Yoga is a science and a discipline that has been handed down to us by seers and mystics who developed techniques to enhance our physical and mental agility. It is said that the practice of Yoga brings flexibility to the spine and joints, keeps the muscles of the body pliable and youthful, increases circulation of blood and strengthens our internal organs; Yoga is all this and more. Yoga improves the power of concentration, cures constipation, relieves stomach disorders, improves coordination of muscles and reduces body fat, strengthens the mind-body connection, calms and relaxes the mind, enhances self-confidence, strengthens self-discipline and self-resolve, reduces stress and increases vitality and energy throughout the body – in fact it can be presumed that NO other form of known exercise can provide all these benefits at one go. Hence, it would appear that yoga has extensive benefits and can help us to be more balanced, relaxed, focused, efficient and effective – professionally and personally.
In today’s competitive professional world physical fitness can be directly proportional to an individual’s and team’s performances. Technological developments have reduced effort which has impacted people’s physical and mental ability. People are willing to invest in developing professional skills towards advancing one’s career but it is crucial that one needs to support that will a healthy body that is flexible, strong and controlled. In ability to move one’s body with dexterity performances will be lacklustre. Similarly, if one is not able to maintain endurance or stamina for a considerable duration of time, the performance will be diminished. In order to be successful, it is also necessary that a person stays focussed and concentrate with confidence on the task without distraction. Dealing with distractions, adversity and stress is an important component of everybody’s life. So, how can yoga assist in strengthening the mind and body of a person and help him/her perform his/her best?
Yoga is the application of physical postures, control of breath, purification and relaxation of mind body and spiritual principles aimed at bringing greater unity and balance to the mind and body. Yoga teaches a person how to link the mind and body and to come into the present. The breathing techniques in pranayama helps a person to focus on breath, calm the mind and build concentration ability.
Yoga asanas also help to develop the control and concentration of the mind. Being able to hold a posture with steadiness, relaxation and comfort requires that a person is able to focus their mind for an extended period of time. This leads to developing strength and concentration in mind / body and is beneficial to be alert and fit even in competitive scenarios.
Asanas also help to create harmony and balance between the mind and body and help to achieve a healthy body and stable mind, It is all about controlling the mind through physical discipline. Asana aid the efficient functioning of internal organs this leads to an overall positive effect on our body. Yoga Asanas are designed to keep the proper curvature of the spine and to increase its flexibility, develop the function of the cerebellum, the brain centre that controls the body when in motion, improve muscle coordination which improves the grace and fluidity.
YOGA IS FOR EVERYONE. Yoga works on strength, flexibility, balance, agility, endurance, core, and overall strength, among other things. Anybody can benefit hugely by adding yoga to his / her daily regimen. Here’s how Yoga can enhance your life…
Strength: consistently holding up your own body weight will give you more strength than lifting weights
Flexibility: Regular practice of yoga increases flexibility and ease of movement, therefore increasing range of motion. In particular, athletes in sports that require swinging action (tennis, golf, etc.) can benefit greatly. Flexibility in general also helps to prevent injury.
Balance: yoga improves overall balance in everything you do, preventing falls and injury. When you learn how to be soft and go with the flow, you can easily bend more and are less likely to break or fall over.
Endurance: yoga gives you the endurance. When you learn to tune into your body and mind, any profession can be a form of meditation. Yoga teaches how to pace yourself, slow and steady, for the long haul.
Core: Every asana and pranayama in yoga works on your core strength. Strong core is essential for a healthy back and a healthy body.
Stability: Yoga helps strengthen and stabilize the little muscles that other forms of physical workouts miss and are important in protecting your joints and spine.
Recovery: Yoga also helps bounce back after injuries or ailments by tuning into your body and nurturing it with the care it wants and needs.
Most importantly, yoga changes the way you breathe, walk, think and approach everything in life: Your nimbleness with help you build confidence, help you perform better in a professional world and thereby make you successful. Your body will work in tandem with your mind, increasing your flexibility and performance all around.
To all the youngsters, I say, now is the time to set aside just 30 minutes a day and indulge in this ancient form of exercise that can have life-altering benefits for the rest of your lives!
The author of this blog article is Madhuri Reddy, Assistant Professor,Department of Engineering Sciences, International Institute of Information Technology, (I²IT), Pune (www.isquareit.edu.in) (madhurir@isquareit.edu.in)
January 22, 2020
Space weather which is governed by solar activities is the key cause of Sun-Earth interaction system. The Sun-Earth interaction significantly affects the magnetosphere- ionosphere- thermosphere system leading to diverse physical phenomena. The earth’s ionosphere is highly affected with the space weather events such as Coronal mass ejections, geomagnetic storms, solar winds and solar flares. The earth’s ionospheric parameters such as electron density, total electron content (TEC), electron and ion temperature behaves in a varied fashion to the incoming solar flux, electrodynamic drift, which makes its prediction very complicated. These parameters are found to be varying with local time, season, latitude, longitude, solar activity and with the conditions on sun and earth’s magnetic field.
Recent increase in solar activities has resulted in an increased concern of the space weather community. These solar activities can lead to the major catastrophic events. It has been proven that every 11 years or so, the solar activities reaches to a peak; this phenomenon is referred to as solar cycle. And peak observed during these solar cycles is termed as ‘Solar Maximum’. The solar maximum can extend to several years either side of the actual peak and the Earth gets subjected to by intense space weather. When these solar storms released due to severe solar activities are punched on to Earth, it may lead to various exceptional activities as well as major disasters leaving Earth out of electric power for years to come.
Solar Activities
Solar activities refer to any natural phenomenon happening on or in the Sun. These activities may be classified into:
“Solar flares are abrupt bursts in the concentration of solar emission.”
“The solar wind is composed of particles charged with very high energy that are radiated from the sun.”
“A Coronal Mass Ejection (CME) is an immense explosion of solar wind into space.”
“Sunspots are momentary phenomenon on the photosphere of the Sun that appears as dark spots contrast to neighbouring regions.”
“Solar Cycle is a period of 11 years or so where the sunspot number reaches to the peak and solar activities increases.”
“Space Weather are the fluid environmental conditions of space, especially near- Earth Space or the space from the Sun’s atmosphere to the Earth’s atmosphere.”
“A Geomagnetic Storm is an impermanent commotion of the Earth’s magnetosphere caused by the turbulence in space weather.” (Information source: Helios, Goddard Space Flight Centre web link, NASA)
Solar Flares
An abrupt, rapid and severe variation in brightness is termed as a ‘Flare’. When the magnetic energy which has built up in the solar surface is abruptly released, a solar flare takes place. Due to this sudden eruption, radiation is produced across almost the whole electromagnetic spectrum from long wavelength to short wavelength end. The quantity of energy erupted during the emission of solar flares is equivalent to millions of 100 megaton hydrogen bombs bombarding at the same instance. On 1st September 1859, first solar flare was recorded in astronomical records. And the scientists who viewed the large solar flare, separately experimenting were Richard C. Carrington and Richard Hodgson.
Due to this eruption of the magnetic energy, particles including heavy nuclei, protons and electrons are excited and accelerated in the solar environment. The amount of energy observed during mission of a flare is generally on the order of 1027 ergs/sec. And large flares can release energy up to 1032 ergs/sec. This energy released is 10 million times greater than the energy evolved from a volcanic eruption. And on the counterpart, it is less than 1/10th of total amount of energy emitted by the Sun per second.
A solar flare consists of typically three stages. First is the precursor stage, where magnetic energy release is triggered. Soft X-ray emission is observed during this stage. In the second or impulsive stage, electrons and protons are accelerated to energies beyond 1 MeV. During this stage, radio waves, gamma rays and hard X-rays are released. The steady build up and perish of soft X-rays can be observed in the final stage named as decay stage. The existences of these stages can be as tiny as a few seconds or as extensive as an hour.
The frequency of occurrence of these solar flares coincides with the solar cycle of 11 years. During solar minimum, dynamic regions are small and rare, thus the minority solar flares are identified. As the sun reaches its solar maximum, an increase in rate of eruption of solar flares is observed due to increase in the number of active regions on the solar atmosphere. This time sun will reach its peak early 2014. (Information source: flares, Goddard Space Flight Centre web link, NASA)
The author of this blog article is Dr. Rajesh V Chowdhary, Associate Professor, Department of Electronics & Telecommunication, International Institute of Information Technology, (I²IT), Pune (www.isquareit.edu.in) (rajeshv@isquareit.edu.in)
January 22, 2020
Many Space agencies across the globe have launched satellites in particular to observe and understand the changes happening on the surface of the sun. Many instruments specifically for measuring various parameters have been orbited through these space agencies. To understand the dynamics of Sun and the related activities and to derive the affects of these activities on our daily life is the main goal of research carried out by many space agencies & their mission. NASA has dedicated a centre named Space Weather Prediction Centre (SWPC) for maintaining a real time data centre for measuring certain parameters and predicting the occurrence of extreme events. A database of real time monitoring is being updated on their website for every 15minutes. A few agencies have dedicated their work to examine the effect of these solar activities on ‘Interplanetary Magnetic Field’ (IMF) and Geomagnetism of Earth.
Similarly, the Air Force Research Laboratory of USA has established Scintillation Network and Decision Aid (SCINDA), as a set-up of ground based stations that monitor trans-ionospheric signals at the UHF and L Band frequencies. The main purpose of SCINDA is to serve as regional specification and short term forecasts of scintillation occurring onto UHF and L Band frequencies i.e., especially on communication and navigation signals. These scintillations onto signals are caused due to irregular distribution of electron density across the equatorial ionosphere region.
Image Courtesy : K Groves
The SCINDA station gives information of two eminent parameters namely, S4 (Scintillation Index) and TEC (Total Electron Content), which are the measures of how much signal has undergone modulation during transverse and total number of electrons present in a 1-m2 cross-sectional area between satellite and station respectively. S4 is indicated by values ranging from 0 to 1, where ‘0’ represents no modulation in the signal and ‘1’ being 100% modulated signal. ‘Scintillation’ word is characteristically referred to quick amplitude and phase variations in a received electromagnetic wave. The cause may be diffractive when electromagnetic waves are spread in an asymmetrical medium composed of several little changes in the refractive index. The variations in the intensity of the signal are deliberated by the scintillation intensity index S4. Due to solar activities it is obvious that our ionosphere will expand and become more ionized. Thus the solar activity occurrences and evidences can be correlated with SCINDA network data. Thus severe to extreme events impacts on to TEC and S4 can be realized using these stations data. Also, the diurnal, seasonal and annual variations of TEC can be studied for a specific geographic location by post processing SCINDA data.
GPS datasets can also be freely accessed at International GNSS Service Network
https://www.igscb.org/igs-stations
Image Courtesy : IGS
The author of this blog article is Dr. Rajesh V Chowdhary, Associate Professor, Department of Electronics & Telecommunication, International Institute of Information Technology, (I²IT), Pune (www.isquareit.edu.in) (rajeshv@isquareit.edu.in)
Sorting is one of the major problems in Computer Science and Engineering. One needs to sort data in an ascending or a descending order based on problem statement. Efficient sorting is required to optimize the performance of other algorithms such as searching and merging. There are two types of sorting methods viz. Comparison Based Sorting and Non-comparison Based Sorting. In Comparison Based Sorting methods, elements are compared with each other to get the sorted order. In Non-comparison Based Sorting methods, elements are not compared with each other to get sorted order. Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, Heap Sort, and Shell Sort are examples of Comparison Based Sorting methods whereas Radix Sort, Count Sort, and Bucket Sort are examples of Non-comparison Based Sorting methods.
Worst case time complexity of Bubble Sort is O (n2), where n indicates the number of elements to be sorted. It is applicable to small size data and usually is not used to solve problems in real-world applications. The best case and worst case time complexities of Insertion Sort are Ω (n) and O (n2) respectively. These are used in situations when the data to be sorted is small in size and is almost in a sorted order. The best case and worst case time complexities of Selection Sort are Ω (n2) and O (n2) respectively. These too are used when the data size is small. The space complexity of all the above sorting methods is equal to O (1), as these methods don’t require additional memory to get sorted order data. One can choose the above sorting methods when memory requirement is very high.
Merge Sort, Quick Sort, and Heap Sort are efficient sorting methods wherein time complexity is less. Among these sorting methods, Quick Sort is the fastest sorting method but its worst case time complexity is O (n2). Merge Sort and Heap Sort give O (nlogn) time complexity in all cases, i.e., best case, average case, and worst case. Quick Sort is preferred over Merge and Heap Sort if the researcher or developer is confident about balanced partitioning of the data.
Merge Sort is applicable in situations, when data size is very large and is unable to fit in a computer’s main memory like RAM although its space complexity is O (n). If there is a high requirement of memory and one wants to find the smallest or the largest element quickly, Heap Sort is preferred because its space complexity is O (1).
Radix Sort or Bucket Sort is specially used to sort out fixed size integers, fixed size strings and floating points of certain range and leads to worst case time complexity of O (d*(n+b)), where d is number of digits required to represent maximum number from the list, n indicates number of elements to be sorted and b indicates number of buckets required to sort out elements. For integers b=10, as there are 10 digits and for strings b=26, as there are 26 alphabets.
Counting Sort is one of the non-comparison based sorting methods, which runs in linear time, i.e., O (n+k), where n shows number of elements to be sorted and k is the maximum element. It is used when the range of the values is small and the list contains duplicate entries of the elements.
The author of this article is Prof. Ajitkumar Shitole with the Department of Computer Engineering at International Institute of Information Technology, I²IT, Pune
e-commerce is transforming rapidly and with many new technological advancements in the 21st century, the world is going crazy with online shopping. Customers check product availability across various e-commerce stores. Details like product specification star ratings, reviews to choose the right product, etc are all available just a click away. Similarly, vendors are vigilant of their customers’ likings.
Though buying a product online has become easier, it is not yet possible to conduct a detailed manual analysis of all the reviews to discern feature-specific opinions. Presently, in many cases, opinion analysis is done only for the overall reviews and we cannot get the feature specific opinions of the product. So, there is a need to analyze the opinions specified for the different aspects of a product, which can be done by using an ‘Aspect Specific Opinion Analysis’. This analysis is carried out in two steps:
- Aspect Extraction
- Sentiment Classification for that Aspect
Aspect Specific Opinion Analysis is done using recent technologies such as Machine Learning and Natural Language Processing (NLP). Many a times NLP is used to extract features and a Machine Learning Algorithm is trained to predict aspect category.
Applications of aspect specific opinion analysis:
For example, let us consider Mobile Phone Reviews. Major aspect categories can be processor, RAM, battery backup, camera, picture quality, color, screen size, etc. The desired aspect is extracted from a review and then the sentiment about it is determined.
Approaches for aspect extraction:
- Frequency based approach: Frequent noun and noun-noun terms are extracted to predict the aspect. These are selected based on frequency/occurrence count.
- Syntax based approach: Candidate features to predict aspect category are selected based on POS tags or some grammatical rules which show the relationship between words in a sentence. So here we need to study different NLP rules which show relationships between words in a sentence. Then use those applicable rules to extract features to predict aspect category.
- Unsupervised approach: Clustering techniques can be used to group terms which reflect the same aspect.
- Hybrid approach: It can be a combination of many approaches such as, frequency, syntax, supervised, unsupervised.
The above listed approaches work on terms specified in the review. But the same aspect may be represented by using multiple terms. So, there is a need to think about the semantic approaches that will consider rational knowledge while predicting the aspects. One of the semantic solutions for the aspect extraction problem is ontology. The level one nodes in ontology represent aspects, and the following nodes represent opinion terms related to those aspects. As per the applications, depth of the ontology can be decided. Ontologies represent the knowledge of a domain and can be the best semantic solution.
Challenges:
The aspects to extract, may be explicit or implicit.
“The cell phone is overpriced” – The aspect, ‘price’ is specified explicitly.
“It is not affordable”- ‘Price’ is the implicit aspect that is represented by the word ‘affordable’.
The challenge is to identify the implicit aspects.
The another issue is to extract aspects from a sentence containing multiple aspects.
“It is overpriced but the picture quality is good”
In this review sentence, one aspect is, ‘price’ (explicit) and another is ‘camera quality’ (implicit).
The complexity of language and choice of words, make NLP that more challenging and exciting. Currently the areas that are open for research aspect-specific opinion mining are:
- Finding implicit aspects from reviews.
- Extract aspects from sentences containing multiple aspects.
- Semantic strategy for the aspect class prediction.
- Unbalanced data problems.
- Negation handling
The author of this article is Prof. Bhavana Kanawade with the Department of Information Technology at International Institute of Information Technology, I²IT, Pune.
Atmospheric aerosols have been found to affect the Earth’s climate in many characteristic ways. They can affect the energy balance of Earth’s atmosphere system by producing a direct or indirect change in the weather and climate system. The direct interaction of aerosols involves both scattering and absorption of radiation, and the relative importance of these processes depends on their chemical composition, refractive index, and size distribution. The indirect effect of aerosols on climate occurs when cloud optical properties are modified. Thus, the concentration, size and composition of aerosols which can act as cloud condensation nuclei determine the cloud properties, evolution and development of precipitation. Aerosols modify cloud properties and precipitation via a variety of mechanisms with varying and contradicting consequences.
Aerosols’ cloud interaction is speculated to be important to perceptive of the global climate change because clouds take part in a crucial task of controlling incoming radiations as well as outgoing radiation. A large number of studies showed that the anthropogenic aerosols change clouds and their optical properties. Atmospheric aerosols change the concentration and size of the cloud droplets which in turn lead to a change in cloud albedo, its lifetime and thereby affect the precipitation. Also, the reduction in cloud effective radius due to the increase in cloud droplet number concentration (CDNC) leads to the increase in cloud lifetime. The possible repercussion of this process is to decrease the rate of surface evaporation which results in stable and drier atmosphere as a result of the reduction in cloud formation. Anthropogenic aerosols influence mixed-phase clouds in a number of ways and needs comprehensive study to understand the precise phenomenon. A great number of studies were conducted on the possible modification of cloud properties via the interaction with atmospheric aerosol particles, as this may lead to important changes in the Earth’s climate. Biomass burning aerosols have been shown to affect clouds through both microphysical and radiative mechanisms. Burning of agriculture waste and deforestation are the major sources of origin of large particles in the Southern Hemisphere commonly called as ‘biomass burning’. Those aerosols are hygroscopic and can add to the CCN (cloud condensation nuclei). Recent research studies such as satellite analyses have reported persistent correlation between cloud fraction and aerosol optical depth in regions influenced by several aerosols present.
The first indirect effect known as the Twomey effect produces the reduction in cloud effective radius due to the increase in aerosol loading for fixed cloud water path (CWP). Opposite of this effect (i.e. as aerosol loading increases cloud effective radius also increases) were observed over some parts of the world in certain environmental conditions. The Twomey effect and Albrecht effect (i. e. lifetime effect) facilitate cooling of the atmosphere by increasing cloud optical depth (COD) and cloud fraction (CF) respectively. This causes a reduction in the net solar radiation at the top of the atmosphere and hence at the surface. Several other studies have pointed out that the aerosol-cloud interactions are not determined by aerosols alone, but the regional meteorological conditions can play a significant role in this relationship.
Extensive studies were conducted on various mechanisms of cloud properties through the interaction of atmospheric aerosol particles with cloud parameters which further influence the Earth’s climate. It was found that at low AODs, cloud optical depth (COD) increases with increasing AOD while COD decreases with increasing AOD at higher AODs. This increase was attributed to a combination of microphysical and dynamical effects while the decrease was due to the dominance of radiative effects that thin and darken the clouds.
Sandeep Varpe
Department of Engineering Sciences
International Institute of Information Technology (I²IT), Pune