Miracle-dz/UniFAR-FTUs huggingface dataset:
Short Communication Relationships between acylated ghrelin with growth hormone, insulin resistance, lipid profile, and cardio respiratory function in lean and obese men

BACKGROUND: Acylated ghrelin, biologically active form of ghrelin, activates growth hormone (GH) secretagogue receptor 1a and play a role in regulating of energy balance. The purpose of this study was to survey relationships between acylated ghrelin with GH, insulin resistance, lipid profile, and cardio respiratory function in lean and obese men. METHODS: Nineteen obese men (body mass index 31.0 ± 3.5 kg/m, aged 27.5 ± 5.8 year) and the same number of lean men (body mass index = 18.47 ± 2.1 kg/m, aged 26.9 ± 5.6 year) were selected if they had no experience of regular physical activity during six month ago. After 12 hour fasting, blood samples were collected and blood parameters as well as maximal oxygen uptake (as indicator of cardiorespiratory function) was assessed. RESULTS: Insulin levels and HOMA-IR (homeostasis model assessment of insulin resistance) were higher, and GH, acylated ghrelin and maximal oxygen uptake levels were lower, in obese versus lean men (p < 0.01). No significant differences were observed in systolic and diastolic blood pressure, fasting blood glucose, and lipid profiles between the two groups (p > 0.01). Plasma acylated ghrelin concentrations in obese and lean men were negatively correlated to body weight (r= -0.50, r= -0.43, respectively), body fat percent (r = -0.53, r = -0.44, respectively), body mass index (r = -0.53, r = -0.49, respectively), insulin (r = -0.42, r = -0.40, respectively) and HOMA-IR (r = -0.48, r = -0.45, respectively), and positively correlated to GH levels (r = 0.37, r = 0.31, respectively) and maximal oxygen uptake (r = 0.33, r = 0.31, respectively) (p < 0.01). No significant correlations were observed between plasma acylated ghrelin concentrations and systolic and diastolic blood pressure, fasting blood glucose, and lipid profiles in both groups (p > 0.01). CONCLUSIONS: Obese and lean inactive young men had different levels of acylated ghrelin, GH, insulin, insulin resistance index, cardiorespiratory function and body fat percent. Body fat percent, insulin, and GH levels appear to be best determinant factors of acylated ghrelin levels. Also, in both obese and lean young men, higher levels of cardiovascular function were associated with higher levels of acylated ghrelin.
A cross-sectional study of the individual, social, and built environmental correlates of pedometerbased physical activity among elementary school children

Background: Children who participate in regular physical activity obtain health benefits. Preliminary pedometerbased cut-points representing sufficient levels of physical activity among youth have been established; however limited evidence regarding correlates of achieving these cut-points exists. The purpose of this study was to identify correlates of pedometer-based cut-points among elementary school-aged children. Method: A cross-section of children in grades 5-7 (10-12 years of age) were randomly selected from the most (n = 13) and least (n = 12) ‘walkable’ public elementary schools (Perth, Western Australia), stratified by socioeconomic status. Children (n = 1480; response rate = 56.6%) and parents (n = 1332; response rate = 88.8%) completed a survey, and steps were collected from children using pedometers. Pedometer data were categorized to reflect the sex-specific pedometer-based cut-points of ≥15000 steps/day for boys and ≥12000 steps/day for girls. Associations between socio-demographic characteristics, sedentary and active leisure-time behavior, independent mobility, active transportation and built environmental variables collected from the child and parent surveys and meeting pedometer-based cut-points were estimated (odds ratios: OR) using generalized estimating equations. Results: Overall 927 children participated in all components of the study and provided complete data. On average, children took 11407 ± 3136 steps/day (boys: 12270 ± 3350 vs. girls: 10681 ± 2745 steps/day; p < 0.001) and 25.9% (boys: 19.1 vs. girls: 31.6%; p < 0.001) achieved the pedometer-based cut-points. After adjusting for all other variables and school clustering, meeting the pedometer-based cut-points was negatively associated (p < 0.05) with being male (OR = 0.42), parent self-reported number of different destinations in the neighborhood (OR 0.93), and a friend’s (OR 0.62) or relative’s (OR 0.44, boys only) house being at least a 10-minute walk from home. Achieving the pedometer-based cut-points was positively associated with participating in screen-time < 2 hours/day (OR 1.88), not being driven to school (OR 1.48), attending a school located in a high SES neighborhood (OR 1.33), the average number of steps among children within the respondent’s grade (for each 500 step/day increase: OR 1.29), and living further than a 10-minute walk from a relative’s house (OR 1.69, girls only). Conclusions: Comprehensive multi-level interventions that reduce screen-time, encourage active travel to/from school and foster a physically active classroom culture might encourage more physical activity among children.
Avoid Postoperative Pain To Prevent Its Chronification: A Narrative Review

Acute postoperative pain is a normal and expected part of the patient’s postsurgical trajectory, and its intensity, severity, and duration vary with surgery-related and patient factors. In a subset of patients, postoperative pain does not resolve as the tissue heals but instead transitions to chronic postoperative pain, a challenging condition to treat and one associated with decreased quality of life, sleep and mood disorders, and neuropathy. Promptly and adequately treating acute postoperative pain can reduce the risk that it will transition into chronic postoperative pain. Numerous agents are available that may help treat postoperative pain, including nonsteroidal anti-inflammatory drugs, opioids, antidepressants, anticonvulsants, and others. In this connection, it is also important to consider patient factors, such as mental health status and comorbidities, as well as the type and duration of surgery. A multimodal approach is recommended, which uses two or more agents with complementary mechanisms of action, working at different targets. Multimodal analgesia may also reduce adverse events and lessen opioid consumption after surgery. A particularly useful fixed-dose combination product is dexketoprofen/tramadol (DEX-TRA), which is safe and effective in numerous clinical trials. This review is based on a presentation from the Roma Pain Days scientific sessions of 2021.
Theoretical Anlysis and Experiment on Active Vibration Control of a Shaft-hull System

Propeller thrust fluctuation is the major factor that induces vibration in a shaft-hull system.Based on the frequency domain model of the shaft-hull system,dynamic characteristics of this system are discussed and the mechanism of suppressing the system vibration by applying control force on the shaft is analyzed.An adaptive feedforward control strategy is proposed in terms of the mechanism.Numerical results demonstrate that control forces applied on the shaft are able to cancel the fluctuating thrust force to reduce the longitudinal vibration of the shaft and the hull.The influence of parameters on the coupled system is also discussed and the results show that location of the control forces have little effect on the control performance,different thrust stiffness results in different control force level,and the convergence rate of the control error changes with the thrust stiffness,but it will keep unchanged when the stiffness is large enough.An experimental model is established to verify numerical analyses.Stiffness of the thrust bearing changes the longitudinal vibration frequency of the shaft,and the proposed active control strategy is able to suppress the shaft and hull vibration effectively.
Modified Salp Swarm Optimization Algorithm (MSSOA) based Implementation of Intelligent Fault Detection and Isolation System for Smart Wireless Sensor Network

This paper presented a design and implementation of fault detection and isolation (FDI) in wireless sensor network (WSN) by using a Modified Salp Swarm Optimization Algorithm (MSSOA). Recently, there has been a great development of wireless sensor networks in many fields and applications. In this paper. The design of the hardware circuit is Raspberry Pi kit where received (temperature, humidity and gas) from the sensors. So that allows each user of the wireless system to use the system securely. Where it enables the ability to monitor the occurrence of an any fault such as fire, high gas or open circuit. Using the data collected when a fault is detected with control levels exceeding a pre-set threshold, communication is made with computer by WIFI to process this data by python. A sensor network has been designed and implemented using Raspberry Pi so that this design is low-cost and is subject to development, which makes this design an efficient system for monitoring and fault detection. The proposed improvement of SSA is called MSSOA; a new term has been developed to make the algorithm more targeted in finding the optimal solution.
Acceleration of Infrared Target Detection via Efficient Channel Pruning

Deployment of modern detection models is difficult for infrared target detection used in robot vision systems due to their heavy computational burden. To alleviate this situation, a simple but efficient channel pruning method is proposed for model acceleration. Specifically, a soft-gated module combined with batch normalization (SGBN) is designed as a standalone layer to substitute the standard batch normalization (BN) layer during training. The conversion between SGBN and BN is easy, and the training overhead introduced is almost negligible after replacement. By controlling the sparsity of the scaling factor in SGBN, unimportant channels with small output are blocked automatically and globally, which is simultaneous with model training. Removing these redundant channels no longer requires fine-tuning, thus significantly speeding up the pruning process. Experiments of pruning different detection models on the infrared dataset show the effectiveness of our method. For example, the parameters and FLOPs of pruned CetnerNet are reduced by 72.70% and 40.20%, respectively, without accuracy loss. The inference speed on the CPU is 12.01ms faster. Extended studies on the classification task also demonstrate its great potential when transferring to other applications.
A Novel Brain Tumor Segmentation Method Based on Im-proved Spatial Attention Mechanism and Multi-path Deep Neural Network

Preoperative Magnetic Resonance Image (MRI) brain tumor diagnosis is an effective technical approach. To accurately segment tumor regions, we propose a novel brain tumor segmentation method based on improved Spatial Attention mechanism and Multi-path neural network (SAMPU-Net). Firstly, we propose a multipath input method to extract feature information of different scales by using convolution kernels of different sizes, so as to fully extract MRI feature information. Secondly, we improve the spatial attention mechanism by adding convolution layer of pyramid structure to it to obtain the features of different receptive fields. In the convolution layer of this pyramid structure, the larger the convolution kernel is, the more global features will be extracted; conversely, the smaller the convolution kernel is, the more local features will be extracted. Thirdly, we use more multi-mode MRI information to segment the brain tumor images. In practical application, due to the fuzzy tumor regions in some MRIs, we use the method of restricted contrast adaptive histogram equalization to perform local enhancement of images. The proposed model and several other mainstream segmentation methods are trained and tested on the BraTS2019 public dataset. Experimental results indicate that using our method, the Dice coefficient of tumor core and tumor enhancement region is increased by 2.4% and 1.3% respectively, and our proposed method has better segmentation effect than other methods.
Research on Object Detection in Campus Scene Based on Faster R-CNN

This paper focuses on the research of object detection technology in Smart Campus scene. Firstly, an object detection algorithm is constructed based on Faster R-CNN two-stage detector, which is trained on COCO2017 and VOC0712 public datasets, and the performance of the trained detector is evaluated. On this basis, the actual image data is collected from the campus scene, and divided into different groups according to the shooting angle, lighting conditions, occlusion, etc. After using the algorithm to detect, the results are analyzed systematically. In general, occlusion between objects is more likely to lead to unsatisfactory detection results. In the future, we still need to find a non-maximum suppression method which can effectively distinguish the same object repeated detection and occlusion between objects.
FEATURES OF MSW INCINERATION CONTROL IN STOCKER-FIRED BOILER

Combined with engineering application example of distributed control system (DCS) in Nanshan Municipal Solid Waste (MSW) Power Plant, Shenzhen City, the technological process and control features of mechanical stocker-fired boiler for MSW incineration have been presented,. Unique control schemes of furnace draft and of spray-back flow for leaching liquid have been worked out for the said power plant in this project, obtaining good effectiveness after implementation of the said schemes.
Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants

The dust extinction laws and dust properties in M31 are explored with a sample of reddened O-type and B-type supergiants obtained from the Local Group Galaxies Survey (LGGS). The observed spectral energy distributions (SEDs) for each tracer are constructed with multiband photometry from the LGGS, PS1 Survey, UKIRT, PHAT Survey, Swift/UVOT, and XMM-SUSS. We model the SED for each tracer in combination with the intrinsic spectrum obtained from the stellar model atmosphere extinguished by the model extinction curves. Instead of mathematically parameterizing the extinction functions, the model extinction curves in this work are directly derived from the silicate–graphite dust model with a dust size distribution of dn/da∼a−αexp(−a/0.25),0.005<a<5μm . The extinction tracers are distributed along the arms in M31, with the derived Milky Way (MW)–type extinction curves covering a wide range of R V (≈ 2–6), indicating the complexity of the interstellar environment and the inhomogeneous distribution of interstellar dust in M31. The average extinction curve with R V ≈ 3.51 and dust size distribution dn/da∼a−3.35exp(−a/0.25) is similar to that of the MW but rises slightly less steeply in the far-UV bands, implying that the overall interstellar environment in M31 resembles the diffuse region in the MW. The extinction in the V band of M31 is up to 3 mag, with a median value of A V ≈ 1 mag. The multiband extinction values from the UV to IR bands are also predicted for M31, which will provide a general extinction correction for future works.
Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding

Most current knowledge base question answering models mainly use RNN and its various derivative versions such as BiLSTM to model the problem, which limits Parallel computing capabilities of the model. In response to this problem, we try to use TransformerEncoder instead of BiLSTM to model and encode the problem, and hope to improve the parallel computing efficiency of the model. At the same time, to solve the problem of insufficient relative position information obtained by using absolute position coding in TransformerEncoder, it is proposed to use relative position coding instead of absolute position coding. According to the experimental results, our model effectively reduces a certain amount of training time and has achieved certain results on the WebQuestions benchmark data set.
YOPCB: A Single-stage Multi-attention Detection Network for PCB Surface Defects

With the miniaturization of electronic components, printed circuit boards (PCB) as a important component of them has become more and more complex and exquisite. Different defects in PCB can be found in modern industrial manufacture, such as lack of weld, lacking components, continuous welding, and the color ring resistance, resulting in low production. Accordingly, it is necessary to achieve the high rate of precision and efficiency when defecting during PCBs industrial production process. Considering that existing detection of PCB defects have a low efficiency, we propose an improved model YOPCB defecting PCB surface defects. Our improved model can achieve the precision of 97.6% mAP, which means our method is more efficient.
Solar energy research for the design and manufacturing of oxygen aerator model for shrimp farming

This project refers to the research, design, and manufacturing of an oxygen aerator prototype for shrimp farming using solar energy. An offline solar system is calculated, designed to drive a 24VDC motor that rotates the propellers to generate oxygen in shrimp ponds. This system is researched for shrimp farming areas without electricity in order to expand shrimp farming capacity for coastal areas. The system can connect to the phone via Bluetooth to transmit the model’s parameters to the user interface. An app that connects the phone to the oxygen aerator is also designed to control and monitor data. Solar-powered oxygen aerators for shrimp ponds can help farmers save 2 to 3 times the cost compared to gas and oil-powered oxygen aerators.
Wheel’s Slip Ratio and Sinkage Estimation for Planetary Rovers Moving on Deformable Terrain

Planetary rover plays a significant role in the lunar and Mars exploration missions and wheel’s move status such as sinkage and slip ratio are the critical Factor to estimate the moving ability of the Mars rover. A method is proposed to estimate wheel’s sinkage and skid angle based on terramechanic model and estimate slip ratio based on rover’s dynamic model using Extended Kalman Filter algorithm. The proposed method can avoid the errors caused by Visual Odometry and improves the estimation accuracy of rover state through the fusion with other sensors’ data. The effectiveness of the estimation method for planetary rovers is verified by comparing with the results obtained from dynamic simulation of Mars rovers.
Survey of Object Tracking Algorithm Based on Siamese Network

The network model using deep learning have been widely used in the sphereof visual object tracking for the past few years. The Siamese network can utilize the model based on deep learning to achieve a balance between the tracking accuracy and speed in the visual object tracking. This work mainly introduces the development process of the visual target tracking field and traditional target tracking algorithms. It focuses on the Siamese network structure and the improved the Siamese algorithm, and compares tracking results of related algorithms. Aiming at the deficiencies of existing Siamese object tracking algorithms, the future development trend is prospected.
Terrain Estimation with Least Squares and Virtual Model Control for Quadruped Robots

In this paper, a method to estimate the terrain based on footholds and least-squares is presented to make quadrupedal locomotion stable on the sloped terrains, with joint encoders, force sensors and inertial measurement unit (IMU) instead of visual sensors. This method which is tested successfully in simulation can predict two types of terrain, one for adjusting the robot pose and the other for adjusting the trajectory of swing phase. And to make full use of these new capabilities, general balance and locomotion controller is presented. The virtual model controller is embedded into architecture and it allows the robot to handle unexpected terrain. The quadrupedal locomotion in terrain with different inclination prove the robustness of this method.
26 June 2006

While a growing number of countries and sub-national localities are banning smoking in hospitality workplaces, extant research on the impacts of smoke-free legislation has centered on hospitality employees and industries in developed countries. Hoping to assist in filling this void, this research empirically explores the relationships among café employees’ attitudes, demographics, work-related variables (WRV), and job satisfaction before the introduction of a smoke-free legislation in one transition economy, i.e. Bosnia-Herzegovina. Results revealed that café area served, gender, average weekly workload, café seating allocation, and education were for the most part not significant in explaining different perceptions toward a smoking ban. However, respondents’ preferred café smoking policy, smoking status, hospitality work experience, job satisfaction, and age did influence how respondents viewed the smoking ban. In terms of respondents’ preferred café smoking policy, significant differences were noted due to smoking status and café seating allocation. In regards to job satisfaction, staff with more positive pre-implementation attitudes towards the ban exhibit significantly higher levels of dissatisfaction with the current job. Overall, respondents appear willing to make concessions for both proand anti-smoking patrons, staff, and owners/managers. Therefore, lawmakers should consider population characteristics, seating allocation, and the combination thereof when devising café smoking policies. © 2013 International University College. All rights reserved
A KNOWLEDGE-ENABLED PROCEDURE FOR CUSTOMER RELATIONSHIP MANAGEMENT

Rajat Goel Department of Management, Chandigarh University, Punjab, India Abstract The value of knowledge management and customer relationship management is well recognized by many leading companies. This study presents a proposed model of Knowledge-enabled Customer Relationship Management and demonstrates the way in which the presented model can facilitate the identification of important factors that have key impacts on business performance in particular settings. The results show that employees in the steel industry indicate that to provide product and service information for customers and to share internally the best practice information can have benefits for hard measures such as market share, repeat purchases, and customer retention and for soft measures such as customer satisfaction, market leadership and customer loyalty. Employees in the textile industry report that to provide customercomplaintinformationofcustomersandtoshareinternallythebestpracticeinformationcanhavebenefi tsforhardmeasuressuchas customer retention, cost savings, and market share and for soft measures such as customer satisfaction, market leadership, customer loyalty, and customer and employeeproductivity.