Miracle-dz/UniFAR-FTUs huggingface dataset:
Validity and Reliability of the Persian Version of the Forgotten Joint Score-12 Questionnaire in Iranian Patients Following Anterior Cruciate Ligament Reconstruction

Background and Objectives: Forgotten Joint Score-12 is a useful questionnaire to evaluate the level of joint awareness and patients’ ability to forget the involved joint. The objective of the present study was to obtain a tool to evaluate the level of joint awareness for use in Persian-speaking patients following anterior cruciate ligament reconstruction. Methods: The present study was a methodological study in the field of psychometrics, in which after translation and cross-cultural adaptation of the FJS-12 questionnaire according to the ISPOR method, to examine its validity, the questionnaire was given to 114 participants who had passed between 1 and 3 years of their anterior cruciate ligament reconstruction. To evaluate reliability, participants were asked to complete and resubmit the questionnaire after a week. In this study, the intraclass correlation coefficient (ICC) was used to analyze the repeatability of the questionnaire and measure Cronbach’s alpha internal consistency. Structural validity was performed by calculating the correlation between the FJS-12 questionnaire and Western Ontario McMaster Osteoarthritis (WOMAC) and Knee Osteoarthritis Outcome Score (KOOS) questionnaires using Spearman’s correlation coefficient. The level of ceiling and floor effect of the questionnaire was also assessed. Results: Internal consistency and data repeatability were excellent for FJS-12 (Cronbach’s α=0.950 and ICC=0.845, respectively). In examining the validity, a weak to moderate correlation was obtained between the score of the FJS-12 questionnaire and the scores of the subscales of WOMAC (r=0.34) and KOOS (r=0.34). Also, a moderate correlation was obtained between the score of the FJS-12 questionnaire and the first question of the subscale of quality of life in the KOOS questionnaire (r=0.46), which indicates the validity of the average structure of the questionnaire. The floor (4.45%) and ceiling (3.50%) effect was low. Conclusion: According to the results of this study, the Persian version of the FJS-12 questionnaire can be used by Persian speakers. Also, this questionnaire is a reliable and valid tool to evaluate the level of joint awareness in individuals following anterior cruciate ligament reconstruction.
Effectiveness of nutrition education on nutrient intake and pregnancy outcomes in Migori county, western Kenya

Maternal nutrition is critical as it lays fundamental foundation for the successful outcome of pregnancy. Kenya’s high rates of under nutrition among women of reproductive age are due to sub-optimal feeding practices, inadequate nutrient intake and insufficient knowledge on nutritionally adequate diets leading to poor pregnancy outcomes. This study assessed nutrient intakes and pregnancy outcomes among pregnant women of about 22 weeks gestation exposed to a psycho-educational nutrition initiative in Migori County. A prospective cohort study design and simple random sampling was used to obtain a sample of 150 pregnant women who were enrolled into the Intervention Study. Data was collected using 24-hour dietary recall, anthropometric measurements and health records and was analysed by Nutri-Survey and SPSS and tested using t-tests and regression model. The mean of macronutrient intakes at baseline were 1613±439 kcal for energy, protein 62.6±24.6 g, carbohydrates 245±7 g, fat 41±2 g and after the intervention, mean energy intake was 2159±309 Kcal, protein 86±2 g, carbohydrates 312±6 g and fat 61±3 g. There was a significant improvement in the intakes for all the macronutrients after four months exposure to the intervention. The mean micronutrient intakes by the women at baseline were, vitamin A, 1255±2131 µg, folic acid, 197±7 µg, vitamin C 67±4 mg, calcium 300±141 mg, iron 115±3 mg and zinc 12±4 mg. The mean intakes after the intervention were, Vitamin A 1750±3560 µg, folic acid 249±7µg, vitamin C 90±6 mg, calcium 442±2 mg, iron 15±3 mg and zinc 16±4 mg. There was a significant increase in intake of all micronutrients after the intervention. Mean gestation age at recruitment was 21 weeks, gestation age at delivery was 37.7 weeks. The mean birth weight was 3098±5 grams. Babies born before 37 weeks gestation were 32.6 % while 67.4 % were born at ≥37 weeks gestation. Coefficients of determination (R2) from the regression model showed that there were positive significant effects of nutrition knowledge obtained from the psycho-educational initiative on nutrient intake after the intervention and on pregnancy outcomes. The intervention had a significant effect on nutrient intake and pregnancy outcomes and should be strengthened in the healthcare system to improve nutrition knowledge and dietary practices for enhanced nutrient intakes and pregnancy outcomes. Key words: Pregnant women, Nutrition Education, Nutrient intake, Pregnancy outcomes
Machine Learning Applications in Supply Chain Management: A Deep Learning Model Using an Optimized LSTM Network for Demand Forecasting

The covid 19 has caused a strong health and economic crisis, where the business environment is turning into a great uncertainty and customer demand has become more and more fluctuating. As a result, demand forecasting as well as the task of predictive analysis, holds a great attention in the supply chain, in order to meet the needs of customers, avoid any out of stock, and at the same time avoid a waste of resources. In this paper, we propose a deep learning method based on long-term memory multilayer networks (LSTM) for demand forecasting. Using the grid search method, our method has the ability to automatically select the most optimal forecasting model, considering different combinations of LSTM hyperparameters of a time series. The proposed model has shown strength in capturing the existing nonlinear features in time series data compared to some known time series forecasting methods derived from statistical and machine learning approaches, using historical sales data of a Moroccan pharmaceutical manufacturing company. These methods include exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), recurrent neural network (RNN). The evaluation of the proposed method and the comparison methods was performed using the root mean square error (RMSE) and Symmetric mean absolute percentage error (SMAPE). The comparison of the test results showed that the proposed method is the best performing with RMSE of 4487.32 and SMAPE of 0.026 much better than those obtained by the other models © 2022, International Journal of Intelligent Engineering and Systems.All Rights Reserved.
Acquisition of collapse temperature and the influencing factors during freeze-drying of placental decellularized matrix in freeze-drying microscopy based on image processing techniques

Abstract The placenta-derived extracellular matrix is an ideal material for making freeze-dried sponges as a wound dressing. In order to prepare a porous sponge, collapse temperature is one of the most important parameters in process of freeze-drying. This work used an image processing method to determine the collapse temperature of extracellular matrix slurry during freeze-drying. By using this technology, the variation of collapse temperature under different cooling rates, heating rates, annealing conditions are studied, and the influence of ice crystal size on collapse temperature is further analyzed. In addition, an annealing operation is used to control the size of ice crystals inside the sample. The actual results are consistent with the fact that annealing increases the ice crystal size and collapse temperature. The image processing algorithm can greatly improve the accuracy of obtaining the collapse temperature to shorten the period of exploration of the key freeze-drying parameters of solid-liquid mixtures.
Triple gene editing in porcine embryos using electroporation alone or in combination with microinjection

Background and Aim: We previously developed the gene-editing by electroporation (EP) of Cas9 protein method, in which the CRISPR/Cas9 system was introduced into porcine in vitro fertilized (IVF) zygotes through EP to disrupt a target gene. This method should be further developed, and a combination of EP and MI methods should be evaluated in pigs. This study aimed to determine that a combination of microinjection (MI) and EP of CRISPR/Cas9 system could increase the rates of biallelic mutation for triple-gene knockout in porcine blastocysts. We targeted the pancreatic and duodenal homeobox1 (PDX1) gene using cytoplasmic MI 1 h before or after EP, which was used to edit alpha-1,3-galactosyltransferase (GGTA1) and cytidine 32 monophosphate-N-acetylneuraminic acid hydroxylase (CMAH) genes in porcine zygotes. Materials and Methods: We introduced guide RNAs targeting PDX1, GGTA1, and CMAH with the Cas9 protein into IVF zygotes (one-cell stage) through EP 10 h after the start of IVF (IVF; EP group) or in combination with MI (1 h before, MI-EP group, or after EP treatment EP-MI group) and evaluated the blastocyst formation rate and efficiency of target mutations in the resulting blastocysts. Results: Our results revealed a significant reduction in the rate of blastocyst formation in the two groups that underwent MI before and after EP (MI-EP and EP-MI group), compared with that in the groups treated with EP alone (EP group) (p=0.0224 and p<0.0001, respectively) and control (p=0.0029 and p<0.0001, respectively). There was no significant difference in the total mutation rates among the treatment groups in the resulting blastocysts. As an only positive effect of additional MI treatment, the rate of blastocysts carrying biallelic mutations in at least one target gene was higher in the MI-EP group than in the EP group. However, there was no difference in the rates of embryos carrying biallelic mutations in more than 2 target genes. Conclusion: These results indicate that although a combination of MI and EP does not improve the mutation efficiency or biallelic mutation for triple-gene knockout, MI treatment before EP is better to reduce mortality in porcine zygotic gene editing through a combination of MI and EP.
TIMETABLE BASED DESIGN OF RAILWAY INFRASTRUCTURE

Summary: The design of new railway infrastructure is a complex planning process today in most European countries due to several requirements. From an operational point of view new infrastructure basically has to fulfil the requirements defined by the later customers who are the railway undertakings. Hereby passenger traffic is often organised in a periodic timetable with well defined arrival and departure times in the hubs. These time constraints allow designing the graphical timetable which is indicating the minimum design speed. As a rule, the number of required tracks in a section or station can be also taken out of the graphical timetable. A further optimisation can be carried out between available running time reserves or additional infrastructure. The design of railway infrastructure is strongly related with the demand of expected traffic which is defined by a timetable. Therefore the timetable is predicting the layout of the infrastructure. Typically the timetable can be displayed in a graphical way which is also used in daily operation. In the graphical timetable all running trains are displayed with their time slots including the blocking time stairway. The time slots are designed by calculation of the technical running times including some supplements. Based upon the graphical timetable it is possible to estimate the demand of infrastructure. So at every location where time slots are crossing each other, there will be the necessity to have at least a second track. Following this rule, the number of required tracks is dedicated by the number of crossing time slots in a certain area. Additionally the arrival and departure times can be taken into consideration as a boundary condition which have to be respected especially when an integrated timetable shall be realised. This paper starts with the basic principles of an integrated timetable and shows then how a methodology can be developed to estimate the demand for the infrastructure.
Constraining simulated black hole feedback in the Perseus Cluster

Active galactic nuclei in galaxy clusters can produce tightly collimated jets that inject energy into the surrounding intracluster medium (ICM). This feedback process can offset cooling losses from X-ray radiation that otherwise would result in cooling flow structures. Jet-inflated bubbles can heat the cluster via a turbulent cascade. The density fluctuation spectrum inferred from X-ray maps of the cluster shows different levels and scales of turbulence in different regions of the clusters, and can be used to determine the contribution of different driving mechanisms. By applying observational power spectrum techniques to synthetic X-ray data from feedback simulations of the Perseus cluster, we find that the density fluctuation spectra produced by a single episode of jet activity are broadly consistent with analysis of Chandra observations inside 60 kpc. The velocity dispersion effected by the AGN approaches Hitomi's measurement of turbulence in Perseus during the AGN episode, but is underpredicted later in the simulation. A fully established volume-filling turbulent cascade therefore requres multiple periods of jet activity, along with turbulence driven by other processes outside of the inner 60 kpc core. This also suggests the X-ray fluctuations directly associated with the jet bubble substantially contribute to the overall density fluctuation spectrum.
Exudate Segmentation for Diabetic Retinopathy Using Modified FCN-8 and Dice Loss

: The severity of diabetic retinopathy can lead to blindness if undiagnosed and untreated. The presence of hard exudates is one of the diabetic retinopathy symptoms. Therefore, automatic segmentation of hard exudates can provide an important diagnosis for diabetic retinopathy. Due to the relatively small dimensions of the exudates and the availability of the optic disc that has similar color, the exudate segmentation is a challenge in itself. In this study, we propose a modification of the fully convolutional network model (FCN-8) by combining FCN-8 and shortcuts to improve the performance of FCN-8. Each shortcut consists of a convolutional layer and batch normalization to reduce input degradation. Prior to processing the hard exudates using a modified FCN-8, the optic disc was removed from the retinal image by detecting the area using Faster R-CNN based on the Alexnet architecture. For training and testing, we applied the IDRiD dataset to evaluate the performance of our proposed architecture. Experiments show that our proposed architecture provides accuracy, sensitivity, specificity of 98.18 %, 81.7%, and 98.37 % respectively. Our proposed method gives higher sensitivity compared to Autoencoder, U-Net , FCN-32, FCN-16, and FCN-8.
Corporate Governance, Political Connection, Family Ownership and Tax Aggressiveness in Indonesia

Tax aggressiveness is a strategy of companies to present lower taxable earnings through tax planning without being accused of committing tax fraud, hence considered as one of the weaknesses of the self-assessment system. The purpose of this study was to examine and analyze the effect of corporate governance and political connection on tax aggressiveness with family ownership as the moderator. This research was conducted on manufacturing companies listed on the Indonesia Stock Exchange from 2016 to 2020. Using purposive sampling method, 49 companies were selected as the sample, resulting in 245 observations. The data were analyzed using multiple regression analysis and moderated regression analysis. This study found that corporate governance does not influence tax aggressiveness, that political connection has a negative effect on tax aggressiveness, and that family ownership does not moderate the influence of corporate governance and political connection on tax aggressiveness. Keywords: tax aggressiveness, corporate governance, political connection, family ownership
Imperceptible Improvement using Edge Area Selection for Robust Video Watermarking Using Tchebichef - Singular Value Decomposition

: Watermarking is a technology to protect the copyright of multimedia data. Video is one of the most complex media compared to image or audio. In invisible watermarking, imperceptible aspects are important besides robustness. This research proposes a frame selection method based on the edge area. The frame with the most edge area is selected and used for watermark embedding with Tchebichef transformation and singular value decomposition. A dual-stage Arnold transform was carried out to spread the edge area, and the watermark was embedded more evenly to improve the imperceptibility quality. Based on the test results, this method can improve the peak signal to noise ratio (PSNR) quality by around 1dB to 2dB while still maintaining the robustness of the watermark compared to the previous method without edge area selection, and dual-stage Arnold transform.
Half-metallic Characteristics of the Novel Half Heusler Alloys XCrSb (X=Ti, Zr, Hf )

Ab-initio calculations are performed to examine the structural, mechanical, electronic, magnetic and thermodynamic properties of the half-Heusler ternary alloys XCrSb (X = H f , Ti, Zr). In this study, the spin-polarized density functional theory (DFT) method that is spin-polarized with generalised gradient approximation (GGA) are used to perform ab-initio calculations to investigate the physical properties of a novel half-Heusler ternary alloys XCrSb (X = H f , Ti, Zr). It was confirmed that the alloys are stable mechanically and exhibit ferromagnetic states (FM). The study reveals that the alloys portray half-metallic character with narrow energy gaps. And it also shows that they have a total magnetic moment of approximately 3ub. From the formation energy calculation, it shows that the alloys can be synthesized experimentally. Also, it was observed that they are mechanically stable. The heat capacities and Debye temperatures were also computed and they show high thermodynamic stability.
Structural design of The Mega Tower of China World Trade Center(phase 3A)

The Mega Tower of China World Trade Centre(phase 3A) is a 330m high building composed of a five-star hotel and grade-A office buildings and multi-purpose spaces.Building height is associated with high seismic design intensity,especially considering various stringent requirements by Chinese codes.Various structural types and utilizing different materials have been explored until the final design was accomplished,which comprises a composite braced frame core-tube and a composite outer frame,linked by two outrigger systems at relevant EM floors.The tapered elevation of the tower building emplays three transfer belt trusses that allow the reduction in number of perimeter columns in middle and high zone of the tower.The use of 8 storeys high V-shaped columns with transfer belt truss admit the grand entrances with wide spacing columns at ground floor,and meanwhile provide a smooth structural transition to the upper moment frame.Composite elements of various types are extensively used and positioned carefully to meet the combined requirements for stiffness,ductility,redundancy,and cost-effectiveness.The composite steel plate wall(C-SPW) is implemented in the structure,to increase the shear capacity and stiffness and improve the ductile behaviour at specific zone.Accurate finite-element analysis and advanced non-linear elasto-plastic time history analysis have been carried out to evaluate the structural behaviour and ensure the building safety under different seismic levels.
3D Microseismic Monitoring Using Machine Learning

Microseismic source localization is important for inferring the dynamic status of the subsurface stress field during hydraulic fracturing. Traditional deterministic methods for 3D microseismic source localization require either ray tracing or full waveform modeling, thus are computationally expensive. We propose a very efficient (e.g., within 1 s) microseismic source localization method based on machine learning. First, three‐dimensional (3D) ray tracing is performed with hypothetical event locations and realistic acquisition geometry to calculate the theoretical travel‐times. The theoretical travel‐time differences and the spatial locations of the stations are treated as the input features of the training data set, and the corresponding source locations are used as the labels. The manually or automatically picked arrival time differences between different stations and a reference station after a microseismic event and the actual station locations are fed into the well‐trained model for a fast and accurate location prediction. The proposed method is efficient enough to be widely applied for the real‐time monitoring of hydraulic fracturing. The machine learning model is analogous to 3D grid search but performs 3D ray tracings before the actual localization, and needs to be retrained when applied to a new study area. We use several synthetic tests and a real data example from the Weiyuan hydraulic fracturing experiment in Sichuan, China, to demonstrate the effectiveness of the proposed method. The application of the proposed method to earthquake localization is also demonstrated to be straightforward.
Early career researchers as active followers: perceived demands of supervisory interventions in academic workplaces

ABSTRACT Academic institutions are characterized by specific dynamics between leaders and followers. Academics prefer work autonomy but expect quality leadership. In this article, we explore how early career researchers (ECRs) constructed followership identities through their expectations of supervisory interventions. We used thematic analysis to analyze 39 interviews. The identified themes of ECR followership identity constructions were as follows: 1) Followers as apprentices: Seeking support in reaching career milestones; 2) Followers as motivated performers: Expecting efficient work organization; and 3) Followers as team members: Calling for a shared vision. We argue that the ECR demand for proactive supervisory interventions and the adoption of active followership identities is necessary to foster full academic autonomy development and to overcome negative aspects of experienced isolation in academic life. Acknowledging hierarchical relationships between leaders and followers in HE has the potential to contribute to enhanced career support for ECRs.
Enhancing the Transient Performances and Stability of AVR System with BFOA Tuned PID Controller

This paper proposes an optimal tuning of Proportional Integral Derivative (PID) controller using Bacterial Foraging Optimization Algorithm (BFOA), to enhance the control performances and stability of Automatic Voltage Regulator (AVR) system. A new objective function designed with necessary time domain specifications is considered in this research work for tuning the gain parameters of PID controller. The effectiveness of the proposed BFOA tuned PID controller is extensively absorbed by analyzing the output performance, stability and robustness of the system. The superiority of the proposed approach is distinctly demonstrated by comparing their results with recently documented results of Swarm Intelligence (SI) techniques such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) algorithms and Many Optimizing Liaisons (MOL) algorithm.