Reviewerly/exHarmony huggingface dataset:
Surveillance Data Yoğun Bakım Ünitelerinde COVID-19’un Santral Kateter İlişkili Kan Dolaşımı Enfeksiyonları Standardize

Introduction: Healthcare-associated infections have increased during the Coronavirus disease-2019 (COVID-19) pandemic because of inadequate adherence to infection control measures.Central line-associated bloodstream infections (CLBSI) are one of the infections with an increased incidence.In this study, CLBSI standardized infection ratio (SIRs) in three periods were compared, and the effect of the COVID-19 pandemic on CLBSI observed in intensive care units (ICUs) was evaluated.The hand hygiene compliance rates were also examined for the same period.Materials and Methods: The 3-year (2019, 2020, and 2021) SIR, standardized utilization ratio (SUR), and SIR change rates between years in 12 adult ICUs were compared.Calculations were made using the SIR and SUR calculation tool prepared by the General Directorate of Public Health of Turkey.The formula [(SIR/SUR of year Y-SIR/SUR of year X) / SIR/SUR x 100 of year X] was used when calculating the SIR/SUR change rates between periods.Results: In 2019 and 2020, SIR was detected as >1 only in the Burn ICU.Anesthesiology and Reanimation 1 and 2 and Internal Medicine 3 were the ICUs with the highest SIR increase rate in 2020.In 2021, the SIR was <1 in all units.In Anesthesiology and Reanimation 2, Internal Medicine 1 and 3, and Cardiovascular Surgery ICUs, the SUR was >1 in all periods.When the mean SIR values of ICUs between 2019, 2020, and 2021 were compared, the rate was significantly higher in 2020 (p<0.05).A significant increase was found in hand hygiene compliance in 2021 compared with previous years (p<0.05). Conclusion:With the COVID-19 pandemic, an increase in CLBSI-SIR was observed in 2020.In 2021, a decrease in CLBSI-SIR and an increase in hand hygiene compliance were achieved by gaining experience in the follow-up and treatment of patients with COVID-19, implementing infection control programs, and conducting on-site training and inspections.
Weight enumerators of all irreducible cyclic codes of length <i>n</i>

Let [Formula: see text], where [Formula: see text] are distinct odd primes, be an integer and [Formula: see text] be a finite field of order [Formula: see text] with [Formula: see text]. We determine the weight enumerators of all irreducible cyclic codes of length [Formula: see text] over [Formula: see text] when multiplicative order of [Formula: see text] modulo [Formula: see text] is [Formula: see text]; [Formula: see text] and [Formula: see text]; [Formula: see text], where [Formula: see text].
3D technology has many uses in higher education instruction

The first time I saw a computer I was 10 years old. My father was a comptroller for a wholesale distribution company. While visiting him at work, I was able to enter the computer room. The entire room was the computer. I'm sure many of you remember the old, huge mainframe computers. My dad would give us boxes of used punch cards to play office and school.
LIFELONG TEACHERS TRAINING FRAMEWORK

This paper focuses on the continuity in lifelong teachers training development framework, provided by all stages of education. Presently, continuity is a factor of competitiveness and the main component of its development. The purpose of the study is to identify the conceptual foundations of the continuity in lifelong teachers training development framework within the pedagogical studio. The research methods are theoretical, general scientific and empirical methods, statistical methods, etc. Firstly, we highlighted the factors of continuity in lifelong teachers training development framework such as targeted, substantive, technological, psychological and pedagogical, managerial, procedural. Secondly, we highlighted the components of the system of continuing professional pedagogical education. They are conceptual, substantive, procedural, emotional, methodological and analytical. Thirdly, we considered the pedagogical studio as a means of implementing continuity. Then, individual educational path were examined. Theoretical and practical results are the following: based on the program-target method the continuity in lifelong teachers training development framework contributes to teacher's personality development.
Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction

Medical image semantic segmentation is essential in computer-aided diagnosis systems. It can separate tissues and lesions in the image and provide valuable information to radiologists and doctors. The breast ultrasound (BUS) images have advantages: no radiation, low cost, portable, etc. However, there are two unfavorable characteristics: (1) the dataset size is often small due to the difficulty in obtaining the ground truths, and (2) BUS images are usually in poor quality. Trustworthy BUS image segmentation is urgent in breast cancer computer-aided diagnosis systems, especially for fully understanding the BUS images and segmenting the breast anatomy, which supports breast cancer risk assessment. The main challenge for this task is uncertainty in both pixels and channels of the BUS images. In this paper, we propose a Spatial and Channel-wise Fuzzy Uncertainty Reduction Network (SCFURNet) for BUS image semantic segmentation. The proposed architecture can reduce the uncertainty in the original segmentation frameworks. We apply the proposed method to four datasets: (1) a five-category BUS image dataset with 325 images, and (2) three BUS image datasets with only tumor category (1830 images in total). The proposed approach compares state-of-the-art methods such as U-Net with VGG-16, ResNet-50/ResNet-101, Deeplab, FCN-8s, PSPNet, U-Net with information extension, attention U-Net, and U-Net with the self-attention mechanism. It achieves 2.03%, 1.84%, and 2.88% improvements in the Jaccard index on three public BUS datasets, and 6.72% improvement in the tumor category and 4.32% improvement in the overall performance on the five-category dataset compared with that of the original U-shape network with ResNet-101 since it can handle the uncertainty effectively and efficiently.
Breast Cancer Detection Using Automated Segmentation and Genetic Algorithms

Breast cancer is the most common cancer among women worldwide, after lung cancer. However, early detection of breast cancer can help to reduce death rates in breast cancer patients and also prevent cancer from spreading to other parts of the body. This work proposes a new method to design a bio-marker integrating Bayesian predictive models, pyRadiomics System and genetic algorithms to classify the benign and malignant lesions. The method allows one to evaluate two types of images: The radiologist-segmented lesion, and a novel automated breast cancer detection by the analysis of the whole breast. The results demonstrate only a difference of 12% of effectiveness for the cases of calcification between the radiologist generated segmentation and the automatic whole breast analysis, and a 25% of difference between the lesion and the breast for the cases of masses. In addition, our approach was compared against other proposed methods in the literature, providing an AUC = 0.86 for the analysis of images with lesions in breast calcification, and AUC = 0.96 for masses.
Complexity, Performance, and Search Efficiency: An Eye-Tracking Study on Assembly-Based Tasks among Construction Workers (Pipefitters)

Past studies have used eye-tracking glasses to analyze people’s perception of visual stimuli, usually regarding wayfinding, safety, or visual appeal. Some industries, such as the automotive industry, studied the effects of visual stimuli on task completion. However, the architecture and construction industries have mainly conducted eye-tracking experiments with surveys or search tasks instead of performing a task. This paper uses eye-tracking glasses to analyze people’s perception of visual stimuli while completing tangible tasks that simulate real-world applications. This research studies how people look at visual stimuli that influence their ability to interpret drawings with varying degrees of complexity, assess task completion performance, and inspect how people search for information. Twenty pipefitters wore eye-tracking glasses to record their eye movement patterns while completing a model pipe spool assembly. The eye-tracking glasses and Visual Eyes software measured visit metrics, fixations, fixation durations, convex hull coverage, assembly time, rework, and errors. Unlike previous studies, convex hull areas are calculated and used to measure search efficiency. This research found that people interacted more frequently with more complex visual stimuli but did not necessarily require more time to complete a task. People with lower search efficiency visited the drawings more frequently than people with higher search efficiency. People with higher search efficiency made fewer mistakes, redid less work, and completed tasks quicker than those with lower search efficiency. Search efficiency was found to be a good predictor of task performance.
Information Technology Innovation for Android-Based Letter and Number Recognition to Improve Learning Outcomes for Kindergarten Students

Children aged 4-6 years are referred to as preschool age, where children are being prepared to enter the learning process, namely school.This age is a sensitive period for children who are sensitive to receive and respond to various stimuli.Education at preschool age is best applied to children at that age because children begin to be sensitive in receiving and responding to stimuli received from their environment, as well as early for children to learn basic things to develop spiritual, emotional, intelligence, skills that will be useful later.The golden age of children is an important period and is observed in the growth and development of children's learning.The use of information technology as a learning medium is an innovation in the teaching and learning process that supports children's learning success.This research was conducted to know the effectiveness of information technology innovation as an educational medium for the ability to recognize letters and numbers of kindergarten students.The research method used is Research and Development with the SDLC (System Development Life Cycle) Software Engineering development procedure.The model used in this study is the Waterfall model which consists of 5 stages, namely Communication, Planning, Modeling, Construction, and Deployment.The data collection methods in this study were pretest and posttest.Data analysis used descriptive percentages.The results of the research test show an increase in learning outcomes for kindergarten students and the implementation of information technology can help teachers in the learning process.From these results, it is known that the innovation of information technology for recognizing letters and numbers based on Android is effective in teaching and learning activities.
Student Autonomy, Self-efficacy on Engagement, and Student Engagement and Service Quality on Student Satisfaction

Online learning is currently being carried out to decrease interaction between students and lecturers to minimize the consequences of corona virus transmission.In such a learning experience, it is believed that the contact between lecturers and students would drive students to engage in the learning that takes place, hence not lowering the learning process's quality.Student satisfaction is an evaluation that the quality of information or knowledge that meets students' expectations.This paper aimed to examine the effect of student autonomy and student self-efficacy on student engagement, and student engagement and service quality to student satisfaction.Variables were measured using a questionnaire adopted from previous research.This research focused on accounting students at three universities in Surabaya, a city in Indonesia.Purposive sampling method was used to recruit respondents who were from batch 2018-2021 which had experienced learning online.An online survey was distributed through social media platform and had gathered 323 valid responses.The Partial Least Squares (PLS) method was used to evaluate the hypotheses in this research.This study found that students with higher self-efficacy and higher autonomy will have higher level of engagement.Higher student engagement and higher service quality will lead to higher student satisfaction.
The Influence of Reading Interest and Learning Environment on Indonesian Language Learning Achievement of Fifth Grade Elementary School Students in Cluster 01 Kretek Bantul

The aim of this study is to determine the effect of reading interest, and learning environment on Indonesian language learning achievement, both individually and collectively in fifth grade elementary school students in Cluster 01 Kretek Bantul.This research was carried out in the Kretek District of Bantul in the 2021/2022 academic year.The research population is all class V SDN in cluster 01 who are registered in the 2021/2021 academic year totaling 112 students.The research sample was taken using proportional random sampling technique totaling 91 students.Data collection techniques used are questionnaires and documents.Questionnaires are used to collect data on reading interest, and learning environment, documentation is used to collect data on Indonesian language learning achievement.Data analysis was carried out quantitatively, using multiple linear regression analysis techniques.The results of the study found (1) there was an effect of reading interest on Indonesian learning achievement with a regression coefficient of 1.729 at p = 0.000, (2) there was no positive influence of the learning environment on Indonesian learning achievement with a regression coefficient of .476 at p = 0.905, (3) There is an effect of interest in reading and the learning environment together on students' Indonesian learning outcomes with an F of 10,184 at p = 0.00 meaning that the higher the interest in reading and the learning environment, the higher the learning achievement of Indonesian language students in class V SD Negeri Se-Cluster 01 Kretek District, Bantul.
Improving College Students’ Information Management Ability

This paper studies the current situation of college students' information literacy through the investigation of the website, and constructs a model to improve college students' information literacy.In order to verify the validity of the model, 15 college students are selected for interviews, and it is found in the interview that information ethics also affects college students.Therefore, we put forward a hypothesis with information ethics, and used the method of questionnaire survey to verify again.First, we used SPSS to carry out statistical analysis on the reliability and validity of the questionnaire data, and then we used Amos to verify our hypothesis, and according to these research results, it proposes ways to improve the information literacy of college students.
Geochemical functions Add-In to MS Excel for geochemical and mineralogical data processing

«Geochemical functions» is a new free Add-In to MS Excel for geochemists and mineralogists. It implements useful functions for routine operations in geochemical and mineralogical calculations: abundance normalization, calculations of empirical formula coeffi cients from chemical and EMPA analyses, statistical operations with partial and unequal data.
Generalized multiple dependent state sampling plan for quality inspection in ocean dataset

The present article develops the generalized multiple dependent state sampling plan for testing the quality of the ocean data. The plan parameters for the inspection of the ocean data are selected through the optimization process. The operational procedure for the inspection of ocean data is given under the proposed sampling scheme. The efficiency of the proposed plan is compared with the existing plan used for the inspection of the quality of ocean data in terms of sample size. From the comparative study, it is concluded that the proposed plan is efficient than the existing plan in an inspection of the ocean data.
Generative prototypes to promote participation in the collaborative design of wearable health technologies

The potential of design artefacts as generative tools is well documented in the early stages of the design process. Yet, how prototypes continue to act generatively in the later stages is less well understood. This paper shares observations from two projects spanning the entire design process, from conceptual ideas to mass-produced objects. Through quotes and observations documenting participants interacting with prototypes, we illustrate the importance of designed physical artefacts as tools to promote expansive conversations with end-users. We find that generative prototypes foster empathy; support decision-making and communication across disciplines; allow participants to enact use and explore user experience within the early stages of a design process and provide permission to imagine possible futures. These findings encourage design practitioners to think about using high-fidelity generative prototypes throughout the design process as a powerful tool for design thinking, collaboration, and co-creation that embrace speculation and playful creativity.
Investigating the use of an autonomous robot assistant to improve the wellbeing of institutionalized older adults in Armenia

Researchers investigated the use of an emotional support robot to improve the well-being of institutionalized older adults in Armenia. The autonomous assistant called Robin the Robot assessed the cognitive and psychosocial needs of elders in a nursing home in Armenia. Robin the Robot is a semi-autonomous robot that interprets facial expressions and conversational contextual clues to understand emotions from individuals, and then uses artificial intelligence to guide its responses and develop a therapeutic interaction with patients METHOD: The twelve-week study was a randomized control trial of using Robin the Robot as an intervention to improve well-being of older individuals in long-term nursing care facilities in Armenia. Upon enrollment, participants completed a brief demographics interview. Evaluation of study participants, both pre and post-test included: the International Short-Form of the Positive and Negative Affect Schedule (I-PANAS-SF)2 ; Montreal Cognitive Assessment (MoCA)8 and the Geriatric Anxiety Scale (GAS-10)9 and Geriatric Depression Scale (GDS)10,11 RESULT: The results of the study revealed Robin the Robot's interventions improved the elder's cognitive functioning. On the Montreal Cognitive Assessment test (MoCA), the elder's score improved an average of 3.29 points per person. Their word recall had the largest numerical improvement. On the Geriatric Depression Test, Geriatric Anxiety Test and the PANAS Test (positive and negative affect test) there were no significant differences between the pre and post intervention, although a slight improvement on the Geriatric Depression Test, however no statistical difference.The use of emotional support robots improved memory and cognitive functions of the elders, as well as slight improvement in mood. This project made a significant contribution in improving health outcomes for the elderly, bringing more attention to nursing facilities, and contributing to research on health and prevention.
Automatic Generation System of Computer Oil Painting Based on Red Spirit

In the oil painting creation in the direction of red spirit, the oil painting is generally in a realistic style, and the emphasis is on highlighting the historical stories in the oil painting.In the optimized creation of the red spirit, computer technology can be used to quickly complete the creation from historical images to oil paintings.In this paper, according to the requirements of the red spirit for optimized creation, an optimized generation system based on computer image processing technology is constructed.After the system receives and processes the original image, it can obtain sampling points in the form of a grid, and determine the optimized stroke direction, brush pixel value and radius according to the pixel value of the source image, and finally realize the conversion from other images to oil paintings.This system can improve the efficiency of red spirit oil painting creation, and can make the processed images have the characteristics of optimized drawing, so as to meet people's individual needs for oil painting creation.
Intra Prediction Method for Depth Video Coding by Block Clustering through Deep Learning

In this paper, we propose an intra-picture prediction method for depth video by a block clustering through a neural network. The proposed method solves a problem that the block that has two or more clusters drops the prediction performance of the intra prediction for depth video. The proposed neural network consists of both a spatial feature prediction network and a clustering network. The spatial feature prediction network utilizes spatial features in vertical and horizontal directions. The network contains a 1D CNN layer and a fully connected layer. The 1D CNN layer extracts the spatial features for a vertical direction and a horizontal direction from a top block and a left block of the reference pixels, respectively. 1D CNN is designed to handle time-series data, but it can also be applied to find the spatial features by regarding a pixel order in a certain direction as a timestamp. The fully connected layer predicts the spatial features of the block to be coded through the extracted features. The clustering network finds clusters from the spatial features which are the outputs of the spatial feature prediction network. The network consists of 4 CNN layers. The first 3 CNN layers combine two spatial features in the vertical and horizontal directions. The last layer outputs the probabilities that pixels belong to the clusters. The pixels of the block are predicted by the representative values of the clusters that are the average of the reference pixels belonging to the clusters. For the intra prediction for various block sizes, the block is scaled to the size of the network input. The prediction result through the proposed network is scaled back to the original size. In network training, the mean square error is used as a loss function between the original block and the predicted block. A penalty for output values far from both ends is introduced to the loss function for clear network clustering. In the simulation results, the bit rate is saved by up to 12.45% under the same distortion condition compared with the latest video coding standard.
Enriched formulas to conjugate gradient method for removing impulse noise images

The formula conjugate is usually the focal point in conjugate gradient techniques. In this paper, the Perry’s conjugacy condition and quadratic model are used to derive a new coefficient conjugate for the conjugate gradient technique, which is used to solve picture restoration issues. The algorithms show global convergence and have the required descent property. The new technique has showed substantial improvement in numerical testing. It has been demonstrated that the novel conjugate gradient approach outperforms the traditional FR conjugate gradient method. The new technique has showed substantial improvement in numerical testing. It has been demonstrated that the novel conjugate gradient approach outperforms the traditional FR conjugate gradient method.