Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular docking and dynamics simulation of Orthosiphon stamineus against SGLT1 and SGLT2

Abstract Orthosiphon stamineus Benth a traditional medicine used in the treatment of diabetes and kidney diseases. Sodium–glucose co-transporter (SGLT1 and SGLT2) inhibitors are the novel group of drugs used to treat patients with type 2 diabetes mellitus. In this study 20 phytochemical compounds from Orthosiphon stamineus Benth were obtained from 3 databases viz Dr.Duke’s phytochemical, Ethno botanical database and IMPPAT. They were subjected to physiochemical, drug likeliness, and ADMET and toxicity predictions. Homology modeling and molecular docking against SGLT1 and SGLT2 were performed and the stability of the selected drug molecule was validated by molecular dynamic (MD) simulation for 200 ns. Among the 20 compounds, 14-Dexo-14-O-acetylorthosiphol Y alone showed higher binding affinity with SGLT1 and SGLT2 protein with the binding energy of −9.6 and −11.4 Kcal/mol respectively and had highest affinity towards SGLT2 inhibitor. This compound also satisfied Lipinski rule of 5 and had a good ADMET profile. The compound is non-toxic to marine organisms and to normal cell lines and non-mutagenic. The RMSD value attained equilibrium at 150 ns with the stability around 4.8 Å and no significant deviation was reported from 160 to 200 ns for SGLT2. Our study suggests that 14-Dexo-14-O-acetylorthosiphol Y showed promising results against the SGLT2 and could be considered as a potent anti-diabetic drug. Communicated by Ramaswamy H. Sarma
Imperatorin inhibits LPS-induced bone marrow-derived macrophages activation by decreased NF-κB p65 phosphorylation

Abstract Background Imperatorin (IMP) is a secondary metabolite of plants and is the most abundant in Angelica dahurica. Previous studies showed that IMP exhibited anti-inflammatory activity in RAW264.7 cell line. Here, we aim to investigate the roles and mechanisms of IMP in bone marrow-derived macrophages (BMDMs), in view of the difference between primary macrophages and cell lines. Methods BMDMs were stimulated with LPS for the inflammation model. Flow cytometry was performed with BMDMs treated with different doses of IMP (0–20mg/L) within staining Annexin V-APC for 5 min. The cytokines and inflammatory mediators were detected by RT-PCR or ELISA. RNA-seq was performed in IMP-treated BMDMs or control, stimulated with LPS for 6h. Western blotting is carried out to determine the phosphorylation of p65, ERK1/2, JNK1, p38, and Akt. Results Our results showed that IMP inhibited IL-12p40, IL-6, TNF-α and IL-1β in LPS-stimulated BMDMs. RNA-seq analysis suggested that IMP inhibits Toll-like receptor signaling pathway (KEGG), TNF signaling pathway (KEGG), NF-κB signaling pathway (KEGG), Inflammatory Response (GO). In addition, IMP inhibited myd88, tpl2, cxcl1, ptgs2(COX-2) expression in mRNA level. Finally, we found decreased phosphorylation of NF-κB p65 in IMP-treated BMDMs, after stimulated with LPS. Conclusion IMP inhibits IL-12p40, IL-6, TNF-α, and IL-1β expression in LPS-stimulated BMDMs. IMP inhibits macrophage activation, which maybe resulted in decreased phosphorylation of NF-κB p65. Furthermore, IMP may protect against the progress of inflammatory-related diseases.
Wavelet-based local region-of-interest reconstruction for synchrotron radiation x-ray microtomography

Synchrotron radiation x-ray microtomography is becoming a uniquely powerful method to nondestructively access three-dimensional internal microstructure in biological and engineering materials, with a resolution of 1μm or less. The tiny field of view of the detector, however, requires that the sample has to be strictly small, which would limit the practical applications of the method such as in situ experiments. In this paper, a wavelet-based local tomography algorithm is proposed to recover a small region of interest inside a large object only using the local projections, which is motivated by the localization property of wavelet transform. Local tomography experiment for an Al–Cu alloy is carried out at SPring-8, the third-generation synchrotron radiation facility in Japan. The proposed method readily enables the high-resolution observation for a large specimen, by which the applicability of the current microtomography would be promoted to a large extent.
Near-field evolution and scaling of shear layer instabilities in a reacting jet in crossflow

Abstract This study analyses the stability characteristics of the shear layer vortices (SLV) in a reacting jet in crossflow, analysing effects of flame position, momentum flux ratio ($J$) and density ratio ($S$). It utilizes 40 kHz particle image velocimetry to characterize the dominant SLV frequencies, streamwise evolution and convective/global stability characteristics for three different canonical configurations, one non-reacting and two reacting (‘R1’ and ‘R2’). In the non-reacting case, both convective and global instability is observed, depending upon $S$ and $J$. Qualitatively similar $S$ dependencies occur for the R1 reacting case where the radial flame position lies outside the jet shear layer, albeit with slower SLV growth rates. When the flame lies inside the jet shear layer, the R2 reacting case, a qualitatively different behaviour is observed, as vorticity concentration in the shear layers is suppressed almost completely. Finally, we show that frequency and stability characteristics of the non-reacting and R1 cases can be scaled in a unified manner using a counter-current shear layer model. This model relates these SLV behaviours to a vorticity layer thickness, a velocity scale and an effective density ratio (noting that there are three distinct densities associated with the jet, the crossflow and the burned gases). These parameters were extracted from the data and used to collapse the frequency scaling, and to explain the transition to self-excited oscillatory behaviour.
Maximum drag reduction state of viscoelastic turbulent channel flow: marginal inertial turbulence or elasto-inertial turbulence

Abstract The essence of the maximum drag reduction (MDR) state of viscoelastic drag-reducing turbulence (DRT) is still under debate, which mainly holds two different types of views: the marginal state of inertial turbulence (IT) and elasto-inertial turbulence (EIT). To further promote its understanding, this paper conducts a large number of direct numerical simulations of DRT at a modest Reynolds number Re with $Re = 6000$ for the FENE-P model that covers a wide range of flow states and focuses on the problem of how nonlinear extension affects the nature of MDR by varying the maximum extension length $L$ of polymers. It demonstrates that the essence of the MDR state can be both IT and EIT, where $L$ is somehow an important parameter in determining the dominant dynamics. Moreover, there exists a critical $L_c$ under which the minimum flow drag can be achieved in the MDR state even exceeding the suggested MDR limit. Systematic analyses on the statistical properties, energy spectrum, characteristic structures and underly dynamics show that the dominant dynamics of the MDR state gradually shift from IT-related to EIT-related dynamics with an increase of $L$. The above effects can be explained by the effective elasticity introduced by different $L$ at a fixed Weissenberg number (Wi) as well as the excitation of pure EIT. It indicates that larger $L$ introduces more effective elasticity and is favourable to EIT excitation. Therefore, we argue that the MDR state is still dominated by IT-related dynamics for the case of small $L$, but replaced by EIT-related dynamics at high $L$. The obtained results can harmonize the seemingly controversial viewpoints on the dominant dynamics of the MDR state and also provide some ideas for breaking through the MDR limit, such as searching for a polymer solution with a proper molecular length and concentration.
Bulk optical properties in binary mixtures of antiferroelectric liquid crystal compounds showing V-shaped switching

In order to clarify the origin of the V-shaped switching observed in thin cells ofantiferroelectric/ ferrielectric liquid crystals, bulk properties have been studied by means of helical pitch and conoscope measurements using thick free-standing films of binary mixtures with various mixing ratios. In the temperature range showing V-shaped switching in thin cells, helical structure clearly exists, indicating the existence of ordered phases. Some indistinct phase changes with temperature, coexistence of phases and quasi-continuous phase changes with an applied electric field were observed, suggesting a system with weak inter-layer correlation. By comparing the phase diagrams made using thin homogeneous cells and thick free-standing films, it was found that V-shaped switching occurs in the region where various subphases exist in the bulk. The appearance of many indistinct phases is consistent with the weak interlayer correlation. In this way, it was concluded that the V-shaped switching occurs in tilted smect.
Bootstrap Critical Values for Testing Homogeneity of Covariance Matrices

Abstract Bartlett's modified likelihood ratio statistic A is often suggested in multivariate analysis for testing equality of covariance matrices. Unfortunately, the χ 2-approximation to the null distribution of −2 log Λ is useful only when the data is normally distributed. This article presents a pooled bootstrap procedure that replaces the χ 2-approximation and makes Bartlett's statistic a useful tool for data analysis. This procedure also applies to most quadratic form test statistics.
Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method

ABSTRACT Smart personal assistants (SPAs), such as Alexa for example, promise individualized user interactions owing to their varying interaction possibilities, knowledgeability, and human-like behaviors. To support the widespread adoption and use of SPAs, organizations such as Google or Amazon provide low code environments that support the development of SPAs (e.g., for Google Home or Amazon’s Alexa). These so-called low code platforms enable domain experts (e.g., business users without programming skills or experience) to develop SPAs for their purposes. However, using these platforms alone does not guarantee a useful and good conversation with novel SPAs due to non-intuitive design choices. Following a design science research approach, we propose the Smart Personal Assistant for Domain Experts (SPADE) method to address the missing link. This method supports domain experts in the development and contextualization of sophisticated SPAs for various application scenarios and focuses especially on conversational and anthropomorphic design steps. Our proof of concept and proof of value results show that SPADE is useful for supporting domain experts to create effective SPAs in different domains beyond private set-ups.
Learning with Digital Agents: An Analysis based on the Activity Theory

ABSTRACT Digital agents are considered a general-purpose technology. They spread quickly in private and organizational contexts, including education. Yet, research lacks a conceptual framing to describe interaction with such agents in a holistic manner. While focusing on the interaction with a pedagogical agent, that is, a digital agent capable of natural-language interaction with a learner, we propose a model of learning activity based on activity theory. We use this model and a review of prior research on digital agents in education to analyze how various characteristics of the activity, including features of a pedagogical agent or learner, influence learning outcomes. The analysis leads to identification of information systems research directions and guidance for developers of pedagogical agents and digital agents in general. We conclude by extending the activity theory-based model beyond the context of education and show how it helps designers and researchers ask the right questions when creating a digital agent.
Concentration Monitoring in Online Classes for Smart Education Applications Based on Neural Network and C-PAD Emotion Model

The arrival of COVID-19 has changed the way traditional classes are conducted, and online teaching has never been more popular. While there are many advantages to online teaching, there are also extremely obvious disadvantages, one of which is the tendency to lack concentration. For this reason, this study uses video images from the DAiSee dataset, a new sampling script, deep learning neural networks, and a new PAD emotion model to systematically assess student concentration. Our test set uses 21 short videos from the DAISee dataset, sampling a total of 1,866 frames. The final results showed that the accuracy of the neural network was approximately 80%. The results of the test set on the PAD model showed that the percentage of attentive listeners was 65.9%, while the percentage of highly inattentive listeners was 6.2%. This study constructed a complete concentration monitoring system for online classrooms centred on smart education which can provide the information of students' concentration in real time.
Design and Method Research of Wind Tunnel Test Scheduling System for Multi-source Heterogeneous Data

Clarify the relationship between wind tunnel test and power resource guarantee, use the existing information system resources, construct the test scheduling system framework, guide and promote the wind tunnel test scheduling to the direction of integration and intelligence development. An MDP wind tunnel test scheduling method is proposed to explore the application of artificial intelligence in the area of the wind tunnel test scheduling, which lay a theoretical and technical foundation for opening up the channel of the multi-source heterogeneous information system related to the wind tunnel test scheduling next, and forming the close relation between the situation awareness and prediction system of the wind tunnel test scheduling.
Optimization Method of 3D Point Cloud Generation for Underwater Target

Due to the influence of machine movement, environmental interference and other factors in ROV remote control operation, it is difficult to identify the image features collected underwater, feature points are difficult to extract, and the quality of 3D reconstruction of underwater target 3D model is low. Therefore, an optimization method of underwater target 3D point cloud generation is proposed. The experimental results of underwater data set based on this method show that the detection amount of underwater target image feature points is nearly doubled, the error rate of feature matching is low, the high-quality point cloud data is doubled, and the effect of 3D model reconstruction is remarkable.
A Think-Aloud Study of Novice Debugging

Debugging is a core skill required by programmers, yet we know little about how to effectively teach the process of debugging. The challenges of learning debugging are compounded for novices who lack experience and are still learning the tools they need to program effectively. In this work, we report a case study in which we used a think-aloud protocol to gain insight into the behaviour of three students engaged in debugging tasks. Our qualitative analysis reveals a variety of helpful practices and barriers that limit the effectiveness of debugging. We observe that comprehension, evidence-based activities, and workflow practices all contribute to novice debugging success. Lack of sustained effort, precision, and methodical processes negatively impact debugging effectiveness. We anticipate that understanding how students engage in debugging tasks will aid future work to address ineffective behaviours and promote effective debugging activities.
A Review Selection Method Based on Consumer Decision Phases in E-commerce

A valuable small subset strategically selected from massive online reviews is beneficial to improve consumers’ decision-making efficiency in e-commerce. Existing review selection methods primarily concentrate on the informativeness of reviews and aim to find a subset of reviews that can reflect the informational properties of the original review set. However, changes in consumers’ review diets during the two-phase decision process are not fully considered. In this study, we propose a novel review selection problem of finding a diet-matched review subset with high diversity and representativeness, which can better adapt to consumers’ review-diet conversion from attribute-oriented to experience-oriented reviews between two decision phases. A novel decision-phase-based review selection method named DPRS is further proposed, which involves two steps: review classification and review selection. In the review classification step, the probability of a review being attribute-oriented or experience-oriented is estimated by prior knowledge-aware attentive neural network. In the second step, a novel heuristic algorithm, namely, stepwise non-dominated selection with superiority strategy, is introduced to seek the solution to the review selection problem. Extensive experiments on a real-world dataset demonstrate that DPRS outperforms state-of-the-art methods in terms of both review classification and review selection.
W-prize-collecting scheduling problem on a single machine

In this paper, we consider the W-prize-collecting scheduling problem on a single machine, where each job has a profit. The objective is to minimize the makespan of the accepted jobs and the total rejection cost of the rejected jobs, conditional on their total profit no less than a given threshold. We first analyze the computational complexities when all jobs have the same parameter. Then, we present a 2-approximation algorithm. Furthermore, we provide two pseudo-polynomial time algorithms. Finally, a fully polynomial time approximation scheme (FPTAS) is given for this problem. Our numerical tests indicate that both dynamic programming algorithms can easily solve large-size problems.
Prediction method of shared bicycle traffic based on Prophet-BiLSTM combined model

Shared bicycles provide citizens with a flexible travel mode, which not only reduces the generation of greenhouse gases, but also brings convenience and benefits, and is increasingly popular in urban transportation. At present, the biggest problem in the management of shared bicycles is excessive delivery, and how to accurately predict the number of them has become the key to standardized management. In order to solve the problem of traffic prediction accuracy of shared bicycles, a combined prediction model based on Prophet and Bidirectional long-short term memory (BiLSTM) network is proposed. First, the data set is preprocessed, then a prediction model based on Prophet and BiLSTM is established, and the two methods are combined with different weights using the least squares method, and the combined model is used as a new model for prediction. Taking the data obtained in the UCI machine learning library as an example, the data of shared bicycle rental is analyzed. Compared with the single-item forecasting model and three typical time series forecasting models, its forecasting performance is the best.
Research on Quantification of Personalized Risk Perception Based on Driving Style

Driver's risk perception characteristics are uncertain factors in the road traffic environment. To improve driving safety, the quantification of risk perception of drivers with different styles is studied. First, typical dangerous scenarios with different risk states are designed. The data of drivers' subjective cognition and objective behavior are obtained through questionnaires and driving simulation. The Pearson correlation analysis is used to determine the evaluation indicators of driver's risk perception. The fuzzy C-means clustering model is used to classify drivers into four styles: radical, mature, ordinary and conservative. A multiple linear regression model is constructed to analyze the change rule of risk perception of drivers with different styles in different risk scenarios. The results show that the risk perception of aggressive drivers is smaller than that of other types of drivers, and the driver's risk perception increases with the increase of scene risk level, in addition, the driver's style changes with the change of risk scenarios. This study can provide a theoretical basis for the driver's personalized strategy of safe driving.
Detecting Mental Distresses Using Social Behavior Analysis in the Context of COVID-19: A Survey

Online social media provides a channel for monitoring people’s social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published since the COVID-19 outbreak. We analyze relevant research and its characteristics and propose new approaches to organizing the large amount of studies arising from this emerging research area, thus drawing new views, insights, and knowledge for interested communities. Specifically, we first classify the studies in terms of feature extraction types, language usage patterns, aesthetic preferences, and online behaviors. We then explored various methods (including machine learning and deep learning techniques) for detecting mental health problems. Building upon the in-depth review, we present our findings and discuss future research directions and niche areas in detecting mental health problems using social media data. We also elaborate on the challenges of this fast-growing research area, such as technical issues in deploying such systems at scale as well as privacy and ethical concerns.
A Proposed Method of Literature Analysis Based on Natural Language Processing and Network Analysis

The number of scientific publications is increasing each year, and researchers' demand for literature analysis is also growing. This study aims to propose a computer-aided literature analysis method based on natural language processing technology. Our seven-step method includes the tasks of literature search, data preprocessing, word frequency calculation, word extraction, word labeling, co-word analysis, and network visualization. Demonstrated with an empirical study for exploring the development of artificial intelligence in a medical area, this method can produce data and visualizations that help researchers to explore literature content based on their research objectives.