SciGaze Group

Introduction Environmental DNA (eDNA) is the genetic information or material released by an organism into its surroundings through activities like skin shedding, mucus, urine or other secretions. These materials contain DNA fragments that can be found in soil, water or air samples. The concept of eDNA is based on the fact that organisms constantly leave organic traces behind them. By analysing the eDNA, researchers can detect the presence of any species in that area. This method is a valuable technique for biodiversity monitoring and ecological research.    Unlike traditional methods that involve direct observation or capturing of organisms, eDNA analysis allows researchers to study biodiversity without directly interacting with the species. This non-invasive approach is particularly useful for studying elusive or endangered species. eDNA analysis has been applied in various ecosystems, including freshwater, marine, and terrestrial environments. It is used to study aquatic org...

Introduction: In recent years, neuroimaging has been a research attraction, especially due to fMRI (Functional Magnetic Resonance Imaging) advancements. The fMRI data comprises of often several individuals. This data is used by researchers to study the association between the cognitive states of an individual and the underlying brain activities. The fMRI data used for neuroimaging is ideally suited for deep learning applications. The large, structured datasets of fMRI can be used for representation-learning methods of the DL. Generally, DL can be defined as a learning method with multiple levels of abstraction, where at each level the input data is transformed by a simple non-linear function which enables the model to recognize complex patterns. With higher-level representation, DL methods can associate a target variable with variable patterns in input data. Also, DL techniques can independently acquire these transformations from the data, eliminating the need for a comprehensive preex...

Introduction Our current generation is facing the challenges of climate change and global warming. The series of feedback events due to the constant degradation of the environment resulted in anthropogenic defaunation and declination in the quality of the entire ecosystem. For centuries, humans have been keeping a watch on species using various methods like drawings, writings and paintings. Then photographs and videos were introduced to study and track various species. The introduction of video tracking played an impactful role in studying marine biodiversity. Oceans cover 71% of the Earth, which is around 361 million sq km. Monitoring marine species has been a challenge for researchers for decades. Ocean provides a home to a wide range of species, some are even yet to be discovered. Video surveys are making things easy for the researchers. They have the capability to monitor underwater activities with much greater accuracy and precision. These surveys can search for species and mo...

Introduction Since the 1960s, after the successful development (at least with respect to the existing technologies of that time) of the first perceptron by Frank Rosenblatt, researchers around the world have been trying to make computers more brain-like. In other words, they are constantly trying to develop programs and hardware that work much like our brains. However, creating a machine that thinks and programs like a human brain is still impossible, given the limitations of present technologies. But, we are successful in this direction up to an agreeable extent. The perceptron developed by Rosenblatt can be considered one of the first implementations of neural networks. It was a huge hardware model, rather than a Python program like today's perceptrons.  Rosenblatt's perceptron was a simple binary input-output system, with various limitations. It was not trainable for different patterns, rather but for simple image recognization. The major drawback was its inability to perfo...

What is a Gravitational Wave? Albert Einstein in his theory of relativity predicted the existence of gravitational waves. The general theory of relativity stated that the mass bends space-time curvature, creating gravity and space-time tells mass how to move. Space-time fabric is a four-dimensional quantity including the three normal dimensions and a fourth dimension of time!  Consider Earth as a ball with a specific mass and space-time as an elastic sheet of rubber (or a trampoline). If we place Earth at the center of this sheet, then it will obviously bend the sheet creating a curvature. Now, place a smaller ball with less mass, and call it the Moon. If we place the moon on the sheet, then will not directly collide with the Earth, rather it will travel in rotational motion around the Earth, before colliding. This simple movement of the smaller ball (Moon) revolving around the larger ball (Earth) explains the formation of gravity.  We all know, for any binary system in the un...

What is Gaxaly Morphology? Galaxy morphology refers to the study and classification of the shapes, structures, and visual appearances of galaxies. It is a fundamental aspect of observational astronomy, as it provides valuable insights into the formation, evolution, and dynamics of galaxies. The study of galaxy morphology dates back to the pioneering work of astronomers like Edwin Hubble, who developed the Hubble morphological classification system in the 1920s. Hubble's classification scheme, known as the Hubble sequence or tuning fork diagram, categorized galaxies into different types based on their visual characteristics. This system remains widely used today as a basic framework for galaxy classification. Galaxies exhibit a remarkable diversity of morphologies, ranging from smooth and featureless elliptical galaxies to majestic spiral galaxies with well-defined arms, to irregular galaxies lacking a distinct structure. Some of the common morphological features observed in galaxie...

What is JWST? The James Webb Space Telescope (JWST) is a highly anticipated and technologically advanced space observatory to revolutionize our understanding of the universe. Equipped with a massive segmented primary mirror, the JWST is designed to observe infrared light, allowing it to penetrate dust clouds and reveal hidden celestial objects and events. Its advanced suite of scientific instruments will enable scientists to study distant galaxies, star formation, exoplanets, and more, with remarkable clarity and sensitivity. The images produced by the JWST are expected to surpass those of its predecessor, offering intricate details and revealing the universe in a completely new light.  By capturing stunning visuals of distant galaxies, stellar nurseries, and other cosmic wonders, these images will help astronomers unravel the mysteries of our universe, deepen our knowledge of its origins, and expand our understanding of the fundamental laws that govern it. Recently, researchers ha...

Introduction : Since the discovery of pulsars in 1967, pulsar searching has evolved significantly. Modern surveys employ high-performance computing and advanced signal processing algorithms to detect weak pulsar signals amidst radio frequency interference and binary systems. However, the final stage of selecting credible pulsar candidates still relies on human judgment, which can be time-consuming and inefficient for large-scale surveys producing millions of candidates. Large-scale pulsar surveys have greatly increased our knowledge of pulsars and their properties. Future surveys will utilize next-generation radio telescopes like LOFAR, FAST, and SKA, benefiting from their large collecting areas and wide fields of view. The sheer number of pulsar candidates detected by these instruments necessitates multi-person or machine-based candidate selection. In some cases, machine solutions have been developed, such as candidate ranking based on likelihoods or sorting based on similarity scores...

Introduction Automated data mining has emerged as a highly valuable approach to knowledge discovery in various subdisciplines within astronomy. It has revolutionized the way astronomers analyze and interpret vast amounts of data, enabling them to uncover hidden patterns, make predictions, and generate new insights. While data mining techniques have been widely adopted across the field, there is a noticeable shift in the discussion towards placing greater emphasis on machine learning (ML) and, to a lesser extent, artificial intelligence (AI). Data mining encompasses the processes of extracting useful information and knowledge from large datasets. In astronomy, where massive volumes of data are generated by telescopes, satellites, and other astronomical instruments, automated data mining techniques have proven indispensable. By leveraging computational algorithms, astronomers can uncover valuable insights and discoveries that would be challenging, if not impossible, to achieve through tr...

Introduction AI, or artificial intelligence, is a technology that can simulate human intelligence, learning, and decision-making processes. It has been applied in various industries, including aviation, and has the potential to revolutionize the way the industry operates. AI can be used to automate repetitive tasks, process large amounts of data, and make accurate predictions, leading to increased safety, efficiency, and cost savings. In the aviation industry, AI can be used in various ways, including aircraft maintenance, flight planning, air traffic management, and customer experience. By analyzing data from aircraft sensors, AI can predict when aircraft parts will need maintenance, reducing downtime and costs. It can also optimize flight routes based on weather, traffic, and other factors, reducing fuel consumption and emissions. Additionally, AI can help air traffic controllers manage air traffic flow more efficiently and safely, reducing delays and improving safety. AI can also en...