Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of 3-D Quantitative Analysis of Multi-Photon Microscopy Images

Motivation: Cartilage is a robust but flexible connective tissue found in most joints of the body. The collagen fibres present in the extracellular matrix of cartilage contribute to its tensile strength and stiffness. The purpose of this study is to develop and implement methods to determine the orientation and anisotropy of collagen fibres in 3-D images gen- erated with multi-photon microscopy. The motivation behind developing these techniques is to improve the foundation for further studies on understanding the characteristics of the cartilage matrix. This in turn would give a better foundation for developing artificial matrices and mechanical models, as well as improve diagnostics. Material and methods: The two methods developed in this study are based on analysing the frequency domain. One is an expansion of a previous developed method by Chaudhuri et al. [1]. This method is based on evaluating the average intensity at different directions in the frequency domain. The direction with the least average intensity is equivalent to the direction of the fibres. The other method is based on thresholding the frequency domain according to intensity followed by fitting an ellipsoid to the remaining data set. The direction of the collagen fibres is equivalent to the direction of the shortest axis of the ellipsoid. These methods are called the sector and ellipsoid method, respectively. To determine how robust these methods are a series of tests were developed. The focus of these tests was to determine if the methods are rotational invariant and if the results are influences by different preprocessing techniques. These preprocessing techniques are: median filtering, deconvolution and skeletonization of the original image containing the collagen fibres. It is also important to determine the sensitivity of the ellipsoid method according to the chosen threshold value. In addition data generated fibres and frequency domains were made to determine the accuracy of the methods. Results and conclusion: The sector method was not very robust. For most cases there is not one specific direction that has the least average intensity in the frequency domain. Instead there is a quite large minimum area. The ellipsoid method shows promising results. It managed to find the correct direction both for the data generated data sets, but also for the real images. It seems like no preprocessing nor frequency filtering, except for thresholding, is needed to still find the correct direction and its anisotropy. The only remark is that the automatically chosen threshold value was to low for one of the samples. This can probably be improved by making a slight change in the process for choosing a threshold value.
Virtual Experiments and Their Use in Teaching Experimental Design

The ability to design experiments in an appropriate and efficient way is an important skill, but students typically have little opportunity to get that experience. Most textbooks introduce standard general‐purpose designs, and then proceed with the analysis of data already collected. In this paper we explore a tool for gaining design experience: computer‐based virtual experiments. These are software environments which mimic a real situation of interest and invite the user to collect data to answer a research question. Two prototype environments are described. The first one is suitable for a course that deals with screening or response surface designs, the second one allows experimenting with block and row‐column designs. They are parts of a collection we developed called ENV2EXP, and can be freely used over the web. We also describe our experience in using them in several courses over the last few years.
Quantum Fokker-Planck equation for interacting waves

The quantum Fokker—Planck equation for the irreversible evolution of a bath of interacting oscillators is derived with sufficient generality that both the action and angle dependence of the distribution function are maintained in the classical limit. This approach unifies Landau —Rumer processes, spontaneous phonon decay, quantum renormalization of the speed of sound, and quantum wave turbulence.
Designing a Biomaterial Approach to Control the Adaptive Response to a Skin Injury

The goal of this review is to explain how to design a biomaterial approach to control the adaptive response to injury, with an emphasis on skin wounds. The strategies will be selected based on whether they have a reasonable probability of meeting the desired clinical outcome vs. just comparing the pros and cons of different strategies. To do this, the review will look at the normal adaptive response in adults and why it does not meet the desired clinical outcome in most cases. In addition, the adaptive response will be looked at in cases where it does meet the clinical performance requirements including animals that regenerate and for fetal wound healing. This will lead to how biomaterials can be used to alter the overall adaptive response to allow it to meet the desired clinical outcome. The important message of the review is that you need to use the engineering design process, not the scientific method, to design a clinical treatment. Also, the clinical performance requirements are functional, not structural. The last section will give some specific examples of controlling the adaptive response for two skin injuries: burns and pressure ulcers. For burns, it will cover some preclinical studies used to justify a clinical study as well as discuss the results of a clinical study using this system. For pressure ulcers, it will cover some preclinical studies for two different approaches: electrical stimulation and degradable/regenerative scaffolds. For electrical stimulation, the results of a clinical study will be presented.
Flavonoid Baicalein Suppresses Oral Biofilms and Protects Enamel Hardness to Combat Dental Caries

The objectives of this study were to investigate the effects of a novel method using flavonoids to inhibit Streptococcus mutans (S. mutans), Candida albicans (C. albicans) and dual-species biofilms and to protect enamel hardness in a biofilm-based caries model for the first time. Several flavonoids, including baicalein, naringenin and catechin, were tested. Gold-standard chlorhexidine (CHX) and untreated (UC) groups served as controls. Optimal concentrations were determined by cytotoxicity assay. Biofilm MTT, colony-forming-units (CFUs), biofilm biomass, lactic acid and polysaccharide production were evaluated. Real-time-polymerase-chain reaction (qRT-PCR) was used to determine gene expressions in biofilms. Demineralization of human enamel was induced via S. mutans-C. albicans biofilms, and enamel hardness was measured. Compared to CHX and UC groups, the baicalein group achieved the greatest reduction in S. mutans, C. albicans and S. mutans-C. albicans biofilms, yielding the least metabolic activity, polysaccharide synthesis and lactic acid production (p < 0.05). The biofilm CFU was decreased in baicalein group by 5 logs, 4 logs, 5 logs, for S. mutans, C. albicans and S. mutans-C. albicans biofilms, respectively, compared to UC group. When tested in a S. mutans-C. albicans in vitro caries model, the baicalein group substantially reduced enamel demineralization under biofilms, yielding an enamel hardness that was 2.75 times greater than that of UC group. Hence, the novel baicalein method is promising to inhibit dental caries by reducing biofilm formation and protecting enamel hardness.
Characteristic concept representations

In machine learning, concepts have traditionally been represented and learned using algorithms that represent only those characteristics that discriminate between two or more classes. Representations such as decision trees and rules provide increased classification ability, but do not provide a general characteristic description of the concepts. This dissertation explores the use of characteristic descriptions for concepts in the classification task. In our research, characteristic descriptions are represented by prototypes. The methods described focus on the issue of learning multiple prototypes for a class when necessary. Two diverse methods are developed. The first method, PL (Prototype Learner), attempts to separate examples into smaller subgroups with similar characteristics. The second method, SNMC (Symbolic Nearest Mean with Clustering), applies clustering techniques to separate groups of examples, using classification accuracy on the training set as its heuristic. The groups of examples in each of these algorithms are simplified into prototypes representing the examples and a nearest neighbor classification approach is used for class prediction. Our empirical results show that SNMC has an increase in average classification accuracy of about 2% over C4.5 and PEBLS on 20 domains from the UCI data repository. The experimental results on the UCI domains showed that SNMC classifies the best in average rank and accuracy of the six algorithms compared. PL classifies favorably to C4.5 and PEBLS on the same domains. These results show that prototype concept representations can be successfully applied to the classification task. The last portion of this dissertation introduces a task, inductive inference, which is a generalization of classification. In this task the algorithm must make predictions about any of the attribute values. We discuss five subtasks of the inductive inference task, including the classification task. We also discuss metrics that can be used to evaluate algorithms on these tasks. We contend that these metrics used in conjunction with classification accuracy provides a more general evaluation methodology than classification accuracy alone. We provide comparisons among PL, C4.5, PEBLS, and COBWEB.
TCAD Simulation of Single Event Transient in Si Bulk MOSFET at Cryogenic Temperature

In this paper, the functional relationship between temperature and single event transient currents caused by heavy-ion striking using TCAD simulation is investigated from 77K to 300 K on 65nm Si bulk n MOSFET. TCAD simulation shows that temperature has a significant influence on the trends of heavy-ions-induced current. The peak value of drain current and collected charge in MOSFET reach the maximum value at 200 K, while the SET fall time decreases monotonously as temperature decrease from 300 K to 77 K at four LET values. The extracted analysis found that the enhancement of bipolar amplification effect is the main reason for the increase of collected charge and broader pulse width at high temperatures. The above conclusions provide a theoretical basis for the wide application of 65nm devices in 77 K-300 K space environment.
Deep Brain Stimulation Surgery Using a Mobile Intraoperative CT Scanner

Introduction Deep brain stimulation (DBS) is widely used for the treatment of movement disorders. Precise placement of electrodes is critical for treatment success. The aim of this study was to analyze the accuracy of the intraoperative computer tomography (CT) images compared to that of a traditional fixed CT for patients undergoing DBS procedures. Methods We retrospectively analyzed the charts from 30 patients who underwent DBS. In group 1, 10 patients underwent electrode implantation surgery using a fixed CT scanner for pre- and post-operative (OP) images. In group 2, 20 patients underwent surgery using an intraoperative CT scanner for pre- and post-operative images, as well as a fixed CT scanner for post-operative images. We compared the average pre-operative localizer box registration error acquired in these two groups. We also analyzed, in group 2, the final electrode position given on each post-operative CT images. We compared the average Euclidean distances between each set of cartesian coordinates to assess target accuracy between both scanning methodologies. Results Thirty patients had ages ranging from 40 to 88 years, with a median of 69 years old. In the 20 patients who utilized an intraoperative CT scanner pre-operatively in group 2, the mean error, given by the Medtronic software (Medtronic Minimally Invasive Therapies, Minneapolis, MN) with the Leksell frame on, was 0.37. For the 10 pre-operative scans with the stealth fixed CT scanner in group 1, the mean error was 0.44 (p = 0.13). In group 2, the average of the 20 Euclidean distances for each target, in those 20 patients who had post-operative images with both scanners, was 0.36. Conclusion We concluded that the accuracy of the intraoperative CT scanner is comparable to the gold standard fixed CT scanner for DBS electrode planning and placement, as well as for positioning confirmation after the electrodes are in place.
Risk Assessment of Water Inrush in Tunnels: A Case Study of a Tunnel in Guangdong Province, China

Fractured tectonic zones with developed groundwater are one of the major causes of water inrush in the construction of igneous tunnels; thus, it is highly important to assess the risk of water inrush. In this study, a total of six evaluation attributes, groundwater level, amount of inrush water, permeability coefficient, strength of the surrounding rock, rock integrity, and width of the jointed and fault fracture zone, were selected for the risk assessment of water inrush, and fuzzy theory was applied to the treatment of the uncertainty in the evaluation attributes. On this basis, the MULTIMOORA (multiple multi-objective optimization by ration analysis) and the model of nearness degree of incidence were combined to obtain the new model of MULTIMOORA–nearness degree of incidence for the risk assessment of water inrush. A deep-underground, extra-long tunnel under construction in southern China was used as an example for validation. The six tunnel sections assessed on site were ZK91 + 195~236, K91 + 169~186.5, K91 + 203~238, ZK94 + 238~198, K94 + 112~82, and K94 + 076~034. K94 + 112~82 was assessed at risk Level III, while the remainder were assessed at risk Level IV. The site conditions were also Level III for section K94 + 112~82 and Level IV for the remainder. The assessment results for the above six tunnel sections are consistent with the field conditions, which verified the validity of the model of the MULTIMOORA–nearness degree of incidence.
Endoscopic Ultrasound-Guided Fine-Needle Biopsy versus Fine-Needle Aspiration in the Diagnosis of Focal Liver Lesions: Prospective Head-to-Head Comparison

Endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) or fine-needle aspiration (EUS-FNA) from focal liver lesions are indicated in selected cases, but there has been no previous comparison of needle types of the same size. The aim of our study was to compare the histologic diagnostic accuracy and adequacy of cores obtained with EUS-FNB needles in contrast to those obtained with FNA needles in focal liver lesions. This prospective one-center study included patients with left lobe hepatic focal lesions with contraindications for percutaneous liver biopsy or need for EUS for concomitant lesions. Each patient had one pass of 22G EUS-FNB (Franseen) needle and one pass of 22G EUS-FNA in a crossover manner, without macroscopic on-site evaluation. Each sample was analyzed separately for histologic adequacy and diagnosis. The final diagnosis was based on histology results or on imaging follow-up in the case of negative biopsies. The EUS-FNB samples (n = 30) were found to be more adequate for histologic analysis, with more cellularity and longer tissue aggregates than the EUS-FNA samples (n = 30). The accuracy of EUS-FNB was 100%, whereas that of EUS-FNA was 86.7% (p = 0.039). No post-procedure complications were noted. The 22G EUS-FNB needle proved superior to 22G EUS-FNA in terms of tissue acquisition diagnostic accuracy and histologic adequacy in focal liver lesions.
The Forward and Lateral Tilt Angle of the Neck and Trunk Measured by Three-Dimensional Gait and Motion Analysis as a Candidate for a Severity Index in Patients with Parkinson’s Disease

(1) Objective: To evaluate the usefulness of a three-dimensional motion-analysis system (AKIRA®) as a quantitative measure of motor symptoms in patients with Parkinson’s disease (PD). (2) Method: This study included 48 patients with PD. We measured their motion during 2 m of walking using AKIRA®, we calculated the tilt angles of the neck and trunk, ankle height, and gait speed, then we compared these parameters with the MDS-UPDRS and the Hoehn and Yahr scale. Furthermore, we measured these AKIRA indicators before and after 1 year of observation. (3) Results: The forward tilt angle of the neck showed a strong correlation with the scores on parts II, III, and the total MDS-UPDRS, and the tilt angle of the trunk showed a moderate correlation with those measures. The lateral tilt angle of the trunk showed a moderate correlation with a freezing of the gait and a postural instability. Regarding changes over the course of 1 year (n = 34), the total scores on part III of the MDS-UPDRS and the forward tilt angle of the neck improved, while the lateral tilt angle of the trunk worsened. (4) Conclusion: Taken together, the forward and lateral tilt angles of the neck and trunk as measured by AKIRA® can be a candidate for quantitative severity index in patients with PD.
Impact of the Digital Transformation of Small- and Medium-Sized Listed Companies on Performance: Based on a Cost-Benefit Analysis Framework

The ultimate goals of digital transformation, which is the only means of realizing the sustainable development of current enterprises, are to achieve cost reduction, efficiency improvement, and innovation for enterprises. However, there are limited empirical quantitative studies on the investment costs and impact of organizational performance related to the digital transformation of small- and medium-sized listed companies. This study first uses Excel VBA to sort the digital transformation panel data of 319 small- and medium-sized listed companies in China’s Shanghai and Shenzhen A-share markets from 2007 to 2020. Thereafter, Stata, a general-purpose statistical software package, is used to analyze the impact of digital transformation on the performance of small- and medium-sized listed companies. The results show that the digital transformation of small- and medium-sized listed companies has a positive correlation with operational performance, and an inverted U-shaped relationship with innovation performance; however, the U-shaped relationship with financial performance is not significant. For small- and medium-sized listed companies to promote digital transformation, first, it must help the organization to improve its internal operational efficiency; second, it can optimize and improve the organization’s financial efficiency; and finally, through innovative efficiency, the organization can continue to develop steadily and improve its organizational resilience.
A Comparative Study of Bayesian and Dempster-Shafer Fusion on Image Forgery Detection

With the advent of digital imaging, it has become fairly easy to modify the content of an image in many different ways while leaving no obvious visual clue. This has further challenged many existing image forensic techniques. The techniques which perform well with one specific kind of forgeries still suffer from strong limitations when dealing with realistic tampered images. Therefore, an effective strategy for tampering detection and localization requires the application of fusion technique. Although there have been extensive researches on fusion technique on different fields, there has never been a systematic study about fusion technique in image forensic domain. In this paper, we provide a thorough review on the state-of-the-art of fusion methods applied in tampering image detection and localization domain. We then present a practical comparison of two popular fusion techniques: Bayesian and Dempster-Shafer theory (DST) based fusion. The comparison relies on two applications which leverage the two aforementioned fusion techniques. In the first case, aggregating the decision maps of two forensic approaches: Photo Response Non Uniformity (PRNU) and statistical features based approaches has improved the forgery detection performance on saturated and dark regions of images. In the second case, integrating the decision maps of the forensic approach using demosaicing artifacts and the forensic approach using SIFT descriptors and local color dissimilarity maps has enhanced the detection performance on both copy-moved and copy-pasted forgeries images. Experiments show that the DST based fusion performs better in the first case while the Markov Random Field (MRF) based fusion performs better in the second case. It can be concluded that each technique has its own advantages and the best choice depends on each situation and users’ requirements.
Energy Efficiency Optimization in Adaptive Transmit Antenna Selection Systems With Limited Feedback

We investigate the energy efficiency (EE) of cellular downlink transmissions with limited feedback, where the channel state information (CSI) assumed to be available at the destination is quantized and fed back to the multiantenna base station for transmitting precoding purposes. Typically, the employment of antennas with poor channel quality marginally enhances system performance but substantially consumes energy resources such as circuit power. Thus, we propose an adaptive transmit antenna selection (ATAS) scheme via setting a channel gain threshold to select transmit antennas with relatively good channel condition for reducing power resource consumption. Meanwhile, the conventional single transmit antenna (STAS) scheme is considered as a baseline. In order to improve EE while considering the impact of feedback overhead, we conduct the effective EE analysis for both ATAS and STAS schemes and show that a maximal effective EE of the proposed ATAS scheme can be achieved by jointly adjusting the channel gain threshold and transmit power. As a consequence, we propose the ergodic effective EE optimization of ATAS (eEEEO-ATAS) scheme to maximize the effective EE. Numerical simulation results demonstrate that the proposed ATAS scheme performs better than the STAS scheme in terms of effective EE.
FASTNN: A Deep Learning Approach for Traffic Flow Prediction Considering Spatiotemporal Features

Traffic flow forecasting is a critical input to intelligent transportation systems. Accurate traffic flow forecasting can provide an effective reference for implementing traffic management strategies, developing travel route planning, and public transportation risk assessment. Recent deep learning approaches of spatiotemporal neural networks to predict traffic flow show promise, but could be difficult to separately model the spatiotemporal aggregation in traffic data and intrinsic correlation or redundancy of spatiotemporal features extracted by the filter of the convolutional network. This can introduce biases in the predictions that interfere with subsequent planning decisions in transportation. To solve the mentioned problem, the filter attention-based spatiotemporal neural network (FASTNN) was proposed in this paper. First, the model used 3-dimensional convolutional neural networks to extract universal spatiotemporal dependencies from three types of historical traffic flow, the residual units were employed to prevent network degradation. Then, the filter spatial attention module was constructed to quantify the spatiotemporal aggregation of the features, thus enabling dynamic adjustment of the spatial weights. To model the intrinsic correlation and redundancy of features, this paper also constructed a lightweight module, named matrix factorization based resample module, which automatically learned the intrinsic correlation of the same features to enhance the concentration of the model on information-rich features, and used matrix factorization to reduce the redundant information between different features. The FASTNN has experimented on two large-scale real datasets (TaxiBJ and BikeNYC), and the experimental results show that the FASTNN has better prediction performance than various baselines and variant models.