Effects of MgF2anti-reflection coating on optical losses in metal halide perovskite solar cells.

The importance of light management for perovskite solar cells (PSCs) has recently been emphasized because their power conversion efficiency approaches their theoretical thermodynamic limits. Among optical strategies, anti-reflection (AR) coating is the most widely used method to reduce reflectance loss and thus increase light-harvesting efficiency. Monolayer MgF2is a well-known AR material because of its optimal refractive index, simple fabrication process, and physical and chemical durabilities. Nevertheless, quantitative estimates of the improvement achieved by the MgF2AR layer are lacking. In this study, we conducted theoretical and experimental evaluations to assess the AR effect of MgF2on the performance of formamidinium lead-triiodide PSCs. A sinusoidal tendency to enhance the short-circuit current density (JSC) was observed depending on the thickness, which was attributed to the interference of the incident light. A transfer matrix method-based simulation was conducted to calculate the optical losses, demonstrating the critical impact of reflectance loss on theJSCimprovement. The predictedJSCs values, depending on the perovskite thickness and the incident angle, are also presented. The combined use of experimental and theoretical approaches offers notable advantages, including accurate interpretation of photocurrent generation, detailed optical analysis of the experimental results, and device performance predictions under unexplored conditions.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation.

As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.

Deep Generative Models-Based Anomaly Detection for Spacecraft Control Systems.

A spacecraft attitude control system provides mechanical and electrical control to achieve the required functions under various mission scenarios. Although generally designed to be highly reliable, mission failure can occur if anomalies occur and the attitude control system fails to properly orient and stabilize the spacecraft. Because accessing spacecraft to directly repair such problems is usually infeasible, developing a continuous condition monitoring model is necessary to detect anomalies and respond accordingly. In this study, a method for detecting anomalies and characterizing failures for spacecraft attitude control systems is proposed. Herein, features are extracted from multidimensional time-series data of a simulation of the attitude control system. Then, the artificial neural network learning algorithms based on two types of generation models are applied. A Bayesian optimization algorithm with a Gaussian process is used to optimize the hyperparameters for the neural network to improve the performance. The performance is evaluated based on the reconstruction error through the algorithm using the newly generated data not used for learning as input data. Results show that the detection performance depends on the operating characteristics of each submode in the operation scenarios and type of generation model. The diagnostic results are monitored to detect anomalies in operation modes and scenarios.

Three-dimensional assessment of condylar position following orthognathic surgery using the centric relation bite and the ramal reference line: A retrospective clinical study.

Orthognathic surgery (OGS) is a relatively common procedure for solving functional and aesthetic problems in facial and jaw areas in patients with dentofacial deformities. The positioning of the mandibular condylar segment during OGS has an impact on the surgical outcome. This study aimed to investigate the changes in the condyle-fossa relationship three dimensionally after OGS using the centric relation (CR) bite and the ramal reference line (RRL).Thirty-two patients with skeletal malocclusion underwent OGS. Condylar repositioning was performed using the CR bite, as previously reported. A RRL was added to the existing method and used during the surgery. Cone-beam computed tomography scans were acquired at 4 time points. Sixty-four condyles were evaluated in the coronal, sagittal, and axial views. Two groups were created according to the amount of mandible setback (SB1 vs SB2), and another 2 groups were created according to the maxillary operation (1-jaw vs 2-jaw). Each was then compared at the 4 time points. Differences between the values before (T0) and a year after surgery (T3) were also investigated. The positions of the pogonion and the menton were examined at T2 and T3 for the simple evaluation of relapse.The change in the condylar position was significant over a time-course (P < .001) but not between T0 and T3 (P > .05). Neither the setback amount nor the maxillary operation affected the positional change (P > .05). There were no significant changes between T2 and T3 in the relapse evaluation.This condylar repositioning method using the CR bite and a RRL showed stable results after OGS. This method is noninvasive and cost-effective and can be easily performed even by an inexperienced surgeon because it reduces errors in repositioning the condyle during OGS.