PurposeSystematic reviews of patient-reported outcome measures (PROMs) differ from reviews of interventions and diagnostic test accuracy studies and are complex. In fact, conducting a review of one or more PROMs comprises of multiple reviews (i.e., one review for each measurement property of each PROM). In the absence of guidance specifically designed for reviews on measurement properties, our aim was to develop a guideline for conducting systematic reviews of PROMs.MethodsBased on literature reviews and expert opinions, and in concordance with existing guidelines, the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) steering committee developed a guideline for systematic reviews of PROMs.ResultsA consecutive ten-step procedure for conducting a systematic review of PROMs is proposed. Steps 1–4 concern preparing and performing the literature search, and selecting relevant studies. Steps 5–8 concern the evaluation of the quality of the eligible studies, the measurement properties, and the interpretability and feasibility aspects. Steps 9 and 10 concern formulating recommendations and reporting the systematic review.ConclusionsThe COSMIN guideline for systematic reviews of PROMs includes methodology to combine the methodological quality of studies on measurement properties with the quality of the PROM itself (i.e., its measurement properties). This enables reviewers to draw transparent conclusions and making evidence-based recommendations on the quality of PROMs, and supports the evidence-based selection of PROMs for use in research and in clinical practice.
The article studies the evolution of the constitutional arrangements in seventeenth-century England following the Glorious Revolution of 1688. It focuses on the relationship between institutions and the behavior of the government and interprets the institutional changes on the basis of the goals of the winners—secure property rights, protection of their wealth, and the elimination of confiscatory government. We argue that the new institutions allowed the government to commit credibly to upholding property rights. Their success was remarkable, as the evidence from capital markets shows.
Macroautophagy/autophagy is a lysosome-dependent degradation process involved in cellular energy metabolism, recycling and quality control. Autophagy is a highly dynamic and precisely regulated process, which contains four major steps: autophagic membrane initiation and cargo recognition, autophagosome formation, autophagosome-lysosome fusion and lysosomal degradation. During the terminal phase of autophagy, the merging of the autophagosome and lysosome membranes is critical for the effective breakdown of sequestered cargoes. However, the participated molecules and the interplay among them have not been fully uncovered. The spatiotemporal property of these molecules is crucial for maintaining the orderly fusion of autophagosomes and lysosomes, otherwise it may lead to fusion disorders. In this article, we tend to summarize the molecules mediating autophagosome-lysosome fusion into two categories: effector molecules and regulatory molecules. The effector molecules are soluble N-ethylmaleimide–sensitive factor attachment protein receptor and tethering proteins, and the latter category contains phosphatidylinositol, Rab GTPases and ATG8-family proteins. The spatio-temporal properties of these autophagosome-lysosome fusion mediating molecules will be featured in this review.
Abstract Accurately predicting the specific heat capacity of nanofluids is critical for optimizing their performance in engineering and industrial applications. This study explores twelve machine learning and deep learning models using conventional and stacking ensemble techniques. In the stacking framework, a linear regression model is employed as a meta-learner to improve base model performance. Additionally, two nature-inspired metaheuristic optimization algorithms—Particle Swarm Optimization and Grey Wolf Optimization—were used to fine-tune the hyperparameters of machine learning models. This research is based on a comprehensive dataset of 1,269 experimental nanofluid samples, with key inputs including nanofluid type (hybrid and direct), temperature, and volume concentration. To improve model generalization, data augmentation strategies inspired by polynomial/Fourier expansions and autoencoder-based methods were implemented. The results demonstrate that the stacked multi-layer perceptron model, integrated with linear regression, achieved the highest predictive accuracy, recording an R² score of 0.99927, a mean squared error of 466.06, and a root mean squared error of 21.58. Among standalone machine learning models, CatBoost was the best performer (R² score: 0.99923, MSE: 487.71, RMSE: 22.08), ranking second overall. The impact of metaheuristic optimization was significant; Grey Wolf Optimization, for instance, reduced the LightGBM model’s mean squared error from 29386.43 to 6549.006. These findings underscore the efficacy of hybrid ML/DL frameworks, advanced data augmentation, and metaheuristic optimization in predictive modeling of nanofluid thermophysical properties, providing a robust foundation for future research in heat transfer applications.
Laura Nistor, Cătălin Lisa, Tsuyoshi Michinobu
et al.
Background: 2-[4-(Dimethylamino)phenyl]-3-([4-(dimethylamino)phenyl]ethynyl)buta-1,3-diene-1,1,4,4-tetracarbonitrile (DDMEBT) is a thermally robust organic material of interest for applications requiring controlled volatility. Understanding its thermal stability, decomposition mechanism, and sublimation behavior is critical for optimizing deposition conditions in industrial processes. Methods: A comprehensive set of techniques was employed, including thermogravimetric analysis coupled with mass spectrometry and FTIR spectroscopy (TG/MS/FTIR), differential scanning calorimetry (DSC), ATR-FTIR spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), dynamic vapor sorption (DVS) analysis, polarized light microscopy (POM), and molecular modeling. Sublimation kinetics were investigated under isothermal conditions (130–150 °C) using anthracene as reference. Significant findings: DDMEBT exhibits a sequential three-step degradation mechanism, independent of heating rate, with high thermal stability (final residue ∼77 %) attributed to its nonplanar architecture and intermolecular π–π/dipole–dipole interactions. Thermal analysis revealed melting at ∼190 °C, structural rearrangements (196–230 °C), and an amorphous-to-crystalline transition at 270 °C. Sublimation proceeds via zero-order kinetics with low volatility (0.178 μg/min at 130 °C) and an activation energy of 66.5 kJ/mol. The determined vapor pressure (1998–4000 Pa) and transport coefficients confirm a thermally activated, hydrodynamically stable process. These findings establish a reliable basis for sublimation modeling and provide guidelines for optimizing material processing in high-temperature, low-volatility applications.
Karima Ezziane, Mayouf Sellami, Mostefa Kameche
et al.
Abstract New limited pyrochlore solid solutions of formula Bi1.5-xMxSb1.5-xM'xZnO7 (M= Fe; M'=Fe, Cr) (x=0; 0.10; 0.15) are prepared from simple oxides at 1080°C by using the ceramic method. All the crystal phases are indexed in the cubic system (space group; No.227). Rietveld refinement method of the Bi1.5Sb1.5ZnO7 (x=0) compounds using powder XRD analysis confirms an overall A2B2O7 cubic pyrochlore structure according to B i 1 . 5 3 + Z n 0 . 5 2 + S b 1 . 5 5 + Z n 0 . 5 2 + O 7 formula with 10.44425(3) Å and F d 3 ¯ m space group. The substitution of Bi(III) and Sb(V) by Fe(III) and Cr(III) in the Bi1.5Sb1.5ZnO7 phase shows the appearance of solid solutions limited to x=0.15. The variation of the cell’s parameter is recorded to the element’s ionic radii. The paramagnetic character is observed in all substituted compounds. The measurements of the electrical conductivity as a function of the temperature, make evidence of the semi-conductive property. Whilst, the magnetic susceptibility satisfies the modified Curie Weiss (CW) and shows the magnetic behavior due to the magnetic moments of the iron and chromium ions being involved in the synthesis of the compounds. Besides, the UV-Visible reflectance displays light absorption in the visible domain.