Miracle-dz/UniFAR-FTUs huggingface dataset:
Nomogram prediction for cervical lymph node metastasis in multifocal papillary thyroid microcarcinoma

Aim Accurate preoperative prediction of cervical lymph node metastasis (LNM) in patients with mPTMC provides a basis for surgical decision making and the extent of tumor resection. This study aimed to develop and validate an ultrasound radiomics nomogram for the preoperative assessment of LN status. Methods A total of 450 patients pathologically diagnosed with mPTMC were enrolled, including 348 patients in the modeling group and 102 patients in the validation group. Univariate and multivariate logistic regression analyses were performed on the basic information, ultrasound characteristics, and American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) scores of the patients in the modeling group to identify independent risk factors for LNM in mPTMC and to construct a logistic regression equation and nomogram to predict the risk of LNM. The validation group data were used to evaluate the predictive performance of the nomogram. Results Male sex, age <40 years, a single lesion with a maximum diameter >0.5 cm, capsular invasion, a maximum ACR score >9 points, and a total ACR score >19 points were independent risk factors for the development of cervical LNM in mPTMC. Both the area under the curve (AUC) and concordance index (C-index) of the prediction model constructed from the above six factors were 0.838. The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, decision curve analysis (DCA) demonstrated a significantly greater net benefit of the model. The external validation demonstrated the reliability of the prediction nomogram. Conclusions The presented radiomics nomogram, which is based on ACR TI-RADS scores, shows favorable predictive value for the preoperative assessment of LNs in patients with mPTMC. These findings may provide a basis for surgical decision making and the extent of tumor resection.
Spatial modeling with system of stochastic partial differential equations

To define a spatial process as the solution to a stochastic partial differential equation (SPDE) is an approach to spatial modeling that is gaining popularity. The model corresponds to a Gaussian random spatial process with Matérn covariance function. The SPDE approach allows for computational benefits and provides a framework for making valid complex models (e.g., nonstationary spatial models). Using systems of SPDEs to define spatial processes extends the class of models that can be specified as SPDEs, while the computational benefits are kept. In this study, we give an overview of the current state of spatial modeling with systems of SPDEs. Systems of SPDEs have contributed toward modeling and computational efficient inference for spatial Gaussian random field (GRF) models with oscillating covariance functions and multivariate GRF models. For multivariate GRF models special systems of SPDEs corresponding to models known from the literature are set up. Little work has been done for exploring opportunities and properties of spatial processes defined as systems of SPDEs. We also describe some of the interesting topics for further research. WIREs Comput Stat 2016, 8:112–125. doi: 10.1002/wics.1378
Weighted k-core community search on heterogeneous information networks

Community search is a widely used technique in graph data mining that aims to find communities containing a given query node. While existing works have mainly focused on homogeneous information networks, most real-world networks are heterogeneous. To address this, this paper proposes a weighted k-core community search method designed for heterogeneous information networks. Firstly, the influence of the association weight between nodes based on meta-paths on the community search results is considered, and a weighted k-core community model (k, P)-Wcore is established, thereby improving the accuracy of community search. Subsequently, in order to improve search efficiency, an optimization algorithm OptWcore based on graph traversal search space is designed. This algorithm can effectively reduce redundant calculations and reduce the depth of path search, thereby improving search efficiency. Finally, experiments conducted on four real-world heterogeneous information network datasets demonstrate the effectiveness and efficiency of the proposed method.
Semantic segmentation method based on improved DeeplabV3+

The semantic segmentation model Deeplabv3+ model in deep learning has a great deal of parameters and spend plenty of training time, and the accuracy of small objects and edge segmentation is low. To solve this problem, an improved semantic segmentation algorithm for DeepLabv3+ network is proposed. Firstly, MobileNetv2 is used instead of Xception to reduce model complexity and speed up model training; secondly, the attention mechanism is introduced to improve segmentation accuracy; thirdly, a Strip Pooling branch is connected in parallel to the Atrous Spatial Pyramid Pooling module (ASPP) to improve the characteristics of the model extraction ability; finally, the Swish activation function is used to replace the Relu function. Experimental results show that compared with the original model, the proposed algorithm can still maintain a high accuracy when the number of parameters is greatly reduced.
A clothing classification method based on residual network

In view of the e-commerce platform merchants need to upload clothing labels to the system, but the manual labeling of clothing appeared cumbersome information error, error-prone and other problems, this paper proposes a ResNet50-DS clothing classification algorithm to automatically label by using machines, to complete the classification of clothing. Taking the residual network as the basic network structure, the SE-Net module is embedded in the model, and a dropout layer is added to reduce the generalization error of the model. The experiment shows that the clothing classification algorithm improves the accuracy of clothing classification, which can better help the e-commerce platform merchants to improve the efficiency of clothing classification, and effectively avoids the information error of manual annotation.
Research on comprehensive energy metering acquisition and monitoring based on feature detector

As an important direction of energy reform, comprehensive energy system has become the focus of research and attention in the world. Improving the efficiency of comprehensive energy utilization and bringing good social and economic benefits as well as social and environmental benefits is the only way for the country to achieve high-quality economic development, and it is also the current national policy. But what are the ways to use energy in a comprehensive way? So, in addition to improving the energy utilization rate of equipment, reduce the energy loss of each production link, but also need to take corresponding management measures and technology to reduce energy consumption. Based on this, the acquisition and detection of comprehensive energy can effectively promote the development of energy conservation, emission reduction and low-carbon economy. In this paper, for the comprehensive utilization of energy, a feature detector is introduced into the comprehensive energy system to monitor the comprehensive energy processing time cycle, power consumption and other performance parameters. Experimental results show that: under the condition of appropriate sensing threshold, the feature detector has a good time sensing effect, which is efficient and feasible for energy collection and monitoring. It also provides a theoretical basis for the supervision and use of the production energy management department, so that the management department can grasp the energy consumption situation of the enterprise at any time and make timely scheduling adjustment according to this.
IPDF: directed fuzzer for input parsing program

Directed greybox fuzzing aims to test specific code and has made many advances in several areas. However, most vulnerabilities of input parsing programs are triggered in the particular state of the program, so existing directed greybox fuzzing works face path explosion problem when they fuzz the input parsing program and need more ability to explore the particular state of the program. To address the above problem, we propose a call-relationship-based fitness function. The main idea is to use the function call relationship to guide directed fuzzing before reaching the target. Call-relationship-based fitness function extracts the function calls and call relationship from the program, uses an intra-procedural reachability analysis to get all concerned edges, and constructs the fitness function based on the edges. Based on the above method, we implemented the directed greybox fuzzing IPDF and evaluated it with the mainstream directed greybox fuzzers Beacon and AFLGo on real-world programs. Evaluation of IPDF showed that IPDF found vulnerabilities faster than the state-of-the-art directed greybox fuzzers. The experimental results showed that the speed of MDGF is 3.01 times faster than that of AFLGo and 1.15 times faster than Beacon.
RSSI centroid node location algorithm based on chimpanzee optimization

In order to improve the positioning accuracy of the centroid algorithm, this paper proposes the RSSI centroid node positioning algorithm based on chimpanzee optimization (SRSSI-CHOA). The algorithm first optimizes the received signal strength value by velocity constant filtering, and then calculates the unknown node coordinates using the distance-weighted centroid algorithm, and then constructs the fitness function based on the distance information and the obtained position information; Finally, the chimpanzee algorithm is used to find the optimal solution position and optimize the node coordinates. The simulation results show that the proposed algorithm is 30.78%, 24.42% and 8.3% lower than the RSSI - centroid, Gaussian model RSSI centroid average and RSSI-PSO algorithm.
Equipment maintenance data mining based on FP-growth algorithm

This paper extracts the maintenance records of the equipment in the equipment big data platform, uses the FP-growth algorithm to mine association rules, obtains typical association rules, and mines the associations of various faults occurring in regions, motorcycle hours, etc., to provide decision support for maintenance plan formulation, maintenance equipment procurement and storage, and to provide technical support for improving the effectiveness of equipment support.
Research on heterogeneous network fusion of industrial internet

In complex industrial scenarios, multiple networks and different protocols exist, and communication between devices is difficult. To solve this problem, we looked at Modbus, OPC, MQTT protocols, and API interfaces. Based on software definition technology, the digital device model of controller, data pump and connection pool is constructed. The controller collects the original data. The data pump processes the collected data according to the rules and can trigger notification or alarm. Connector connects to a gateway or database. Developed the industrial Internet heterogeneous network integration management software, with driver management, connection management, event management, user management, configuration management and other functions. The system realizes the integration of heterogeneous network and edge calculation of industrial Internet and has been applied in engineering and obtained good results.
Falls during oxaliplatin-based chemotherapy for gastrointestinal malignancies – (lessons learned from) a prospective study

Abstract This prospective cohort study aimed to characterise the impact of oxaliplatin-based chemotherapy and its neurotoxic side effects (i.e., chemotherapy-induced neuropathy) on functional fall-risk and falls. Twenty chemotherapy-naïve participants (mean age, 59 years; 16 males) were consecutively included. A multimodal fall risk assessment was performed at four time points within 6 months. Polyneuropathy was assessed using the Neurologic Disability Scale; the fall risk was assessed by functional tests (Tinetti Test, Chair-Rising Test, and Timed up and Go Test). Patient-reported outcomes comprised the Hospitality Anxiety and Depression Scale (HADS), the Falls Efficacy Scale – International (FES-I) to assess the fear of falling, and the Physical Activity for the Elderly (PASE) questionnaire. Three falls occurred during the study. All fallen participants had a high fall risk-index (≥4 more risk factors) compared to only 30% of the non-fallen participants (p = 0.03) and suffered more frequently from pre-existing mild polyneuropathy (p = 0.049). Study discontinuation (n = 12) was associated with a higher rate of polypharmacy (p = 0.045), anxiety (HADS-A, p = 0.03), and specific fear of falling (FES-I, p = 0.025). In contrast, study completers (n = 8) reported an improvement in physical activity (PASE) (p = 0.018). In summary, pre-existing fall-risk factors impacted more falls than chemotherapy. A fall risk index offers a time-efficient screening option in an outpatient oncological setting.
A New Biological Product Shows Promising Control of the Northern Root-Knot Nematode, Meloidogyne hapla, in Greenhouse Tomatoes

Abstract Tomato plants are susceptible to significant yield losses when infested by the northern root-knot nematode, Meloidogyne hapla. While there are many options for conventional chemical management, few of these options offer effective control for organic growers or those who seek to adopt more environmentally considerate strategies. In this study, we showed that a new, biologically based product (referred to as “MN21.2”) has potential for controlling populations of the northern root-knot nematode, Meloidogyne hapla, as a pest of susceptible tomato (cv. Rutgers) in a greenhouse trial. This is significant because if this product’s efficacy is supported under field conditions, it may provide organic tomato growers with a valuable tool for fighting the plant-parasitic nematode pest, M. hapla.
An improved GPSR algorithm based on geographic position information

An improved routing algorithm, the GPSR-S algorithm, is proposed for the problem that the GPSR protocol based on geographic position information in Vehicular Ad Hoc Networks (VANET) is not stable enough for communication links when forwarding data packets. According to the geographical position information of any vehicle nodes at different times, the algorithm calculates their running speed and movement direction respectively, and then calculates the Link Expiraion Time (LTE) of the communication link between the nodes by the speed and direction, taking into account the link stability and distance, and selects a reliable next hop. The network simulation platform NS-3 is used to simulate GPSR and GPSRS. In terms of packet delivery ratio and end-to-end delay, the GPSR-S algorithm outperforms the GPSR method, according to the results, making it more suitable for use in VANETs.
A new dynamic clustering scheme for VANETs driven by deep reinforcement learning

Vehicular nodes in Vehicular Ad Hoc Networks (VANETs) are featured with high speed, unpredictable sudden acceleration, and constantly changing positions. Due to the continuous mobility, the topology of VANETs becomes highly dynamic, which causes difficulties in effective data transmissions among V2V and V2I. The difficulties are originated from high communication overheads for exchanging and updating the topology information, transmission delay or interruption caused by high-speed movements. In order to boost up the performance of VANETs, we take the clustering as an efficient method to segment the moving vehicles in the traffic environment into different subgroups. In regards to the service of vehicle network, some tasks like video transmissions with long periods or intensive data interactions require a stable connection within the cluster, the longer lifetime is one of important factors for maintaining the dynamics as well as the cluster stability. To improve the life time of cluster, we develop the reinforcement learning (RL) agents into self-organized VANETs to form stable clusters and therefore increase the average lifetime, which can interact with the dynamic vehicle environment and output the optimal policy about cluster creation. A simulation is conducted to compare our RL-based scheme against other schemes with no RL factors. The results show that our method outperforms other dynamic schemes in the context of a high lifetime in comparison with the fixed policy. The average life time of our scheme has 6.5 % and 13.6 % improvement approximately in the statics of cluster change time and average vehicle’s speed.
Research and optimization of massive small file processing performance based on Ceph

In today’s computing era, lots of files are generated from various areas due to the rapid development of technologies. Storing and processing massive, small files is one of the significant challenges for Ceph. Ceph is a scalable, reliable, high-performance storage solution widely used in cloud computing. However, for a large number of small files, Ceph has problems such as write amplification will cause performance bottlenecks. This paper proposes a novel technique Extended Small Files Processing Framework (ESFPF). Firstly, for efficient storage of files, the small files are merged after deduplication, which will effectively reduce the data blocks of Ceph to reduce load to achieve high-efficiency data processing operation. Secondly, a prefetching mechanism and file index is introduced to improve the efficiency of accessing small files. The experimental results indicate that the proposed approach can improve the efficiency of storing and accessing massive, small files on Ceph.
A novel process monitoring method of heat treatment furnace based on ReliefF-SFA

A novel process monitoring method based on ReliefF-slow feature analysis (ReliefF-SFA) is proposed, to solve the problem of the large variable dimensions and feature selection. First, the data is mapped into a new space, and the slow characteristics of the data is obtained. The new obtained can not only extract the slowest changing components, but represent the inherent characteristics of the system. Second, the feature selection algorithm ReliefF is used to select the more weighted features, to achieve dimensionality reduction. The main subspace is established based on the obtained features, and the remaining features are put into the residual space. The corresponding statistics and control limits are calculated based on the two subspace. Finally, the process monitoring using the proposed algorithm is performed by testing the actual heat treatment furnace process data, and the results show the effectiveness of the proposed method.
Facial landmark detection algorithm based on depth structure model

In order to solve the problem that the existing face key point detection methods based on depth learning do not explicitly embed the structural dependency between face key points, and most networks have high memory access costs and energy consumption, this paper proposes a depth structure face key point detection method combining depth convolution neural network and all connected conditional random field. This method uses an efficient VoVNet depth neural network to replace the previous CNN model, reduce the memory access cost and energy consumption, and capture the structural relationship changes caused by posture and deformation. The experimental results show that this method is superior to the previous methods in face key point detection, the access cost is greatly increased, and it has good generalization ability for challenging data sets.
Detection method of solar cell surface defects based on C4.5-G algorithm

It is a very important method in nondestructive testing to detect defects using infrared or electroluminescent (EL) images of solar cell modules. Traditionally, this work is completed by skilled technicians, which is not only time-consuming but also vulnerable to subjective factors. The surface defect detection method of solar cells based on machine learning has become one of the main research directions for its high efficiency and convenience. Therefore, this paper proposes a defect detection method based on machine learning for solar cell EL image defect detection. First, the EL image is preprocessed to provide a clearer image with more obvious defects for subsequent feature extraction; Then, the gray, contrast, brightness and edge gradient features of the preprocessed image are extracted, and the edge gradient information of the defect is extracted using the improved LBP algorithm. Combined with the gray value, contrast and brightness information of the image, the training and testing data set of the classifier model is formed. Finally, the optimized decision tree algorithm - C4.5-G algorithm is used to classify the surface defects of the battery chip. While improving the performance of the algorithm, the convergence speed is accelerated and the over fitting phenomenon is avoided. The experimental results show that the method is feasible, effective and high detection rate in the defect detection of solar cells.
Deafness and Other Communication Disorders

Abstract The National Institute on Deafness and Other Communication Disorders (NIDCD) web site provides the latest information on the normal and disordered processes of hearing, balance, taste, smell, voice, speech, and language. The NIDCD is part of the National Institutes of Health and strives to advance the science of communication to improve lives. While the focus of NIDCD is conducting and supporting research related to disease prevention and health promotion, the web site contains many resources for consumer health.
The Effect of the Health Literacy Levels of Teachers on Their Vaccine Hesitancy: The Example of Turkey

Abstract Teachers assume important responsibilities in improving the health literacy levels of the society. Health literacy and vaccination are important prerequisites for health protection and promotion. This study aimed to investigate the effect of teachers’ health literacy levels on their vaccine hesitancy. This is a descriptive cross-sectional study. The health literacy and vaccine hesitancy levels of the teachers were found to be moderate. A negative and weak significant relationship was observed between health literacy and vaccine hesitancy, indicating that as health literacy increases, vaccine hesitancy may decrease. In addition, it was revealed that general health perception and over-the-counter drug use are associated with health literacy, and age, income status, and place of residence are associated with vaccine hesitancy. The health literacy and vaccine hesitancy levels of teachers should be improved. As health literacy increases, vaccine hesitancy decreases, which is an important finding in terms of increasing the effectiveness of vaccination programs.