have contributed to some increase in E. coli concentrations at LP Beach from the external sources considered. However, construction of a new 150 m jetty in 2018, in place of the 25 m jetty separating Belle River from LP Beach, is expected to reduce the E. coli concentrations at LP Beach from the same sources by about 80%

Fast and accurate classification of echocardiograms using deep learning

Echocardiography is essential to modern cardiology. However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and research potential for precision medicine. Deep learning is a cutting-edge machine-learning technique that has been useful in analyzing medical images but has not yet been widely applied to echocardiography, partly due to the complexity of echocardiograms' multi view, multi modality format. The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views. To this end, we anonymized 834,267 transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51 percent female, 26 percent obese) seen between 2000 and 2017 and labeled them according to standard views. Images covered a range of real world clinical variation. We built a multilayer convolutional neural network and used supervised learning to simultaneously classify 15 standard views. Eighty percent of data used was randomly chosen for training and 20 percent reserved for validation and testing on never seen echocardiograms. Using multiple images from each clip, the model classified among 12 video views with 97.8 percent overall test accuracy without overfitting. Even on single low resolution images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5 percent for board-certified echocardiographers. Confusional matrices, occlusion experiments, and saliency mapping showed that the model finds recognizable similarities among related views and classifies using clinically relevant image features. In conclusion, deep neural networks can classify essential echocardiographic views simultaneously and with high accuracy. Our results provide a foundation for more complex deep learning assisted echocardiographic interpretation.Comment: 31 pages, 8 figure

AGAMA DAN PENDIDIKAN: ANALISIS RELASI DAN IMPLIKASINYA DALAM UPAYA PENGEMBANGAN EKONOMI

Bagi orang yang beriman, agama merupakan elemen yang paling fundamental dalam kehidupan.Dikatakan demikian, karena ia tidak hanya terkait dengan keimanan pemeluknya, namun lebih dari itu agama juga merupakan perangkat prinsip- prinsip dan nilai-nilai sakral yang sangat diyakini kebenarannya, sempurna dan komprehensif. Prinsip-prinsip dan nilai- nilai agama yang tidak lekang ditelan zaman tersebut dianggap mampu menjadi energi yang positif tidak hanya pada prilaku individu tetapi juga perilaku sosial- ekonomi. Agama dengan demikian, tidak hanya berkaitan dengan persoalan individu tetapi juga berkaitan dengan permasalahan sosial- ekonomi. Islam misalnya, bukan hanya sekedar agama yang bersifat individual tetapi juga sosial. Bahkan banyak ayat- ayat Al-Qur’an mengandung dimensi sosial termasuk ekonomi. Proses internalisasi prinsip-prinsip dan nilai-nilai agama oleh individu pemeluk agama dalam banyak segi berkaitan erat dengan pendidikan yang bersifat sosial. Transformasi prinsip-prinsip dan nilai-nilai serta pewarisan budaya Islam dari generasi ke generasi berikutnya melaui proses pendidikan. Hal ini mengindikasikan bahwa terdapat relasi yang erat antara agama, ekonomi dan pendidikan.   [1]Istilah agama (religion) dalam Islam dikenal dengan term din, menurut al-Attas maknanya tidak sama dengan konsep agama sebagaimana yang biasanya diinterpretasikan dan dipahami dalam sejarah dan peradaban keagamaan Barat. Pengertian agama sebagai din dimana semua konotasi dasar yang berkaitan dengan term din dilukiskan sebagai terpadukan kedalam satu kesatuan yang saling kait mengkait seperti tercermin dalam Al-Qur’an dan Bahasa Arab. Dalam Bahasa Arab arti utama dari istilah din dapat diringkas menjadi empat yaitu: (1) keberhutangan; (2) kepatuhan (3) kekuasaan bijaksana (4) kecenderungan alami atau tendensi. Lihat dalam Syed Muhammad Al-Naquib Al-Attas. Konsep Pendidikan dalam Islam, (Bandung: Mizan,1984), h. 71-72)

Implementasi Kompetensi Inti 1 Kurikulum 2013 Pada Mata Pelajaran PAI di Masa Pandemi Covid-19

Pembelajaran daring atau online dalam rangka untuk mencegah tersebarnya Covid 19 pada semua kehidupan, khususnya bagi kehidupan peserta didik. Keselamatan peserta didik menjadi prioritas utama dikeluarkannya kebijakan pemerintah yang berupa Surat Keputusan Bersama (SKB) empat Menteri, yakni Menteri Penddikan, Kebudayaan dan Riset dan Teknologi, Menteri Agama, Menteri Kesehatan, dan Menteri Dalam Negeri. Penelitian ini merupakan penelitian konseptual yang mencoba mengakaji dari beberapa literatur yang relevan dengan judul dan kemudian dianalisis dengan fenomena pembelajaran di masa pendemi covid 19. Penelitian yang relevan dengan penelitian ini sebenarnya sudah pernah dilakukan oleh peneliti sebelumnya, namun dilakukan secara umum. Dalam penelitian ini, peneliti mencoba fokus pada implementasi kompetensi inti 1 kurikulum 2013 di masa pandemi covid 19. Penelitian ini bertujuan: 1) bagaimana pembelajaran di masa Pandemi Covid-19; apa kompetensi inti 1 dalam kurikulum 2013; dan 3) implementasi kompetensi inti 1 pada mata pelajaran PAI di masa pandemi covid 19. Harapan penelitian ini: 1) menjadi bahan evaluasi bagi guru dalam melaksanakan proses pembelajaran daring atau online di masa pandemi covid 19; 2) menjadi bahan evaluasi dalam implementasi kompetensi inti 1 pada mata pelajaran PAI serta sebagai solusi problem pembelajaran daring atau online

Response of Sunflower Yield and Phytohormonal Changes to Azotobacter,Azospirillum,Pseudomonas and Animal Manure in a Chemical Free Agroecosystem

There are new trends in agriculture to move toward the low input systems with the lower application of chemical fertilizers. To reach this goal, different methods, such as the application of biofertilizers, may be used. So this experiment was conducted in 2010 at a research farm in Arak, Iran, in factorial in the form of a randomized complete block design with three replications and four factors: animal manure (M), Pseudomonas putida (P), Azotobacter chroococcum (A)and Azospirillum lipoferum (Z). Results indicated that manure significantly affected grain yield (P≤0.01); the highest grain yield was achieved in the interaction of manure × Azotobacter × Pseudomonas (4.556 ton/ha). Grain yield was not significantly affected by the microorganisms. Moreover, the four factors of the experiment significantly affected auxin, gibberellin and cytokinin content of plant. Overall, this experiment indicated that desirable yield can be achieved by the application of manure and biofertilizers, in a sustainable agriculture

REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization

Human-annotated textual explanations are becoming increasingly important in Explainable Natural Language Processing. Rationale extraction aims to provide faithful (i.e., reflective of the behavior of the model) and plausible (i.e., convincing to humans) explanations by highlighting the inputs that had the largest impact on the prediction without compromising the performance of the task model. In recent works, the focus of training rationale extractors was primarily on optimizing for plausibility using human highlights, while the task model was trained on jointly optimizing for task predictive accuracy and faithfulness. We propose REFER, a framework that employs a differentiable rationale extractor that allows to back-propagate through the rationale extraction process. We analyze the impact of using human highlights during training by jointly training the task model and the rationale extractor. In our experiments, REFER yields significantly better results in terms of faithfulness, plausibility, and downstream task accuracy on both in-distribution and out-of-distribution data. On both e-SNLI and CoS-E, our best setting produces better results in terms of composite normalized relative gain than the previous baselines by 11% and 3%, respectively

Isolation and identification of inhibitory bacteria against pathogenic fungi from Isfahan using molecular method

زمینه و هدف: گونه های باسیلوس منبعی از متابولیت های ضد قارچی با توان مهار عفونت های قارچی هستند. هدف از این مطالعه، جداسازی و شناسایی باکتری مهارکننده ی رشد قارچ های پاتوژن از اصفهان با استفاده از روش مولکولی بود. روش بررسی: در این مطالعه توصیفی- مقطعی، تعداد 150 نمونه (خاک، هوا و سطوح) از شهر اصفهان تهیه و تأثیر مهاری باکتری های رشد یافته بر روی محیط کشت نوترینت آگار بر رشد قارچ های آسپرژیلوس نایجر، آسپرژیلوس فلاووس و موکور هیمالیس بررسی شد. بررسی کیفی مهار رشد قارچ با روش نشاکاری و جهت بررسی کمی مهار رشد قارچ ها تلقیح سوسپانسیون قارچی حاوی 104 اسپور بر میکرولیتر به صورت کشت خطی در فواصل 5/0، 1، 5/1، 2، 5/2 و 3 سانتی متری از مرکز (محل تلقیح سوسپانسیون 5/0 مک فارلند باکتری ها) انجام شد. نمونه ها در دمای 30 درجه سانتی گراد به مدت 96 ساعت نگهداری و شناسایی باکتری مهاری با تست های بیوشیمیایی و روش مولکولی انجام گرفت. یافته ها: تأثیر مهاری باکتری ها بر رشد قارچ های آسپرژیلوس نایجر، آسپرژیلوس فلاووس و موکور هیمالیس در فواصل 5/0 تا 3 سانتی متر مشاهده شد. بر اساس نتایج تست های بیوشیمیایی و روش کلنی- PCR، باکتری با بیشترین اثر مهاری نسبت به قارچ های مذکور باسیلوس آتروفئوس سویه ی HNSQJYH170 شناسایی شد. نتیجه گیری: باسیلوس آتروفئوس سویه ی HNSQJYH170 بومی اصفهان قابل استفاده برای تولید آنتی بیوتیک و مصارف کنترل بیولوژیک است

Outage probability based on telecommunication range for multi-hop HALE UAVs

Cooperative relaying increases telecommunication range, improves the connectivity, and increases the reliability of data transmission; however, the transmitted power does not change. This paper analyzes the extended telecommunication range of a multi-hop cascaded network comprising N–cooperative relaying high-altitude long endurance (HALE) unmanned aerial vehicles (UAVs) under ambient conditions. A notable ambient condition is rain, which causes signals to scatter in different directions; hence, one should model the communication channel for HALE UAV as a Rayleigh channel. This paper proposes a statistical model that is based on the effect of the telecommunication range on the outage probability in an N-Rayleigh fading channel. The simulation results show that as the telecommunication range increases, the outage probability (Poutage) also increases, whereas when both the telecommunication range and the number of relays increase, Poutage decreases. An issue that has been highlighted in this paper is that, by increasing number of relays from N=1 to N=5 the telecommunication range increases and Poutage about 40% decreases. Moreover, in rainy conditions and with a fixed number of relays, when both the intensity of rainfall and telecommunication range increases, Poutage increases. For example by increasing rate of rain (Rr) from 1mm/h to 100 mm/h, Poutage increases around 30% in 100 Km with two relays