Miracle-dz/UniFAR-FTUs huggingface dataset:
BcGR1.1, a Cytoplasmic Localized Glutathione Reductase, Enhanced Tolerance to Copper Stress in Arabidopsis thaliana

Copper is a mineral element, which is necessary for the normal growth and development of plants, but high levels of copper will seriously damage plants. Studies have shown that AtGR1 improves the tolerance of Arabidopsis to aluminum and cadmium stress. However, the role of GR in the copper stress response of plants is still unclear. Here, we identified four genes (named BcGR1.1, BcGR1.2, BcGR2.1 and BcGR2.2, respectively) encoding glutathione reductase (GR) in non-heading Chinese cabbage (Brassica campestris (syn. Brassica rapa) ssp. chinensis), which could be divided into two types based on the subcellular localization. Among them, BcGR1.1, which belonged to the cytoplasmic localization type, was significantly upregulated under copper stress. Compared to WT (the wild type), Arabidopsis thaliana heterologously overexpressed BcGR1.1 had longer roots, higher fresh weight, higher GSH levels and GSH/GSSG (oxidized form of GSH) ratio, and accumulated more superoxide dismutase and peroxidase under copper stress. However, in the AsA-GSH cycle under copper stress, the contents of AsA and AsA/DHA were significantly downregulated, and the contents of DHA and T-AsA (total AsA) were upregulated, in the BcGR1.1-overexpressing Arabidopsis. Therefore, BcGR1.1 could improve the scavenging ability of reactive oxygen species (ROS) by increasing the activity of GR, antioxidant enzymes and the utilization of AsA, and then enhance the copper stress tolerance of plants.
Sensitivitätssteigerung bei der inversen Materialparameterbestimmung für Piezokeramiken

Zusammenfassung Für eine realitätsnahe Finite-Elemente-Simulation von Piezokeramiken ist es notwendig, dass deren Materialparameter präzise bekannt sind. Zur Bestimmung werden zunehmend numerische Verfahren auf Basis inverser Methoden eingesetzt, die einen Einfluss aller Materialparameter auf den als Messgröße genutzten Impedanzverlauf voraussetzen. Dieser kann durch die Nutzung einer segmentierten Elektrode in Kombination mit einem elektrischen Vornetzwerk vergrößert werden, wodurch die Sensitivität erhöht und die Bestimmung aller relevanter Materialparameter an einer einzelnen Probe ermöglicht wird.
Tangible interfaces for navigation in virtual environments: a Argonauta Reactor case study

This work presents a interaction system for virtual environments that allows users control platform without the need of using mouse and keyboard. Through the head movement and voice commands is possible the navigation and control of virtual human (avatar), thus, better human-computer integration. The control system called SOMI (Sounds and Motion Interface) is based on speech recognition using artificial neural networks (ANN), where once the ANN are trained for the different users it possible direct a vocal command to a command of avatar, resulting in the possibility of control by voice. Head movements are recognised using the system infrared(IR) head tracking, this system is based in a IR camera detecting the position of IR leds positioned on the user's head to put the avatar vision in accordance with the vision of the user. The SOMI was integrated into a system called Virtual Argonauta (VA), this system configures itself as a virtual platform for operations training at nuclear reactor research of the Nuclear Engineering Institute (IEN/CNEN), until the present work the virtual person was all controlled by mouse and keyboard, preventing the use of head mounted display (HMD) and decreasing user immersion. The results show an interface that promotes a more effective and engaging manner allowing the use of HMD and with that greater immersion of human beings to the virtual environment, more specifically the VA environment.
Efficacy of Vitamin D Supplements in Prevention of Acute Respiratory Infection: A Meta-Analysis for Randomized Controlled Trials

Background: Previous systematic reviews and meta-analyses of randomized controlled trials (RCTs) have reported inconsistent results regarding the efficacy of vitamin D supplements in the prevention of acute respiratory infections (ARIs). Methods: We investigated these efficacy results by using a meta-analysis of RCTs. We searched PubMed, EMBASE, and the Cochrane Library in June 2021. Results: Out of 390 trials searched from the database, a total of 30 RCTs involving 30,263 participants were included in the final analysis. In the meta-analysis of all the trials, vitamin D supplementation showed no significant effect in the prevention of ARIs (relative risk (RR) 0.96, 95% confidence interval (CI) 0.91–1.01, I2 = 59.0%, n = 30). In the subgroup meta-analysis, vitamin D supplementation was effective in daily supplementation (RR 0.83, 95% CI, 0.73–0.95, I2 = 69.1%, n = 15) and short-term supplementation (RR 0.83, 95% CI, 0.71–0.97, I2 = 66.8%, n = 13). However, such beneficial effects disappeared in the subgroup meta-analysis of high-quality studies (RR 0.89, 95% CI, 0.78–1.02, I2 = 67.0%, n = 10 assessed by the Jadad scale; RR 0.87, 95% CI, 0.66–1.15, I2 = 51.0%, n = 4 assessed by the Cochrane’s risk of bias tool). Additionally, publication bias was observed. Conclusions: The current meta-analysis found that vitamin D supplementation has no clinical effect in the prevention of ARIs.
Risk Factors of Premarital Sex Among University Girl Students: A Qualitative Study

Background: In recent years, the median age of marriage has increased in Iran. Premarital sex has been socially, culturally, religiously, and legally banned in Iran. Changes in sexual and social criteria, as well as communications technology, have resulted in the alteration of traditional values. Also, free time and adventure-seeking behaviors have increased unhealthy and dangerous conducts, which could lead to the prevalence of some sexually transmitted diseases (STDs). Hence, in-depth studies are required to determine the factors affecting premarital sexual relationships in young females. Objectives: This study investigated the challenges and facilitators of premarital sex among female university students in Iran. Methods: This is a qualitative study with a content analysis approach. The participants were 24 female university students in four universities of Fars province, who were selected through purposive sampling method, followed by the snowball sampling technique. Semi-structured interviews were used for data collection, and the data were analyzed using content analysis. Results: The findings highlighted three main determinants of premarital sexual relationships among university students, including individual determinants, family environment, and community-related determinants. Conclusions: In individual determinants, policy makers and planners should improve individual skills such as self-control, resilience to peer pressure, and the skill of saying no, along with teaching self-knowledge. To control the effect of family environment, educating parents and promoting moderate behavior with their children can be effective. To reduce social pressure, programs should be considered for young girls to express themselves and their emotions. Moreover, some new policies are needed to reduce legal restrictions for unmarried females.
Performance Improvement for SAR Tomography Based on Local Plane Model

Multilook approaches have been applied in synthetic aperture radar (SAR) tomography (TomoSAR), for improving the density and regularity of persistent scatterers reconstructed from multipass SAR images in both rural and urban regions. Multilook operations assume that all scatterers in a given neighborhood are similar in height, thereby providing additional data for recovering the position and reflectivity of a single scatterer, so that a higher signal-to-noise ratio can be achieved. This is equivalent to assuming that scatterers belonging to a local neighborhood of range–azimuth cells are located on horizontal planes. The present article generalizes this approach by adopting the so-called local plane (LP) model for TomoSAR imaging in urban areas, accounting for local variations in the height of scatterers that are not negligible. Furthermore, an LP-generalized likelihood ratio test (LP-GLRT) algorithm is developed to implement the previous idea. Compared with the multilook generalized likelihood ratio test algorithm, LP-GLRT shows better performance in the case of urban structures and terrains in experiments based on both simulated data and TerraSAR-X images.
Prediction of Ozone Concentration in Semi-Arid Areas of China Using a Novel Hybrid Model

The semi-arid regions of China support delicately balanced ecosystems that are likely to be significantly affected by small changes in the level of atmospheric ozone. To predict ozone concentrations in such areas, this paper proposes a new hybrid forecasting model called an ARMT-CPSO-BP neural network, which is based on the association rule mining technique (ARMT), a chaotic particle swarm optimization algorithm (CPSO) and a back-propagation (BP) neural network (CPSO-BP neural network). This paper first uses the ARMT to find correlations between meteorological variables and ozone concentrations. The significant correlation coefficients are then fed into the CPSO-BP neural network to obtain a prediction. Finally, the predicted results are compared with the results obtained from the BP neural network and the regression model. The comparison shows that the proposed hybrid model is superior to both the BP neural network and the regression model. The hybrid model reduces the square root mean square error (RMSE) by 31.6% compared to the BP model and by 23.8% compared to the regression model. The hybrid model is promising for forecasting ozone concentration in arid and semi-arid areas.
Removal of systematic distance error due to correlated stray light in laser scanning-based AMCW time-of-flight sensor

With the increasing demand for 3D depth information in various industrial applications, light detection and ranging (LiDAR) has been emerged as one of the solutions to measure the distance of objects. However, existing AMCW-based indirect ToF sensors have problems with measurement accuracy since the measured depth is sensitive to unwanted error sources such as ambient light, wide-band random noises, and stray light. In this paper, the effects of such stray light i.e. systematic error source are thoroughly analyzed in a cause-and-effect manner in terms of the signal’s amplitude and measured phase changes. Furthermore, a pre-compensation method to remove the effects of stray light is validated under various practical experimental conditions. According to the experimental results, the proposed pre-compensation method improves the measurement accuracy with mm-level depth error.
Multi-method simulation approach: multiple restaurants to multiple customers on-demand food delivery services in Dubai

ABSTRACT Since early 2020, the COVID-19 outbreak has disrupted various supply chains including the on-demand food delivery sector. As a result, this service industry has witnessed a tremendous spike in demand that is affecting its delivery operations at the downstream level. Previous research studies have explored one-to-one and many-to-one solutions to the virtual food court delivery problem (VFCDP) to optimize on-demand food delivery services in different cities. However, research efforts have been limited to multiple restaurant orders from only one customer which does not apply to traditional systems where multiple customers request on-demand food delivery from multiple restaurants. This study rigorously analyses multiple restaurants to multiple customers (Many-to-many) food delivery simulation models in ideal weather conditions that are constrained with multiple key performance indicators (KPIs) such as delivery fleet utilization (the number of couriers utilized over the fleet size), average order delivery time, and fuel costs. This research also benchmarks the on-demand food delivery queueing methodologies using system dynamics and agent-based simulation modeling where three on-demand food delivery routing methodologies are simulated including First-in-First-Out (FIFO), Nearest, and Simulated Annealing using AnyLogic. The results suggest that the Many-to-many (Nearest) method outperforms other delivery routing methods which would have positive implications on optimizing existing food delivery systems and managerial decisions.
Integrated routing and fill for self-aligned double patterning (SADP) using grid-based design

Self-aligned double patterning (SADP) has been proposed as an alternative patterning solution for sub-10nm technology because of delay of advanced lithography beyond 193nm ArF. In conventional SADP, line and space style of dummy metal fills are inserted once main design is complete. A large buffer distance is required around the main design because no further verification of main design (in presence of fills) is performed. This causes irregular pattern density, which becomes a source of process variations. We propose integrated-fill, in which main design and dummy fill insertion are performed together. This requires a change in overall design flow, which we discuss. Integrated-fill is demonstrated in M2 layer of SADP process; M2 density increases by 15.7% with 2.3% reduction in standard deviation of density distribution; metal thickness variation is also reduced by 24%. More dummy fills cause increased coupling capacitance, which however is shown to be insignificant.
Animal Assisted Therapy for Older Adults in Aged Care Facilities: A Rapid Review

Purpose: Research has demonstrated the effectiveness of animal assisted therapy (AAT) for improving the mental health of older adults in residential care. The aim of this rapid review was to synthesise existing research evidence to determine the approaches that AAT should take to enhance outcomes for older adults living in residential care. Methods: A systematic literature search was conducted to identify studies published between 2009 and 2019 that investigated AAT and improvement in physical and/or psychosocial outcomes for adults aged over 65 years, living in residential care. Studies were critically appraised to determine methodological quality, key data were extracted, and a critical narrative synthesis was conducted to determine features of effective AAT intervention. Results: Eighteen studies were identified for inclusion in this review. All eligible studies utilised dogs for AAT. Nearly all studies found positive outcomes from the AAT; however, several features of AAT were associated with better outcomes. AAT was shown to be effective at improving depressive symptoms and socioemotional behaviours regardless of the frequencies, durations, and overall intervention periods employed. Participant quality of life only improved when AAT was conducted up to twice weekly. Physical interaction and combined physical interaction and walking were both associated with positive outcomes. The use of trained/certified therapy dogs was more likely to improve outcomes than using dogs with no reported training. Facilitators provided by AAT organisations, and facilitators with veterinary, nursing, or AAT training were associated with improved outcomes. Group AAT was associated with greater effectiveness than AAT conducted with individual participants. Studies where AAT was conducted in a combined indoor/outdoor or solely indoor setting appeared most likely to improve outcomes. Conclusion: AAT involving dogs was typically associated with positive outcomes for older adults living in residential care; however, some features of AAT were associated with better outcomes. To enhance outcomes, it is recommended that AAT be implemented in a group setting, include physical interaction or combined physical interaction, and walking, and be conducted by trained facilitators.
Survival Rate and Prognostic Factors in Turkish Women Patients with Breast Cancer

Background: The study aimed to estimate the overall and disease-free survival rates of breast cancer patients and the factors affecting these rates. Methods: In this retrospective study, data were obtained from 686 patients diagnosed with breast cancer in Sivas Cumhuriyet University Faculty of Medicine Research and Application Hospital Oncology Center between 1988 and 2014. Total population sampling method was used. The survival rates at certain periods were determined by creating a Life Table. By using the Kaplan-Meier Analysis, the mean survival times and rates were determined, and whether the variables had an impact on survival was examined. By applying Cox regression analysis, the effect of prognostic factors that are significant on the survival time of breast cancer patients was examined. Results: Overall mean survival time was found as 208.4±11.8 months. According to Kaplan-Meier analysis, 1, 5, 10 and 20-years overall survival rates were 96.6 ± 0.07%, 82.3 ± 1.7%, 64.4 ± 3.4% and 49%± 7.4%, respectively. According to Cox regression analysis results, variables that influence overall survival time were found as disease stage, multicentricity status, ECOG (performance status), presence of diabetes, CA15-3 value, neutro-phil/lymphocyte ratio. Moreover, variables that had an impact on the disease-free survival time were found as tumor grade, multicentricity, and ECOG. Conclusion: Many factors other than disease can prolong survival or accelerate death. Considering the findings of this study may be useful in planning the treatment of breast cancer patients have positive affect on overall survival rates.
Generating Virtual Wire Sculptural Art from 3D Models

Wire sculptures are objects sculpted by the use of wires. In this article, we propose practical methods to create 3D virtual wire sculptural art from a given 3D model. In contrast, most of the previous 3D wire art results are reconstructed from input 2D wire art images. Artists usually tend to design their wire art with a single wire if possible. If not possible, they try to create it with the least number of wires. To follow this general design trend, our proposed method generates 3D virtual wire art with the minimum number of continuous wire lines. To achieve this goal, we first adopt a greedy approach to extract important edges of a given 3D model. These extracted important edges become the basis for the subsequent lines to roughly represent the shape of the input model. Then, we connect them with the minimum number of continuous wire lines by the order obtained by optimally solving a traveling salesman problem with some constraints. Finally, we smooth the obtained 3D wires to simulate the real 3D wire results by artists. In addition, we also provide a user interface to control the winding of wires by their design preference. Finally, we experimentally show our 3D virtual wire results and evaluate these created results. As a result, the proposed method is computed effectively and interactively, and results are appealing and comparable to real 3D wire art work.
Comparing Monocular Camera Depth Estimation Models for Real-time Applications

: Monocular Depth Estimation (MDE) is a fundamental problem in the ﬁeld of Computer Vision with ongoing developments. For the case of challenging applications such as autonomous driving, where highly accurate results are required in real-time, traditional approaches fall short due to insufﬁcient information to understand the scene geometry. Novel approaches utilizing deep neural networks show signiﬁcantly improved results, especially in autonomous driving applications. Nevertheless, there now exists a number of promising approaches in literature and their performance has never been compared head-to-head. In this paper, a detailed evaluation of the performance of four selected deep learning networks is presented. We identify a set of metrics to benchmark the selected approaches from different aspects, especially those related to real-time applications. We analyze the results and present insights into the performance levels of the various approaches.
THE SEISMIC RESPONSE OF INELASTIC STRUCTURES

A thirteen storey, two bay, reinforced concrete framed structure is subjected to a series of non-linear, dynamic analyses in an attempt to find some correlation between the damaging potential of various digitised earthquakes and their relative strengths which have been computed in, a variety of ways. Much of the previous work in this field has been with respect to simple one degree of freedom systems and these do not appear to give any indication of the correlation that could be expected for a non­ linear multi-degree of freedom structure. The results show the effects of the different scalings of the various earthquakes and compare these with those obtained for the familiar NorthSouth component of the May 18, 1940 El Centro earthquake. These results highlight the difficulty of trying to relate the use of such a dynamic earthquake analysis to the present pseudo-static code requirements. Further, the results of the analyses show also the great difference between the present assumption of the plastic hinge distributions, used in the ultimate seismic design method, and those observed during the earthquake excitation with the consequences on the lower-floor column axial loads.
Uncertainty quantification in modeling and measuring components with resonant ultrasound spectroscopy

Resonant Ultrasound Spectroscopy (RUS) is a nondestructive evaluation (NDE) method which can be used for material characterization, defect detection, process control and life monitoring for critical components in gas turbine engines, aircraft and other systems. Accurate forward and inverse modeling for RUS requires a proper accounting of the propagation of uncertainty due to the model and measurement sources. A process for quantifying the propagation of uncertainty to RUS frequency results for models and measurements was developed. Epistemic and aleatory sources of uncertainty were identified for forward model parameters, forward model material property and geometry inputs, inverse model parameters, and physical RUS measurements. RUS model parametric studies were then conducted for simple geometric samples to determine the sensitivity of RUS frequencies and model inversion results to the various sources of uncertainty. The results of these parametric studies were used to calculate uncertainty bounds assoc.
An Enhanced Rsa Algorithm For Data Security Using Gaussian Interpolation Formula

Data security is a crucial concern that ought to be managed to help protect vital data. Cryptography is one of the conventional approaches for securing data and is generally considered a fundamental data security component that provides privacy, integrity, confidentiality, and authentication. In this paper, a hybrid data security algorithm is proposed by integrating traditional RSA and Gaussian Interpolation formulas. The integration raises the security strength of RSA to the fifth degree. The Gaussian First Forward Interpolation is used to encrypt the ASCII values of the message after which the traditional RSA is used to encrypt and decrypt the message in the second and third levels. The last stage employs Gaussian Backward Interpolation to decrypt the data again. The integration helps to cater to the factorization problem of the traditional RSA. Comparative analysis was performed using four different algorithms; RSA, SRNN, 2-Key pair algorithms, and the proposed algorithm. It is proven that when the data size is small the encryption and decryption times are lower for the proposed algorithm but higher when the data size is big.
Discussion on “Black-Start” Scheme of Power System

Some problems about the"black-start"scheme are discussed in this paper. "Black-start"scheme is an im-por tant part of power system collapse prevention,we must depend on the collapse automatic control system which main body is computer in order to implement the scheme precisely.The"black-stat"scheme is implemented by on-line com puter partly or by the organization of power system dispatcher entirely,and it also plays a very important role in the re sume control of power system collapse.