Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Stage-Matched Educational Intervention on Behavior Change and Glycemic Control in Elderly Patients with Diabetes

Objective: Diabetes is a chronic and common global dis-ease that requires particular care behaviors for the rest of life. People with diabetes can be trained to achieve optimal self-care. The present study was conducted to determine the effect of stage-matched educational intervention on behavior change and glycemic control in elderly patients with diabetes. Matherials and methods: In this controlled clinical trial, 110 type 2 elderly patients with diabetes were selected randomly from health centers in Malard city, Iran, and were assigned to the control and experimental group equally. Data collection instruments were: demographics questions, scales to assess transtheoretical model constructs and stage of change, and blood samples for determining FBS and HbA1c. The mentioned variables were measured before, 1, and 3 months after the stage-matched educational intervention. The educational intervention consisted of 2 group intensive training sessions of about 45 minutes and daily messages via mobile phone for 3 months. The educational topics were about healthy diet and caring behaviors. Data were analyzed using SPSS 25.0 and independent & paired t-test, chi-square, one-way ANOVA, and ANCOVA. The significance level was con sidered less than 0.05. Results: The mean age of study participants was (66.5 ± 3.91) years. Also, the average duration of diabetes was (7.18 ± 4.21) years, and 50% had a Junior high school education. A significant decrease was found in mean FBS (from 187 ± 43.4 to 136.42 ± 36.34, p < 0.05) and HbA1c (from 8.28 ± 1.26 to 7.26 ± ± 1.10, p < 0.05) in the experimental group, and also the mean of self-care behaviors in the experimental group increased significantly from baseline (from 1.51 ± ± 1.71 to 1.45 ± 0.92, p < 0.05). Conclusions: Educational intervention based on the stages-of-change model can improve glycemic control and behaviors related to controlling diabetes, so it is necessary to pay attention to essential psychological variables in designing educational interventions in order to control blood glucose in patients with diabetes. (Clin Diabetol 2022, 11; 4: 232–238)
Managing Hospital Employees’ Burnout through Transformational Leadership: The Role of Resilience, Role Clarity, and Intrinsic Motivation

Medical errors have been identified as one of the greatest evils in the field of healthcare, causing millions of patient deaths around the globe each year, especially in developing and poor countries. Globally, the social, economic, and personal impact of medical errors leads to a multi-trillion USD loss. Undoubtedly, medical errors are serious public health concerns in modern times, which could be mitigated by taking corrective measures. Different factors contribute to an increase in medical errors, including employees’ risk of burnout. Indeed, it was observed that hospital employees are more exposed to burnout situations compared to other fields. In this respect, managing hospital employees through transformational leadership (TL) may reduce the risk of burnout. However, surprisingly, studies on the relationship between TL and burnout are scarce in a healthcare system, indicating the existence of a critical knowledge gap. This study aims to fill this knowledge gap by investigating the role of TL in reducing the risk of burnout among hospital employees. At the same time, this study also tests the mediating effects of resilience and role clarity with the conditional indirect effect of intrinsic motivation in the above-proposed relationship. To test different hypotheses, a hypothetical model was developed for which we collected the data from different hospital employees (n = 398). Structural equation modeling (SEM) was considered for statistical validation of hypotheses confirming that TL significantly reduces burnout. The results further indicated that resilience and role clarity mediate this relationship significantly. Lastly, the conditional indirect effect of intrinsic motivation was also confirmed. Our results provide meaningful insights to the hospital administrators to combat burnout, a critical reason for medical errors in hospitals. Further, by incorporating the TL framework, a hospital may reduce the risk of burnout (and, hence, medical errors); on the one hand, such a leadership style also provides cost benefits (reduced medical errors improve cost efficiency). Other different theoretical and practical contributions are discussed in detail.
Dietary Intakes of Slovenian Adults and Elderly: Design and Results of the National Dietary Study SI.Menu 2017/18

Incomparable and insufficiently detailed information on dietary intakes are common challenges associated with dietary assessment methods. Being a European Union country, Slovenia is expected to conduct national food consumption studies in line with harmonised EU Menu methodology. The present study aimed to describe the methodology and protocols in the Slovenian nationally representative dietary survey SI.Menu 2017/18, and to assess population dietary habits with respect to food consumption and energy and macronutrient intakes. While the study targeted various population groups, this report is focused on adults. A representative sample of participants was randomly selected from the Central Register of Population according to sex, age classes and place of residency, following a two-stage stratified sampling procedure. Information on food consumption was collected with two non-consecutive 24-h dietary recalls using a web-based Open Platform for Clinical Nutrition (OPEN) software. Data were complemented with a food propensity questionnaire to adjust for usual intake distribution. Altogether, 364 adults (18–64 years) and 416 elderlies (65–74 years) were included in the data analyses. Study results highlighted that observed dietary patterns notably differ from food-based dietary guidelines. Typical diets are unbalanced due to high amounts of consumed meat and meat products, foods high in sugar, fat and salt, and low intake of fruits and vegetables and milk and dairy products. Consequently, the energy proportion of carbohydrates, proteins, and to some extent, free sugars and total fats, as well as intake of dietary fibre and total water deviates from the reference values. Age and sex were significantly marked by differences in dietary intakes, with particularly unfavourable trends in adults and men. Study results call for adoption of prevention and public health intervention strategies to improve dietary patterns, taking into account population group differences. In addition, all developed protocols and tools will be useful for further data collection, supporting regular dietary monitoring systems and trend analyses.
Daring to Be Different: The First-Person HIV-Positive Narrator in Two South African Novels

Masande Ntshanga’s novel The Reactive (2014) is the first South African novel written by a black male writer to feature the first-personvoice of an HIV-positive man, Lindanathi. Following Kgebetli Moele’s The Book of the Dead (2009), which gave the virus itself a voice, The Reactive heralds a significant shift in the portrayal of HIV in South African literature. Eben Venter’s Afrikaans novel Ek Stamel, EkSterwe (1996) which was translated into English by Luke Stubbs as My Beautiful Death (2004), and which has – significantly – received little critical review in English as an HIV narrative, tells the story of a white South African man, Konstant, in the Australian diaspora whoeventually succumbs to AIDS. Both novels complicate ideas of masculinity and can be described as ‘coming of age’ narratives or bildungsromans. Both novels sit historically on the cusp of change, before and after the widespread availability of ARVs. Given theircommonality of subject and narrative perspective, these texts seem ripe for comparison despite their authors’ different backgrounds. The shifts and continuities in the representation of HIV/AIDS found between these two novels, published 18 years apart, seem to disrupt the trajectory of the post-colonial bildungsroman as it is mediated (for the first time? ) through the HIVpositive narrator. Reading these two novels together helps us to understand literary patterns, associations and dissociations, which reveal a cultural symbology of HIV/AIDS, part of a wider cultural symbology of illness in South African literature.
A Level-set Algorithm for Simulating Wildfire Spread

Level-set approaches are efficient and versatile methods for solving interface tracking problems and have been used in recent years to describe wildland fire propagation. Being based on an Eulerian description of the spread problem, their numerical implementation offers improved computational agility and better portability to parallel computing environments with respect to vector-based simulators. The use of a continuous representation of the fire perimeter in place of the binary formulation used in Cellular Automata avoids the commonly observed distortion of the fire shape. This work presents an algorithm for fire-spread simulation based on a level-set formulation. The results are compared to the ones obtained by two well-known Cellular Automata simulators under homogeneous conditions, and to the ones given by a well-know vector-based fire-spread simulator under realistic slope and wind conditions. According to this work, the level-set approach provides better results, in terms of accuracy, at a much reduced computational cost.
Elliptic Flowers: New Types of Dynamics to Study Classical and Quantum Chaos

We construct examples of billiards where two chaotic flows are moving in opposite directions around a non-chaotic core or vice versa; the dynamics in the core are chaotic but flows that are moving in opposite directions around it are non-chaotic. These examples belong to a new class of dynamical systems called elliptic flowers billiards. Such systems demonstrate a variety of new behaviors which have never been observed or predicted to exist. Elliptic flowers billiards, where a chaotic (non-chaotic) core coexists with the same (chaotic/non-chaotic) type of dynamics in flows were recently constructed. Therefore, all four possible types of coexisting dynamics in the core and tracks are detected. However, it is just the beginning of studies of elliptic flowers billiards, which have already extended the imagination of what may happen in phase spaces of nonlinear systems. We outline some further directions of investigation of elliptic flowers billiards, which may bring new insights into our understanding of classical and quantum dynamics in nonlinear systems.
Sampling-Frequency-Independent Convolutional Layer and its Application to Audio Source Separation

Audio source separation is often used for the preprocessing of various tasks, and one of its ultimate goals is to construct a single versatile preprocessor that can handle every variety of audio signal. One of the most important varieties of the discrete-time audio signal is sampling frequency. Since it is usually task-specific, the versatile preprocessor must handle all the sampling frequencies required by the possible downstream tasks. However, conventional models based on deep neural networks (DNNs) are not designed for handling a variety of sampling frequencies. Thus, for unseen sampling frequencies, they may not work appropriately. In this paper, we propose sampling-frequency-independent (SFI) convolutional layers capable of handling various sampling frequencies. The core idea of the proposed layers comes from our finding that a convolutional layer can be viewed as a collection of digital filters and inherently depends on sampling frequency. To overcome this dependency, we propose an SFI structure that features analog filters and generates weights of a convolutional layer from the analog filters. By utilizing time- and frequency-domain analog-to-digital filter conversion techniques, we can adapt the convolutional layer for various sampling frequencies. As an example application, we construct an SFI version of a conventional source separation network. Through music source separation experiments, we show that the proposed layers enable separation networks to consistently work well for unseen sampling frequencies in objective and perceptual separation qualities. We also demonstrate that the proposed method outperforms a conventional method based on signal resampling when the sampling frequencies of input signals are significantly lower than the trained sampling frequency.
Modulation Bandwidth Enhancement and Frequency Chirp Suppression in Two-Section DFB Laser

We demonstrate a two-section distributed feedback (DFB) laser for direct modulation based on the reconstruction equivalent chirp (REC) technology. The two-section DFB laser exhibits a stable single-mode characteristic. Utilizing the photon-photon resonance (PPR) effect and detuned-loading effect, the 3-dB modulation bandwidth of the two-section DFB laser is improved from 8 GHz to 23 GHz. A 25 Gb/s non-return to zero (NRZ) signal is applied to the two-section DFB laser and eye diagrams with large open extent are obtained. In addition, the relative intensity noise is reduced to below −143 dB/Hz. The frequency chirp is also suppressed. The chirp parameter is reduced from 5.67 to 1.37, which is a small value in directly modulated DFB laser. We compared the eye diagrams after transmitting different distances, the results show that the two-section structure reduces the frequency chirp significantly. The modulation of the two-section DFB laser also has good linearity, with a 1-dB compression point of more than 18 dBm.
A comparative study of Machine learning Algorithms on the UNSW-NB 15 Dataset

The world has experienced a radical change due to the internet. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. This safety is handled using systems to detect network intrusion called Intrusion Detection Systems (IDS). Machine learning techniques are being implemented to improve these systems. In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a good one for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. Nevertheless, most researchers have focused on the confusion matrix as measurements of classification performance. Therefore, many papers use this matrix to present a detailed comparison with the dataset, data preprocessing, feature selection technique, algorithms classification and performance evaluation. The goal of this paper is to present a comparison of application of different Machine Learning algorithms used to build and improve intrusion detection systems in terms of confusion matrix, accuracy, recall, precision, FAR, specificity and sensitivity using the UNSW-NB15 Dataset. Furthermore, we introduce some lesson learnt to shoot more researchers in their future works.
Manufacture and Quality Control of Human Umbilical Cord-Derived Mesenchymal Stem Cell Sheets for Clinical Use

Human umbilical cord-derived mesenchymal stem cell (UC−MSC) sheets have attracted much attention in cell therapy. However, the culture media and coating matrix used for the preparation of UC−MSC sheets have not been safe enough to comply with current clinical drug standards. Moreover, the UC−MSC sheet preservation systems developed before did not comply with Good Manufacturing Practice (GMP) regulations. In this study, the culture medium and coating matrix were developed for UC−MSC sheet production to comply with clinical drug standards. Additionally, the GMP-compliant preservation solution and method for the UC−MSC sheet were developed. Then, quality standards of the UC−MSC sheet were formulated according to national and international regulations for drugs. Finally, the production process of UC−MSC sheets on a large scale was standardized, and three batches of trial production were conducted and tested to meet the established quality standards. This research provides the possibility for clinical trials of UC−MSC sheet products in the development stage of new drugs and lays the foundation for industrial large-scale production after the new drug is launched.
Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation

Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The first step is to replace the original U-Net’s backbone feature extraction structure with VGG16 to segment the potato crop rows. Secondly, a fitting method of feature midpoint adaptation is proposed, which can realize the adaptive adjustment of the vision navigation line position according to the growth shape of a potato. The results show that the method used in this paper has strong robustness and can accurately detect navigation lines in different potato growth periods. Furthermore, compared with the original U-Net model, the crop row segmentation accuracy is improved by 3%, and the average deviation of the fitted navigation lines is 2.16°, which is superior to the traditional visual guidance method.
ICT Based Learning Solutions for Children with ASD: A Requirement Engineering Study

"This research explores the explicit requirements to design and develop ICTbased learning solutions for children with Autism Spectrum Disorder (ASD). A requirement elicitation study has been conducted for six weeks of the study’s in Pune, India. The study sample size is fifteen (N=15), with an age limit between five to twelve years old. All the participants have been diagnosed with ASD and undergoing a skills development process in the same institution for at least six months. The outcomes of this research are tri-fold. Firstly, identify the twelve explicit requirements to design and develop ICT-based learning solutions considering the challenges due to autism. Secondly, conformity to the existing Multi Agent based Persuasive Education (MAPE) model with revealed requirements. Thirdly, propose an Internet of Things (IoT) based framework to observe and record the learning performance of ASD-affected children. The unique contribution of this study is to develop a Level-5 IoT framework to incorporate the requirements revealed through the participatory design approach for implementing ICT-based learning solutions. The proposed framework forms the basis for developing a dataset to identify the learning patterns and new requirements for ASD-affected children through Artificial Intelligence (AI) and Machine Learning (ML). "
Reconstruction-Based Hand–Eye Calibration Using Arbitrary Objects

This article introduces a flexible hand–eye calibration technique for a 3-D sensor from a reconstruction perspective, with no need for a specialized and accurate calibration rig. Our intention is to find the hand–eye relation that simultaneously aligns multiview point clouds of a common scene into the robot base frame, namely simultaneous calibration and reconstruction. To achieve this goal, a novel variant of iterative closest point (ICP) algorithm based on the Gauss–Newton method and Lie algebra is proposed, which iteratively transforms multiview point clouds into the robot base frame, estimates point-to-point correspondences between point clouds then refines the hand–eye relation to minimize the Euclidean distance between corresponding points. In addition to the calibration result, it returns a preliminary reconstruction as a byproduct. Cases of degeneracy and applicable conditions are given and proved. Using arbitrary daily objects with no prior information and a real robotic eye-in-hand system, we verify our method feasible and effective.
Metal Ions and Chemical Modification Reagents Inhibit the Enzymatic Activity of Lecithin-Dependent Hemolysin from Vibrio parahaemolyticus

Lecithin-dependent thermolabile hemolysin (LDH) is a virulence factor excreted by Vibrio parahaemolyticus, a marine bacterium that causes important losses in shrimp farming. In this study, the function of LDH was investigated through its inhibition by metal ions (Mg2+, Ca2+, Mn2+, Co2+, Ni2+ and Cu2+) and chemical modification reagents: β-mercaptoethanol (βME), phenylmethylsulfonyl fluoride (PMSF) and diethyl pyrocarbonate (DEPC). LDH was expressed in the Escherichia coli strain BL-21, purified under denaturing conditions, and the enzymatic activity was evaluated. Cu2+, Ni2+, Co2+ and Ca2+ at 1 mmol/L inhibited the LDH esterase activity by 20–95%, while Mg2+ and Mn2+ slightly increased its activity. Additionally, PMSF and DEPC at 1 mmol/L inhibited the enzymatic activity by 40% and 80%, respectively. Dose-response analysis showed that DEPC was the best-evaluated inhibitor (IC50 = 0.082 mmol/L), followed by Cu2+ > Co2+ > Ni2+ and PMSF (IC50 = 0.146–1.5 mmol/L). Multiple sequence alignment of LDH of V. parahaemolyticus against other Vibrio species showed that LDH has well-conserved GDSL and SGNH motifs, characteristic of the hydrolase/esterase superfamily. Additionally, the homology model showed that the conserved catalytic triad His-Ser-Asp was in the LDH active site. Our results showed that the enzymatic activity of LDH from V. parahaemolyticus was modulated by metal ions and chemical modification, which could be related to the interaction with catalytic amino acid residues such as Ser153 and/or His 393.
Development of Small and Medium Enterprises during Covid-19 Period: The Role of Literacy, Attitude and Productive Economic Behavior

The purpose of this study is to examine the model of developing SMEs in the socio-economically unpredictable crisis of the COVID-19 pandemic. Work from home and all activities are carried out online, the implementation of Social Restrictions has an impact on the economic crisis. Many SMEs survive or close their businesses, on the other hand, new entrepreneurs emerge. Economic literacy (EC), digital literacy (DLT), entrepreneurial attitude (EAT), productive economic behavior (PEB), and entrepreneurial business development (EBD) were analyzed as research models with a survey to obtain the latest field data, analyzed by exploratory factor analysis and AMOS confirmatory factor analysis 25. The theory of planned behavior from Ajzen was developed as a predictor behavior model for SMEs in developing businesses. Quantitative research with an entrepreneurial population in garment SMEs, a total of 203 entrepreneurs who have an online production and sales business as a sample. This study succeeded in testing 6 of the proposed hypotheses and one hypothesis was rejected. The model results have met the suitability criteria, that the development of SMEs based on high economic literacy skills will increase good entrepreneurial attitudes so that productive economic behavior is formed which will ultimately increase the ability of SMEs to survive, grow and develop during the COVID-19 pandemic crisis. For further researchers to develop research on a wider sample area as well as other potential factors to develop SMEs such as entrepreneurial skills. Received: 17 April 2022 / Accepted: 16 August 2022 / Published: 2 September 2022