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Pain mechanistic networks: the development using supervised multivariate data analysis and implications for chronic pain
No full textChronic postoperative pain is present in approximately 20% of patients undergoing total knee arthroplasty. Studies indicate that pain mechanisms are associated with development and maintenance of chronic postoperative pain. The current study assessed pain sensitivity, inflammation, microRNAs, and psychological factors and combined these in a network to describe chronic postoperative pain. This study involved 75 patients with and without chronic postoperative pain after total knee arthroplasty. Clinical pain intensity, Oxford Knee Score, and pain catastrophizing were assessed as clinical parameters. Quantitative sensory testing was assessed to evaluate pain sensitivity and microRNAs, and inflammatory markers were likewise analyzed. Supervised multivariate data analysis with "Data Integration Analysis for Biomarker Discovery" using Latent cOmponents (DIABLO) was used to describe the chronic postoperative pain intensity. Two DIABLO models were constructed by dividing the patients into 3 groups or 2 defined by clinical pain intensities. Data Integration Analysis for Biomarker discovery using Latent cOmponents model explained chronic postoperative pain and identified factors involved in pain mechanistic networks among assessments included in the analysis. Developing models of 3 or 2 patient groups using the assessments and the networks could explain 81% and 69% of the variability in clinical postoperative pain intensity. The reduction of the number of parameters stabilized the models and reduced the explanatory value to 69% and 51%. This is the first study to use the DIABLO model for chronic postoperative pain and to demonstrate how different pain mechanisms form a pain mechanistic network. The complex model explained 81% of the variability of clinical pain intensity, whereas the less complex model explained 51% of the variability of clinical pain intensity.</p
Suppressing the thermal conduction in glass–ceramic foams by controlling crystallization
Get PDFGlass-based insulating materials have attracted considerable attention owing to their tailorable properties. It is known that the thermal conductivity of glass ceramics can be greatly influenced by varying their crystallinity. However, the mechanism of such influence in glass–ceramic foams remains poorly understood. In this study, we demonstrate our new findings regarding the correlation between thermal conductivity and crystallinity in silicate glass–ceramic foams. The foams were produced by mixing ZrO2-containing soda-lime glass powder with CaCO3 as foaming agent and foam them using a thermochemical approach. ZrO2 was introduced as a nucleation agent. The crystallinity of the foams was varied by adjusting the heating protocol, i.e., by varying temperature, time, and number of heating cycles. The glass–ceramic foams exhibited relative crystallinities of <30%. The identity of the crystalline phases in the glass–ceramic foams varies with crystallinity. Specifically, cristobalite diminished, but devitrite grew with increasing crystallinity. It was observed that the crystallinity had a nonmonotonic impact on the thermal conductivity of the glass–ceramic foams. The optimum crystallinity for achieving the lowest thermal conductivity was 8–10%, resulting in an approximately 20% lower thermal conductivity compared to noncrystalline. Our findings have implications for the future design of glass–ceramic foams
Plötzlich Führungskraft:Relevanzeinschätzung von Führungskompetenzen durch neue wissenschaftliche Führungskräfte
No full textThe importance of effective leadership is increasing within the scientific community, particularly within universities and non-university research institutions. Despite this growing significance, scientifically trained personnel often find themselves unprepared for the demands of leadership roles and only rarely see being an academic leader as their primary career goal. In this study, newly appointed professors were surveyed regarding which aspects of leadership competence had become important to them since taking over the leadership role, and whether these aligned with their initial expectations. The findings offer valuable insights for universities and other research institutions, enabling them to better recognize and tailor leadership development initiatives to meet the needs of emergingscientific leaders in their institutions
Nearest Kronecker product decomposition based multichannel filtered-x affine projection algorithm for active noise control
No full textThe filtered-x affine projection (FxAP) algorithm is an appealing choice for active noise control (ANC) systems. The main reason for its popularity is its fast convergence rate, especially for correlated input signals. However, this algorithm has a high computational complexity when the length of the filter is long. In this paper, we focus on a nearest Kronecker product decomposition method to improve the efficiency of the FxAP algorithm. The basic idea is to decompose a long filter into several short filters and then update the filter coefficients separately. Besides the development of the FxAP algorithm based on the nearest Kronecker product decomposition, we also propose a partially update strategy to further reduce the computational burden. Then, the computational complexity of the proposed algorithms is analyzed and compared with the original FxAP algorithm. Finally, simulation results show the advantages of the proposed algorithms for simulated and real acoustic paths in multichannel ANC systems.</p
Quantitative Assessment Mechanism of Low Frequency Oscillations in Train-Network Systems
Get PDFLow frequency oscillations (LFOs) in the electrified train-network system can lead to serious traction blockade accidents. Although impedance models and stability analysis tools have been applied in existing studies to address specific cases, a generalized mechanism to address LFO is still not established so far. This paper proposes a quantitative assessment method to reveal the underlying mechanism of LFOs. Founded on the improved Nyquist criterion, a stability margin indicator is defined to concretely describe system stability, then its corresponding expression is derived by combining the simplified impedance model as the basis for quantitative analysis. To this end, the identified negative resistances in the impedance model are revealed as the root cause of LFOs. Besides, theoretical justification for the impact of parameter tuning on the system stability is provided based on the explicit formula of the stability margin indicator. Finally, the effectiveness and accuracy of the theoretical analysis are verified under simulations and hardware-in-the-loop experimental conditions
Direct connection between secondary relaxation mode and fracture toughness in alkali-aluminosilicate glasses
No full textOxide glasses are intrinsically brittle, lacking sufficient atomic-scale mechanisms that can relax mechanical stresses in the vicinity of a propagating crack. As a result, fracture is typically well-captured by considering local bond rupture at the crack tip. Here we demonstrate that barrier energies related to the low-temperature γ-relaxation mode in alkali-aluminosilicate glasses are inversely related to the fracture toughness measured via standardized three-point bending fracture experiments. This holds true for both a series with varying cations (Li, Na, K) and one with varying Li concentration. The structural rationale for this finding is gained via Raman spectroscopy. The findings suggest that a fundamental structural relaxation mode measured on bulk specimens can serve as an effective guideline for fracture toughness of oxide glasses. Data for additional silicate glasses support this conclusion.</p
Likelihood ratio estimation of partial Y-STR profile matches using discrete Laplace models and marginalisation
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Inertia in Renewable Power Systems: A Review of Estimation Methods and Prac-tical Implementation
Get PDFThe dynamic behavior of modern power systems is being fundamentally reshaped by the increasing penetration of renewable energy sources with low or zero inertia, such as wind and solar PV. Consequently, in many regions, the rotational inertia traditionally provided by conventional synchronous generators has significantly declined. Since virtual inertia–achieved through synthetic inertia control– is not yet widely implemented, the overall system inertia has fallen well below that of traditional power systems. Accurate estimation of critically low inertia levels is therefore essential to ensure reliable and stable system operation. This review paper presents a comprehensive assessment of existing methods for inertia estimation in both conventional and renewable-rich power systems. It systematically compares techniques adopted by utilities, highlighting their practical applications, strengths, and limitations. Furthermore, the paper evaluates the feasibility of these approaches from an implementation perspective and discusses emerging challenges. Finally, it outlines future directions toward robust, adaptive, and real-time inertia estimation methods capable of supporting the secure operation of next-generation power systems.<br/
