Application of digital impression and model in removable partial dentures for Kennedy classâ andâ ¡dentition defects.

OBJECTIVES: This study aimed to evaluate the application of digital impression and resin model technology in removable partial dentures (RPD) for Kennedy classⅠandⅡdentition defects. METHODS: Patients with Kennedy classⅠorⅡdental defect were selected and grouped in accordance with the following denture production processes: digital impression/resin model/cast cobalt-chromium alloy framework group (group A), digital impression/resin model/laser printed titanium framework group (group B), alginate impression/plaster model/cast cobalt-chromium alloy framework group (group C), and alginate impression/plaster model/laser printed titanium framework group (group D), with 40 cases in each group. The final RPD was examined in place in the mouth, and the evaluation indicators included the retention force of clamp ring, the tightness of connector and base, and the accuracy of occlusion. The evaluation scores of each index were used for analysis on the Kruskal-Wallis rank-sum test. RESULTS: No statistically significant difference in the score of each index was found among the four groups in RPD. CONCLUSIONS: The cast cobalt-chromium alloy and laser-printed titanium framework RPD using digital impression and resin model can meet the clinical restoration requirements of patients with Kennedy classⅠandⅡdentition defects.

Online inspection of blackheart in potatoes using visible-near infrared spectroscopy and interpretable spectrogram-based modified ResNet modeling.

Introduction: Blackheart is one of the most common physiological diseases in potatoes during storage. In the initial stage, black spots only occur in tissues near the potato core and cannot be detected from an outward appearance. If not identified and removed in time, the disease will seriously undermine the quality and sale of theentire batch of potatoes. There is an urgent need to develop a method for early detection of blackheart in potatoes. Methods: This paper used visible-near infrared (Vis/NIR) spectroscopy to conduct online discriminant analysis on potatoes with varying degrees of blackheart and healthy potatoes to achieve real-time detection. An efficient and lightweight detection model was developed for detecting different degrees of blackheart in potatoes by introducing the depthwise convolution, pointwise convolution, and efficient channel attention modules into the ResNet model. Two discriminative models, the support vector machine (SVM) and the ResNet model were compared with the modified ResNet model. Results and discussion: The prediction accuracy for blackheart and healthy potatoes test sets reached 0.971 using the original spectrum combined with a modified ResNet model. Moreover, the modified ResNet model significantly reduced the number of parameters to 1434052, achieving a substantial 62.71% reduction in model complexity. Meanwhile, its performance was evidenced by a 4.18% improvement in accuracy. The Grad-CAM++ visualizations provided a qualitative assessment of the model's focus across different severity grades of blackheart condition, highlighting the importance of different wavelengths in the analysis. In these visualizations, the most significant features were predominantly found in the 650-750 nm range, with a notable peak near 700 nm. This peak was speculated to be associated with the vibrational activities of the C-H bond, specifically the fourth overtone of the C-H functional group, within the molecular structure of the potato components. This research demonstrated that the modified ResNet model combined with Vis/NIR could assist in the detection of different degrees of black in potatoes.

CO2 Photoreduction over Semiconducting 2D Materials with Supported Single Atoms: Recent Progress and Challenges.

In the face of the growing energy crisis and environmental challenges, substantial efforts are now directed toward sustainable clean energy as a replacement for traditional fossil fuels. CO2 photoreduction into value-added chemicals and fuels is widely recognized as a promising approach to mitigate current energy and environmental concerns. Photocatalysts comprising single atoms (SAs) supported on two-dimensional (2D) semiconducting materials (SAs-2DSemi) have emerged as a novel frontier due to the combined merits of SA catalysts and 2D materials. In this study, we review advancements in metal SAs confined on 2DSemi substrates, categorized into four groups: (1) metal oxide-based, (2) g-C3N4-based, (3) emerging, and (4) hybridized 2DSemi, for photocatalytic CO2 conversion over the past few years. With a particular focus on highlighting the distinct advantages of SAs-2DSemi, we delve into the synthesis of state-of-the-art catalysts, their catalytic performances, and mechanistic elucidation facilitated by experimental characterizations and theoretical calculations. Following this, we outline the challenges in this field and offer perspectives on harnessing the potential of SAs-2DSemi as promising photocatalysts. This comprehensive review aims to provide valuable insights for the future development of 2D photocatalytic materials involving SAs for CO2 reduction.