We develop a spectral based coefficient of determination to measure how well the spectral density of a stationary process is represented by the class of MA(q) models. Using periodogram-based estimators, we establish asymptotic normality, derive tests for the MA(q) hypothesis, and construct procedures for determining the smallest order q achieving a prescribed approximation quality.
This study presents a numerical investigation of a novel star-shaped twin core photonic crystal fiber (TC-PCF) sensor for refractive index sensing of sucrose concentrations. The structural design and electromagnetic analysis are carried out using the finite element method implemented in COMSOL Multiphysics. Sensor performance is evaluated in the mid-infrared wavelength range, where key parameters such as confinement loss, wavelength sensitivity and amplitude sensitivity are extracted through MATLAB-based analysis. The proposed geometry incorporates a compact and well-defined micro-structured arrangement, which enhances the interaction between the guided modes and the analyte-filled region. Variations in sucrose concentration led to changes in refractive index, which modulates the modal properties and transmission characteristics of the fiber. The sensor achieves a maximum wavelength sensitivity of 8262.76 nm/RIU and an amplitude sensitivity of 3.3849 RIU−1, while maintaining negligible confinement loss. The combination of high sensitivity, low loss and compact design demonstrates the potential of this PCF sensor for biochemical sensing applications. These findings indicate its effectiveness for precise and reliable detection of sucrose concentration in real-world environments.
Fast and transferable interatomic potentials are in high demand for atomistic simulations of multicomponent systems, particularly those involving compounds and hydrides. In this work, we present a new classical interatomic model for the Fe–Cr–Ni–Ti–H system in the form of an angular-dependent potential (ADP). The potential accurately reproduces key thermodynamic and kinetic properties, phase stability, elastic properties, defect energetics and migration behavior in different materials: alloys, compounds, and hydrides. Benchmark tests demonstrate that the reported classical potential achieves an accuracy comparable to that of modern machine-learning interatomic models. At the same time, its classical functional form provides high computational efficiency, making the potential well suited for a variety of large-scale simulations involving millions of atoms over extended time scales. As representative examples, we present two large-scale atomistic simulation studies. The first examines strain-induced transformations near a crack tip in Fe-based alloys. The second case investigates hydride formation in FeTi compounds upon exposure to a molecular hydrogen environment. These examples demonstrate that the developed ADP model is capable of capturing complex phenomena in technologically relevant materials.
OBJECTIVES:This study aimed to investigate the efficacy and safety of filgotinib, a JAK1 preferential inhibitor, in patients with axial spondyloarthritis (axSpA). METHODS:The phase 3 OLINGUITO trial comprises 2 international, randomised, double-blind, placebo (PBO)-controlled studies. Patients with an established diagnosis of radiographic (r) or nonradiographic (nr) axSpA and an inadequate response/intolerance to ≥2 nonsteroidal anti-inflammatory drugs were randomised 1:1 to filgotinib 200 mg or PBO once daily through week (W) 16 (double-blind period; stratified by high-sensitivity C-reactive protein [hs-CRP] level and prior biologic disease-modifying antirheumatic drug [bDMARD] use). After W16, patients received open-label filgotinib 200 mg through W52; patients ≥65 years and/or with prespecified risk factors received response-based dosing (100 or 200 mg). The primary (Assessment of SpondyloArthritis international Society ≥40% response [ASAS40 response]) and secondary efficacy endpoints were assessed at W16. Efficacy and safety were assessed through W52. RESULTS:At W16, the primary endpoint was met in both studies (ASAS40 response rates: r = axSpA [n = 258], 39.5% filgotinib vs 20.9% PBO [P = .001]; nr-axSpA [n = 237], 34.5% vs 17.8% [P = .003], respectively). ASAS40 improvements, irrespective of hs-CRP level/prior bDMARD use, were observed as early as W1. For secondary endpoints, significant improvements were seen in Axial Spondyloarthritis Disease Activity Score and Spondyloarthritis Research Consortium of Canada magnetic resonance imaging sacroiliac joint inflammation in both studies, and in Bath Ankylosing Spondylitis Functional Index and Ankylosing Spondylitis Quality of Life Questionnaire in patients with r-axSpA. Improvements with filgotinib were maintained/increased further through W52. Overall, 2 cases of myocardial infarction (PBO: n = 1; PBO-filgotinib: n = 1), 3 of herpes zoster (PBO-filgotinib), and 4 of malignancies (excluding nonmelanoma skin cancer; filgotinib: n = 3; PBO-filgotinib: n = 1) occurred. CONCLUSIONS:Across the whole spectrum of axSpA, filgotinib provided rapid and significant patient-relevant improvements in axSpA signs and symptoms and was well tolerated.
The use of Artificial Intelligence (AI) chatbots is becoming increasingly popular worldwide. To prevent potential negative consequences associated with the use at an early stage, it is crucial to understand its associations with mental health. Therefore, the present study compared people who use AI chatbots with those who do not engage in AI chatbot-interactions in terms of mental health and loneliness. Moreover, we investigated potential mechanisms associated with addictive AI chatbot use. Data of 7,312 persons (3,729 users: 42.1% men, 57.9% women; Mage = 42.23, SDage = 13.33; 3,583 non-users: 42.6% men, 57.4% women; Mage, = 54.28, SDage = 14.49) from the German adult population were collected via an online survey. AI chatbot users had significantly higher levels of loneliness, depressive, anxiety and stress symptoms, and significantly lower levels of life satisfaction than non-users. Users primarily engaged in AI chatbot-interactions for informational and practical reasons, such as obtaining information and inspiration, and receiving text-related assistance. The frequency of AI chatbot use to escape negative emotions was significantly positively associated with addictive tendencies. Loneliness statistically mediated the association between addictive AI chatbot use and the mental health variables. Most observed effect sizes were small. The present findings show that using AI chatbots for emotion regulation is positively associated with higher addictive tendencies and lower levels of mental health. Further experimental longitudinal research is urgently needed to clarify causal mechanisms of these associations.
