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ABSTRACT Migratory fishes are renowned for their ability to home to natal streams for spawning. Learned olfactory cues play a critical role in natal homing of Pacific salmon and other fishes, but the underlying chemical signature of streams remains poorly understood after decades of study. The molecules that convey a stream‐specific odour must differ among sites but remain constant over time. Among leading odorant candidates are amino acids; however, little research has assessed the spatial and temporal variability of amino acid profiles in streams. We report a comprehensive chemical study of dissolved amino acids as potential olfactory cues for homing by migratory fish. Specifically, we profiled amino acids in water from 23 streams in the upper Laurentian Great Lakes basin over 2 years. We investigated variation in amino acid profiles (1) among regions and rivers within a year, (2) between years and (3) among sites and across the seasons of migration and early life history within a stream. Liquid‐chromatography tandem mass spectrometry revealed nanomolar concentrations for most of the 20 L‐amino acids measured, above the levels detectable by studied migratory fishes. Moreover, amino acid profiles were temporally stable between 2 years and across an annual season from adult spawning migration through offspring early‐life development within a stream. However, spatial differences in amino acid profiles were evident primarily over large geographic distances (among regions) but not among tributaries within regions or among sites within a stream. Collectively, our results indicate dissolved amino acids may be consistent components of rivers' odorant profiles but suggest additional molecules are likely important for natal homing of migratory fishes to specific spawning sites. We suggest that future studies consider the combined importance of amino acids and molecules from other chemical classes. Understanding the chemical basis of olfactory‐guided natal homing is especially important as human activities could alter the odorant profiles of streams and thereby disrupt fish migrations and negatively impact population recruitment.

Proportional-Integral-Derivative (PID) controllers are prominent due to their superior functionality and ease of use. However, optimizing their parameters presents a significant challenge. Adjusting parameters must be done carefully and cautiously because improper calibration can compromise the system’s stability. Although classic tuning techniques, such as the Ziegler-Nichols (ZN), are frequently employed, their efficiency is restricted due to the intricate and ever- changing nature of the systems, often leading to parameter settings that could be more optimal. Therefore, the need for a more accurate parameter-tuning technique is urgent. Various optimization strategies are used to fine-tune parameters with more precision. These methods include Gray Wolf Optimization (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). These methods are applied to fine-tune the PID parameters for a Direct Current (DC) motor to achieve optimal performance, and a comparative analysis of the results is conducted. Various fitness functions encompass performance metrics such as rise time, overshoot, peak time, settling time, and mean square error (MSE). These metrics are incorporated into the corresponding optimization approaches to quantitatively assess the controller’s performance. Various test cases have been utilized and the GA outperforms other algorithms ranging from 17% to 28% where rise time, settling time, and MSE are significant in the fitness function.

Equity research analysts and investors remain divided on the treatment of stock-based compensation (SBC) in firm valuation, particularly in discounted cash flow (DCF) and earnings multiples techniques. Many firms and analysts exclude SBC, viewing it as a non-cash expense, while others argue this leads to overvaluation. SBC usage is especially high in the tech sector. This study examines five large semiconductor firms, analyzing SBC levels, its treatment in adjusted earnings, and its impact on share dilution, valuation multiples, and free cash flow calculations. Findings indicate that SBC treatment significantly affects valuation, particularly within the semiconductor industry.

We develop a mechanistic model that classifies individuals both in terms of epidemiological status (SIR) and vaccination attitude (Willing or Unwilling/Unable), with the goal of discovering how disease spread is influenced by changing opinions about vaccination. Analysis of the model identifies the existence and stability criteria for both disease-free and endemic disease equilibria. The analytical results, supported by numerical simulations, show that attitude changes induced by disease prevalence can destabilize endemic disease equilibria, resulting in limit cycles.

The vacuum is now understood to have a rich and complex structure, characterized by fluctuating energy fields¹ and a condensate of virtual quark-antiquark pairs. The spontaneous breaking of the approximate chiral symmetry², signalled by the nonvanishing quark condensate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>⟨</mml:mo> <mml:mi>q</mml:mi> <mml:mover><mml:mrow><mml:mi>q</mml:mi></mml:mrow> <mml:mo>¯</mml:mo></mml:mover> <mml:mo>⟩</mml:mo></mml:mrow> </mml:math> , is dynamically generated through topologically nontrivial gauge configurations such as instantons³. The precise mechanism linking the chiral symmetry breaking to the mass generation associated with quark confinement⁴ remains a profound open question in quantum chromodynamics (QCD)-the fundamental theory of strong interaction. High-energy proton-proton collisions could liberate virtual quark-antiquark pairs from the vacuum that subsequently undergo confinement to form hadrons, whose properties could serve as probes into QCD confinement and the quark condensate. Here we report evidence of spin correlations in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Λ</mml:mi> <mml:mover><mml:mrow><mml:mi>Λ</mml:mi></mml:mrow> <mml:mo>¯</mml:mo></mml:mover> </mml:mrow> </mml:math> hyperon pairs inherited from spin-correlated strange quark-antiquark virtual pairs. Measurements by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory reveal a relative polarization signal of (18 ± 4)% that links the virtual spin-correlated quark pairs from the QCD vacuum to their final-state hadron counterparts. Crucially, this correlation vanishes when the hyperon pairs are widely separated in angle, consistent with the decoherence of the quantum system. Our findings provide a new experimental model for exploring the dynamics and interplay of quark confinement and entanglement.

The STAR experiment reports new, high-precision measurements of the transverse single-spin asymmetries for π^{±} within jets, namely the Collins asymmetries, from transversely polarized p^{↑}p collisions at sqrt[s]=510 GeV. The energy-scaled distribution of jet transverse momentum, x_{T}=2p_{T,jet}/sqrt[s], shows a remarkable consistency for Collins asymmetries of π^{±} in jets between sqrt[s]=200 GeV and 510 GeV. This indicates that the Collins asymmetries are nearly energy independent, with, at most, a very weak scale dependence in p^{↑}p collisions. These results extend to high-momentum scales (Q^{2}≤3400 GeV^{2}) and enable unique tests of evolution and universality in the transverse-momentum-dependent formalism, thus providing important constraints for the Collins fragmentation functions.