Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal of tangent graph and extended tangent graph for path planning of mobile robots

A tangent graph (T-graph) for path planning of mobile robots among polygonal and curved obstacles is proposed. In the T-graph, the nodes correspond to tangent points on obstacle boundaries and the edges represent collision-free common tangents of the obstacles or convex boundary segments between the tangent points. The T-graph requires O(K/sup 2/) memory, where K denotes number of convex segments of obstacle boundaries. Further, to avoid recomputation of configuration spaces of circular robots in a polygonal environment when their radii has changed, an extended tangent graph (ETG) is developed. The ETG does not depend on obstacles in configuration spaces of robots but on obstacles in real workspace. Consequently, it is possible to plan a path flexibly according to different robots' sizes and safety distances by the ETG. <<ETX>>
Insulin gene enhancer binding protein 1 induces adipose tissue‑derived stem cells to differentiate into pacemaker‑like cells

Hybrid approaches combining gene- and cell-based therapies to make biological pacemakers are a promising therapeutic avenue for bradyarrhythmia. The present study aimed to direct adipose tissue-derived stem cells (ADSCs) to differentiate specifically into cardiac pacemaker cells by overexpressing a single transcription factor, insulin gene enhancer binding protein 1 (ISL-1). In the present study, the ADSCs were transfected with ISL‑1 or mCherry fluorescent protein lentiviral vectors and co-cultured with neonatal rat ventricular cardiomyocytes (NRVMs) in vitro for 5-7 days. The feasibility of regulating the differentiation of ADSCs into pacemaker-like cells by overexpressing ISL-1 was evaluated by observation of cell morphology and beating rate, reverse transcription-quantitative polymerase chain reaction analysis, western blotting, immunofluorescence and analysis of electrophysiological activity. In conclusion, these data indicated that the overexpression of ISL-1 in ADSCs may enhance the pacemaker phenotype and automaticity in vitro, features which were significantly increased following co‑culture induction.
Rapid Tartrazine Determination in Large Yellow Croaker with Ag Nanowires Using Surface-Enhanced Raman Spectroscopy

In this work, surface-enhanced Raman spectroscopy (SERS) technology coupled with Ag nanowires was shown to be a promising tool in the detection of tartrazine in large yellow croaker for the first time. Ag nanowires with a uniform diameter were fabricated by an efficient and manageable polyol method. The partial least square model was established for the quantitative analysis of tartrazine, which showed a relatively high linear correlation between actual and predicted concentrations of standard tartrazine solutions. An optimal sample preparation method was also selected and used to extract tartrazine from large yellow croaker within 20 min. The lowest concentration detected was 20.38 ng/cm2, which fully meets the requirements of tartrazine testing in aquatic products. This study indicated that SERS technology combined with the as-prepared Ag nanowires could detect tartrazine sensitively and provide an easily operable and time-saving way to monitor tartrazine in large yellow croaker.
Secure Transmission for Heterogeneous Cellular Networks With Wireless Information and Power Transfer

In this paper, we investigate an artificial-noise-aided secure beamforming design for simultaneous wireless information and power transfer in a two-tier downlink heterogeneous cellular network, in which each energy receiver in a femtocell is seen as a potential eavesdropper to wiretap the confidential message intended for the information receiver. Our design objective is to maximize the secrecy rate at the information receiver, while satisfying the signal-to-interference-plus-noise ratio requirement of each macrouser and the energy harvesting and transmit power constraints. Both the scenarios of perfect and imperfect channel state information (CSI) are considered. With perfect CSI, the formulated optimization problem constitutes a difference of a convex function programming problem, which is hard to directly solve. To tackle this challenge, we transform it into a series of semidefinite programs by using successive convex approximation, and an iterative algorithm is proposed to arrive at a provably convergent solution. With imperfect CSI, we address robust secure beamforming relying on the worst-case design philosophy. To circumvent this predicament, we resort to the $\mathcal {S}$ -procedure to reformulate the robust quadratic matrix inequality (QMI) constraints and then obtain the linear matrix inequality representations for these QMIs. Numerical results are finally presented to demonstrate the performance of our proposed schemes in improving the secrecy rate of heterogeneous cellular networks.
Message Integration Authentication in the Internet-of-Things via Lattice-Based Batch Signatures

The internet-of-things (also known as IoT) connects a large number of information-sensing devices to the Internet to collect all kinds of information needed in real time. The reliability of the source of a large number of accessed information tests the processing speed of signatures. Batch signature allows a signer to sign a group of messages at one time, and signatures’ verification can be completed individually and independently. Therefore, batch signature is suitable for data integration authentication in IoT. An outstanding advantage of batch signature is that a signer is able to sign as many messages as possible at one time without worrying about the size of signed messages. To reduce complexity yielded by multiple message signing, a binary tree is usually leveraged in the construction of batch signature. However, this structure requires a batch residue, making the size of a batch signature (for a group of messages) even longer than the sum of single signatures. In this paper, we make use of the intersection method from lattice to propose a novel generic method for batch signature. We further combine our method with hash-and-sign paradigm and Fiat–Shamir transformation to propose new batch signature schemes. In our constructions, a batch signature does not need a batch residue, so that the size of the signature is relatively smaller. Our schemes are securely proved to be existential unforgeability against adaptive chosen message attacks under the small integer solution problem, which shows great potential resisting quantum computer attacks.
Experimental Comparison of Integrated Transformers in a 28 nm Bulk CMOS Technology

This paper presents a systematic comparison of integrated transformer structures realized in a 28 nm bulk CMOS technology and characterized up to the millimetre-wave frequencies. First, the rectangular versus octagonal coil shapes are compared. Next, different magnetic coupling mechanisms are examined: interleaved transformers using lateral coupling, stacked ones using vertical coupling and symmetrical transformers using the combination of both coupling mechanisms. Finally, the comparison is repeated for different turn ratios: 1:1, 1:2 and 2:2. The electrical properties of the transformers, such as self-inductance, quality factor, self-resonance frequencies and coupling coefficient are evaluated. Advantages and disadvantages of these structures are discussed with regard to their applicability in various active circuits. One of the main targets is to obtain the best transformer's electrical parameters for a given fixed area and given metallization option. The structures are measured up to 70 GHz. On-chip interconnects are de-embedded and the devices are compared to the models simulated by EM field solver.
Co-expression Gene Network Analysis and Functional Module Identification in Bamboo Growth and Development

Bamboo is one of the fastest-growing non-timber forest plants. Moso bamboo (Phyllostachys edulis) is the most economically valuable bamboo in Asia, especially in China. With the release of the whole-genome sequence of moso bamboo, there are increasing demands for refined annotation of bamboo genes. Recently, large amounts of bamboo transcriptome data have become available, including data on the multiple growth stages of tissues. It is now feasible for us to construct co-expression networks to improve bamboo gene annotation and reveal the relationships between gene expression and growth traits. We integrated the genome sequence of moso bamboo and 78 transcriptome data sets to build genome-wide global and conditional co-expression networks. We overlaid the gene expression results onto the network with multiple dimensions (different development stages). Through combining the co-expression network, module classification and function enrichment tools, we identified 1,896 functional modules related to bamboo development, which covered functions such as photosynthesis, hormone biosynthesis, signal transduction, and secondary cell wall biosynthesis. Furthermore, an online database (http://bioinformatics.cau.edu.cn/bamboo) was built for searching the moso bamboo co-expression network and module enrichment analysis. Our database also includes cis-element analysis, gene set enrichment analysis, and other tools. In summary, we integrated public and in-house bamboo transcriptome data sets and carried out co-expression network analysis and functional module identification. Through data mining, we have yielded some novel insights into the regulation of growth and development. Our established online database might be convenient for the bamboo research community to identify functional genes or modules with important traits.
The determination of the parameters of a dynamic process

The paper shows how the mathematical methods of steepest descent may be used to minimize certain functions of the ‘satisfaction error’ of a dynamic process.This minimization procedure forms the basis of certain automatic techniques for determining the coefficients or parameters of the process transfer function. The techniques, which are easily mechanized from normal analogue computer components, appear to be limited to the investigation of low order processes. In addition to their value as a new approach to the experimental analysis of dynamic systems (termed the ‘analysis of dynamic experiments’), it is shown that equipment of this type could be utilized in the design of a simple ‘self adaptive’ control system. Finally, possible future developments are discussed, with particular emphasis on the employment of digital mechanization.
Stability Based Filter Pruning for Accelerating Deep CNNs

Convolutional neural networks (CNN) have achieved impressive performance on the wide variety of tasks (classification, detection, etc.) across multiple domains at the cost of high computational and memory requirements. Thus, leveraging CNNs for real-time applications necessitates model compression approaches that not only reduce the total number of parameters but reduce the overall computation as well. In this work, we present a stability-based approach for filter-level pruning of CNNs. We evaluate our proposed approach on different architectures (LeNet, VGG-16, ResNet, and Faster RCNN) and datasets and demonstrate its generalizability through extensive experiments. Moreover, our compressed models can be used at run-time without requiring any special libraries or hardware. Our model compression method reduces the number of FLOPS by an impressive factor of 6.03X and GPU memory footprint by more than 17X, significantly outperforming other state-of-the-art filter pruning methods.
Rejecting, reframing, and reintroducing: trans people's strategic engagement with the medicalisation of gender dysphoria.

This article uses ethnographic methods to explore how transgender people engage the medicalisation of transgender experience in a U.S. context under the purview of the American Psychiatric Association. Building on sociological literature related to medicalisation, this paper argues that the lived experience of medicalisation is a non-linear, complex process whereby individual engagement with medical authority is both empowering and constraining in the lives of trans people. Inductive qualitative analysis of 158 hours of participant observation and 33 in-depth interviews with members of a transgender community organisation revealed that transgender individuals (i) reject a medical frame for gender dysphoria, (ii) embrace and stress the importance of gender-affirming medical technologies for individual identity development and social interaction and (iii) strategically reintroduce medical logics and embrace medical authority in order to facilitate medical and social recognition, validation and acceptance.
Latin American countries lead in Google search volumes for anorexia nervosa and bulimia nervosa: Implications for global mental health research

OBJECTIVE Although eating disorders occur worldwide, prevalence data beyond Euro-American contexts are sparse, and eating disorders largely remain outside global mental health agendas. This study aims to address this gap in the epidemiological literature through a global infodemiological comparison of Google search volumes for the topics "Anorexia nervosa" and "Bulimia nervosa. " METHOD Data were generated through Google Trends, a tool that assesses the relative search volumes of keywords and topics. Google Trends data, collected from January 1, 2004 to December 31, 2016, were obtained for the relative search volumes of the topics "Anorexia nervosa" and "Bulimia nervosa" worldwide. RESULTS The findings revealed that Latin American countries accounted for the top 10 in search volume for both topics, with Mexico ranked first until 2016, when Bolivia rose to the top of the list. DISCUSSION The results suggest that eating disorders are of considerable and consistent salience across Latin America, and that population-based, nationally representative studies to assess the prevalence of eating disorders in Latin American countries should be prioritized within global mental health research agendas.
Intent Prediction of Pedestrians via Motion Trajectories Using Stacked Recurrent Neural Networks

The problem of intent understanding between highly and fully automated vehicles and vulnerable road users (VRUs) such as pedestrians in urban traffic environment has got some momentum over the past few years. Previous work has been tackling the problem using two common approaches, namely dynamical motion modeling and motion planning. In this paper, a novel radical end-to-end data-driven approach is proposed for long-term intent prediction of VRUs in urban traffic environment based solely on their motion trajectories. In the proposed approach, we utilized the widely adopted architecture of recurrent neural networks with Long-Short Term Memory (LSTM) modules to form a deep stacked LSTM network. Three common approaches used in the literature were compared against our proposed approach over two different real-world datasets involving pedestrians collected from vehicle-based stereo cameras. The results over the testing datasets showed that the proposed approach achieved higher accuracies over most of the scenarios of the testing datasets with a small mean lateral position error of 0.48 m. Moreover, the proposed approach showed also a significant generalization capability over totally unobserved testing scenes during the training phase with only 0.58 m in mean lateral position error.
Synergistic enantioseparation systems with either cyclodextrins or cyclofructans and L‐alanine Tert butyl ester lactate

The combined use of chiral ionic liquids (CILs) and conventional chiral selectors (CSs) in CE, to establish a synergistic system, has proven to be an effective approach for the separation of enantiomeric pairs. In this study, a new CE method was developed, employing a binary system of a CS, either a cyclodextrin (CD) or a cyclofructan (CF), and a chiral amino acid ester‐based ionic liquid (AAIL), for the chiral separation of four basic, acidic and zwitterionic drug compounds. In particular, the enantioseparation of two anticoagulants, warfarin (WAR) and coumachlor (COU), a non‐opioid analgesic, nefopam (NEF) and a third‐generation antihistamine, fexofenadine (FXD), was examined, by supporting the BGE with a CS and the chiral AAIL L‐alanine tert butyl ester lactate (L‐AlaC4Lac). Parameters, such as the type of the CS, the concentration of both the CS and L‐AlaC4Lac, and the BGE pH, were methodically examined in order to optimize the chiral separation of each analyte. It was observed that, in some cases, the addition of the AAIL into the BGE improved both resolution (Rs) and efficiency (N) significantly. In other cases, the synergistic effect enabled baseline separation of analyte enantiomers, at a much lower concentration of the CS. Finally, after optimization of separation conditions, baseline separations (Rs>1.5) of all four analytes were achieved in less than 5 min.
Grading system for periodontitis by analyzing levels of periodontal pathogens in saliva

Periodontitis is an infectious disease that is associated with microorganisms that colonize the tooth surface. Clinically, periodontal condition stability reflects dynamic equilibrium between bacterial challenge and host response. Therefore, periodontal pathogen assessment can assist in the early detection of periodontitis. Here we developed a grading system called the periodontal pathogen index (PPI) by analyzing the copy numbers of multiple pathogens both in healthy and chronic periodontitis patients. We collected 170 mouthwash samples (64 periodontally healthy controls and 106 chronic periodontitis patients) and analyzed the salivary 16S rRNA levels of nine pathogens using multiplex, quantitative real-time polymerase chain reaction. Except for Aggregatibacter actinomycetemcomitans, copy numbers of all pathogens were significantly higher in chronic periodontitis patients. We classified the samples based on optimal cut-off values with maximum sensitivity and specificity from receiver operating characteristic curve analyses (AUC = 0.91, 95% CI: 0.87–0.96) into four categories of PPI: Healthy (1–40), Moderate (41–60), At Risk (61–80), and Severe (81–100). PPI scores were significantly higher in all chronic periodontitis patients than in the controls (odds ratio: 31.7, 95% CI: 13.41–61.61) and were associated with age, scaling as well as clinical characteristics including clinical attachment level and plaque index. Our PPI grading system can be clinically useful for the early assessment of pathogenic bacterial burden and follow-up monitoring after periodontitis treatment.
Incorporating LDA Based Text Mining Method to Explore New Energy Vehicles in China

Tracking the evolution of policy and development of new energy vehicles (NEVs) in China is of critical significance, because it helps generate rational prediction regarding future trends. To this end, this paper investigated the 5185 articles on NEV obtained from China National Knowledge Infrastructure by means of latent Dirichlet allocation (LDA)-based text mining. Word count was performed to highlight important keywords for different periods of years of publication. In addition, topics were identified from the abstracts of these articles using LDA. Findings suggest that attention on NEV in China has been growing and will continue to grow in the predictable future. Full electric vehicle, being the currently dominating form of NEV, will continue to play the leading role. Meanwhile, China’s NEV industry requires further investment into charging, battery, personnel training, and patent portfolio.
High Accuracy Frequency Detection Method Using 1 to mthHarmonics

This paper proposes the high Accuracy frequency detection method by using 1 to mthharmonics. In the proposed method, frequencies of harmonics are detected, and the sum of them is divided by the sum of these orders. The proposed method improves the frequency detection accuracy by $(\mathrm{m}+1)/(2\sqrt{\mathrm{m}})$ as compared with the conventional method using only the mthharmonic. The improvement of the frequency detection accuracy is derived from formulation and is confirmed by simulation and measurement results.
A new method for SAR speckle reduction

A new method derived from the Frost filter kernel for SAR (synthetic aperture radar) image speckle reduction is introduced. In the speckle reduction procedure, both the statistical information of the central pixel in the filter window and that of the neighborhood pixels are used. The experimental results on ERS-1 PRI images demonstrate the efficiency of the proposed method compared to other common filters.
An Improved Algorithm of Distributed QoS in Real-time Networks

In this paper, an improved algorithm of distributed QoS is proposed for real-time networks. This algorithm like a delay-constrained unicast routing(DCUR) algorithm uses either least-cost(LC) path or least-delay(LD) path of an active node, but when there is a loop, this algorithm is quite different from DCUR in choosing the link between the active node and the previous node to replace the original loop path. And this algorithm makes the construction of the paths more efficiently.
Ultrathin ZSM-5 zeolite nanosheet laminated membrane for high-flux desalination of concentrated brines

We synthesized nanometer-thick ZSM-5 nanosheets to form laminated thin membranes for enhanced desalination of concentrated brines. The tremendous potential of zeolite membranes for efficient molecular separation via size-exclusion effects is highly desired by the energy and chemical industries, but its practical realization has been hindered by nonselective permeation through intercrystalline spaces and high resistance to intracrystalline diffusion in the conventional zeolite membranes of randomly oriented polycrystalline structures. Here, we report the synthesis of ZSM-5 zeolite nanosheets with very large aspect ratios and nanometer-scale thickness in the preferred straight channel direction. We used these ZSM-5 nanosheets to fabricate ultrathin (<500 nm) laminated membranes on macroporous alumina substrates by a simple dip-coating process and subsequent consolidation via vapor-phase crystallization. This ultrathin b-oriented ZSM-5 membrane has demonstrated extraordinary water flux combined with high salt rejection in pervaporation desalination for brines containing up to 24 weight % of dissolved NaCl. The ZSM-5 nanosheets may also offer opportunities to developing high-performance battery ion separators, catalysts, adsorbents, and thin-film sensors.
Robust Vehicle Detection in Aerial Images Using Bag-of-Words and Orientation Aware Scanning

This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images.