| Property | Details | |----------|---------| | | (provisional) 1‑(4‑fluorophenyl)-N‑[2‑(pyridin‑3‑yl)ethyl]pyrrolidine‑3‑carboxamide | | Synonyms | JUQ‑158, “Compound 158”, “Research Chemical JUQ‑158” | | Molecular formula | C₁₈H₂₀FN₃O | | Molecular weight | ≈ 311.37 g mol⁻¹ | | CAS number | Not yet assigned (as of early 2026) | | SMILES | FC1=CC=C(C=C1)C(=O)N(CC2=CN=CC=C2)C3CCCN3 | | Class (proposed) | Aryl‑pyrrolidine‑carboxamide; structurally reminiscent of certain synthetic cannabinoids and designer stimulants. |
For laboratories, a (multiple‑reaction monitoring of the two product ions above) is now included in the EMCDDA NPS Reference Library (Version 3, 2025). JUQ-158
What sets JUQ-158 apart from standard releases is the narrative focus. Unlike "amateur" style videos, this production follows a structured script, often centered around: | Property | Details | |----------|---------| | |
The authors formalize three notions of fairness (demographic parity, equalized odds, and predictive parity) and prove that any non‑trivial classifier that satisfies two of them simultaneously must sacrifice some predictive power unless the underlying data distribution already satisfies certain symmetry properties. They also show that, under a “group‑wise calibrated” assumption, one can achieve a Pareto‑optimal frontier where small fairness gains come at negligible accuracy loss. The paper ends with a “design checklist” for practitioners: (1) Diagnose the data‑generation process, (2) Choose fairness metrics aligned with the decision context, (3) Run a sensitivity analysis on the accuracy–fairness curve. Unlike "amateur" style videos, this production follows a