Miracle-dz/UniFAR-FTUs huggingface dataset:
Porous construction and surface modification of titanium-based materials for osteogenesis: A review

Titanium and titanium alloy implants are essential for bone tissue regeneration engineering. The current trend is toward the manufacture of implants from materials that mimic the structure, composition and elasticity of bones. Titanium and titanium alloy implants, the most common materials for implants, can be used as a bone conduction material but cannot promote osteogenesis. In clinical practice, there is a high demand for implant surfaces that stimulate bone formation and accelerate bone binding, thus shortening the implantation-to-loading time and enhancing implantation success. To avoid stress shielding, the elastic modulus of porous titanium and titanium alloy implants must match that of bone. Micro-arc oxidation technology has been utilized to increase the surface activity and build a somewhat hard coating on porous titanium and titanium alloy implants. More recently, a growing number of researchers have combined micro-arc oxidation with hydrothermal, ultrasonic, and laser treatments, coatings that inhibit bacterial growth, and acid etching with sand blasting methods to improve bonding to bone. This paper summarizes the reaction at the interface between bone and implant material, the porous design principle of scaffold material, MAO technology and the combination of MAO with other technologies in the field of porous titanium and titanium alloys to encourage their application in the development of medical implants.
Robust pyroptosis risk score guides the treatment options and predicts the prognosis of bladder carcinoma

Background Bladder carcinoma (BLCA) is a heterogeneous disease that makes it difficult to achieve proper individual treatment and predict prognosis. This study aimed to develop a risk score from a new perspective of pyroptosis and guide accurate treatment and prognosis prediction for BLCA. Methods The TCGA-BLCA cohort data were downloaded from The Cancer Genome Atlas database. Two external validation cohorts were collected from the Gene Expression Omnibus. Another independent validation cohort (the Xiangya cohort) was recruited from our hospital. The least absolute shrinkage and selector operation (LASSO) algorithm and Cox regression models were used to establish the pyroptosis risk score. Thereafter, we correlated the pyroptosis risk score with prognosis, tumor microenvironment (TME) immune hallmarks, and multiple treatments, including anticancer immunotherapy, chemotherapy, radiotherapy, and targeted therapy. Results The pyroptosis risk score was an independent prognostic predictor of BLCA. We found that the activities of multiple steps of the anticancer immune response cycle, such as the release of cancer cell antigens, CD8 T cell recruitment, and NK cell recruitment, were significantly higher in the high-risk score group than in the low-risk score group. In addition, the infiltration levels of the corresponding tumor-infiltrating immune cells (TIICs), such as CD8 T cells and NK cells, were positively correlated with the pyroptosis risk score. Thus, BLCA with a high-risk score may be associated with inflamed phenotypes. Simultaneously, the expression of multiple immune checkpoints (such as PD-L1, CTLA-4, and PD-1) and enrichment scores of gene signatures positively correlated with immunotherapy response were positively correlated with the pyroptosis risk score. Therefore, patients with a high pyroptosis risk score may be more sensitive to immunotherapy. In addition, patients with high pyroptosis risk scores may be more sensitive to chemotherapeutic drugs, such as cisplatin, docetaxel, and paclitaxel. In addition, the pyroptosis risk score accurately predicted the molecular subtypes of BLCA, which were cross-validated in several independent systems. Conclusions This study developed and validated a robust pyroptosis risk score that can predict the clinical outcomes and TME immune phenotypes of BLCA. In summary, the pyroptosis risk score helps drive precision therapy in patients with BLCA.
Transcranial current stimulation in epilepsy: A systematic review of the fundamental and clinical aspects

Purpose Transcranial electrical current stimulation (tES or tCS, as it is sometimes referred to) has been proposed as non-invasive therapy for pharmacoresistant epilepsy. This technique, which includes direct current (tDCS) and alternating current (tACS) stimulation involves the application of weak currents across the cortex to change cortical excitability. Although clinical trials have demonstrated the therapeutic efficacy of tES, its specific effects on epileptic brain activity are poorly understood. We sought to summarize the clinical and fundamental effects underlying the application of tES in epilepsy. Methods A systematic review was performed in accordance with the PRISMA guidelines. A database search was performed in PUBMED, MEDLINE, Web of Science and Cochrane CENTRAL for articles corresponding to the keywords “epilepsy AND (transcranial current stimulation OR transcranial electrical stimulation)”. Results A total of 56 studies were included in this review. Through these records, we show that tDCS and tACS epileptic patients are safe and clinically relevant techniques for epilepsy. Recent articles reported changes of functional connectivity in epileptic patients after tDCS. We argue that tDCS may act by affecting brain networks, rather than simply modifying local activity in the targeted area. To explain the mechanisms of tES, various cellular effects have been identified. Among them, reduced cell loss, mossy fiber sprouting, and hippocampal BDNF protein levels. Brain modeling and human studies highlight the influence of individual brain anatomy and physiology on the electric field distribution. Computational models may optimize the stimulation parameters and bring new therapeutic perspectives. Conclusion Both tDCS and tACS are promising techniques for epilepsy patients. Although the clinical effects of tDCS have been repeatedly assessed, only one clinical trial has involved a consistent number of epileptic patients and little knowledge is present about the clinical outcome of tACS. To fill this gap, multicenter studies on tES in epileptic patients are needed involving novel methods such as personalized stimulation protocols based on computational modeling. Furthermore, there is a need for more in vivo studies replicating the tES parameters applied in patients. Finally, there is a lack of clinical studies investigating changes in intracranial epileptiform discharges during tES application, which could clarify the nature of tES-related local and network dynamics in epilepsy.
Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization

Intraoperative assessment of tissue can be guided through fluorescence imaging which involves systemic dosing with a fluorophore and subsequent examination of the tissue region of interest with a near-infrared camera. This typically involves administering indocyanine green (ICG) hours or even days before surgery and intraoperative visualization at the time predicted for steady-state signal-to-background status. Here, we describe our efforts to capture and utilize the information contained in the first few minutes after ICG administration from the perspective of both signal processing and surgical practice. We prove a method for characterization of cancerous versus benign rectal lesions now undergoing further development and validation via multicenter clinical phase studies.
Analysis of Service Value and Changing Characteristics of Ecological Land in Simao Basin in Recent 20 Years Based on Visualization of Remote Sensing

Ecological land has irreplaceable functions such as regulation of drought and flood, conservation of biodiversity, aesthetic education, and heritage preservation. It's helpful to grasp the response of ecological land to the process of urbanization and provide localized case for reference by identifying the changed characteristics of ecological land and analyzing its service value. Based on the five-phase Landsat remote sensing images from 1997 to 2017 as the data source, combined with the visualization technology of remote sensing, which is used for the dynamic analysis and thematic map expression of service value and changed characteristics of ecological land in Simao basin. The results show that:(1) the classification method combined with support vector machine (SVM) after the decomposition of mixed pixels is more suitable for the identification of ecological land information in the study area, and the accuracy can reach 85.53%. (2)The overall number of dynamical change of ecological land in the study area shows a downward and convergent trend, from 3164.13hm2 in 1997 to 2745.63hm2 in 2017. The woodland area as a whole shows a decreasing trend of varying degrees, but there is a small increase in between 2012 and 2017; and the garden land area shows the “inverted V -shaped” trend in 2002 when is the main boundary line, while the increase and decrease of farmland area are not fixed and alternate, showing irregular changes. (3)The change of the total service value of ecological land in the study area is declining, which is from 12.7217 × 107 yuan in 1997 to 9.6565 × 107 yuan in 2017, the ecological service value decrease by 3.0652 × 107 yuan during the period, and the reduction of average annual is 0.1533 × 107 yuan/a. The changing characteristics of ecological land in Simao basin within 20 years reflects the process of urban expansion, which aggravates the fragmentation of the ecological landscape pattern to a certain extent, interferes with the ecological self-efficacy of local ethnic groups, and damages the stability and diversity of the ecosystem in study area.
Resilience-Focused Monitoring Framework for Edge Systems

Developing applications on the edge is a difficult task, as issues arise when multiple nodes have to operate in unison. Common challenges comprise correctness, performance, and forensics analysis after a system crash. The challenges are aggravated when both edge and fog devices get in the picture, since the limited resources of some of them exacerbate any possible issue, and create the need for a robust mechanism to collect log data to make sense of the system's behavior. The envisioned monitoring layer should collect log data regarding performance and errors, protect the data until they are delivered to an external data processing facility, and be easy to integrate into the deployed system. This paper proposes C-Mon, a resilience-focused monitoring framework for service-oriented applications that tests timing constraints against live log data. The monitoring framework is integrated with OpenAPI Generator, the mainstream ReST code generator we used to generate client and server interfaces and monitoring mechanisms. Log data are cached locally on the disk of the fog/edge devices and transferred to the monitoring server only when enough CPU cycles and network bandwidth are available, at the same time enabling forensics analysis.
Parcel-level Land Boundary Extraction Based on High-resolution Remote Sensing Image with Convolutional Neural Networks

Cultivated land is an important basis for human production and life. The rapid development of high-resolution satellite remote sensing technology and the deep learning method makes it possible to finely identify farmland parcels in large re-gions with high efficiency. In this paper, GaoFen-2(GF-2) remote sensing image is used as the data source to extract farmland parcels, and a data-driven, robust and general automatic extraction method of farmland parcels is proposed. The extraction task of farmland parcels is divided into two aspects: the extraction of farmland parcels' extent and the extraction of farmland parcels' boundary. The extraction results of these two aspects are integrated, and on this basis, an effective post-processing mechanism based on expert rules is established, to realize the accurate identification of farmland parcels. Experimental results show that our method can effectively extract farmland boundary.
GolfPose: Golf Swing Analyses with a Monocular Camera Based Human Pose Estimation

With the rapid developments of computer vision and deep learning technologies, artificial intelligence takes a more and more important role in sports analyses. In this paper, to attain the objective of automated golf swing analyses, we propose a lightweight temporal-based 2D human pose estimation (HPE) method, called GolfPose, which achieves improved performance than the state-of-the-art image-based HPE methods. Unlike traditional image-based methods, our temporal-based method, designed for efficient and effective golf swing analyses, takes advantage of the temporal information to improve the estimation accuracy of fast-moving and partially self-occluded keypoints. Furthermore, in order to make sure the golf swing analyses can run on mobile devices, we optimize the model architecture to achieve real-time inference. With around 10% of the parameters and half of the GFLOPs used in the state-of-the-art HRNet, our proposed GolfPose model can achieve 9.16 mean pixel error (MPE) in our golf swing dataset, compared with 9.20 MPE for HRNet. Furthermore, the proposed temporal-based method, facilitated with golf club detection(GCD), significantly improves the accuracy of keypoints on the golf club from 13.98 to 9.21 MPE.
Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for Intelligent Vehicles

Intelligent vehicles can effectively improve traffic congestion and road traffic safety. Adaptive cruise following-control (ACFC) is a vital part of intelligent vehicles. In this paper, a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model, type-2 fuzzy control, feedforward + fuzzy proportion integration (PI) feedback (F+FPIF) control, and inverse longitudinal dynamics model of vehicles. Firstly, a traditional variable time headway model is improved considering the acceleration of the lead car. Secondly, an interval type-2 fuzzy logic controller (lT2 FLC) is designed for the upper structure of the ACFC system to simulate the driver's operating habits. To reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration, the control strategy of F+FPIF is given for the lower control structure. Thirdly, the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method (no lower controller for tracking desired acceleration) separately. Meanwhile, the proportion integration differentiation (PID), linear quadratic regulator (LQR), subsection function control (SFC) and type-l fuzzy logic control (Tl FLC) are respectively compared with the IT2 FLC in control performance under different scenes. Finally, the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.
Automatic detection of WCE bleeding frames using hybrid features and machine learning algorithms

Wireless capsule endoscopy (WCE), an emerging non-invasive, imaging technology is used by clinicians to detect anomalies like polyp, ulcer, bleeding, etc., especially from diseased small bowel of the human body. The lengthy duration of captured WCE video is difficult to analyze and prone to physician error. So, machine learning (ML) algorithms are being used to automatically detect such anomalies and aid in their early diagnosis, management, and treatment. This work proposes an automatic detection of WCE bleeding and non-bleeding frames using five different ML algorithms namely k-nearest neighbors, random forest, decision tree, and support vector machine (SVM). Statistical features and convolution based features computed from MICCAI 2017 red lesion dataset have been used as input. Correlation matrix, filter, and feature mapping analysis has been done during feature estimation. All the pipelines have been evaluated based on accuracy, precision, F1-score, recall, and specificity. Convolution feature matrix and SVM-based pipeline performed best and achieved a precision and specificity up-to 98.11% and 98.55% respectively on multiple runs. The proposed work has performed well when compared with existing state-of-the-art (SOTA) ML works and precisely detects bleeding frames.
Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media

Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this dis-parity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, pro-vaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics.
Development and Control of Underwater Gliding Robots: A Review

As one of the most effective vehicles for ocean development and exploration, underwater gliding robots (UGRs) have the unique characteristics of low energy consumption and strong endurance. Moreover, by borrowing the motion principles of current underwater robots, a variety of novel UGRs have emerged with improving their maneuverability, concealment, and environmental friendliness, which significantly broadens the ocean applications. In this paper, we provide a comprehensive review of underwater gliding robots, including prototype design and their key technologies. From the perspective of motion characteristics, we categorize the underwater gliding robots in terms of traditional underwater gliders (UGs), hybrid-driven UGs, bio-inspired UGs, thermal UGs, and others. Correspondingly, their buoyancy driven system, dynamic and energy model, and motion control are concluded with detailed analysis. Finally, we have discussed the current critical issues and future development. This review offers valuable insight into the development of next-generation underwater robots well-suited for various oceanic applications, and aims to gain more attention of researchers and engineers to this growing field.
Fabric Defect Detection VIA Unsupervised Neural Networks

Surface defect detection is a necessary process for quality control in the industry. Currently, popular neural network based defect detection systems usually need to use a large number of defect samples for training, and it takes a lot of manpower to make marks and clean the subsequent data. This is a time-consuming process, and it makes the whole system less effective. In this paper, a deep neural network based model for fabric surface defect detection is proposed and it only uses positive clean samples for training. Since the proposed model does not collect negative defective samples for learning, the landing time of whole system is greatly reduced. In the experiment, we use RTX3080 in the TensorRT model with 250 FPS, and the detection accuracy is 99%, which is suitable for production lines with real time requirements.
An Approximating Twiddle Factor Coefficient Based Multiplier for Fixed-Point FFT

Fast Fourier Transform (FFT) is playing an important role in signal processing. This paper proposes a FFT -specific approximate multiplier design to improve the energy efficiency. The approximate multiplier is based on the approximated twiddle factor and compressor to reduce the energy during partial product accumulation. Instead of complex arithmetic operations, the design can achieve reduced power consumption and hardware cost with limited error.
Doppelgänger Saliency: Towards More Ethical Person Re-Identification

Modern surveillance systems have become increasingly dependent on artificial intelligence to provide actionable information for real-time decision making. A critical question relates to how these systems handle difficult ethical dilemmas, such as the re-identification of similar looking individuals. Potential misidentification of individuals can have severe negative consequences, as evidenced by recent headlines of individuals who were wrongly targeted for crimes they did not commit based on false matches. A computer vision-based saliency algorithm is proposed to help identify pixel-level differences in pairs of images containing visually similar individuals, which we term "doppelgängers. " The computed saliency maps can alert human users of the presence of doppelgängers and provide important visual evidence to reduce the potential of false matches in these high-stakes situations. We show both qualitative and quantitative saliency results on doppelgängers found in a video-based person re-identification dataset (MARS) using three different state-of-the-art models. Our results suggest that this novel use of visual saliency can improve overall outcomes by helping human users in the person re-identification setting, while assuring the ethical and trusted operation of surveillance systems.
An Adaptive Beamforming Approach Applied to Planar Antenna Arrays Using Neural Networks

Future wireless networks depend on the improvement of current smart antenna operations so that they maintain high accuracy levels at low response times. Utilizing machine learning techniques, it is possible to replace the currently used algorithms with a much faster yet reliable alternative. In this study, we focus on adaptive beamforming applied to a planar antenna array using the null steering beamforming algorithm (NSB). We test different types of deep neural networks (DNNs) as potential alternative beamformers, by comparing their accuracy to that of the NSB algorithm. The application concerns an 8×8 planar antenna array composed of isotropic elements. The DNNs tested here are the traditional feedforward neural networks and recurrent neural networks using either gated recurrent units or long short-term memory units. In addition, we investigate each DNN type to make sure we are utilizing the best version of each neural network architecture.