Teaching learning process in ancient ayurvedic text - Charak Samhita

The word Ayurveda consists of two words ‘Ayu’ means life and ‘Veda’ means knowledge. Therefore the word Ayurveda, means knowledge of life i.e. study of life. In Ayurveda the teaching and learning was based on “Gurukula” system of education. A “Gurukula” was a place where a teacher (Guru) and students lived together. There were three ways for obtaining the knowledge i.e. Adhyayanam (Study/learning), Adhyapanam (teaching) and Tadvid Sambhasha (discussions) with the learned persons. Ayurveda suggest that in order to get expertise in any subject one should have the basic knowledge of the concepts. Other teaching and learning methods should be adopted which help to acquire more and accurate knowledge in that subject while practical training should be provided for application of that knowledge. Study of a single science gives only limited understanding, therefore one should also get knowledge of other sciences. To explain different concepts in Ayurveda Acharya Charak has given various methods of teaching and learning. The Roots of most of the current teaching methodology resides in ancient teaching and learning skills. Hence here is an attempt to review various teaching, learning methods used in Charak Samhita. Teaching learning process is very well applied in Charak Samhita. It is the very first school of Ayurveda with various techniques and scholarly approach of subjects to students. Charak Samhita develops its own teaching learning process. It is the most important text in the field of Ayurveda Teaching Learning Process.

Inferring the instability of a dynamical system from the skill of data assimilation exercises

Data assimilation (DA) aims at optimally merging observational data and model outputs to create a coherent statistical and dynamical picture of the system under investigation. Indeed, DA aims at minimizing the effect of observational and model error and at distilling the correct ingredients of its dynamics. DA is of critical importance for the analysis of systems featuring sensitive dependence on the initial conditions, as chaos wins over any finitely accurate knowledge of the state of the system, even in absence of model error. Clearly, the skill of DA is guided by the properties of dynamical system under investigation, as merging optimally observational data and model outputs is harder when strong instabilities are present. In this paper we reverse the usual angle on the problem and show that it is indeed possible to use the skill of DA to infer some basic properties of the tangent space of the system, which may be hard to compute in very high-dimensional systems. Here, we focus our attention on the first Lyapunov exponent and the Kolmogorov–Sinai entropy and perform numerical experiments on the Vissio–Lucarini 2020 model, a recently proposed generalization of the Lorenz 1996 model that is able to describe in a simple yet meaningful way the interplay between dynamical and thermodynamical variables.

Asthma in Morocco: Retrospective Analysis of Case Characteristics reported by Hospital Units

Asthma is a serious public health problem. This study aimed to identify the characteristics of asthma cases reported by Agadir's Souss-Massa Regional Hospital Center (SMRHC). A retrospective analysis was carried out at the SMRHC's pneumology and paediatrics departments in 2019. As data support, reporting records and a data collection worksheet were used. This year, 141 cases were reported. The highest frequencies were observed in February (21.9%) and April (26.6%). Both males and females were affected (sex ratio Male/Female = 0.98). The asthmatics were, on average 40.7 ± 25.1 years old. The majority of the cases are from areas that are easily accessible for medical consultation at the SMRHC. To obtain more accurate knowledge and contribute to the research, related studies should be undertaken on this topic. Our findings, we hope, will act as a foundation for future research into improving the case registration system (digital support) and upgrading patient data in accordance with WHO and GINA guidelines.

Lost Horizon: Quantifying the Effect of Local Topography on Global 21 cm Cosmology Data Analysis

We present an investigation of the horizon and its effect on global 21 cm observations and analysis. We find that the horizon cannot be ignored when modeling low-frequency observations. Even if the sky and antenna beam are known exactly, forward models cannot fully describe the beam-weighted foreground component without accurate knowledge of the horizon. When fitting data to extract the 21 cm signal, a single time-averaged spectrum or independent multi-spectrum fits may be able to compensate for the bias imposed by the horizon. However, these types of fits lack constraining power on the 21 cm signal, leading to large uncertainties on the signal extraction, in some cases larger in magnitude than the 21 cm signal itself. A significant decrease in uncertainty can be achieved by performing multi-spectrum fits in which the spectra are modeled simultaneously with common parameters. The cost of this greatly increased constraining power, however, is that the time dependence of the horizon’s effect, which is more complex than its spectral dependence, must be precisely modeled to achieve a good fit. To aid in modeling the horizon, we present an algorithm and Python package for calculating the horizon profile from a given observation site using elevation data. We also address several practical concerns such as pixelization error, uncertainty in the horizon profile, and foreground obstructions such as surrounding buildings and vegetation. We demonstrate that our training-set-based analysis pipeline can account for all of these factors to model the horizon well enough to precisely extract the 21 cm signal from simulated observations.

Using drone technology for preserving the economic sustainability of the agricultural holdings

In the present era, precision agriculture, through the set of innovative technologies that it uses, allows to effectively manage the terrain, machinery, and input acquisition, considering the specific natural variation of the environmental conditions. One of such innovations is the unmanned aerial vehicle (drone) technology which has gained popularity and has been widely used in adopting efficient strategies for preserving the economic sustainability of the agricultural holdings. The need for an efficient management, the complex climatic, technological, economic, and biological changes that have recently occurred at the level of agro-systems impose a continuous and accurate knowledge of the growing production resources and the vegetation state in cultures. In this context, the article investigates a series of particularities regarding the use of geospatial and informational technology in the process of taking, storing, analysing, and interpreting them to optimize inputs, considering the state of the crops and the degree of soil supply in each relatively homogeneous area of the terrain..

Nonlinear stochastic filtration of satellite measurements

Existing methods for processing satellite measurements are based on the use of either the least squares method in different versions, or with the known model of motion of an object – various modifications of the Kalman filter. At the same time, the Kalman approach is more accurate, since it takes into account the dynamics of the movement of the object and the history of estimates, but its significant drawback is the need for a priori knowledge of the equations of motion of the object. In this regard, a new approach is proposed to assess the navigation parameters of the object by satellite measurements. On the one hand, this approach takes into account the dynamic nature of motion parameters and the history of estimates, and on the other hand, free from restriction in the form of accurate knowledge of the equations of motion of an object. The effectiveness of the considered approach in comparison with the existing traditional methods of processing satellite measurements is confirmed by the results of numerical modeling.

(Over)Estimating Shared Cultural Knowledge: Evidence for early use of a knowledge prediction heuristic in children

<p>Both adults and children accurately and efficiently predict what other people know, despite interacting with a diverse range of individuals who each have different knowledge sets. To reduce the cognitive cost of predicting each individual’s knowledge, there is evidence that we use heuristics to make generalisable predictions about the way specific kinds of knowledge are shared with others. Yet, little research examines the function of a knowledge prediction heuristic, the input needed to produce accurate knowledge predictions, or changes across development. I propose that children use a heuristic to predict others’ knowledge, and that this heuristic functions by considering the type of knowledge being predicted, and characteristics of the individual whose knowledge is being predicted. Chapter 2 demonstrates that 3- to 6-year-old children accurately and selectively predict who shares different pieces of their knowledge. Children also predict knowledge accurately in a third-party task, providing evidence for the use of a generalisable heuristic rather than simple associations or personal experience. Chapter 3 and Chapter 4 demonstrate knowledge overestimation errors, predicted by the heuristic I propose. 4-year-olds, but not 6-year-olds, overattribute knowledge to others if the knowledge item being predicted is an example of a cultural knowledge item (typically shared with strangers from the same social groups). Yet, even 4-year-olds do not make this over-attribution error when predicting an example of a typically episodic knowledge item (not typically shared with any strangers). Chapter 4 provides initial evidence that feelings of closeness or shared episodic knowledge with a partner (but not simply shared group membership) decrease 4- and 6-year-olds consideration of this partner’s perspective. Taken together, these findings provide evidence for an early-emerging knowledge prediction heuristic which considers the type of knowledge being predicted and characteristics of the individual whose knowledge is being predicted to facilitate accurate yet efficient knowledge predictions.</p>

Contracting COVID-19: a longitudinal investigation of the impact of beliefs and knowledge

Recent work has found that an individual’s beliefs and personal characteristics can impact perceptions of and responses to the COVID-19 pandemic. Certain individuals—such as those who are politically conservative or who endorse conspiracy theories—are less likely to engage in preventative behaviors like social distancing. The current research aims to address whether these individual differences not only affect people’s reactions to the pandemic, but also their actual likelihood of contracting COVID-19. In the early months of the pandemic, U.S. participants responded to a variety of individual difference measures as well as questions specific to the pandemic itself. Four months later, 2120 of these participants responded with whether they had contracted COVID-19. Nearly all of our included individual difference measures significantly predicted whether a person reported testing positive for the virus in this four-month period. Additional analyses revealed that all of these relationships were primarily mediated by whether participants held accurate knowledge about COVID-19. These findings offer useful insights for developing more effective interventions aimed at slowing the spread of both COVID-19 and future diseases. Moreover, some findings offer critical tests of the validity of such theoretical frameworks as those concerning conspiratorial ideation and disgust sensitivity within a real-world context.

(Over)Estimating Shared Cultural Knowledge: Evidence for early use of a knowledge prediction heuristic in children

<p>Both adults and children accurately and efficiently predict what other people know, despite interacting with a diverse range of individuals who each have different knowledge sets. To reduce the cognitive cost of predicting each individual’s knowledge, there is evidence that we use heuristics to make generalisable predictions about the way specific kinds of knowledge are shared with others. Yet, little research examines the function of a knowledge prediction heuristic, the input needed to produce accurate knowledge predictions, or changes across development. I propose that children use a heuristic to predict others’ knowledge, and that this heuristic functions by considering the type of knowledge being predicted, and characteristics of the individual whose knowledge is being predicted. Chapter 2 demonstrates that 3- to 6-year-old children accurately and selectively predict who shares different pieces of their knowledge. Children also predict knowledge accurately in a third-party task, providing evidence for the use of a generalisable heuristic rather than simple associations or personal experience. Chapter 3 and Chapter 4 demonstrate knowledge overestimation errors, predicted by the heuristic I propose. 4-year-olds, but not 6-year-olds, overattribute knowledge to others if the knowledge item being predicted is an example of a cultural knowledge item (typically shared with strangers from the same social groups). Yet, even 4-year-olds do not make this over-attribution error when predicting an example of a typically episodic knowledge item (not typically shared with any strangers). Chapter 4 provides initial evidence that feelings of closeness or shared episodic knowledge with a partner (but not simply shared group membership) decrease 4- and 6-year-olds consideration of this partner’s perspective. Taken together, these findings provide evidence for an early-emerging knowledge prediction heuristic which considers the type of knowledge being predicted and characteristics of the individual whose knowledge is being predicted to facilitate accurate yet efficient knowledge predictions.</p>

Overuse-related instability of the elbow: the role of CT-arthrography

The elbow is a complex joint whose biomechanical function is granted by the interplay and synergy of various anatomical structures. Articular stability is achieved by both static and dynamic constraints, which consist of osseous as well as soft-tissue components. Injuries determining instability frequently involve several of these structures. Therefore, accurate knowledge of regional anatomy and imaging findings is fundamental for a precise diagnosis and an appropriate clinical management of elbow instability. This review focuses particularly on the varied appearance of overuse-related elbow injuries at CT-arthrography.