Use of Strengthening Cementation When Civil Structures on Karsted Territories Construction

The paper describes measures on strengthening cementation of karsted territories both when erection of new houses and in failure situations. The result of strengthening cementation is water resistant strong base preventing development of karst-suffosion processes

Strengthening of Clay Soils of Buildings Bases under Reconstruction by Means of Alkalization

The experience of use of natrium hydroxide solutions for strengthening of clay soils of buildings bases under reconstruction in city Ufa is described. The method applied allows to increase the design soil resistance from 0.16 to 0.4 MPa and decrease the labour expenditures at site by 40 %

TinyML based Deep Learning Model for Activity Detection

Our physical and emotional well-being are directly impacted by our body positions. In addition to promoting a confident, upright image, maintaining good body posture during various activities also ensures that our musculoskeletal system is properly aligned. On the other side, bad posture can result in a number of musculoskeletal conditions, discomfort, and reduced productivity. Accurate systems that can detect posture in real time, activity detection, are required due to the rising use of wearable technology and the growing interest in health and fitness tracking. The goal of this project is to create a TinyML model for wearable activity detection that will allow users to assess their posture and make necessary corrections in order to improve their health and general well-being. The project intends to contribute to the creation of useful posture detection technologies that can be quickly implemented on wearable devices for widespread usage by leveraging machine learning algorithms and wearable sensor data. For reliable posture categorization, the model architecture combines deep neural networks (DNN) and LSTM layers. With the development and implementation of the TinyML model, a significant decrease in the model's power consumption, memory, and latency was achieved without any compromise in the accuracy. This work can be used in the fields of health, wellness, rehabilitation, corporate life, sports and fitness to keep track of calories burned, activity duration, distance traveled, posture analysis, and real-time tracking

Growth Kinetics and Production of Glucose Oxidase Using Aspergillus niger NRRL 326

In this paper, we demonstrate the substrate inhibition phenomena for growth kinetics of Aspergillus niger NRRL 326 grown on sucrose during glucose oxidase production. The initial set of experiments were carried out using three different substrates, viz., glucose, sucrose and raffinose of which it was observed that sucrose serves better for higher production of glucose oxidase. Experiments involving sensitivity studies conveyed that substrate inhibition became predominant when sucrose mass concentration was above γ = 30 g L-1 in the cultivation medium. The later part of the work was dovetailed towards validation of substrate inhibited growth kinetics with established models such as Haldane, Andrews, Luong, Han-Levenspiel and Aiba. Finally, it was observed that none of the classical models explains the kinetics well

Growth Kinetics and Production of Glucose Oxidase Using Aspergillus niger NRRL 326

In this paper, we demonstrate the substrate inhibition phenomena for growth kinetics of Aspergillus niger NRRL 326 grown on sucrose during glucose oxidase production. The initial set of experiments were carried out using three different substrates, viz., glucose, sucrose and raffinose of which it was observed that sucrose serves better for higher production of glucose oxidase. Experiments involving sensitivity studies conveyed that substrate inhibition became predominant when sucrose mass concentration was above γ = 30 g L-1 in the cultivation medium. The later part of the work was dovetailed towards validation of substrate inhibited growth kinetics with established models such as Haldane, Andrews, Luong, Han-Levenspiel and Aiba. Finally, it was observed that none of the classical models explains the kinetics well

Differentiating Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features from Classic Papillary Thyroid Carcinoma: Analysis of Cytomorphologic Descriptions Using a Novel Machine-Learning Approach.

Background:Recent studies show various cytomorphologic features that can assist in the differentiation of classic papillary thyroid carcinoma (cPTC) from noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Differentiating these two entities changes the clinical management significantly. We evaluated the performance of support vector machine (SVM), a machine learning algorithm, in differentiating cases of NIFTP and encapsulated follicular variant of papillary thyroid carcinoma with no capsular or lymphovascular invasion (EFVPTC) from cases of cPTC with the use of microscopic descriptions. SVM is a supervised learning algorithm used in classification problems. It assigns the input data to one of two categories by building a model based on a set of training examples (learning) and then using that learned model to classify new examples. Methods:Surgical pathology cases with the diagnosis of cPTC, NIFTP, and EFVPTC, were obtained from the laboratory information system. Only cases with existing fine-needle aspiration matching the tumor and available microscopic description were included. NIFTP cases with ipsilateral micro-PTC were excluded. The final cohort consisted of 59 cases (29 cPTCs and 30 NIFTP/EFVPTCs). Results:SVM successfully differentiated cPTC from NIFTP/EFVPTC 76.05 ± 0.96% of times (above chance, P \u3c 0.05) with the sensitivity of 72.6% and specificity of 81.6% in detecting cPTC. Conclusions:This machine learning algorithm was successful in distinguishing NIFTP/EFVPTC from cPTC. Our results are compatible with the prior studies, which show cytologic features are helpful in differentiating these two entities. Furthermore, this study shows the power and potential of this approach for clinical use and in developing data-driven scoring systems, which can guide cytopathology and surgical pathology diagnosis

Endoscopic resection of sinonasal inverted papilloma: a multivariate retrospective analysis of factors affecting recurrence and persistence

Sinonasal inverted papilloma (IP) is the most common benign epithelial tumor in the nasal cavity and paranasal sinuses, with a worldwide incidence between 0.6 and 1.5/100 000 persons per year. However, only a few studies have investigated patient-dependent factors related to IP recurrence and persistence. According to available evidence, these factors are still debated, and results are contradictory. In this multicenter retrospective study, we analyzed the clinical records of 130 patients who were surgically treated for sinonasal IP to evaluate the factors affecting recurrence and persistence of IP and compared the curative rates of different surgical approaches. Our analysis showed that IP recurrence is strongly related to specific risk factors including incomplete surgical removal, stage of disease, site of the lesion, surgical technique, and malignancy rate. In conclusion, the recurrence of IP may be affected by several risk factors; these factors must be carefully considered during clinical evaluation and especially during the follow-up of patients with IP

Assessment of Marine Weather forecasts over the Indian Sector of Southern Ocean

The Southern Ocean (SO) is one of the important regions where significant processes and feedbacks of the Earth\u27s climate take place. Expeditions to the SO provide useful data for improving global weather/climate simulations and understanding many processes. Some of the uncertainties in these weather/climate models arise during the first few days of simulation/forecast and do not grow much further. NCMRWF issued real-time five day weather forecasts of mean sea level pressure, surface winds, winds at 500 hPa & 850 hPa and rainfall, daily to NCAOR to provide guidance for their expedition to Indian sector of SO during the austral summer of 2014–2015. Evaluation of the skill of these forecasts indicates possible error growth in the atmospheric model at shorter time scales. The error growth is assessed using the model analysis/reanalysis, satellite data and observations made during the expedition. The observed variability of sub-seasonal rainfall associated with mid-latitude systems is seen to exhibit eastward propagations and are well reproduced in the model forecasts. All cyclonic disturbances including the sub-polar lows and tropical cyclones that occurred during this period were well captured in the model forecasts. Overall, this model performs reasonably well over the Indian sector of the SO in medium range time scale