Ultralow lattice thermal conductivity of binary compounds A2B (A = Cs, Rb & B = Se, Te) with higher-order anharmonicity correction

By employing first-principles calculations that integrate self-consistent phonon theory and the Boltzmann transport equation, we have delved into the thermal transport characteristics of hexagonal anisotropic materials A₂B (A = Cs, Rb and B = Se, Te). Our computational results have disclosed that these A₂B materials exhibit ultralow lattice thermal conductivity (κ_L) at room temperature. Specifically, in the case of Cs₂Te, the κ_L values are a mere 0.15 W m^-1 K^-1 in the a(b) direction and 0.22 W m^-1 K^-1 in the c direction, both markedly less than the thermal conductivity of quartz glass, a conventional thermoelectric material (0.9 W m^-1 K^-1). Importantly, our calculations encompass higher-order anharmonic effects while computing the lattice thermal conductivities of these materials. This is essential since pronounced anharmonicity leads to a decrease in phonon group velocity, and consequently, lowers the κ_L values. Our results establish a theoretical foundation for exploring the thermal transport characteristics of anisotropic materials with substantial anharmonicity. Furthermore, the binary compounds A₂B proffer a gamut of possibilities for a wide range of applications in thermoelectrics and thermal management, owing to their ultralow lattice thermal conductivity.

Imaging features of marginal ulcers on multidetector CT

AIM

To assess the inter-reader agreement and frequency of various imaging findings of marginal ulcers on computed tomography (CT) in a series of patients with endoscopically or surgically confirmed marginal ulcer disease.

MATERIALS AND METHODS

This was a institutional review board-approved retrospective analysis involving a single academic institution. Eighty patients with a gastro-enteric anastomosis with confirmed marginal ulcer on endoscopy or surgery and multidetector (MD)CT performed within a month reviewed by two fellowship-trained abdominal radiologists to assess for the presence or absence of predetermined imaging characteristics categorised under signs of inflammation, signs of penetration, signs of perforation, and signs of obstruction. Inter-rater reliability was assessed using the Cohen kappa test.

RESULTS

Findings of perforation had moderate to substantial agreement, including the presence of extraluminal air, extraluminal fluid, and leakage of oral contrast medium (kappa 0.83 [0.61, 1.05], 0.57 [0.35, 0.79] and 0.75 [0.18, 1.31], respectively) although these were present relatively infrequently (23-26%, 30-43%, 3-4%, respectively). Additional imaging characteristics with moderate agreement were the presence of perienteric fat stranding, a dilated stomach proximal to the anastomosis, and penetration into adjacent organ (kappa 0.45 [0.23, 0.69], 0.47 [0.26, 0.69], and 0.47 [0.25, 0.69], respectively) which were variably present (80-88%, 11-16%, 5%, respectively). Wall thickening and contour abnormalities, although frequently present (61-90% and 60-80%, respectively) had only slight to fair agreement (kappa 0.09 [-0.14, 0.30] and 0.29 [0.07, 0.51]).

CONCLUSION

Signs of perforation have high inter-reader agreement but occur relatively infrequently. Fat stranding, wall thickening, and contour abnormalities are much more common; however, only fat stranding had moderate agreement.

Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting

This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE).

Structural stability and evolution of half-metallicity in Ba2CaMoO6: interplay of hole- and electron-doping

Half-metallic ferromagnetic materials have attracted a lot of attention due to their probable technological applications in spintronics. In this respect, doping plays a crucial role in tailoring or controlling the physical properties of the system. Herein, the impact of both hole and electron doping on the structural, electronic and magnetic properties of the recent high pressure synthesized non-magnetic insulator Ba₂CaMoO₆ double perovskite oxide are investigated by replacing one of the Mo ions with Nb and Tc. The structural and mechanical stability of the undoped/doped materials are analyzed by calculating the formation energies and stiffness tensors, respectively, which confirm the system's stability. Interestingly, our results revealed that Nb- and Tc-doped systems display an electronic transition from insulating to p- and n-type half-metallic ferromagnetic states, respectively. The most striking feature of the present study is that oxygen ions become spin-polarized, with a magnetic moment of ∼0.12 μ_B per atom, and are mainly responsible for conductivity in the Nb-doped system. However, the admixture of Tc 4d non-degenerate orbitals are primarily contributing to the metallicity in the Tc-doped structure, with a moment of ∼0.59 μ_B. It is also found that Nb and Tc ions remain in the 5+ and 7+ states with electronic configurations of t22g↑t22g↓e0g↑e0g↓ and t32g↑t22g↓e0g↑e0g↓, with spin states of S = 0 and S = 1/2 in the individual doped systems, respectively. Hence, the present work proposes that a doping strategy with a suitable candidate could be beneficial to tune the physical properties of the materials for their potential utilization in advanced spin-based devices.

Detection of intratumoral calcification in oligodendrogliomas by susceptibility-weighted MR imaging

BACKGROUND AND PURPOSE

SWI is a unique pulse sequence sensitive to both hemorrhage and calcification. Our aim was to retrospectively assess the ability of SWI to detect intratumoral calcification in ODs compared with conventional MR imaging.

MATERIALS AND METHODS

Using CT as criterion standard, the MR imaging findings from 71 patients (33 males, 38 females; mean age, 42.5 years) with pathologically proved OD were retrospectively evaluated. We classified the MR imaging data into SWI data (MRSWI) and traditional pulse sequences (MRnoSWI). The sensitivity and specificity of the MRnoSWI (n = 71) were compared with that of the MRSWI (n = 13) independently and also for matched-paired data (n = 13). The Fisher exact test was applied to the matched-pair data for statistical evaluation.

RESULTS

For paired data of MRSWI and MRnoSWI (n = 13), there was significantly increased sensitivity of MRSWI (86%) for the detection of intratumoral calcification in OD compared with the MRnoSWI (14.3%) (P = .015, Fisher exact test) by using CT as the criterion standard. The overall accuracy of MRSWI for the paired data was also significantly greater (P = .048). The specificities were not significantly different (P = .773). The sensitivity of MRSWI (n = 13) was 86%, and for MRnoSWI (n = 71), it was 33.3%. Specificity of MRSWI was 83%, and for MRnoSWI, it was 95%.

CONCLUSIONS

SWI is better able to detect calcification in ODs than conventional MR imaging pulse sequences.