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HighScore with the Plus option (HighScore Plus) is the commercial powder diffraction analysis software from PANalytical. It has been in constant development over the last 13 years and has evolved into a very complete and mature product. In this paper, we present a brief overview of the suite focusing on the latest additions and its user-friendliness. The introduction briefly touches some basic ideas behind HighScore and the Plus option.

Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X‐ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase‐identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al–Si–Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well‐trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al–Si–Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample.

BackgroundArtificial intelligence (AI) and computer vision (CV) have revolutionized image analysis. In surgery, CV applications have focused on surgical phase identification in laparoscopic videos. We proposed to apply CV techniques to identify phases in an endoscopic procedure, peroral endoscopic myotomy (POEM).MethodsPOEM videos were collected from Massachusetts General and Showa University Koto Toyosu Hospitals. Videos were labeled by surgeons with the following ground truth phases: (1) Submucosal injection, (2) Mucosotomy, (3) Submucosal tunnel, (4) Myotomy, and (5) Mucosotomy closure. The deep-learning CV model—Convolutional Neural Network (CNN) plus Long Short-Term Memory (LSTM)—was trained on 30 videos to create POEMNet. We then used POEMNet to identify operative phases in the remaining 20 videos. The model’s performance was compared to surgeon annotated ground truth.ResultsPOEMNet’s overall phase identification accuracy was 87.6% (95% CI 87.4–87.9%). When evaluated on a per-phase basis, the model performed well, with mean unweighted and prevalence-weighted F1 scores of 0.766 and 0.875, respectively. The model performed best with longer phases, with 70.6% accuracy for phases that had a duration under 5 min and 88.3% accuracy for longer phases.DiscussionA deep-learning-based approach to CV, previously successful in laparoscopic video phase identification, translates well to endoscopic procedures. With continued refinements, AI could contribute to intra-operative decision-support systems and post-operative risk prediction.

X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials.

The continuous scanning laser Doppler vibrometry (CSLDV) technique is usually used to evaluate the vibration operational deflection shapes (ODSs) of structures with continuous surfaces. In this paper, an extended CSLDV is demonstrated to measure the non-continuous surface of the bladed disk and to obtain the ODS efficiently. For a bladed disk, the blades are uniformly distributed on a given disk. Although the ODS of each blade can be derived from its response data along the scanning path with CSLDV, the relative vibration direction between different blades cannot be determined from those data. Therefore, it is difficult to reconstruct the complete vibration mode of the whole blade disk. In order to measure the complete ODS of the bladed disk, a method based on ODS frequency response functions (ODS FRFs) has been proposed. While the ODS of each blade is measured by designing the suitable scanning paths in CSLDV, an additional response signal is obtained at a fixed point as the reference signal to identify the relative vibration phase between the blade and the blade of the bladed disk. Finally, a measurement is performed with a simple bladed disk and the results demonstrate the feasibility and effectiveness of the proposed extended CSLDV method.

The present study was undertaken to examine the effect of iron, manganese, copper and magnesium on the microstructural characteristics of Al-11%Si-2%Cu-Mg-based alloy referred to as 396 under different working conditions. The results show that strontium (Sr) has high affinity to react with magnesium (Mg), resulting in reduced effectiveness as eutectic silicon modifier or age hardening agent. In addition, Sr alters the sequence of the precipitation of the α-AlFeMnSi phase from post-eutectic to pro-eutectic which would harden the soft α-Aluminum matrix. The mechanism is still under investigation. The interactions between iron (Fe) and Mg and Sr-Mg result in the formation of serval dissolvable intermetallics during the solutionizing treatment such as β-AlFeSi, π-AlFeMgSi and Q-AlMgSiCu phases. The study also emphasizes the role of modification and grain refining as well as intermetallics in porosity formation and hardness of samples aged in the temperature range 155–240 °C.

Faulted phase identification is one of the segments of conventional system protection that is severely vulnerable in the presence of inverter-based resources (IBR) such as Type IV wind and solar PV power plants. The work presented in this paper investigates the effect of IBRs on the conventional phasor-based faulted phase identification methods widely implemented in contemporary commercial protection relays using theoretical analysis and simulation results. Moreover, this premise is further validated by testing commercial line protection relays using hardware-in-the-loop simulations. This paper also evaluates the applicability of recently proposed transients/incremental quantities-based techniques to overcome the deficiencies of conventional methods to correctly identify the faulted phase in systems with IBRs through real-time and control hardware-in-the-loop simulations. Comparisons with commercial relays show that transient/incremental quantities-based methods are more suitable for systems with a high penetration of IBRs.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co-association matrix-based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.

Rapid evolution of electricity distribution networks challenges the maintenance of up-to-date information in electricity utility databases. This hinders the ability of utilities to understand phase connectivity and topology of users in their distribution networks. Extensive research has been conducted to develop smart meter data-driven phase identification and topology determination approaches as alternatives to the conventional, time-consuming, and expensive approach of manual inspection. However, the majority of such approaches are challenged by low levels of smart meter penetration in distribution networks, entailing further investigation. The objective of this paper is to contribute to this challenge by proposing an alternative smart meter data-driven approach of user phase identification, verification, and topology determination and testing the method on a real Australian distribution network under varied levels of smart meter penetration. This paper first presents a smart meter data-driven user phase identification tool using k-means clustering. Then, a smart meter data-driven user phase verification and topology determination approach is introduced by analyzing voltage-to-power sensitivities obtained from linear regression. Four distinct linear regression models are developed and compared to recognize relevant parameters and input variables leading to the most reliable sensitivities. The overall process proposed in this study demonstrated high accuracy at original smart meter penetration of 75% of the case study DN. The performance at reduced smart meter penetrations of 50% and 25% is also examined and discussed in the paper.