Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a Short Form of the Japanese Version of a Scale Assessing Clinical Nursing Instructors' Effective Clinical Teaching Behaviors

Objective: The objective of this research was to develop a short version of the scale assessing the Effective Clinical Teaching Behaviors (ECTB) of clinical nursing instructors in Japan. Design: This was a cross-sectional questionnaire survey. Materials and Methods: A total of 784 nursing students undergoing clinical practicum at a university hospital in Japan participated in this study. The ECTB scale, a five-point Likert-type scale comprising 32 items, was used for assessment. This study comprised the following two phases: (a) item analysis and (b) evaluation of the scale's reliability and validity by conducting an exploratory factor analysis (EFA). Results: A total of 723 (92.2%) responses were obtained and 633 (80.7%) effective answers were identified. Each item-total and inter-item correlation coefficient was between 0.48 and 0.76 and between 0.18 and 0.73, respectively. Three items were eliminated on the basis of the item analysis, and 15 items were removed on the basis of the EFA results. Finally, a short version of the ECTB was developed according to two factors comprising 14 items: (i) Factor 1 (promotion of learning experience) and (ii) Factor 2 (enhancement of motivation to learn). Conclusion: In this study, the validity and reliability of the short version of ECTB have been proven. The findings suggest that clinical nursing instructors should acquire teaching behaviors that promote students' learning experience and enhance motivation. Further studies should be conducted in order to establish a clinical nursing education process so that clinical nursing instructors learn the ECTB.
My Chatbot lightblue: Course Project Report

The needs for training data is analyzed by pretraining and literature reviews. I firstly feed in some training data to see the response from the chatbot and I also learned from instructors and papers. The needs for training data may include: Full Sentence Structure and Consistent Grammar (C1) Not Too Long Sentences (C2): I found the chatbot often give the right answer for first half part, but wrong for else. So I guess maybe generator model is used which calculate probabilities word by word. Then shorter sentences can reduce the risk for wrong answers. Wide Coverage of Vocabulary (C3) Commutative Q&A Interactions (C4): I found the chatbot may link the sentences before or after its answers to learn, so it is important to let chatbot not only be an answerer, but also an asker.
Extension Employee Health Behaviors, Needs, and Interests: A Utah-Based Survey

The goal of our study was to better understand the health and wellness practices, needs, and interests within Cooperative Extension. We used a Qualtrics web-based survey to collect data from Extension employees at Utah State University. Extension employees demonstrated interest in making healthier lifestyles a priority through Extension-specific wellness programs focused on exercise and physical activity, promotion of optimal mental health, and opportunities for personal development. Extension leaders and administrators are encouraged to offer targeted programming to enhance health and wellness within Cooperative Extension.
TrimT: A Graphical Password Authentication Scheme

Globally, the task of ensuring that unauthorized users are not given access into a secured system is being given necessary attention. As a result researchers have developed varieties of authentication methods. Alphanumeric password is the foremost and most common authentication method. Researches have shown that, it is difficult for users to remember strong and random alphanumeric password. Sequel to this, graphical password was introduced. Graphical password adopts visual interface. Human brain can remember visual images better than random characters [1]. Many graphical password methods have been proposed. After reviewing some of the existing graphical password methods, the outcome of the review indicated that login time of most of the graphical password methods is high. It is against this backdrop that this research principally focused on developing a new graphical password authentication method aiming at reducing login time.
Mountain waves produced by  a stratified shear flow with a boundary layer. Part III: Trapped lee waves and horizontal momentum transport.

The boundary layer theory for non-hydrostatic mountain waves presented in Part II is extended to include upward propagating gravity waves and trapped lee waves. To do so, the background wind with constant shear used in Part II is smoothly curved and becomes constant above a “boundary-layer” height d which is much larger than the inner layer scale δ. As in Part II, the pressure drag stays well predicted by a gravity wave drag when the surface Richardson number J < 1 and by a form drag due to non separated sheltering when J < 1. As in Part II also, the sign of the Reynolds stress is predominantly positive in the near neutral case (J < 1) and negative in the stable case (J > 1) but situations characterized by positive and negative Reynolds stress now combine when J ∼ 1. In the latter case, and even when dissipation produces positive stress in the lower part of the inner layer, a property we associated with non separated sheltering in Part II, negative stresses are quite systematically found aloft. These negative stresses are due to upward propagating waves and trapped lee waves, the first being associated with negative vertical flux of pseudo-momentum aloft the inner layer, the second to negative horizontal flux of pseudo-momentum downstream the obstacle. These results suggest that the significance of mountain waves for the large-scale flow is more substantial than expected and when compared to the form drag due to non separated sheltering.
Population Data and Internal Validation of the 21 Short Tandem Repeat Loci in Turkish Population

1Institute of Forensic Sciences and Legal Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey 2Department of Biophysics, Faculty of Medicine, Inonu University, Malatya, Turkey 3Department of Health Management, Faculty of Health Sciences, Istanbul Yeni Yuzyil University, Istanbul, Turkey Article Information Received 03 August 2021 Revised 25 September 2021 Accepted 07 October 2021 Available online 08 March 2022 (early access)
Detection and Classification of RBCs and WBCs in Urine Analysis with Deep Network

—Urinary sediment examination is used to evaluate the possible urinary tract diseases of patients. Currently, numerous approaches are applied to automatically detect Red Blood Cells (RBCs) and White Blood Cells (WBCs) from urinary sediment images. However, it is still a challenging task due to the cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on image detection in various tasks. In this paper, we investigate issues involving Faster Regions with Convolutional Neural Network (Faster R-CNN) for the construction of an end-to-end urine analysis system. We propose an effective baseline for RBCs and WBCs detection on urinary sediment images by using a pre-train Faster R-CNN model. We evaluate our urine analysis system on a large dataset of urinary sediment images which consist of more than 6,000 annotated RBCs and WBCs images. Our results show competitive accuracy and acceptable run time. Prospectively, the proposed methods could provide support to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and it could potentially lead to a better understanding of urinary tract diseases. Code and dataset will be made publicly available.
Ransomware Detection & Identification Using AI

Malware is a constantly evolving and rising threat, especially ransomware, a form of malware. The rise of ransomware as a service platform adds to this surge, and malware researchers need options to swiftly and reliably identify a family of ransomware to protect the data of individuals and vital infrastructures. In this study we provide an image-based detection and classification method that can aid researchers in identifying the origins of ransomware by comparing it to known ransomware families. We aimed to reach a high level of accuracy and a low false positive rate on a given ransomware sample using a limited-size training dataset and COTS hardware. We used a dataset of 347,307 Windows executable malware samples obtained from VirusTotal (VT). These samples were collected by VT between 2017 and 2020. From this dataset we selected samples positively identified as known ransomware. We applied a novel AI-driven approach to classify ransomware based on an image representation of the binary file. This approach has been used by security practitioners and academics on malware in general but not on particular types of malware like ransomware. We used a naïve approach to selecting the best-performing convolutional neural networks based on 16 of the available applications in Keras, a Python API for the TensorFlow opensource machine learning platform. These applications were pre-trained on the ImageNet dataset of natural images. The proposed method achieves above 90% accuracy and a high recall rate for a high probability of detection of ransomware based on three channel (RGB) images. The best-scoring models on our dataset were MobileNet and MobileNetV2 .
ONLINE PHM SYSTEM FOR BEARINGS BASED ON TIME-FREQUENCY ANALYSIS AND CNN TRANSFER LEARNING

Bearings are important transmission components for rotating machinery, and their remaining life prediction plays a crucial role in equipment maintenance decisions. In this paper, the gearbox of crane running online is the research target. Because the bearing accounts for a high proportion of the historical machine failure, taking the bearing as an example to detail the overall framework and algorithm details for monitoring, diagnosis, and prognosis. The current bottlenecks in bearing failure prediction are: (1) The bearing health status and failure mode are uncertain during operation. (2) The value density of data is low and cannot support the training of neural networks. (3) Lack of accurate failure data for economic and security reasons. (4) Compared with the run-to-failure experiment, the bearings of different types of gearboxes are different, and the working conditions are quite different. Even for the same type of bearing, according to a large number of data, the life of rolling bearings is presented as a discrete distribution, and the prediction model (curve) is difficult to transfer directly. Therefore, the identification of the degraded state and early failure of in-service bearings is significant for accurately predicting the remaining life. In contrast, bearing datasets obtained by these run-to-failure experiments can accurately obtain the monitoring data of the bearing from health to damage throughout the life cycle, to facilitate the division of the recession phase, training the classifier. This paper proposes a universal transfer learning analysis scheme for online PHM system of rotating machinery: Time-frequency analysis is used to evaluate the performance degradation of the bearing state, determine the time of the early failure, and divide the health and damage data. Transfer the CNN classifier originally trained by the experimental data to online data, which helps the abnormal detection of the online equipment, and finally the life prediction is completed by the piecewise prediction model
Deep Skill Chaining from Incomplete Demonstrations

—In general, humans can easily assign a sequence of simple tasks for performing complex tasks. If a person gives an agent an order of simple tasks to carry out a complex task, we can ﬁnd a skill sequence efﬁciently by learning the corresponding skills. However, independently trained low-level skills (simple tasks) are incompatible, so they cannot be performed in sequence without additional reﬁnement. In this context, we propose a method to create a skill chain by connecting independently learned skills. In the experiment, we perform manipulation tasks with RGBD images as input in the Baxter simulator implemented using MuJoCo. We verify that skill chains can be successfully trained from incomplete data while conﬁrming that the agent can be trained much more efﬁciently and stably than baselines.
In Vitro Micropropagation of Vernonia cinerea (L.) Less.-an important medicinal herb

In vitro micropropagation was achieved from Nodal and leaf explants of Vernonia cinerea L. on MS medium supplemented with different concentrations of BAP (1.0 to 3.0 mg/l), GA3 (1.0 to 5.0 mg/l) and KN (0.5 mg/l) for direct shoot induction. Maximum numbers of shoots were obtained from nodal and leaf explants at BAP (2.5 mg/l) GA3 (2.0 mg/l) and KN (0.5 mg/l) respectively. The multiple shoots elongation and their development were better on MS medium supplemented with 1.5 mg/l BAP and 3.0 mg/l GA3 for both the explants. The regenerated shoots were successfully rooted on MS medium supplemented with IBA 2.0 mg/1, after sequential hardening, survival rate was 91%. The in vitro raised plantlets were acclimatized and established successfully in the field.
University of Groningen Pain response of healthy workers following a Functional Capacity Evaluation and implications for clinical interpretation Soer,

Background and aim Functional Capacity Evaluations (FCEs) are used to quantify physical aspects of work capacity. Safety is a critical issue for clinical use of an FCE. Patients with Chronic Low Back Pain (CLBP) are known to report a temporary increase in pain following an FCE, but it is not known whether this increase is a normal pain response to FCE. It is currently unknown how healthy subjects respond to an FCE and whether this should be interpreted as a normal reaction after physical exercise. This study was performed to quantify the intensity, duration, location and nature of the pain response following an FCE in healthy subjects and to compare this pain response with the pain response of patients with CLBP from a previous study. Methods A total of 197 healthy working subjects between 20 and 60 years of age volunteered to participate in this study. All subjects performed a 12-item FCE. Pain response was measured by a self-constructed Pain Response Questionnaire (PRQ). Descriptive statistics were used to describe the pain response following an FCE. Mann–Whitney and t-tests were performed to compare the data from this study with data of patients with CLBP from a previous study. Results About 82% of all subjects reported a pain response following the FCE. The intensity of the pain response after 24 h post FCE was a median of 3.0 on a numeric rating scale (0–10). About 78% of all pain was reducible to muscle soreness. Pain was most often reported in the upper legs (51%), the lower back (38%) the shoulders (37%) and upper arms (36%). Symptoms decreased to pre-FCE levels in a mean of 3 days. The pain response of 2 subjects (1%) lasted for 3 weeks. The intensity and duration of the pain response of healthy subjects was not significantly different from the response of patients with CLBP. Conclusion Pain response of 99% of all subjects who reported a pain response was interpreted as normal. It was concluded that a pain response following an FCE can be expected in healthy subjects and that this pain response is a normal musculoskeletal reaction. The pain response of patients with CLBP resembles the pain response of healthy subjects.
Research on Text Sentiment Analysis Based on Attention Mechanism Fusing LSTM and CNN Models

Text sentiment analysis is an important branch in the field of natural language processing, widely used in public opinion analysis and other aspects, and is a hot research direction in recent years. In this paper, an LSTM and CNN model are fused to build a fusion model for sentiment analysis based on the attention mechanism, and trained on comment datasets crawled from several social media sites such as Facebook, Twitter, Instagram and WhatsApp. Through experimental comparison studies, it is found that the sentiment analysis fusion model proposed in this paper can better extract the features of text data, thus effectively improving the effect of text sentiment analysis.
System Identification and Adaptive Speed Governing Control Simulation of Marine Diesel Engine

Marine diesel engine has the characteristics of non-linear and time-invariant. Traditional PID controller cannot optimize parameters to adapt to the governor of diesel engine according to different working conditions and control requirements. An adaptive controller based on back-propagation(BP) neural network and Wiener neural network was used to tune PID parameters for speed control system of marine diesel engine. In the controller, the model of diesel engine was expressed by Wiener model, and was identified with neural network method. The sensitivity of diesel engine output with respect to its input was obtained via Wiener model, and was used to optimize PID parameters. It improved the convergence speed of the model. Moreover, a relative error was used in target evaluation function of the BP neural network. A simulation platform based on dSPACE was developed, and a simulation using the adaptive controller was done, which was at 50% of rated load and the speed of the diesel engine alternately changed from 1100(RPM) to 1500(RPM). In the simulation, speed fluctuation rate was less than 0.81%, and the steady state response time was less than 6.5s. The simulation demonstrated that the adaptive controller can meet the demand of diesel engine speed governing.
Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers

We present a novel approach for risk-aware planning with human agents in multi-agent traffic scenarios. Our approach takes into account the wide range of human driver behaviors on the road, from aggressive maneuvers like speeding and overtaking, to conservative traits like driving slowly and conforming to the right-most lane. In our approach, we learn a mapping from a data-driven human driver behavior model called the CMetric to a driver's entropic risk preference. We then use the derived risk preference within a game-theoretic risk-sensitive planner to model risk-aware interactions among human drivers and an autonomous vehicle in various traffic scenarios. We demonstrate our method in a merging scenario, where our results show that the final trajectories obtained from the risk-aware planner generate desirable emergent behaviors. Particularly, our planner recognizes aggressive human drivers and yields to them while maintaining a greater distance from them. In a user study, participants were able to distinguish between aggressive and conservative simulated drivers based on trajectories generated from our risk-sensitive planner. We also observe that aggressive human driving results in more frequent lane-changing in the planner. Finally, we compare the performance of our modified risk-aware planner with existing methods and show that modeling human driver behavior leads to safer navigation.
Machine-in-the-Loop Process in Project Risk Management

— Despite the widespread recognition of the necessity and importance of project risk management, its effective implementation is often difficult. Much of the difficulty could be explained by the effects of cognitive biases related to heuristics when humans make decisions about probability and frequency. In this study, we propose a machine-in-the-loop process in which human and AI model cooperate in a complementary manner to mitigate the influence of cognitive bias in human decision-making and compensate for the lack of domain knowledge in prediction with the AI model. In a case study where the machine-in-the-loop process was applied to project risk management, we conducted an interview survey and confirmed the effectiveness of the machine-in-the-loop process with positive comments which support the reduction of uncertainty and cognitive bias.
Performance enhancement of a scanning electron microscope using a deep convolutional neural network

We report noise reduction and image enhancement in scanning electron microscope (SEM) imaging while maintaining a fast scan rate during imaging, using a deep convolutional neural network (D-CNN). SEM images of non-conducting samples without a conducting coating always suffer from charging phenomenon, giving rise to SEM images with low contrast or anomalous contrast and permanent damage to the sample. One of the ways to avoid this effect is to use fast scan mode, which suppresses the charging effect fairly well. Unfortunately, this also introduces noise and gives blurred images. The D-CNN has been used to predict relatively noise-free images as obtained from a slow scan from a noisy, fast scan image. The predicted images from D-CNN have the sharpness of images obtained from a slow scan rate while reducing the charging effect. We show that by using the proposed method it is possible to increase the scanning rate by a factor of about seven with an output of image quality comparable to that of slow scan mode. We present experimental results in support of the proposed method.
Computing offloading method with low power consumption constraint for smart grid

With the development of the Power Internet of Things, power transmission and transformation business have a wide range of operation, large regional differences, a wide variety of equipment and other problems, making the access and deployment of mass sensors with low power consumption crucial. In order to realize the cloud-side collaborative computing of the Internet of Things, it is necessary to build a low-power information acquisition and transmission network, and realize the low-power deployment of the low-power sensor network and equipment. The embedded node device can be used as the basis of low-power transmission network to realize low-power transmission of cloud-side collaborative computing information, combined with the existing low-power transmission and transformation equipment.
On Single-pulse Energies of Some Bright Pulsars Observed at 1.7 GHz

We present Arecibo observations of the bright pulsars B0301+19, B0525+21, B0540+23, B0611+22, and B0823+26 at 1.7 GHz with 100 MHz bandwidth. No giant pulses were found, except for B0823+26, where we recorded a giant interpulse with 230 times the average peak intensity. The postcursor in B0823+26 shows a symmetric double-peaked structure, indicating that it is frequency dependent. In all pulsars, for a given single-pulse peak intensity there is a range of equivalent widths up to a maximum, which becomes smaller the stronger the pulses are, thereby apparently limiting the energy output. Forming average profiles from pulses with certain equivalent widths leads to profiles with changing component characteristics and could allow exploring the magnetosphere at different heights, assuming a dipolar field geometry. We found that the normalized lag-1 autocorrelation coefficient for single-pulse energies can be over 0.5, indicating high correlations. From the first peak of the energy autocorrelation function a so-far-unobserved 15-period modulation is found for B0540+23, as well as a possible 10-period modulation for B0611+22. We also show that a fit of the Weibull distribution to the cumulative probability for the energies yields a better fit than the usual lognormal distribution. The cumulative probability distributions permit an estimate of the nulling fraction, which ranges from 0.6% for B0611+22 to 24% for B0525+21.