Miracle-dz/UniFAR-FTUs huggingface dataset:
Downregulation of the OsAKT1 K+/Na+ Transporter Gene by CRISPR-Cas9 Mediated Transformation in Sensitive Rice IR29 Makes it Tolerant to Salt Stress

The coastal area of Bangladesh is affected by soil salinity which threatens rice production. Saline soils are mainly caused by excessive Na+ which interferes with the uptake of the essential nutrient K+. Certain K+ transporters may cause leakage of Na+ ions and the OsAKT1 transporter was earlier shown to be downregulated under salt stress in a tolerant rice landrace called Horkuch. We therefore targeted this gene for Crispr-mediated downregulation by Agrobacterium-mediated in planta transformation in salt sensitive IR29 rice. The gene was shown to be significantly downregulated in at least 3 independent transformants. One of the gene edited IR29 transformants showed significantly high survival, low loss in chlorophyll content, low electrolyte leakage as well as low H2O2 after 15-days of salt stress at 120mM NaCl at the T3 generation. Therefore the potassium transporter OsAKT1 gene is a good target for downregulation to produce salt tolerant rice. Plant Tissue Cult. & Biotech. 33(1): 71-84, 2023 (June)
Novel Si1−xGex/Si heterojunction internal photoemission long‐wavelength infrared detectors

A new approach to the design of a Si‐based infrared detector is demonstrated, based on internal photoemission over a Si1−xGex/Si heterojunction barrier. The heterojunction internal photoemission device structure is grown by molecular beam epitaxy (MBE). The detector requires a degenerately doped p+‐Si1−xGex layer for strong infrared absorption and photoresponse. Doping concentrations to 1020 cm−3 are achieved using boron from a HBO2 source during MBE growth of the Si1−xGex layers. The photoresponse of this device is tailorable, and most significantly, can be extended into the long‐wavelength infrared regime by varying the Ge ratio x in the Si1−xGex layers. Results are obtained with x=0.2, 0.28, 0.3, and 0.4 on patterned Si (100) substrates. Photoresponse at wavelengths ranging from 2 to 10 μm is obtained with quantum efficiencies above ∼1% in these nonoptimized structures.
Dynamic analysis of the rope-sheave system of polar crane for nuclear power plant

A systematic method for analyzing the dynamic of the rope-sheave system of polar crane for nuclear power plant is proposed taking physical structure of the system into account based on the virtual power principle in this paper. The dynamical equations of the spatial ropes are established by spline interpolation based on the spatial description parameters, which could be applied to the rope with variable length. For sheaves of different types, the corresponding essential parameters and the virtual power equations are given. Properties and formulations of the variables involved in these equations are studied in detail. In order to improve the solving efficiency, the high-frequency items are filtered out by replacing the instant strain by time- average strain in integral and the degree of freedoms (DOFs) of the spatial ropes are condensed to the boundary nodes of the ropes. Hence, the total virtual power equation in terms of the sheaves’ parameters is obtained. Two numerical examples, including lowering the load and hoisting the load for different types of rope-sheave systems, are given to verify the rationality and feasibility of the method.
POST-COVID-19 PANDEMIC STUDENT MUKHLIS PERSONALITY CHARACTERISTICS

This research was conducted with the aim of knowing the personality characteristics of mukhlis students after the Covid-19 pandemic. The method used in this study is descriptive method with a total sampling technique of 365 students. Based on the results of statistical calculations obtained, an overview of Mukhlis' personality characteristics was obtained for students of the Faculty of Psychology Class of 2021 UMA who had low achievements in Semester 2 of 95% and based on several aspects, namely first, the Al-Khaathir aspect (86.7%), the 'Azam ( 90%), Niyat aspect (66.7%) and Af'al aspect (93.3%). Thus it can be concluded that the Af'al aspect is more dominant in students than the Niyat aspect so that student achievement tends to decrease
Up, Up, and Away: Individual Differences in Imaginative Involvement and Magical Beliefs Relate to Perceiving and Desiring Supernatural Powers

Human beings have relished the idea of possessing supernatural/superhuman powers for millennia. However, do some people desire such powers more than others based on specific individual differences and characteristics? We examined how individual differences in imaginative involvement (fantasy proneness, absorption) and magical beliefs (superstitiousness and religiousness) uniquely related to desire for 12 supernatural powers (final N = 143); participants also rated the supernaturalness of each power and how much they believed humans possess/will possess each power. These individual differences, as predicted, consistently correlated with the desire for supernatural powers and the belief that humans may possess these powers in the future. After controlling for sci-fi and fantasy narrative consumption, only absorption and superstitiousness (chance subscale) uniquely related to desire for supernatural powers; and only absorption uniquely predicted the belief that humans do/would possess these powers. Ultimately, there are important individual differences in desire and perception of supernatural powers.
Digital Communication: Bridge to Repurchase Intention

Background: According to the Top Brand Index (TBI) statistics for 2021, Milo was ranked fifth in the ready-to-drink liquid milk category. Furthermore, in the Milk Powder category, Milo was picked third out of four milk brands in the same year. Aim: This study examines the impact of Brand Familiarity, Brand Love, and e-WOM on Brand Attitude and the Repurchase Intention of Milo brand consumers in Samarinda City. Method: This study uses a questionnaire with the object of research being the Milo Brand product. The sample of this study is 147 respondents conducted in the city of Samarinda. The method of sampling is accidental sampling, which means that data were collected from respondents while found. The research methodology for this study involves using a quantitative approach, including path analysis and Structural Equation Modeling (SEM), which were analyzed using IBM SPSS Statistic 25 software and AMOS 5.0 software. Findings: The analysis of the structural model in this study found that three out of six hypotheses were significant: (1) the effects of Brand Familiarity, e-WOM, and Brand Love on Brand Attitude, and (2) the effects of e-WOM, Brand Love, and Brand Attitude on Repurchase Intention. The other three hypotheses were not significant: (1) the effect of Brand Familiarity on Brand Attitude, (2) the effect of Brand Love on Repurchase Intention, and (3) the effect of e-WOM on Repurchase Intention.
Does Telephone Counselling Promote Breast Feeding Duration in Taiwan?

Background: In Taiwan, actual breastfeeding rates fall short of 6 months recommendation duration. 60.2% of infants are breastfed at 1 month, decreasing to 47.3% by 4 months and 37.9% by 6 months. The study aimed to evaluate the effectiveness of telephone-based counselling in encouraging mothers’ breastfeeding duration at least 3 months in Taiwan. Methods: A quasi-experimental design using pre-and post-tests with intervention and control groups was selected for the study. A total 79 mothers were invited to participate randomized into either experimental group (n=40) or control group (n=39). Both groups received usual care during hospitalization but the experimental group received a scheduled telephone interviews after discharge from hospitals. Both groups will have a pre-test before discharge from hospital, a post-test at 1 month, 2 months, and 3 months. Breastfeeding Self-Efficacy Scale (BSES) were used to measure breastfeeding confidence. Results: The repeat measures ANOVA revealed no interaction effect for time by group (F (3, 44) = . 71, p=. 55). Similarly, there was no significant main effect for time (F (3,44) = . 20, p=. 90) or for group (F (1,22) = . 27, p=. 60). The only predictor and positive related to higher scores in BSES was the length of previous breastfeeding (P<.005). Conclusion: Although not a statistically significant difference, there are 23 out of 40 mothers in experimental group still having breastfeeding compared to 1 out of 39 mothers in control group in the third month.
Grain-Boundary Roughening in Colloidal Crystals

In polycrystals, faceted grains may become round and rough at high temperatures. Such a roughening phenomenon remains poorly understood, partly because of the lack of experimental observations. Here, we directly visualize the roughening dynamics of grain boundaries inside thin-film colloidal crystals at the single-particle level using video microscopy. The thermal fluctuations of grain boundaries appear to exhibit both static and dynamic critical-like behaviors, in contrast to the Kosterlitz-Thouless transition in typical free surface roughening. The roughening point shifts towards the melting point as the grain boundary’s mismatch angle θ decreases and is preempted by melting when θ < 18°. Counterintuitively, the amplitude of grain-boundary fluctuations decreases above the roughening point. This could be attributed to the observed widening of the grain boundary. The roughening strongly affects the mobility of the grain boundary but not the stiffness. These results provide new guidance for the control of microstructures in polycrystals and further development of roughening theory.
Determination value k in k-nearest nieghbor with local mean euclidean And weight gini index

K-Nearest Neighbor is the algorithm that included into the category of algorithms supervaised learning is bound process to distinguish the classes that already exists. Nearest neighbor is calculated based on the value of k that determines how the nearest neighbor in consider on the distance class data for k-nearest neighbor based on the determination of the value of the k. The determination of the class test data local mean based k-nearest neighbor using the measurement of the distance closest to each using eucllidean distance from each class data. Model based approach the weight of Gini Index is in need to give the weight of each attributes to determine the value of k. Research In this time I get the results of k best at the thyroid data which is a type of unbalanced data to obtain the value of the k highest k=44 until k=46 with accuracy of the closest neighbors of 71,19% and the value of the k lowest is k=50 of 69,30%. Then the results of the value of the k=44 until k=46 become k best on the processing of this time. It can be concluded for the data class is not the same will result in class data become random repeatedly until the limit of the determination of the value of k as well as exceeding the value of the k highest.
TWO-DIMENSIONAL MODELING OF THE MOTION IN INDUCTION MOTOR WITH FERROMAGNETIC HYSTERESIS

The paper deals with the analysis of an induction motor, which is obtained from the Problem No. 30a of the TEAM Workshops. The modified problem has been solved by the Finite Element Method (FEM) using the A,V – A – potential formulation taking the nonlinearity of the material into account. The effect of nonlinearity has been modeled by an inverse tangent type analytical model. The nonlinearity is handled by the FixedPoint iteration technique (FPT).
Frame Prediction Using Recurrent Convolutional Encoder with Residual Learning

The prediction for the frame of a video is difficult but in urgent need in auto-driving. Conventional methods can only predict some abstract trends of the region of interest. The boom of deep learning makes the prediction for frames possible. In this paper, we propose a novel recurrent convolutional encoder and deconvolutional decoder structure to predict frames. We introduce the residual learning in the convolution encoder structure to solve the gradient issues. The residual learning can transform the gradient backpropagation to an identity mapping. It can reserve the whole gradient information and overcome the gradient issues in Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Besides, compared with the branches in CNNs and the gated structures in RNNs, the residual learning can save the training time significantly. In the experiments, we use UCF101 dataset to train our networks, the predictions are compared with some state-of-the-art methods. The results show that our networks can predict frames fast and efficiently. Furthermore, our networks are used for the driving video to verify the practicability. Keywordsresidual learning; recurrent convolutional networks; frame prediction.
A Lightweight Convolutional Neural Network Based on Joint Correlation Distance Constraints and Density Peak Clustering for Hyperspectral Target Detection

Deep learning can fully exploit the potential information of data, which facilitates effective target-background separation for hyperspectral target detection (HTD). The deep learning model usually requires a large number of labeled samples yet the available prior target spectra in the hyperspectral image (HSI) are extremely limited. In addition, network training also requires reliable input samples to ensure that the trained model has stronger data discrimination, but current detection methods often suffer from the problem that the background samples are impure. To address the above problems, we propose a lightweight convolutional neural network based on joint correlation distance constraints and density peak clustering (LCNN-CD) for HTD. First, the correlation distance between the prior target and HSI is calculated, and the pixels with high correlation are retained for expanding the target sample, which effectively handles the problem of insufficient target samples. Second, the density peak clustering algorithm is used to extract the main pure background samples, which effectively solves the problem that the background samples are sufficiently impure. Finally, a lightweight convolutional neural network model is designed, which is fed by the training samples (both target and background samples) to obtain the final detection results with low computational efficiency. The proposed method is compared with the classical and recently popular HTD methods on three real HSI datasets. Numerous experiments show that the proposed LCNN-CD method has better target detection performance and effectiveness.
Disturbance-observer-based control for time-delay Markovian jump systems subject to actuator saturation

This paper is concerned with disturbance-observer-based control for Markovian jump systems with time delay and actuator saturation. By virtue of the disturbance-observer-based control technique and an appropriate mode-dependent Lyapunov–Krasovskii functional, an anti-disturbance controller is designed to ensure that the resulting closed-loop systems are stochastically stable, as well as estimation of the domain of attraction. Furthermore, an iterative optimization method is proposed to acquire the maximum estimate of the domain of attraction by solving a set of linear matrix inequalities. Finally, a simulation illustrates the effectiveness of the proposed schemes.
Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm

With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting, with research focused on problems such as long training times, forecasting accuracy and insufficient speed, etc. Using the advantages of swarm intelligence algorithms such as global optimization, strong adaptability and fast convergence, the improved Aquila optimization algorithm (AO) is used to optimize the structure of neural networks, and the optimal solution is chosen as the structure of neural networks used for subsequent prediction. However, its performance in processing sequence data with time characteristics is not good, so this paper introduces a Long Short-Term Memory (LSTM) neural network which has obvious advantages in time-series analysis. The Cauchy variational strategy is used to improve the model, and then the improved Aquila optimization algorithm (IAO) is used to optimize the parameters of the LSTM neural network to establish a model for predicting the actual photovoltaic power. The experimental results show that the proposed IAO-LSTM photovoltaic power prediction model has less error, and its overall quality and performance are significantly improved compared with the previously proposed AO-CNN model.
Micro-shear bond strength of resin cements to Er,Cr:YSGG laser and/or acid etched enamel

The purpose of this study was to evaluate the effect of Er,Cr:YSGG laser irradiation and/or acid etching on the micro-shear bond strength (µSBS) of two different dual-cured resin cements to human enamel. 24 intact, extracted molars were sectioned longitudinally and randomly divided into three subgroups according to three surface pretreatment methods: (1) Er,Cr:YSGG laser (Waterlase MD, Biolase) irradiation was used with a power setting of 2 W (55% water and 65% air), and a repetition rate of 20 Hz for 10 s; (2) 37% phosphoric acid application for 20 s; (3) Re-etching with phosphoric acid for 20 s after the Er,Cr:YSGG laser irradiation as in group1. 12 enamel discs for each of the resin cements (Panavia F2.0 and Variolink N) were prepared and three cylinders of resin cements were bonded to each enamel surface (n = 12). A micro-shear bond test (EZ-test- 500 N) was used at a cross-head speed of 1 mm min−1 to determine the µSBS. One-way analysis of the variance and Tukey post-hoc tests at the 95% confidence level were used to analyze the µSBS values. Panavia F2.0 verified significantly higher µSBS values than Variolink N (p < 0.05) for all the tested surface pretreatment methods. The combination of laser and acid etching on enamel produced higher µSBS values for bonding both resin cements than the use of acid or laser irradiation alone. The combination of the Er,Cr:YSGG laser and phosphoric acid etching of the enamel for the cementation of indirect restorations with Variolink N and Panavia F2.0 produced better µSBS values to enamel than the use of phosphoric acid or Er,Cr:YSGG laser etching alone, holding the potential to become a highly attractive method for routine use.
Waqf and The Theory of Planned Behavior: A Meta-Analysis Review

Based on a thorough literature review and meta-analysis, this research aims to outline the use of the theory of planned behaviour (TPB) in predicting Waqf Intention (WI). A meta-analysis was used as a research approach, and 17 studies with a total population of 4575 persons (mean = 269.118) were included in this study. Previous studies that employed the TPB to predict the authors summarized WI. WI is determined by Attitudes about Waqf, Subjective Norms (SN), and Perceived Behavioral Control (PBC). The fundamental implication of this meta-analysis is to deconstruct the core of Waqf and its determinants for Islamic financial institutions and Waqf Institutions to promote the practice of this behaviour in their respective fields. However, a notable weakness in the present literature is a potential "cooperation bias," in which individuals are inclined to overstate their WI. Therefore, this is the first meta-analysis to explore the TPB's predictive value for identifying WI. While there are various discrepancies noted in the research that employed the TPB in the Waqf setting, the outcomes of this research have been compiled and presented.
Design, Kinematics and Gait Analysis, of Prosthetic Knee Joints: A Systematic Review

The aim of this review article is to appraise the design and functionality of above-knee prosthetic legs. So far, various transfemoral prosthetic legs are found to offer a stable gait to amputees but are limited to laboratories. The commercially available prosthetic legs are not reliable and comfortable enough to satisfy amputees. There is a dire need for creating a powered prosthetic knee joint that could address amputees’ requirements. To pinpoint the gap in transfemoral prosthetic legs, prosthetic knee unit model designs, control frameworks, kinematics, and gait evaluations are concentrated. Ambulation exercises, ground-level walking, running, and slope walking are considered to help identify research gaps and areas where existing prostheses can be ameliorated. The results show that above-knee amputees can more effectively manage their issues with the aid of an active prosthesis, capable of reliable gait. To accomplish the necessary control, closed loop controllers and volitional control are integral parts. Future studies should consider designing a transfemoral electromechanical prosthesis based on electromyographic (EMG) signals to better predict the amputee’s intent and control in accordance with that intent.
Etude des erreurs programmées du ribosome par microscopie de fluorescence en molécule unique

La synthese des proteines est un mecanisme central de la vie cellulaire dont la compre-hension est un enjeu pour la recherche biomedicale. Des phenomenes comme les erreurs programmeesde la traduction eucaryote ou l’initiation par des structures IRES virales sont impliques dans les processusde replication de virus et de bacteries. Mieux apprehender ces processus est une etape essentiellepour aboutir au developpement d’approches therapeutiques innovantes. Les etudes en molecule uniquepermettent d’observer chaque systeme reactionnel individuellement et donnent acces a des evenementsasynchrones difficilement observables en mesure d’ensemble, tels la traduction de proteines.Ce manuscrit de these presente une approche d’etude de la traduction par un ribosome eucaryote(mammifere) en molecule unique. Nous observons les systemes traductionnels grâce a des marqueursfluorescents lies a des oligonucleotides pouvant s’hybrider sur les sequences d’ARN messagers traduites.L’observation de ces marqueurs est faite par microscopie de fluorescence en reflexion totale (TIRFM),les ARNm etant accroches a la surface de l’echantillon. En lisant l’ARNm, le ribosome detache lesmarqueurs, et leurs instants de depart nous permettent de remonter a la dynamique de traductionde ribosomes individuels. Cette methode permet d’obtenir des donnees cinetiques statistiques surun grand nombre de systemes traductionnels en parallele pouvant alors etre ajustees par des lois deprobabilite. Partant de ce principe, mes travaux de these ont eu pour objectif d’etendre nos experiencesa une nouvelle problematique biologique : l’etude des evenements non canoniques de la traductioneucaryote. Pour cela nous avons apporte les modifications et les optimisations necessaires au dispositifet au protocole experimental pour l’adapter a ces nouveaux enjeux.Nos mesures de la cinetique in vitro de l’elongation eucaryote ont mis a jour un delai du a uneinitiation non-canonique. En effet, nous realisons le recrutement du ribosome par l’ARNm grâce aune structure virale de type IRES. Dans nos conditions d’experience, l’incorporation d’un acide amineprend environ une seconde tandis que cette structure induit un retard a la traduction de plusieursdizaines de secondes. Nous avons realise une etude comparative de plusieurs de ces structures viraleset avons montre que le delai mesure etait une caracterisitique conservee dans le cadre de l’initiation noncanonique. Ce resultat ouvre des perspectives d’etudes cinetiques tant pour approfondir nos conclusionssur les IRES que pour aborder d’autres evenements non canoniques tel que le decalage de la phase delecture ou le franchissement du codon stop.