We propose a new analytical approach based on ordinal pattern analysis to investigate respiratory heart rate variability (RespHRV, also called respiratory sinus arrhythmia), specifically modulation of heart rate across different phases of the respiratory cycle. The method uses RR interval time series derived from ECG signals, along with simultaneous respiratory recordings obtained with a respiratory belt. The method produces distributions of ordinal patterns that reflect the dynamics of heart rate variability throughout the respiratory cycle. We systematically test how variations in parameters defining ordinal patterns affect the results and interpretation, and discuss the optimal parameter configuration for quantification of RespHRV in short-term recordings. Finally, we demonstrate the ability of the method to differentiate between healthy controls and patients with obstructive sleep apnea based on daytime cardiorespiratory data.
One of the main challenges in welding dissimilar joints between steels and nickel-based alloys for power engineering applications is carbon diffusion and the formation of brittle interfacial phases. The aim of this work was to evaluate the effect of the buttering layer composition on the microstructure and mechanical behavior of welded joints between AISI 304 H steel and Inconel 617 alloy. Multi-pass Gas Tungsten Arc Welding (GTAW) using Inconel 617 filler, with prior application of Inconel 82 and Inconel 617 buttering layers on the AISI 304 H steel side, was performed and compared with the structural integrity of joints produced without buttering using conventional and pulsed GTAW processes. The welded joints were characterized by optical microscopy (OM), scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS), tensile testing at room and elevated temperatures, Vickers hardness measurements, and Charpy impact testing. Significant metallurgical interactions and local element diffusion during solidification and cooling are reflected in the presence of Type I and Type II grain boundaries as well as intricate morphological characteristics including peninsula structures and isolated austenitic islands near the interfaces. Considerable diffusion of Ni, Cr, and Fe, as well as localized segregation near the AISI 304 H/buttering interface, was observed. Additionally, TiC and NbC precipitates were detected in the Inconel 82 buttering layer, and the Inconel 617 buttering and weld metal were enriched with Cr and Mo-based carbides (M₂₃C₆, Mo₆C). The application of a buttering layer significantly improved the mechanical performance of AISI 304 H-Inconel 617 welded joints. The buttering process resulted in increased and more uniform hardness across the weld metal ( 221 HV0.5 for IN82 and 239 HV0.5 for IN617 in non-buttered welds, with higher values observed in buttered welds). At room temperature, the IN617 buttered joint exhibited the highest ultimate tensile strength (UTS) of approximately 682 MPa, compared with 581 MPa for the IN82 buttered joint. The IN617 buttered joint also exhibited significantly higher ductility ( 50
Chicken feathers, an abundant poultry by-product rich in keratin, remain underutilised due to their recalcitrant structure. The combined effects of feather pre-treatment and room-temperature alkaline hydrolysis conditions on keratin extraction efficiency, degree of hydrolysis, and antioxidant activity were systematically studied. Two pre-treatment methods were compared: a typical method, involving feather grinding and defatting (T-method) and a simplified one based on detergent washing only (S-method). Feather solubilisation ranged from 27 to 72
This study presents a novel intelligent hybrid seismic control system for a 10-storey shear building that integrates base isolation (BI) with an acceleration differential force-enhanced tuned mass damper inerter (BI-ADF-TMDI) and further extends it through active control to create the BI-ADF-ATMDI system. The key innovation lies in the mechanical configuration that connects the roof-mounted TMDI to the BI level via an inerter, as well as the incorporation of a passive acceleration differential force (ADF) mechanism that dissipates energy without requiring external power. To enhance adaptability, the system employs an active TMDI governed by a tilt-integral-derivative (TID) controller, whose parameters are optimally tuned using the multi-objective cheetah optimizer (MOCO) algorithm. Additionally, the MOCO algorithm is used to optimize the parameters of the three proposed systems: BI-TMDI, BI-ADF-TMDI, and BI-ADF-ATMDI. This study investigates the seismic performance of a structure equipped with these systems in comparison to an uncontrolled structure. Performance optimization was conducted using an artificial earthquake record, followed by rigorous validation across 23 diverse near-field seismic events. The results reveal that the BI-ADF-ATMDI system achieves significantly improved seismic performance as compared to its passive counterparts, demonstrating superior reductions in structural responses. All three proposed systems substantially outperform the uncontrolled structure, highlighting their effectiveness. Overall, this research establishes a robust methodology for system optimization and evaluation, contributing meaningfully to the advancement of seismic resilience in structural design.
Current research on bias in machine learning often focuses on fairness, while overlooking the roots or causes of bias. However, bias was originally defined as a "systematic error," often caused by humans at different stages of the research process. This article aims to bridge the gap between past literature on bias in research by providing taxonomy for potential sources of bias and errors in data and models. The paper focus on bias in machine learning pipelines. Survey analyses over forty potential sources of bias in the machine learning (ML) pipeline, providing clear examples for each. By understanding the sources and consequences of bias in machine learning, better methods can be developed for its detecting and mitigating, leading to fairer, more transparent, and more accurate ML models.
