Global Particle Physics Excellence Awards

Introduction Alkali-Activated Materials (AAMs) represent a new generation of sustainable construction materials that align with global green and low-carbon development goals. These materials are synthesized by activating industrial by-products, such as fly ash and slag, with alkaline solutions. Their preparation involves mixing strong bases or weak base salts to initiate geopolymerization, producing binders with high mechanical and chemical stability. Among their many properties, electrical conductivity has emerged as a promising feature for multifunctional applications. This research focuses on understanding how alkali-activated fly ash-slag (AAFS) systems achieve superior conductivity and the mechanisms governing this behavior. Conductivity influencing factors The electrical conductivity of AAFS pastes is significantly affected by multiple variables, including pore structure, water distribution, and ion concentration. This study investigates the role of fly ash-slag mass ratios, alka...

Introduction The Global Particle Physics Excellence Awards stand as a beacon of recognition for outstanding researchers whose work has redefined the future of particle physics. Among its most prestigious honors, the Best Researcher Article Award highlights contributions that push scientific boundaries, emphasizing the importance of impactful publications that transform our understanding of the universe. This award not only acknowledges exceptional talent but also serves as a global symbol of academic excellence and inspiration for future scientific explorations. Recognition of impactful publications This award is designed to honor research articles that hold a significant place in advancing particle physics. Each recognized publication embodies creativity, originality, and scholarly depth, establishing new benchmarks for global research. By acknowledging such impactful work, the award fosters a culture of scientific excellence and inspires both established and upcoming researchers to c...

Introduction The study explores the impact of non-vertical sidewall angles on the band structure of thin-film lithium niobate photonic crystals. Both suspended membranes and lithium niobate on insulator structures are analyzed to highlight fabrication-dependent deviations. By employing the gap-to-midgap ratio as a primary figure-of-merit, the work demonstrates significant performance changes under varying sidewall conditions, thus providing valuable insights into the challenges faced in photonic crystal design. Impact of sidewall angle Non-vertical sidewalls, a common occurrence in fabricated photonic crystals, critically affect the optical performance of lithium niobate devices. For suspended structures, the reduction in bandgap is notable, but for lithium niobate on insulator platforms, the effect is even more severe, emphasizing the necessity of precise control over etching processes. This observation underlines the importance of sidewall angle as a decisive factor in achieving opti...

Introduction Two-dimensional transition metal dichalcogenides (2D TMDs) have emerged as a revolutionary class of materials due to their graphene-like layered structures and tunable band gaps. These properties make them highly attractive for energy conversion and storage applications, particularly in addressing pressing environmental challenges. Among the family of TMDs, molybdenum disulfide (MoS₂) and tungsten diselenide (WSe₂) have attracted significant attention because of their stable structures, fascinating optoelectronic behavior, and ability to form high-quality van der Waals (vdW) heterostructures. Their unique band alignment and mechanical flexibility open new possibilities for next-generation nanoelectronic and thermoelectric devices. Strain engineering in heterostructures Strain engineering has become a powerful method for tuning the intrinsic properties of 2D TMD heterostructures. The MoS₂/WSe₂ bilayer system is an excellent platform to investigate the effects of both biaxia...

Introduction Robotic arm trajectory planning has always faced critical challenges in unstructured environments, where randomness, uncertainty, and dynamic obstacles reduce the efficiency of traditional methods. This research introduces a novel hybrid reinforcement learning framework by combining simulated annealing (SA) with proximal policy optimization (PPO) to overcome local optimum traps, convergence issues, and limited adaptability. The proposed model provides a robust foundation for precise and collision-free grasping, thereby advancing the field of intelligent robotic manipulation in real-world industrial settings. Research challenges One of the main motivations behind this research is addressing the challenges of trajectory planning in unpredictable environments. Traditional reinforcement learning methods often struggle with local optimum traps and slow convergence, while real-time interaction in dynamic spaces remains difficult. By analyzing these limitations, the study emphasi...

Introduction A neutrino factory represents a transformative step in the study of fundamental particle physics. As a potential successor to the current generation of neutrino oscillation experiments and a precursor to muon colliders, it offers a uniquely well-characterized neutrino beam. This facility could provide high-statistics beams of electron, muon, and their corresponding antineutrinos, allowing researchers to probe physics beyond the Standard Model. By investigating scenarios like non-standard interactions and CPT violation, the neutrino factory has the potential to revolutionize our understanding of neutrino properties and their role in the universe. Sensitivity to Non-Standard Interactions One of the most promising research opportunities at a neutrino factory lies in its sensitivity to vector neutrino non-standard interactions (NSI). Unlike conventional oscillation experiments, the facility’s precise beam composition and charge identification capabilities allow for the disenta...

Introduction High-current electron beams are playing an increasingly vital role in advancing industrial and scientific applications, ranging from medical radioisotope production to environmental technologies such as wastewater purification. Their efficiency and productivity are directly linked to the ability to generate beams in continuous wave (CW) mode with high stability and minimal energy loss. Superconducting radio frequency (SRF) linacs are particularly well-suited for such applications because they can sustain CW operation at high power. However, the injector system becomes a critical component in ensuring high-quality beam generation without compromising SRF cavity integrity. SRF Linacs for High-Current Electron Beams Superconducting RF linacs have emerged as the preferred accelerator technology for producing high-current electron beams due to their low energy dissipation and capability to operate in CW mode. Unlike conventional linacs, SRF linacs can maintain efficiency even...

Introduction The study of elastic proton-proton scattering at the Large Hadron Collider (LHC) provides a unique opportunity to probe the dynamics of Quantum Chromodynamics (QCD) at high energies. Recent observations reveal a remarkable phenomenological scaling property in the elastic scattering cross sections at moderate momentum transfer. This finding invites deeper theoretical investigation since such scaling behaviors often indicate underlying universal mechanisms at work. Understanding these features is not only crucial for precision modeling of scattering amplitudes but also for advancing knowledge of QCD saturation physics. Phenomenological Scaling in Elastic Scattering The discovery of scaling in proton-proton elastic scattering cross sections highlights an intriguing pattern that appears across different energy regimes. This suggests that the cross sections exhibit universal behavior governed by deeper QCD mechanisms rather than being mere coincidences. Phenomenological scaling...

Introduction The Best Paper Award under the Global Particle Physics Excellence Awards stands as one of the most prestigious honors in the scientific community. It is dedicated to recognizing researchers whose groundbreaking publications have reshaped the understanding of physics and contributed to the advancement of global knowledge. By highlighting innovation, originality, and academic rigor, this award serves as a beacon of excellence that inspires both early-career and established researchers. Celebrating Research Excellence Research in physics requires years of dedication, critical thinking, and innovation. The Best Paper Award serves as a powerful acknowledgment of these qualities by honoring scholars whose work demonstrates significant contributions to the field. Such recognition not only validates the efforts of individual researchers but also reinforces the importance of research as the foundation of scientific progress. Impact on the Global Academic Community Outstanding resea...

Introduction Delay discounting models have traditionally been grounded in frameworks such as the exponential model, hyperbolic model, and those stemming from nonextensive statistics. These models typically simplify impulsivity into a singular construct, thereby overlooking the complex and multifaceted nature of impulsive behavior. The present research introduces a new perspective by treating impulsivity not as a single parameter but as a dynamic interaction of multiple facets. By doing so, the study paves the way for more accurate predictions of behavioral decision-making and aligns mathematical modeling with real-world psychological complexity. Impulsivity as a multi-faceted construct Impulsivity is increasingly understood as a heterogeneous trait, encompassing both stable personality dimensions and fluctuating state-dependent behaviors. In this research, two distinct but complementary facets of impulsivity are mathematically represented, reflecting the nuanced differences between lon...

Introduction This research explores the intricate connections between creative learning environments, mathematics self-efficacy, and mathematics achievement by analyzing Programme for International Student Assessment (PISA) 2022 data from Korea, Canada, and Türkiye. The study draws on the group socialization development theory and social cognitive theory to explain how social and cognitive dynamics shape student performance. With large, diverse samples from the three countries, the research offers robust cross-national evidence. It highlights the importance of fostering creativity-driven educational settings that enhance confidence in mathematics learning and, consequently, improve achievement outcomes. Theoretical framework The study is grounded in two key perspectives: the group socialization development theory and the social cognitive theory. Group socialization emphasizes the role of peers, family, and social settings in shaping student development, while social cognitive theory un...

Introduction Agentic AI-integrated robots play a vital role in achieving effective, efficient, and safe Human-Robot Collaboration (HRC). For seamless collaboration, robots must interpret and predict human behaviors accurately by understanding the working context. While many existing human motion prediction models emphasize task-related context, they often neglect the influence of the robot itself as a contextual factor. This research addresses that gap by exploring the integration of robot-awareness into prediction frameworks, paving the way for more intelligent and adaptive HRC systems. Background and Motivation Human motion prediction is a critical component in enabling real-time decision-making for collaborative robots. Traditional models predominantly focus on external environmental or task-specific parameters, often overlooking the direct impact of the robot’s actions. Such limitations can lead to inefficiencies or safety concerns in HRC. Motivated by the need for a more holist...

Introduction The field of quantum control has seen remarkable advancements in recent years, particularly in addressing the challenges of managing large ensembles of quantum systems with varying internal parameters. In this research, we introduce a novel probabilistic control framework that enables efficient steering of an ensemble of quantum systems while compensating for external environmental interactions. By targeting the probabilistic description of quantum dynamics rather than deterministic trajectories, the proposed approach provides enhanced robustness and adaptability, opening new pathways for precise quantum ensemble manipulation. Research Motivation Controlling quantum systems at the ensemble level is inherently challenging due to parameter variations, environmental influences, and stochastic nature of quantum processes. Traditional control methods often struggle to maintain accuracy across diverse system configurations. This research is motivated by the need for a unified ...

Introduction Wireless network coverage optimization plays a pivotal role in enhancing service quality and ensuring seamless connectivity in modern communication infrastructures. However, as networks grow in scale and complexity, traditional optimization methods face computational bottlenecks. The advent of quantum computing offers promising alternatives, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era, where hybrid approaches can be leveraged for efficiency. This work introduces a quantum-driven divide-and-conquer method that models coverage as a graph problem, partitions it via QUBO formulation, and employs advanced quantum algorithms to deliver scalable solutions. Quantum Modelling of Network Coverage The research models the wireless coverage optimization problem as a covering graph, enabling a structured representation of network nodes, coverage zones, and overlap constraints. This graph-based model provides a natural mapping to Quadratic Unconstrained Binary Optimiz...