Leveraging generative artificial intelligence to simulate student learning behavior

Student simulation presents a transformative approach to enhance learning outcomes, advance educational research, and ultimately shape the future of effective pedagogy. We explore the feasibility of using large language models (LLMs), a remarkable achievement in AI, to simulate student learning behaviors. Unlike conventional machine learning based prediction, we leverage LLMs to instantiate virtual students with specific demographics and uncover intricate correlations among learning experiences, course materials, understanding levels, and engagement. Our objective is not merely to predict learning outcomes but to replicate learning behaviors and patterns of real students. We validate this hypothesis through three experiments. The first experiment, based on a dataset of N = 145, simulates student learning outcomes from demographic data, revealing parallels with actual students concerning various demographic factors. The second experiment (N = 4524) results in increasingly realistic simulated behaviors with more assessment history for virtual students modelling. The third experiment (N = 27), incorporating prior knowledge and course interactions, indicates a strong link between virtual students' learning behaviors and fine-grained mappings from test questions, course materials, engagement and understanding levels. Collectively, these findings deepen our understanding of LLMs and demonstrate its viability for student simulation, empowering more adaptable curricula design to enhance inclusivity and educational effectiveness

Tactile Bodily Gaze Mapping Could Regulate Human Attention

Increasing individuals' awareness of their own body signals can lead to improved interoception, enabling the brain to estimate current body states more accurately and in a timely manner. However, certain body signals, such as eye movements, often go unnoticed by individuals themselves. This study aimed to test the hypothesis that providing eye-movement-correlated tactile feedback on the body enhances individuals' awareness of their attentive states, subsequently improving attention. Our results demonstrate the effectiveness of such feedback in redirecting and enhancing attention, particularly in the presence of distractions during long-duration tasks. Additionally, we observed that people's gaze behaviors changed in response to the tactile feedback, suggesting an increased self-awareness of current eye movements and attentive states. Ultimately, these changes in gaze behaviors contribute to the modulation of attentive states. Our findings highlight the potential of eye-movement-correlated bodily tactile feedback to increase individuals' self-awareness of their eye movements and attentive states. By providing real-time feedback through tactile stimuli, we can actively engage individuals in regulating their attention and enhancing their overall performance.Comment: 6 pages, 3 figure

Basis, Diagnosis, and Treatment of Uveal Melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy with a strong tendency to metastasize. The prognosis is poor once metastasis occurs. The treatment remains challenging for metastatic UM, even though our understanding of UM has advanced. Risk factors for developing UM include ages, skin colors, and genetic mutations. Many therapies that have applied to cutaneous melanoma have little or no success in UM. Various forms and combinations of radiotherapy, phototherapy, and local resection are utilized for advanced cases. The treatment aims to preserve the eye and useful vision and prevent metastases. This chapter aims to introduce the current study for UM

A Beam-Steering Reflectarray Antenna with Arbitrary Linear-Polarization Reconfiguration

This work presents a beam-steering reflectarray antenna capable of achieving arbitrary linear polarization (LP) reconfiguration. It utilizes a dual-circular polarization (CP) reconfigurable reflectarray, along with an LP feed horn, to synthesize a LP beam by combining two reflected CP beams in the same direction. The LP states can be dynamically adjusted by tuning the phase constants of the array, which correspondingly modify the wave phases. Experimental validation of the proposed polarization synthesis concept is conducted using a 16$\times$16 dual-CP 1-bit reconfigurable reflectarray operating at 16.8 GHz. This reflectarray generates reconfigurable LP waves with polarization states of LP(0$^\circ$), LP(45$^\circ$), LP(90$^\circ$) and LP(135$^\circ$). Furthermore, it demonstrates the capability to perform beam scanning, allowing for versatile beam manipulation. The application of this polarization-reconfigurable beam-steering reflectarray is pertinent to beam alignment and polarization synchronization in various wireless communication scenarios, including satellite communication and mobile communication

Development and Validation of A Rapid LC-MS/MS Method forTthe Determination of JCC76, A Novel Antitumor Agent for Breast Cancer, in Rat Plasma and Its Application to A Pharmacokinetics Study

JCC76 is a novel nimesulide analog that selectively inhibits the human epidermal growth factor receptor 2 (HER2) overexpressing breast cancer cell proliferation and tumor progression. To support further pharmacological and toxicological studies of JCC76, a novel and rapid method using liquid chromatography and electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) has been developed and validated for the quantification of the compound in rat plasma. A C18 column was used for chromatographic separation, and the mobile phase was aqueous ammonium formate (pH 3.7; 5 mm)–methanol (1:9, v/v) with an isocratic elution. With a simple liquid–liquid extraction procedure using the mixture of methyl tert-butyl ether–hexane (1:2, v/v), the mean extraction efficiency of JCC76 in rat plasma was determined as 89.5–97.3% and no obvious matrix effect was observed. This method demonstrated a linear calibration range from 0.3 to 100 ng/mL for JCC76 in rat plasma and a small volume of sample consumption. The intra- and inter-assay accuracy and precision were within ±10%. The pharmacokinetics of JCC76 was also profiled using this validated method in rats. In conclusion, this rapid and sensitive method has been proven to effectively quantify JCC76 for pharmacokinetics study

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

In the fault diagnosis of the shaft orbit of rotating machinery, there are few prejudgments about the severity of the faults, which is very important for fault repair. Therefore, a fine-grained recognition method is proposed to detect different severity faults by shaft orbit. Since different shaft orbits represent different type and different severity of faults, the convolutional neural network (CNN) is applied for identifying the shaft orbits to recognize the type and severity of the fault. The recognition rate of proposed fine-grained fault identification method is 97.96 % on the simulated shaft orbit database, and it takes only 0.31 milliseconds for the recognition of single sample. Experimental result indicated that the classification performance of the proposed method are better than the traditional machine learning models. Moreover, the method is applied for the identification of the measured shaft orbits of rotor with different degree of imbalance faults, and the testing accuracy of the experiments in measured shaft orbits is 97.14 %, which has verified the effectiveness of the proposed fine-grained fault recognition method

A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval

Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the representation learning of queries or explore label-graph and multi-task to facilitate models to learn external information. However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories. This paper proposes a Multi-granularity Matching Attention Network (MMAN), which contains three modules: a self-matching module, a char-level matching module, and a semantic-level matching module to comprehensively extract features from the query and a query-category interaction matrix. In this way, the model can eliminate the difference in expression between queries and categories for query intent classification. We conduct extensive offline and online A/B experiments, and the results show that the MMAN significantly outperforms the strong baselines, which shows the superiority and effectiveness of MMAN. MMAN has been deployed in production and brings great commercial value for our company.Comment: Accepted by WWW 202