Miracle-dz/UniFAR-FTUs huggingface dataset:
Intratumoral Administration of High-Concentration Nitric Oxide and Anti PD-1 Treatment Leads to Higher Tumor Regression Rates and Prolonged Survival in CT26 Tumor-Bearing Mice

Nitric Oxide (NO) is a signaling molecule in multiple diseases, including cancer and has been shown to activate anti-tumor immune responses [3]. Previously, we reported that treatment of CT26 tumor-bearing mice with high-concentration NO (UNO) stimulated anti-tumor immune responses leading to the rejection of a secondarily-induced tumor. More specifically, a significant increase of tumor-infiltrating T-cells and blood and spleen B and T-cells were observed 14-21 days post-UNO treatment.
Blockchain Security Research Progress and Hotspots

—Blockchain is poised to advance the cause of product innovation and technology evolution as a novel de-centralized infrastructure and distributed computing model. However, there are serious security risks in the blockchain itself, which will restrict the development of blockchain and related applications. To better grasp the current status of blockchain security research and explore the research hotspots and frontier, this study used Citespace as a bibliometric analysis tool to collect 3993 research papers between 2014 and 2020 from the WoS Core Collection as a data sample. We conduct a comprehensive overview of blockchain security research in terms of authors, research institutions, publications, countries, citations and keywords in a visual graph. This paper shows that the literature record continues to be refreshed, and it peaked in 2019. China and the USA are the main regions for collaborative research. They have established partnerships with many countries whose research institutions and authors play an important role in the collaborative network. Blockchain security research hotspots are mainly related to “cryptocurrency”, “supply chain”, “smart contract”, “healthcare”, “consensus mechanism”, “fog computing”, “cyber attack” and “Internet of Thing (IoT)”. The lineage of blockchain development has evolved from an early focus on cryptocurrency and blockchain security risk analysis to securing other areas, such as ﬁnancial systems, the IoT and healthcare through blockchain. In the future, blockchain security research will be combined with much newer technologies to form diverse developments.
Do e-WOM Persuade Travelers Destination Visit Intentions? An investigation on how Travelers Adopt the Information from the Social Media Channels

The main aim of this study is to investigate how travelers adopt the information from Facebook (FB) electronic word of mouth (e-WOM) and its impacts on their intention to visit a destination. Purposive sampling was used to gather data from 412 visitors visiting several tourist destinations in major cities of India. To determine the minimum necessary sample size ―G*Power software version 3.1. 9.7‖ was used. For data analysis, Partial Least Square Structural Equational Modelling (PLS-SEM) was used in the study. The results of this study show that all the antecedents except timeliness have a significant influence on travelers' visit intentions through Facebook e-WOM adoption. The outcomes of this study will be beneficial to internet community organizers in terms of better managing their websites for the aim of delivering relevant information. It would also benefit them in attracting users and, as a result, in paying advertisers. This research aids to the knowledge about the use of the content of Facebook e-WOM to help people decide whether or not to visit a certain destination. The major limitation is that this study is more inclined towards the destination visit intentions of travelers rather than actual visit behavior.
The intrinsic relationship between cyclones, anticyclones, and Rossby Wave Breakings in the North-Atlantic

Rossby wave breaking events describe the last stage in the life-cycle of baroclinic atmospheric disturbances. These breaking events can strongly influence the large-scale circulation, and are also related to weather extremes such as heat waves, blockings, and extreme precipitation events. Nonetheless, a complete understanding of the synoptic-scale dynamics involved with wave breaking events is still absent. Here we highlight the fundamental relation between low-level weather systems and upper-level wave breaking events in the North Atlantic region, by combining a storm-tracking technique together with 5 a wave breaking detection algorithm. We show that Anticyclonic Wave Breaking (AWB) events are associated with a strong upper-level ridge and a low-level anticyclone to its east, which are both located in the anticyclonic side of the jet. During the breaking, a strong cyclone is often found to the north-northwest of the anticyclone, while a weaker one is often found to its south-southeast. Time evolution composites centered around anticyclones during AWB show that as the downstream trough wraps anticyclonically to the east and south of the ridge, the upper-level jet weakens to the south, hence the upper-level jet 10 becomes more split into an upstream poleward tilted jet, and a downstream zonal jet. In addition, the Sea Level Pressure (SLP) anomalies rotate in an anticyclonic manner relative to each other, such that the initially zonally oriented low-high SLP anomaly dipole become meridionally oriented by the end of the life-cycle (low-above-high). On the contrary, Cyclonic Wave Breaking (CWB) events are associated with a strong upper-level trough and a low-level cyclone to its east, which are both located in the cyclonic side of the jet. An additional anticyclone is often found to the northeast of the cyclone. Time evolution 15 composites centered around cyclones during CWB show that as the downstream ridge wraps cyclonically to the east and north of the trough, the zonal and more southward shifted upper-level jet weakens further to the north. A relative cyclonic rotation is observed at low-levels, such that a high-above-low SLP anomaly dipole is found by the end of the life-cycle. A simple kinematic interpretation is suggested for the poleward and equatorward shifted jets associated with AWB and CWB events, respectively. Anomalous life-cycles of anticyclones during CWB events and cyclones during AWB events are also discussed.
Boundedness and asymptotic stability in a chemotaxis model with indirect signal production and logistic source

This article concerns the chemotaxis-growth system with indirect signal production $$\displaylines{ u_t=\Delta u-\nabla\cdot(u\nabla v)+\mu u(1-u),\quad x\in \Omega,\; t>0,\cr 0=\Delta v-v+w,\quad x\in \Omega,\; t>0,\cr w_t=-\delta w+u,\quad x\in\Omega,\; t>0, }$$ on a smooth bounded domain \(\Omega\subset \mathbb{R}^n\) (\(n\geq1\) with homogeneous Neumann boundary condition, where the parameters \(\mu, \delta>0\). It is proved that if \(n\leq 2\) and \(\mu>0\), for all suitably regular initial data, this model possesses a unique global classical solution which is uniformly-in-time bounded. While in the case \(n\geq 3\), we show that if \(\mu\) is sufficiently large, this system possesses a global bounded solution. Furthermore, the large time behavior and rates of convergence have also been considered under some explicit conditions.
Review Hearing loss in Waardenburg syndrome: a systematic review

Waardenburg syndrome (WS) is a rare genetic disorder characterized by hearing loss (HL) and pigment disturbances of hair, skin and iris. Classifications exist based on phenotype and genotype. The auditory phenotype is inconsistently reported among the different Waardenburg types and causal genes, urging the need for an up-to-date literature overview on this particular topic. We performed a systematic review in search for articles describing auditory features in WS patients along with the associated genotype. Prevalences of HL were calculated and correlated with the different types and genes of WS. Seventy-three articles were included, describing 417 individual patients. HL was found in 71.0% and was predominantly bilateral and sensorineural. Prevalence of HL among the different clinical types significantly differed (WS1: 52.3%, WS2: 91.6%, WS3: 57.1%, WS4: 83.5%). Mutations in SOX10 (96.5%), MITF (89.6%) and SNAI2 (100%) are more frequently associated with hearing impairment than other mutations. Of interest, the distinct disease-causing genes are able to better predict the auditory phenotype compared with different clinical types of WS. Consequently, it is important to confirm the clinical diagnosis of WS with molecular analysis in order to optimally inform patients about the risk of HL.
Enhanced ultrasonic focusing and temperature elevation via a therapeutic ultrasonic transducer with sub-wavelength periodic structure

In this paper, we report a therapeutic ultrasonic transducer with a sub-wavelength periodic structure, by which ultrasonic focusing and temperature elevation have been significantly enhanced compared to a conventional concave transducer with the same size. Enhanced acoustic focusing was demonstrated by both measuring and simulating acoustic pressure and temperature elevation. Compared to the conventional concave transducer, the proposed transducer exhibited stronger capacity in elevating acoustic pressure and temperature rise in the focal region, in which extraordinary acoustic transmission close to Wood's anomaly could be modulated by the spherically curved surface. This work is believed to possess great clinical potential in the safe and efficient application of ultrasonic therapy.
Automatic Photo Tagging based on Geo-Tags in Social Sites

— Photo tagging is becoming more and more consequential now-a-days to organize and search astronomically immense number of photos on convivial websites. To engender high quality convivial tags and automatic tag recommendation is the main research topic. In this paper main focus is on the personalized and geo-categorical tag recommendation. Consider users and geo locations have different preferred tags assigned to a photo, an incipient subspace learning method is proposed to individually discover both the predilections. The goal is learn coalesced space which is shared by visual domain and textual domain to make visual features and textual features commensurable. Visual feature is considered to be lower caliber representation on semantics than textual feature. Supplement ally intermediate space is introduced for the visual space and expecting it to have consistent local structure with text space. Cumulated space is mapped from the textual space and the intermediate space respectively. When an untagged photo with its geo-location is given predicated on the most proximate neighboring search utilizer preferred and geo-location-concrete tags are found in the corresponding cumulated space. Then cumulate these obtained tags and the visual appearance of the photo to find semantically and visually homogeneous photos, among which the most frequently used tags are suggested to the utilizer and utilizer is sanctioned to cull predicated on his predilection. Conclusively, the tags cognate to the keywords, geo-location and utilizer profile information are recommended to the utilizer automatically.
A Statistical Study of Myanmar’s Integrated Livestock-fish Production using Satellite Imagery

In recent years, integrated livestock-fish production is becoming increasingly popular in Myanmar. Economists hope to study this agricultural mode, however, thoroughly investigating it will be unrealistic. We seek to perform a statistical study with the help of satellite imagery and field investigation data. We formulate the problem as deep learning tasks, including object detection, object segmentation and cluster detection. The results have revealed several economical phenomenons that are worth studying, e
of Birmingham W-Band waveguide filters fabricated by laser micromachining and 3-D printing

—This paper presents two W-band waveguide bandpass ﬁlters, one fabricated using laser micromachining and the other 3-D printing. Both ﬁlters are based on coupled resonators and are designed to have a Chebyshev response. The ﬁrst ﬁlter is for laser micromachining and it is designed to have a compact structure allowing the whole ﬁlter to be made from a single metal workpiece. This eliminates the need to split the ﬁlter into several layers and therefore yields an enhanced performance in terms of low insertion loss and good durability. The second ﬁlter is produced from polymer resin using a stereolithography 3-D printing technique and the whole ﬁlter is plated with copper. To facilitate the plating process, the waveguide ﬁlter consists of slots on both the broadside and narrow side walls. Such slots also reduce the weight of the ﬁlter while still retaining the ﬁlter’s performance in terms of insertion loss. Both ﬁlters are fabricated and tested and have good agreement between measurements and simulations.
Research on quality assessment method of blockchain multi-source heterogeneous business data

The application of blockchain technology in business is more and more widespread. Business data mostly come from different data sources, the information representation is inconsistent, the informati on ambiguity between blocks in the same blockchain is too large, resulting in information consisten cy, credibility and value is difficult to evaluate, and other problems, this paper proposes a multi-sou rce heterogeneous blockchain data quality assessment method for business activities. Firstly, the entit y information representation method based on CEKGRL model is proposed, and the triplet structure of related entities is introduced into the blockchain, which is associated with the business activity c ategory. The similarity is calculated through the context information to realize the consistency evalu ation of blockchain information. Secondly, a credibility representation method based on information source, information comment and information content is proposed, and the credibility assessment of business activity information is obtained by fusing the representation results. Finally, on the basis of information credibility representation, the value evaluation method is introduced to evaluate the tota l value of business information in the blockchain, and the blockchain quality evaluation model is co nstructed. Experimental results show that the proposed method is superior to the existing methods i n evaluating inter-block consistency, intra-block information credibility and value.
Multiple Object Tracking Algorithm based on Joint Target Detection

Due to the limitation of manpower, it is difficult to realize real ‐ time feedback of channel conditions by manual to avoid risks. Real ‐ time video supervision of shipping vessels can achieve dynamic real ‐ time tracking and positioning, especially for specific ships and waters, such as dangerous goods ships, Tracking and monitoring of limited areas has become an urgent problem to be solved. This paper proposes a multiple object tracking algorithm based on target detection, Using CenterNet and YOLOv3 as the basic detectors, combined with DeepSort, to achieve real ‐ time detection and tracking of ships. Through a large number of video tests to compare the actual effects of CenterNet – DeepSORT and YOLOv3 ‐ DeepSORT, the experimental results show that the detection and tracking algorithm of CenterNet ‐ DeepSORT as the main structure can solve the current needs of actual water traffic monitoring scenarios, and the actual accuracy and robustness are both in multiple scenarios. It is stronger than YOLOv3 ‐ DeepSORT and has strong real ‐ time and scalability. The analysis and extraction of trajectory features provide an expanded basis for subsequent intelligent traffic supervision and other auxiliary functions, such as abnormal ship behavior detection.
A Fuzzy Group Decision-making Model for Measurement of Companies' Performance

In the paper, a fuzzy multi-criteria group evaluation model suitable for the measurement of companies´ performance is developed. The constructed model considers in addition to the traditionally used quantitative financial point of view also nonfinancial qualitative criteria such as Corporate Social Responsibility, innovation, or service level containing a considerable degree of uncertainty that can be appropriately modeled by the tools of fuzzy sets theory. Since the evaluations are often done by a group of experts with different opinions, our approach enables one to find at first a set of alternatives that are good enough for the sufficient quantity of relevant experts, and therefore to reach a consensus among evaluators. The best alternative is subsequently chosen from this set using a fuzzy weighted average operation. The model can be used for the comparison of companies in the same field, in one region, or to compare a company before and after managerial interventions.
Entity Based Sentiment Analysis for Textual Health Advice

This work explores entity based sentiment analysis for textual health advice through deep learning. We fine tuned a pretrained BERT model to analyze sentiments across five different predetermined categories which consist of food, medicine, disease, exercise, and vitality for three different sentiments: positive, negative, and neutral. Original set of annotated medical dataset from Dartmouth College’s Persist Lab was used to conduct the experiments. For the aim of tailoring the data for the purpose of entity based sentiment analysis, we explored data transformation techniques to generate optimum training examples. During the experiments, we were able to discover that the wide variety and complexity of terms for the medicine and the disease category posed difficulty on the BERT model which we utilized masking techniques to mitigate. We also demonstrated that our model struggles to learn neutral sentiments in cases where instances of all labels are balanced(in both training and testing). Our system was able to achieve an overall F1 score of 0.739 when conducting the experiment with masked text on a balanced dataset.
Statistical Models for Short Text Clustering

Statistical Models for Short Text Clustering SAMAR HANNACHI A notable rise in the amounts of data collected, which were made available to the public, is witnessed. This allowed the emergence of many research problems among which extracting knowledge from short texts and their different related challenges. In this thesis, we elaborate new approaches to enhance short text clustering results obtained through the use of mixture models. We deployed the collapsed Gibbs sampling algorithm previously used with the Dirichlet Multinomial mixture model on our proposed statistical models. In particular, we proposed the collapsed Gibbs sampling generalized Dirichlet Multinomial (CGSGDM) and the collapsed Gibbs sampling BetaLiouville Multinomial (CGSBLM) mixture models to cope with the challenges that come with short texts. We demonstrate the efficiency of our proposed approaches on the Google News corpora. We compared the experimental results with related works that made use of the Dirichlet distribution as a prior. Finally, we scaled our work to use infinite mixture models namely collapsed Gibbs sampling infinite generalized Dirichlet Multinomial mixture model (CGSIGDMM) and collapsed Gibbs sampling infinite Beta-Liouville Multinomial mixture model (CGSIBLMM). We also evaluate our proposed approaches on the Tweet dataset additionally to the previously used Google News dataset. An improvement of the work is also proposed through an online clustering process demonstrating good performance on the same used datasets. A final application is presented to assess the robustness of the proposed framework in the presence of outliers.
Acadlore Transactions on Applied Mathematics and Statistics

The continuous evolution of consumer behavior in the modern era of consumption has prompted enterprises to explore the underlying behavioral factors of consumers and cater to their particular needs. Moreover, developing a rational operational behavior model and responding effectively to the dynamic market environment have become critical concerns for businesses. This study examines the impact of consumer reference price effects and enterprise short-sighted behavior on strategic selection and performance, employing differential game theory to construct a game model between manufacturers and retailers. Utilizing Behrman’s continuous dynamic programming theory, analytical solutions for various models are derived, followed by comparative analyses and numerical examples. The research reveals that: (1) manufacturers’ behavior patterns are found to be dominant, favoring far-sighted behavior, which not only enhances profits but also enables consumers to access higher quality and costeffective products; retailers should opt for collaboration with far-sighted manufacturers and exhibit a preference for short-sighted behavior. (2) In terms of overall system profit, the FM model emerges as the optimal combination. (3) When the reference price effect has a small impact on market demand, enterprises can make use of the reference price effect to actively promote marketing and gain profit; as the influence increases, intensifying the degree of influence effectively augments profits.
AMERICAN JOURNAL OF TRADITIONAL CHINESE VETERINARY MEDICINE

Cervical stiffness in horses is a common complaint and can adversely affect performance. Presenting signs may include resistance to turning, lameness not localized to the distal limb, poor performance, abnormal gait, loss of power, incoordination, and ataxia. The objective of this randomized, controlled, blinded study was to determine whether acupuncture (AP) can be an effective treatment for cervical stiffness in horses. Eighteen horses were randomly assigned to either the Test Group (acupuncture) or the Control Group (sham-acupuncture) with three treatments given 7-10 days apart. Lateral bend was quantitatively evaluated before the first treatment and one day after the third treatment with analysis on video recordings. Measurements were taken at the point of maximal bend (R1) and pre-compensation bend (R2). In addition, muscle mass of the neck was measured at the level of the caudal aspect of the second cervical vertebra and at the caudal aspect of the fifth cervical vertebra at the same assessment times as for lateral bend. The person taking the measurements was blinded as to treatment group. The results showed that the Test Group had significantly larger mean R1 (p = 0.019) and R2 (p = 0.008), than the Control Group. No significant difference was found in muscle mass between subject groups. These outcomes suggest that acupuncture can improve lateral bend in horses and can be an effective treatment for cervical stiffness in horses.