A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, undesirable student detecting, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions in this research area.Comment: 21 pages, 5 figure

Distribution of multi-level B cell subsets in thymoma and thymoma-associated myasthenia gravis

B-cell subsets in peripheral blood (PB) and tumor microenvironment (TME) were evaluated to determine myasthenia gravis (MG) severity in patients with thymoma-associated MG (TMG) and the distribution of B cells in type B TMG. The distribution of mature B cells, including Bm1-Bm5, CD19+ and CD20+ B cells and non-switched (NSMBCs) and switched (SMBCs) memory B cells, were determined in 79 patients with thymoma or TMG. Quantitative relationships between the T and TMG groups and the TMG-low and TMG-high subgroups were determined. NSMBCs and SMBCs were compared in TME and PB. Type B thymoma was more likely to develop into MG, with types B2 and B3 being especially associated with MG worsening. The percentage of CD19+ B cells in PB gradually increased, whereas the percentage of CD20+ B cells and the CD19/CD20 ratio were not altered. The (Bm2 + Bm2')/(eBm5 + Bm5) index was significantly higher in the TMG-high than in thymoma group. The difference between SMBC/CD19+ and NSMBC/CD19+ B cell ratios was significantly lower in the thymoma than TMG group. NSMBCs assembled around tertiary lymphoid tissue in thymomas of patients with TMG. Few NSMBCs were observed in patients with thymoma alone, with these cells being diffusely distributed. MG severity in patients with TMG can be determined by measuring CD19+ B cells and Bm1-Bm5 in PB. The CD19/CD20 ratio is a marker of disease severity in TMG patients. Differences between NSMBCs and SMBCs in PB and TME of thymomas can synergistically determine MG severity in patients with TMG.</p

Federated Learning with Privacy-Preserving Incentives for Aerial Computing Networks

With the help of artificial intelligence (AI) model, aerial computing can help analyze and predict the network dynamics and support intelligent decision-making to improve the performance of 6G space-air-ground integrated networks. Federated learning has been proposed to tackle the challenges of limited energy and data shortage for the application of AI models in aerial computing networks. A critical problem of FL for aerial computing is the lack of incentives due to privacy concerns. On the one hand, the information needed to measure users’ learning quality may be eavesdropped. On the other hand, users’ real costs for determining payments may also undertake inference attacks. In this paper, we design a privacy-preserving and learning quality-aware incentive mechanism for federated learning in aerial computing networks. We propose differential privacy based scheme to protect the privacy of the real cost. In addition, utilize Combinatorial Multi-Armed Bandit (CMAB) algorithm to evaluate the user learning quality without any participant information. Simulation results demonstrate that our scheme can significantly motivate high-quality participants with guaranteed privacy preservation and achieve effective federated learning under the constraint of the limited budget

Graphene-Based Biosensors for Detection of Composite Vibrational Fingerprints in the Mid-Infrared Region.

In this study, a label-free multi-resonant graphene-based biosensor with periodic graphene nanoribbons is proposed for detection of composite vibrational fingerprints in the mid-infrared range. The multiple vibrational signals of biomolecules are simultaneously enhanced and detected by different resonances in the transmission spectrum. Each of the transmission dips can be independently tuned by altering the gating voltage applied on the corresponding graphene nanoribbon. Geometric parameters are investigated and optimized to obtain excellent sensing performance. Limit of detection is also evaluated in an approximation way. Besides, the biosensor can operate in a wide range of incident angles. Electric field intensity distributions are depicted to reveal the physical insight. Moreover, another biosensor based on periodic graphene nanodisks is further proposed, whose performance is insensitive to the polarization of incidence. Our research may have a potential for designing graphene-based biosensor used in many promising bioanalytical and pharmaceutical applications

Impact of CodY protein on metabolism, sporulation and virulence in Clostridioides difficile ribotype 027

Toxin synthesis and endospore formation are two of the most critical factors that determine the outcome of infection by Clostridioides difficile. The two major toxins, TcdA and TcdB, are the principal factors causing damage to the host. Spores are the infectious form of C. difficile, permit survival of the bacterium during antibiotic treatment and are the predominant cell form that leads to recurrent infection. Toxin production and sporulation have their own specific mechanisms of regulation, but they share negative regulation by the global regulatory protein CodY. Determining the extent of such regulation and its detailed mechanism is important for understanding the linkage between two apparently independent biological phenomena and raises the possibility of creating new ways of limiting infection. The work described here shows that a codY null mutant of a hypervirulent (ribotype 027) strain is even more virulent than its parent in a mouse model of infection and that the mutant expresses most sporulation genes prematurely during exponential growth phase. Moreover, examining the expression patterns of mutants producing CodY proteins with different levels of residual activity revealed that expression of the toxin genes is dependent on total CodY inactivation, whereas most sporulation genes are turned on when CodY activity is only partially diminished. These results suggest that, in wild-type cells undergoing nutrient limitation, sporulation genes can be turned on before the toxin genes

Systemic siRNA Nanoparticle-Based Drugs Combined with Radiofrequency Ablation for Cancer Therapy

Purpose Radiofrequency thermal ablation (RFA) of hepatic and renal tumors can be accompanied by non-desired tumorigenesis in residual, untreated tumor. Here, we studied the use of micelle-encapsulated siRNA to suppress IL-6-mediated local and systemic secondary effects of RFA. Methods: We compared standardized hepatic or renal RFA (laparotomy, 1 cm active tip at 70±2°C for 5 min) and sham procedures without and with administration of 150nm micelle-like nanoparticle (MNP) anti-IL6 siRNA (DOPE-PEI conjugates, single IP dose 15 min post-RFA, C57Bl mouse:3.5 ug/100ml, Fisher 344 rat: 20ug/200ul), RFA/scrambled siRNA, and RFA/empty MNPs. Outcome measures included: local periablational cellular infiltration (α-SMA+ stellate cells), regional hepatocyte proliferation, serum/tissue IL-6 and VEGF levels at 6-72hr, and distant tumor growth, tumor proliferation (Ki-67) and microvascular density (MVD, CD34) in subcutaneous R3230 and MATBIII breast adenocarcinoma models at 7 days. Results: For liver RFA, adjuvant MNP anti-IL6 siRNA reduced RFA-induced increases in tissue IL-6 levels, α-SMA+ stellate cell infiltration, and regional hepatocyte proliferation to baseline (p<0.04, all comparisons). Moreover, adjuvant MNP anti-IL6- siRNA suppressed increased distant tumor growth and Ki-67 observed in R3230 and MATBIII tumors post hepatic RFA (p<0.01). Anti-IL6 siRNA also reduced RFA-induced elevation in VEGF and tumor MVD (p<0.01). Likewise, renal RFA-induced increases in serum IL-6 levels and distant R3230 tumor growth was suppressed with anti-IL6 siRNA (p<0.01). Conclusions: Adjuvant nanoparticle-encapsulated siRNA against IL-6 can be used to modulate local and regional effects of hepatic RFA to block potential unwanted pro-oncogenic effects of hepatic or renal RFA on distant tumor

High-Quality Coherent Plane-Wave Compounding Using Enhanced Covariance-Matrix-Based Statistical Beamforming

Coherent plane-wave compounding (CPWC) enables high-frame-rate ultrasound imaging, but the imaging quality is mainly determined by the beamforming method. Covariance-matrix-based statistical beamforming (CMSB) was previously proposed for synthetic aperture ultrasound imaging, which provides notable improvements in resolution and contrast over conventional delay-and-sum (DAS). However, the speckle quality is inadequate in the phantom experiment, and there exists a tradeoff between the contrast and speckle preservation of CMSB due to the constant diagonal reducing factor. In this paper, we applied CMSB in CPWC ultrasound imaging and propose an enhanced CMSB approach for CPWC to enhance the image quality. First, we introduced lag-one coherence (LOC) as an adaptive weighting factor for CMSB to suppress incoherent noise. Then, we propose adaptive diagonal reducing for CMSB using the coherence factor and amplitude standard deviation, with the aim to further improve the speckle quality. Finally, the combination of LOC weighting and adaptive diagonal reducing is proposed for CMSB to simultaneously improve the contrast and speckle quality. A simulation, experiments, and carotid studies were used to validate the imaging performance of the proposed methods. Results from the experiments show that LOC-weighted CMSB (LOCw-CMSB) with adaptive diagonal reducing improves the average contrast, generalized contrast-to-noise ratio (gCNR), and speckle signal-to-noise ratio (sSNR) by 59.9%, 53.6%, and 77.7%, respectively, in comparison with DMAS. The contrast and sSNR of the LOCw-CMSB with adaptive diagonal reducing were improved by 32.3% and 33.1%, respectively, compared to CMSB. In addition, LOCw-CMSB with adaptive diagonal reducing improves the contrast by 176.6% compared with SLSC in the in vivo carotid study, while it obtains a comparable gCNR. These results demonstrate that the proposed methods are effective in improving the image quality of CPWC imaging

Impact of Nonlocality on Group Delay and Reflective Behavior Near Surface Plasmon Resonances in Otto Structure

In this work, we study the effects of nonlocality on the optical response near surface plasmon resonance of the Otto structure, and such nonlocality is considered in the hydrodynamic model. Through analyzing the dispersion relations and optical response predicted by the Drude’s and hydrodynamic model in the system, we find that the nonlocal effect is sensitive to the large propagation wavevector, and there exists a critical incident angle and thickness. The critical point moves to the smaller value when the nonlocal effect is taken into account. Before this point, the absorption of the reflected light pulse enhances; however, the situation reverses after this point. In the region between the two different critical points in the frequency scan calculated from local and nonlocal theories, the group delay of the reflected light pulse shows opposite behaviors. These results are explained in terms of the pole and zero phenomenological model in complex frequency plane. Our work may contribute to the fundamental understanding of light–matter interactions at the nanoscale and in the design of optical devices