Latest news with #Gauss
New Indian Express
4 days ago
- Science
- New Indian Express
Study sheds light on formation of stars, galaxies
THIRUVANANTHAPURAM: Massive protostars in our galaxy, that would later evolve to have mass of over 8-10 times that of the Sun, have remained an enigma for astrophysicists for decades. For the first time in the world, a team of researchers at the Indian Institute of Space Science & Technology (IIST), Thiruvananthapuram, in association with international scientists, have detected and measured magnetism near an infant massive star. The finding opens an exciting window into the understanding of how massive stars form, which later go on to shape entire galaxies. A protostar is the earliest known stage of a star that is beginning to form. The study was carried out on protostar IRAS 18162-2048, located 4,500 light years away using the National Radio Astronomy Observatory's Karl G Jansky Very Large Array in the US. 'The Astrophysical Journal Letters' brought out by the American Astronomical Society has published the study in detail. The researchers detected radio emission, with a special property known as circular polarisation near IRAS 18162-2048. The emission offers the first direct clue to the strength of magnetic fields in the immediate surroundings of a forming massive star. While strong magnetic fields have been observed earlier in low-mass protostars that go on to form stars like the Sun, measuring such fields around massive protostars has been a tough task until now. The new data allowed researchers to infer the magnetic field near the protostar to be about 20-35 Gauss, roughly 100 times stronger than Earth's magnetic field.
Yahoo
08-07-2025
- Business
- Yahoo
Risk, Reward, and Resilience: Building Insurance Primitives in DeFi
Insurance stands as one of finance's foundational primitives—an essential scaffold that undergirds every major market from commodities to credit. Since the 1600s, no vibrant financial ecosystem has thrived without a robust insurance mechanism: market participants demand quantifiable measures of risk before committing capital. Yet in decentralized finance(DeFi)'s first wave—lending, exchanges, derivatives—insurance remained an afterthought, implemented in rudimentary forms or absent altogether. As DeFi targets its next inflection point, embedding sophisticated, institution-grade insurance models will be critical to unlocking deep pools of capital and delivering enduring resilience. Modern insurance has a long history. In the 16th century, Gerolamo Cardano's early treatises on games of chance pioneered probabilistic thinking, framing uncertainty in mathematical terms (eventually he would give his name to today's blockchain). In the mid-17th century, an epochal correspondence between Blaise Pascal and Pierre de Fermat laid the empirical bedrock for probability theory, transforming chance from mysticism into a quantifiable science. By the 19th century, Carl Friedrich Gauss's formalization of the normal distribution enabled statisticians to model deviations around an expected value systematically—a breakthrough instrumental to actuarial science. At the dawn of the 20th century, Louis Bachelier's seminal work on the random walk of asset prices presaged modern quantitative finance, informing everything from option pricing to risk management. Later in that century, Harry Markowitz's portfolio theory reframed diversification as a quantitative process, offering a rigorous framework for balancing risk and return. The Black-Scholes-Merton model further advanced the field by providing a tractable means to derive implied volatilities and price options—cornerstones of modern derivatives markets. In recent decades, innovators like Paul Embrechts and Philippe Artzner enriched risk theory with copula statistical models and coherent risk measures, enabling the systematic capture of extreme tail risks and systemic dependencies. Insurance requires four core prerequisites: diversified risk vectors, a risk premium exceeding capital costs, scalable pools of capital, and quantifiable exposures. DeFi clearly offers quantifiable hazards—protocol exploits, oracle manipulations, governance attacks—but challenges to insurability remain. Early DeFi insurance initiatives struggled with limited actuarial sophistication, untested capital structures, and prohibitive premiums driven by the high opportunity cost of capital. Moreover, DeFi's rapid innovation cycle creates a shifting threat landscape: vulnerabilities in one protocol seldom translate neatly to another, and the speed of code changes outpaces traditional underwriters' capacity to assess risk. Overcoming these obstacles will require next-generation insurance architectures that can adapt dynamically to evolving hazard profiles. High Price Insurance Capital At the heart of any insurance construct lies the cost of capital. DeFi insurance pools typically accept ETH, BTC, or stablecoins—assets that themselves generate on-chain yield via staking, lending, or liquidity provisions. Insurers must therefore offer returns above these native yields to attract underwriters, driving premiums upward. This results in a classic Catch-22: high premiums deter protocol teams, yet low capital costs undermine coverage capacity and solvent reserves. To break this impasse, market architects must tap alternative capital sources. Institutional investors—pension funds, endowments, hedge funds—possess vast pools of capital with long-term horizons. By designing insurance products aligned to these investors' risk-return benchmarks (e.g., structured tranches offering defined upside in exchange for taking first-loss positions), DeFi insurance constructs can achieve a sustainable cost of capital, balancing affordability with solvency. Jakob Bernoulli's law of large numbers underpins classical insurance: as policy counts grow, actual loss ratios converge toward expected values, enabling precise actuarial pricing. Mortality tables by Edmond Halley and Abraham de Moivre epitomize this principle, translating population statistics into dependable premiums. DeFi's nascent ecosystem, however, features only a finite—and often correlated—set of protocols. Catastrophic events such as multi-protocol oracle manipulations expose systemic dependencies that violate independence assumptions. Instead of relying solely on volume, DeFi insurance must employ layered diversification: reinsurance agreements across independent risk pools, capital tranching to allocate losses by seniority, and parametric triggers that automate coverage payouts based on on-chain metrics (e.g., price slippage thresholds, oracle deviation tolerances). Such architectures can approximate the smoothing benefits achieved by traditional insurers. Quantitative risk modeling in DeFi remains in its formative stages. With only a handful of years of historical data and immense heterogeneity across smart-contract platforms, extrapolating risk from one protocol to another carries significant uncertainty. Past exploits—on Venus, Bancor or Compound—yield forensic insights but limited predictive power for novel vulnerabilities in emerging protocols such as Aave v3 or Uniswap v4. Building robust DeFi risk frameworks demands hybrid approaches: integrating on-chain analytics for real-time exposure tracking, formal security verification of smart-contract code, oracles for external event validation, and comprehensive stress-tests against simulated attack vectors. Machine-learning models can augment these methods—clustering protocols by code patterns, transaction behaviors, or governance structures—yet must be guarded against overfitting sparse data. Collaborative risk consortia, where protocol teams and insurers share anonymized data on exploits and failure modes, could create a richer data foundation for next-generation models. At its current scale, DeFi beckons for a reliable insurance primitive. Embedding sophisticated, scalable insurance solutions will not only shield capital but also translate abstract hazards—flash loan attacks, governance exploits, oracle failures—into measurable financial exposures. By aligning product design with institutional risk appetites, leveraging layered diversification, and advancing quantitative risk models, a vibrant DeFi insurance market could unlock previously inaccessible capital pools. Such an ecosystem promises deeper liquidity, enhanced counterparty confidence, and broader participation—from family offices to sovereign wealth funds—transforming DeFi from an experimental frontier into a cornerstone of global finance. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Time of India
03-06-2025
- Science
- Time of India
UGC NET Physics 2025: Top 20+ most repeated questions for UGC NET Physics exam preparation 2025
UGC NET Physics 2025 Exam: Prepare well for the upcoming UGC NET Physics Exam 2025. Below we've compiled 20+ most repeated questions to help you practice better. The UGC NET 2025 exam will be held in 2025 and lasts for 3 hours. It has three sections — Part A, Part B, and Part C. Part A checks your research aptitude and general science knowledge. Parts B and C test your understanding of core and advanced physics topics. These include Mathematical Methods of Physics, Classical Mechanics, Electromagnetic Theory, Quantum Mechanics, Thermodynamics, Statistical Physics, Electronics, and Experimental Methods. UGC NET Physics 2025 Exam Pattern and Marking Scheme The UGC NET Physics 2025 exam pattern and marking scheme are as follows: The UGC NET exam consists of two papers: Paper 1 and Paper 2. Both papers are conducted in Computer Based Test (CBT) mode. The total duration for both papers combined is 3 hours (180 minutes) without any break. Paper 1 is common for all subjects and contains 50 multiple-choice questions (MCQs). Paper 2 is subject-specific (Physics in this case) and contains 100 MCQs. Each question in both papers carries 2 marks. The medium of the question papers is English and Hindi. UGC NET 2025 Exam: Marking Scheme Each correct answer awards 2 marks. There is no negative marking for incorrect answers. Candidates are encouraged to attempt all questions as no marks are deducted for wrong or unanswered questions. The aggregate score is the sum of marks obtained in both Paper 1 and Paper 2. Top Repeated UGC NET Physics Questions (2025) Below are the required chapter-wise Top Repeated UGC NET Physics Questions 2025 listed: 1. Classical Mechanics State and prove the work-energy theorem. Derive the equation of motion for a simple harmonic oscillator. Explain the concept of Lagrangian and derive the Euler-Lagrange equation. What is the principle of least action? Illustrate with an example. Derive the equations of motion for a particle under a central force. 2. Electromagnetic Theory Derive Maxwell's equations in differential form. Explain the concept of displacement current and its significance. State and prove Gauss's law for electric fields. Derive the wave equation for electromagnetic waves in free space. Explain the boundary conditions for electric and magnetic fields at the interface of two media. 3. Quantum Mechanics State and explain the Heisenberg uncertainty principle. Derive the time-independent Schrödinger equation for a particle in a one-dimensional infinite potential well. Explain the concept of operators in quantum mechanics. What is the significance of the wave function? Write down its properties. Solve the Schrödinger equation for a harmonic oscillator. 4. Thermodynamics and Statistical Mechanics State and prove the first law of thermodynamics. Explain the concept of entropy and the second law of thermodynamics. Derive the Maxwell-Boltzmann distribution law. What is the partition function? Explain its importance in statistical mechanics. Derive the expression for the efficiency of a Carnot engine. 5. Solid State Physics Explain the band theory of solids. What is a semiconductor? Explain intrinsic and extrinsic semiconductors. Derive the expression for the density of states in a three-dimensional free electron gas. Explain the concept of superconductivity and Meissner effect. 6. Nuclear and Particle Physics Explain the liquid drop model of the nucleus. What is radioactive decay? Derive the decay law. Describe the working principle of a nuclear reactor. Explain the concept of quarks and leptons. Preparation Tips for UGC NET Physics 2025 Exam Students, to prepare well for the UGC NET Physics 2025 exam, follow a structured study plan. Study 4–6 hours daily with full focus. Start with core topics like Mathematical Methods of Physics, Classical Mechanics, Electromagnetic Theory, Quantum Mechanics, and Thermodynamics & Statistical Physics. Cover key chapters such as vector calculus, Lagrangian mechanics, Maxwell's equations, Schrödinger equation, and statistical laws. Use the initial weeks to understand basic concepts and solve questions from Part A. Then move on to advanced topics in Parts B and C. These include Atomic & Molecular Physics, Condensed Matter Physics, and Nuclear Physics. Revise regularly and solve previous year papers and mock tests to improve speed and accuracy. Take short breaks during study sessions. Do weekly self-assessments to track your progress. Focus more on weak areas for complete preparation.
Yahoo
07-04-2025
- Business
- Yahoo
airBaltic CEO 'dismissed' from Latvian airline
Latvian Transport Minister Atis Svinka said on Monday that Riga-based carrier airBaltic had parted ways with its German-born longtime CEO Martin Gauss. "I inform you that Martin Gauss has been dismissed from his position. It is important for me to see results," Svinka said on social network X. The Latvian state has a majority stake in the carrier, which last month said it had posted a net loss of 118 million euros ($129 million) last year. "AirBaltic is a company of national importance, and it must be able to independently develop and adapt to external conditions," Svinka added. AirBaltic announced in January that additional engine maintenance demands would force the cancellation of more than 4,600 flights this year. The airline flies exclusively Airbus A220-300, which are equipped with modern fuel efficient Pratt & Whitney engines that have been found to be susceptible to microscopic cracks and require replacement ahead of schedule. Parts shortages mean several aircraft have to be taken out of service during the peak summer travel season. The transport ministry said in a statement that the decision regarding Gauss was taken by the supervisory board after Monday's shareholder meeting. The ministry said it "expressed its loss of confidence in" Gauss at the meeting, "calling for a vote to oust him from the job". Gauss had been with the airline for more than a decade. "Today, my journey as CEO of @airBaltic comes to an end," he wrote on X. "The Latvian government, as majority shareholder, has withdrawn its trust — and the Supervisory Board has acted accordingly," he said. The airline said in a statement on X that management board member and chief operating officer Pauls Calitis would take over as interim CEO. The chairman of the supervisory board, Andrejs Martinovs, said "core objectives remain unchanged, and airBaltic continues to implement its strategy and move forward toward a potential IPO". bur-amj/rl Sign in to access your portfolio
Reuters
07-04-2025
- Business
- Reuters
AirBaltic CEO Martin Gauss steps down
LONDON, April 7 (Reuters) - The Chief Executive Officer for Latvian carrier airBaltic Martin Gauss has stepped down from his role, the company said on Monday, as the airline has repeatedly pushed back a planned initial public offering. Gauss, who is German, has held the position of CEO since 2011. The airline recently sold a 10% stake to German airline group Lufthansa ( opens new tab as it said it struggled with engine delivery delays and sought to push back its planned IPO to 2026. "Our core objectives remain unchanged, and airBaltic continues to implement its strategy and move forward toward a potential IPO," said Andrejs Martinovs, the chairman of the primarily state-owned airline's supervisory board. Pauls Calitis, the current chief operations officer, will take over as interim CEO, the company said in a statement.
