Glass is widely used for various applications, including flat panel displays, solar panels, architectural windows, and exterior materials. These applications exhibit increasing complexity and ...improved functionality. In particular, glass substrates used in display panels require diverse forms of processing, prompting the exploration of laser applications to enhance processing quality, yield, and efficiency. This study aims to investigate the feasibility of using a high absorption, 257 nm femtosecond laser for processing glass substrates. The goal is to minimize damage and eliminate the need for post-processing, and ensuring superior quality and cross-sectional features. The analysis focuses on the influence of energy density and laser beam overlap ratio on processing variations. Point, line, and area processing were conducted within the achievable energy density range of 2.4–10.8 J/cm
2
. The results indicate that as the overlap ratio increases, processing depth, influenced by heat accumulation, exhibits a non-linear growth pattern. Moreover, the phenomenon of excessive processing width, surpassing design specifications, is mitigated by utilizing burst pulses that induce heat accumulation of ultra-short pulse lasers, thereby promoting increased processing depth while restraining width expansion. By comparing the outcomes of glass substrate processing using different laser wavelengths and pulse durations, it is confirmed that employing a 257 nm femtosecond laser minimizes damage, cracks, and chipping in the processed areas, obviating the need for post-processing. This paper presents the pioneering research on glass processing using deep ultraviolet femtosecond lasers. Results indicate that clear glass ablation is achieved without cracks.
Background:
This randomized controlled trial aimed to investigate the effects of dance therapy using telerehabilitation on trunk control and balance training in patients with stroke and compare them ...with the effects of conventional treatment.
Methods:
We enrolled 17 patients with subacute or chronic stroke who were randomly assigned to either an experimental or a control group. In addition to conventional physical therapy, the experimental group (n = 9) participated in 40-minute, non-face-to-face, dance-therapy sessions and the control group (n = 8) received conventional physical therapy. The primary outcome measures were the Trunk Impairment Scale (TIS) scores to assess trunk control and balance function between the 2 groups as a measure of change from baseline to after the intervention.
Results:
We found that the TIS scores of the patients in the experimental group significantly improved (
P
= .017). The TIS results indicated non-inferiority within a predefined margin for dance therapy using telerehabilitation (difference = -0.86, 95% confidence interval CI = -2.21 to 0.50).
Conclusion:
Dance therapy using telerehabilitation significantly improved the TIS scores in the experimental group and was not inferior to conventional rehabilitation treatment when compared in a non-inferiority test. The remote dance program may therefore have similar effects to those of conventional treatment regarding trunk-control improvement in patients with stroke.
The black-box nature of complex Neural Network (NN)-based models has hindered their widespread adoption in security domains due to the lack of logical explanations and actionable follow-ups for their ...predictions. To enhance the transparency and accountability of Graph Neural Network (GNN) security models used in system provenance analysis, we propose PROVEXPLAINER, a framework for projecting abstract GNN decision boundaries onto interpretable feature spaces. We first replicate the decision-making process of GNNbased security models using simpler and explainable models such as Decision Trees (DTs). To maximize the accuracy and fidelity of the surrogate models, we propose novel graph structural features founded on classical graph theory and enhanced by extensive data study with security domain knowledge. Our graph structural features are closely tied to problem-space actions in the system provenance domain, which allows the detection results to be explained in descriptive, human language. PROVEXPLAINER allowed simple DT models to achieve 95% fidelity to the GNN on program classification tasks with general graph structural features, and 99% fidelity on malware detection tasks with a task-specific feature package tailored for direct interpretation. The explanations for malware classification are demonstrated with case studies of five real-world malware samples across three malware families.
Graph Neural Networks (GNNs) require that all nodes have initial representations which are usually derived from the node features. When the node features are absent, GNNs can learn node embeddings ...with an embedding layer or use pre-trained network embeddings for the initial node representations. However, these approaches are limited because i) they cannot be easily extended to initialize new nodes that are added to the graph for inference after training and ii) they are memory intensive and store a fixed representation for every node in the graph. In this work, we present PropInit a scalable node representation initialization method for training GNNs and other Graph Machine Learning (ML) models on heterogeneous graphs where some or all node types have no natural features. Unlike existing methods that learn a fixed embedding vector for each node, PropInit learns an inductive function that leverages the metagraph to initialize node representations. As a result, PropInit is fully inductive and can be applied, without retraining, to new nodes without features that are added to the graph. PropInit also scales to large graphs as it requires only a small fraction of the memory requirements of existing methods. On public benchmark heterogeneous graph datasets, using various GNN models, PropInit achieves comparable or better performance to other competing approaches while needing only 0.01% to 2% of their memory consumption for representing node embeddings. We also demonstrate PropInit's effectiveness on an industry heterogeneous graph dataset for fraud detection and achieve better classification accuracy than learning full embeddings while reducing the embedding memory footprint during training and inference by 99.99%
Background
Little information is available about prognostic factors of arthroscopic capsular repair for peripheral triangular fibrocartilage complex (TFCC) lesions. The purpose of this study was to ...analyze factors that affect the treatment outcomes of arthroscopic capsular repair for peripheral TFCC tears.
Methods
This study retrospectively enrolled 60 patients who were treated with arthroscopic outside-in capsular repair for peripheral TFCC tears. Functional survey, including pain numeric rating scale (NRS) on an ulnar provocation test, distal radio-ulnar joint (DRUJ) stress test, Disability of the Arm, Shoulder, and Hand (DASH) score, and satisfaction with treatment, was conducted at 12-month follow-up. Patients who were enthusiastic or satisfied comprised the satisfied group, and those who were noncommittal or disappointed the dissatisfied group. Demographic, clinical, and arthroscopic findings were compared between the satisfied and dissatisfied groups.
Results
The mean pain NRS and DASH scores exhibited significant clinical improvement at the 12-month follow-up. Out of the total participants, 46 were satisfied and 14 were dissatisfied about the treatment, with significantly more female subjects in the dissatisfied group than in the satisfied one. The patients in the satisfied group had a shorter duration of symptoms, were more likely to have trauma history, and exhibited positive DRUJ stress test results compared to the dissatisfied group. There were no significant group differences in age, hand dominance, work level, and the extent of ulnar plus variance. Multivariable analysis revealed that female gender, a longer duration of symptoms, or negative DRUJ stress test results were associated with an increased disability after arthroscopic TFCC repair.
Conclusion
Female gender, a longer duration of symptom, and a negative DRUJ stress test are associated with a higher likelihood of treatment failure after arthroscopic outside-in capsular repair of peripheral TFCC tears.
Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The dynamics of the volatility surface ...is difficult to estimate. In this paper, we establish a novel architecture based on physics-informed neural networks and convolutional transformers. The performance of the new architecture is directly compared to other well-known deep-learning architectures, such as standard physics-informed neural networks, convolutional long-short term memory (ConvLSTM), and self-attention ConvLSTM. Numerical evidence indicates that the proposed physics-informed convolutional transformer network achieves a superior performance than other methods.
Little information is available about prognostic factors of arthroscopic capsular repair for peripheral triangular fibrocartilage complex (TFCC) lesions. The purpose of this study was to analyze ...factors that affect the treatment outcomes of arthroscopic capsular repair for peripheral TFCC tears.
This study retrospectively enrolled 60 patients who were treated with arthroscopic outside-in capsular repair for peripheral TFCC tears. Functional survey, including pain numeric rating scale (NRS) on an ulnar provocation test, distal radio-ulnar joint (DRUJ) stress test, Disability of the Arm, Shoulder, and Hand (DASH) score, and satisfaction with treatment, was conducted at 12-month follow-up. Patients who were enthusiastic or satisfied comprised the satisfied group, and those who were noncommittal or disappointed the dissatisfied group. Demographic, clinical, and arthroscopic findings were compared between the satisfied and dissatisfied groups.
The mean pain NRS and DASH scores exhibited significant clinical improvement at the 12-month follow-up. Out of the total participants, 46 were satisfied and 14 were dissatisfied about the treatment, with significantly more female subjects in the dissatisfied group than in the satisfied one. The patients in the satisfied group had a shorter duration of symptoms, were more likely to have trauma history, and exhibited positive DRUJ stress test results compared to the dissatisfied group. There were no significant group differences in age, hand dominance, work level, and the extent of ulnar plus variance. Multivariable analysis revealed that female gender, a longer duration of symptoms, or negative DRUJ stress test results were associated with an increased disability after arthroscopic TFCC repair.
Female gender, a longer duration of symptom, and a negative DRUJ stress test are associated with a higher likelihood of treatment failure after arthroscopic outside-in capsular repair of peripheral TFCC tears.
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