International Young Scientist Awards

 Carbon-neutral agriculture focuses on balancing greenhouse gas emissions from farming activities with carbon removal strategies, aiming to achieve net-zero emissions while maintaining productivity and food security. A central pillar of this approach is soil carbon sequestration , the process of capturing atmospheric carbon dioxide and storing it as organic carbon in agricultural soils. Healthy soils act as major carbon sinks, helping to mitigate climate change while improving soil fertility, structure, water retention, and microbial activity. Practices such as conservation tillage, cover cropping, crop rotation, agroforestry, organic amendments, and biochar application enhance soil organic carbon by reducing carbon losses and increasing carbon inputs. These methods not only lower emissions from intensive land use but also increase resilience to climate extremes such as droughts and floods. Precision agriculture and digital monitoring tools further support carbon-neutral goals by ...

Machine learning (ML) has become a transformative force in statistical research, reshaping how data are analyzed, interpreted, and applied across diverse scientific domains. By integrating computational algorithms with classical statistical theory, machine learning enables researchers to uncover complex patterns, model nonlinear relationships, and make accurate predictions from large and high-dimensional datasets that were previously difficult to analyze using traditional methods alone. In statistical research, machine learning enhances exploratory data analysis, model selection, and inference. Techniques such as supervised learning (e.g., regression, classification, and ensemble methods) allow statisticians to predict outcomes with high precision, while unsupervised learning methods (such as clustering and dimensionality reduction) help identify hidden structures and groupings within data. These methods complement statistical models by improving flexibility and scalability without aba...

  Topological condensed matter physics has emerged as one of the most exciting and debated fields in modern physics. At its core, the debate revolves around how topology—an abstract mathematical concept—can govern real, measurable physical properties of materials. Unlike conventional phases of matter, which are classified by symmetry breaking, topological phases are defined by global invariants that remain robust against disorder and perturbations. Supporters argue that topological materials such as topological insulators, Weyl semimetals, and topological superconductors represent a paradigm shift in condensed matter physics. Their edge or surface states are protected by topology, leading to dissipationless transport and robustness against impurities. These properties open pathways for revolutionary technologies, including fault-tolerant quantum computing, spintronics, and low-power electronic devices. However, critics highlight several unresolved challenges. Experimentally disting...

 Machine learning (ML) models are transforming the discovery of antibacterial compounds by enabling faster, more cost-effective identification of potential drug candidates. Traditional antibiotic discovery relies on extensive laboratory screening, which is time-consuming and expensive. In contrast, ML-driven approaches can analyze vast chemical and biological datasets to predict antibacterial activity with high accuracy, significantly accelerating early-stage drug discovery. These models learn patterns from known antibacterial and non-antibacterial compounds using features such as molecular descriptors, chemical fingerprints, physicochemical properties, and genomic or proteomic data of target bacteria. Popular algorithms include random forests, support vector machines, gradient boosting, and deep learning models such as convolutional neural networks (CNNs) and graph neural networks (GNNs). GNNs, in particular, are powerful because they directly model molecular structures as graphs...

Scientists have made a surprising discovery inside NASA’s spacecraft assembly cleanrooms—facilities designed to be among the most sterile environments on Earth. Despite rigorous cleaning protocols, researchers identified 26 previously unknown bacterial species thriving in these ultra-clean spaces. These microbes were found in cleanrooms used to assemble spacecraft destined for missions to Mars and other celestial bodies, raising important questions about planetary protection and microbial resilience.   The newly discovered bacteria exhibit extraordinary survival capabilities. They can withstand extreme conditions such as intense radiation, chemical disinfectants, desiccation, and nutrient scarcity—conditions that would normally eliminate most life forms. Genetic analysis revealed that these microbes possess unique DNA repair mechanisms and stress-response genes, allowing them to adapt and persist in harsh environments. This makes them particularly interesting to scientists studyin...

The rapid diffusion of Artificial Intelligence (AI) is transforming labor markets worldwide, yet its implications for emerging economies remain underexplored. Unlike advanced economies, where AI adoption often complements high-skilled labor, emerging economies face distinct structural challenges such as informality, skill mismatches, and limited social protection. This research examines how AI-driven task reallocation affects employment structures and wage inequality in emerging economies, with a particular focus on sectoral heterogeneity and skill polarization. The study builds on task-based economic theory, which suggests that AI substitutes routine and codifiable tasks while complementing non-routine cognitive and socio-emotional tasks. In emerging economies, a large share of employment is concentrated in routine-intensive manufacturing, clerical services, and informal sectors, making these labor markets particularly vulnerable to AI-induced displacement. At the same time, AI adopt...