Miracle-dz/UniFAR-FTUs huggingface dataset:
An Examination of Employee Rewards and Work Environment on Employee Creativity and Innovation

Purpose - The provision of rewards to employees for their innovative ideas and creative ideas acts as a motivational factor, encouraging them to sustain their efforts in developing novel and innovative solutions. The objective of this research is to examine the effects of employee rewards and work environment on employee creativity and innovation.Design/Methodology/Approach - This research used a quasi-experimental approach (specifically, an explanatory design). This inquiry relied on secondary sources. The collection of secondary material was conducted by using multiple online search platforms and specialist journals. Research Findings - Employees' ability to be creative and innovative is bolstered by all three types of rewards: intrinsic rewards, which have a positive effect on intrinsic motivation, extrinsic non-financial rewards, and extrinsic financial rewards, which can either encourage or discourage employees' efforts to be creative and innovative. Organizational environments that promote and cultivate employee creativity and receptiveness to novel ideas are more inclined to cultivate a culture that fosters creativity and then innovation. Originality - The significance of this research study adds value to the existing corps of information about understanding the effects of employee rewards with the aid of the work environment on employee creativity and innovation. Practical Implications - Drawing from the findings of this study it has been proven that in the corporate world employees are different with various types of needs. Hence to successfully motivate them to be creative and innovative organizations must provide employee rewards that are comprehensive and at the same time must balance the employee rewards they offer with the conducive work environment with employees' individual needs.
Simultaneous Estimation of Curcumin and Gentamicin by UV-VIS Spectrometric Methods or Derivative Spectroscopic Techniques

A simultaneous study is very important analytical parameter which helps to assess compatibility of mixture of drugs. UV spectrophotometer is one of the simplest and efficient methods to assess simultaneous parameters. Due to its economical approach, we have used UV spectrophotometer. Curcumin and Gentamicin sulphate have zero crossing points of 420 nm and 244 nm, respectively, in first-order derivative spectroscopy. The solvent for the spectrophotometric process was 0.1 N NaOH. Curcumin and Gentamicin sulphate linearity was established over range of concentrations of 2–12 g/ml, with correlation coefficients of 0.995 and 0.993. The mean percent recoveries Curcumin and Gentamicin sulphate were found to be in the range of 98.88 percent and 98.54 percent, respectively. The approach has been found to be repeatable in both inter day and intraday testing. The approach was proven to be both precise and reliable. According to the recovery investigation, the approach was effectively applied to pharmaceutical formulation with no interference from excipients. The results of the analysis were statistically evaluated, as well as by recovery trials. The LOD and LOQ for Gentamicin sulphate in phosphate buffer were found to be 0. 024µg/ml and 0. 045µg/ml, respectively and values for Curcumin were 0. 024µg/ml and 0.037 µg/ml, respectively which indicates adequate sensitivity of method
TCNet: Multiscale Fusion of Transformer and CNN for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images plays a critical role in areas such as urban change detection, environmental protection, and geohazard identification. Convolutional Neural Networks (CNNs) have been excessively employed for semantic segmentation over the past few years; however, a limitation of the CNN is that there exists a challenge in extracting the global context of remote sensing images, which is vital for semantic segmentation, due to the locality of the convolution operation. It is informed that the recently developed Transformer is equipped with powerful global modeling capabilities. A network called TCNet is proposed in this article, and a parallel-in-branch architecture of the Transformer and the CNN is adopted in the TCNet. As such, the TCNet takes advantage of both Transformer and CNN, and both global context and low-level spatial details could be captured in a much shallower manner. In addition, a novel fusion technique called Interactive Self-attention is advanced to fuse the multilevel features extracted from both branches. To bridge the semantic gap between regions, a skip connection module called Windowed Self-attention Gating is further developed and added to the progressive upsampling network. Experiments on three public datasets (i.e., Bijie Landslide Dataset, WHU Building Dataset, and Massachusetts Buildings Dataset) depict that TCNet yields superior performance over state-of-the-art models. The IoU values obtained by TCNet for these three datasets are 75.34% (ranked first among 10 models compared), 91.16% (ranked first among 13 models compared), and 76.21% (ranked first among 13 models compared), respectively.
Exponential Fusion of Interpolated Frames Network (EFIF-Net): Advancing Multi-Frame Image Super-Resolution with Convolutional Neural Networks

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel accuracy using an affine motion model to capture the camera platform motion. The frames are externally upsampled using single-image interpolation. The interpolated frames are then fused with the custom EWF layer, employing subpixel registration information to give more weight to pixels with less interpolation error. Realistic image acquisition conditions are simulated to generate training and testing datasets with corresponding ground truths. The observation model captures optical degradation from diffraction and detector integration from the sensor. The experimental results demonstrate the efficacy of EFIF-Net using both simulated and real camera data. The real camera results use authentic, unaltered camera data without artificial downsampling or degradation.
Biosynthetic pathway of prescription bergenin from Bergenia purpurascens and Ardisia japonica

Bergenin is a typical carbon glycoside and the primary active ingredient in antitussive drugs widely prescribed for central cough inhibition in China. The bergenin extraction industry relies on the medicinal plant species Bergenia purpurascens and Ardisia japonica as their resources. However, the bergenin biosynthetic pathway in plants remains elusive. In this study, we functionally characterized a shikimate dehydrogenase (SDH), two O-methyltransferases (OMTs), and a C-glycosyltransferase (CGT) involved in bergenin synthesis through bioinformatics analysis, heterologous expression, and enzymatic characterization. We found that BpSDH2 catalyzes the two-step dehydrogenation process of shikimic acid to form gallic acid (GA). BpOMT1 and AjOMT1 facilitate the methylation reaction at the 4-OH position of GA, resulting in the formation of 4-O-methyl gallic acid (4-O-Me-GA). AjCGT1 transfers a glucose moiety to C-2 to generate 2-Glucosyl-4-O-methyl gallic acid (2-Glucosyl-4-O-Me-GA). Bergenin production ultimately occurs in acidic conditions or via dehydration catalyzed by plant dehydratases following a ring-closure reaction. This study for the first time uncovered the biosynthetic pathway of bergenin, paving the way to rational production of bergenin in cell factories via synthetic biology strategies.
Institutional Policy Implementation and Uganda’s Mineral Resources Management

Purpose: This study examines the Mediating effect of Institutional Policy Implementation on Uganda’s Mineral Resource Management. Materials and Methods: The study uses a cross-sectional design suggested by Bell et al., (2018) to examine Mediating effect of Institutional Policy Implementation on Uganda’s Mineral Resource Management. This design involves data collection at a single point in time to identify patterns between variables. The study population consisted of 39 institutions that are involved in the management of mineral resources in Uganda (Ministry of energy and mineral development, 2022). The study targeted a sample size of 34 institutions, determined using the sample determination table developed by Krejcie and Morgan (1970). Findings: The results in this study indicate that Managerial Competence has a direct and significant impact on Uganda’s Mineral Resource Management. This means that competent managers are better equipped to make informed decisions, efficiently allocate resources, and implement best practices in the mining sector. The study also reveals that the relationship between Managerial Competence and Mineral Resource Management outcomes is not entirely independent of external factors. Implications to Theory, Practice and Policy: The Institutional Theory, developed by Meyer and Rowan in 1977, provides a theoretical framework to understand how institutions and organizational structures influence behavior and decision-making within organizations and it helped to introduced Institutional policy implementation which acted as a mediator in this relationship, meaning that the effectiveness of policies set by relevant institutions can influence how managerial competence translates into actual outcomes in Uganda’s mining industry. The policy implication in this study is that Uganda’s policy makers should prioritize strengthening institutional capacity utilization through targeted training programs and capacity-building initiatives as this would promote the achievement of sustainable and responsible Mineral Resource Management in the country.
Mental health and social media in the U.S.: A review: Investigating the potential links between online platforms and mental well-being among different age groups

This study investigates the complex relationship between social media usage and mental well-being across different age groups in the United States. Employing a systematic literature review and content analysis, the research aims to unravel the multi-dimensional impacts of online platforms on mental health. The methodology involves a comprehensive search of peer-reviewed journals, conference proceedings, and digital libraries, focusing on studies conducted from 2000 to the present. Inclusion and exclusion criteria are meticulously applied to ensure the relevance and quality of the selected literature. Key findings reveal that the impact of social media on mental health is nuanced and varies significantly across age groups. Positive aspects such as enhanced connectivity and community support coexist with negative impacts like exposure to cyberbullying, addiction, and exacerbation of mental health issues. The study highlights the importance of considering psychological, social, and cultural dimensions in understanding these impacts. The future landscape of social media and mental health presents both challenges and opportunities. The digital age necessitates adaptive strategies to mitigate the risks associated with social media use while leveraging its potential benefits. Recommendations are provided for industry leaders, healthcare professionals, and policymakers, emphasizing the need for safer social media platforms, integration of social media literacy in healthcare, and robust regulatory frameworks. Conclusively, this study contributes to the understanding of social media's role in mental health, suggesting the need for ongoing research, particularly in exploring causal relationships and developing age-specific and culturally sensitive interventions. The findings underscore the importance of a collaborative approach among various stakeholders to foster a healthier digital environment.
The effect of abatacept on T cell activation is not long-lived in vivo

Abatacept, a co-stimulatory blocker comprising the extracellular portion of human CTLA-4 linked to the Fc region of IgG1, is approved for the treatment of rheumatoid arthritis. By impairing the interaction between CD28 on T cells and CD80/CD86 on APCs, its mechanisms of action include: the suppression of follicular T helper cells (preventing the breach of self-tolerance in B cells), inhibition of cell cycle progression holding T cells in a state described as “induced naïve” and reduction in DC conditioning. However, less is known about how long these inhibitory effects might last, which is a critical question for therapeutic use in patients. Herein, employing a murine model of OVA-induced DTH, we demonstrate that the effect of abatacept is short-lived in vivo and that the inhibitory effects diminish markedly when treatment is ceased.
Taking Up the Challenges Faced by Higher Education Institutions in Technology to Create Smart Campus

Universities face big challenges in technology to create smart campuses to increase higher education efficiency, productivity, quality and innovation. This research aims to analyze universities' efforts to utilize technology to create smart campuses. The research uses descriptive qualitative with a phenomenological approach. Data was collected through open interviews at four universities. Research subjects include top level management, middle level management, lecturers and staff. Data analysis was performed using Bogdan & Biklen which is called data reduction, the interconnection between data and verification. The research results show that universities have made various efforts to introduce digital technology to create smart campuses. This research describes the ability of universities to realize Smart Campus in 4 levels, namely adoption, implementation, integration and optimization. However, the biggest challenge is the integration and optimization of systems used for education, research and service services. Then optimization requires policies that involve goals and targets related to integration, technological sustainability and improving the learning environment, so that it will create a smart campus.
A Hybrid LSTM Network for Long-Range Vehicle Trajectory Prediction Based on Adaptive Chirp Mode Decomposition

Vehicle trajectory prediction is essential for intelligent transportation systems and smart cities, but the prediction of long-range trajectories is still challenging. Since the speed of a moving vehicle is relatively high, coordinates of discrete trajectory points may vary from 0 to 10 km in long-range trajectory prediction. Moreover, the vehicle trajectory contains random noises due to the instability of GPS signals in urban areas and the error accumulation effect of the other sensors for vehicle positioning. To address this issue, the discrete trajectory data is treated as a signal, and multiple signal components are extracted from the original data through the adaptive chirp mode decomposition (ACMD) algorithm to capture spatial information at different spatial scales and filter out chaotic noises caused by other assisted positioning sensors simultaneously. To overcome the error propagation of long short term memory (LSTM), since the quality of long-range predictions relies on short-range prediction accuracies, a hybrid LSTM model is proposed based on a combination network of forward LSTM, bidirectional LSTM (BILSTM), and backward LSTM. Taking advantage of an improved whale optimization algorithm (IWOA) to optimize the hyperparameters of the hybrid-LSTM model, the embedding layer decomposes the input trajectories and further learns and trains through the hybrid LSTM layer to effectively overcome the large prediction errors. Extensive experiments based on our dataset and a public taxi trajectory dataset show that the hybrid LSTM model based on ACMD and IWOA outperforms the existing state-of-the-art methods in terms of accuracy and stability.
Impact of Land Use on Urban Land Surface Temperature——A Case Study of Dongguan,Guangdong Province

As a typical city of rapid urbanization,the urban land surface temperature of Dongguan City was retrieved through interpretation of Landsat TM6,combined with land use map and meteorological observational data,and three thermal effect estimating indices(thermal effect contribution index,thermal pixels proportion index,regional thermal pixels proportion index) were designed to discuss the impact of various types of land use on urban thermal environment,which shows that urban and rural construction land for industry and residence take an important role,while water area and grassland gives little impact,and the thermal contribution of heath land to regional thermal environment is the least one.The result of the three indices also proved that it maybe untrue if we only consider the contribution of average temperature to urban thermal environment.The research also shows that the average temperature in winter had obviously been increased from 1988 to 1998 in Dongguan,and the land surface temperature was 2.21℃ in 1998 higher than that of 1988,while the water surface temperature had been changed little(0.76℃),in a word,the difference in surface temperature between water surface and land surface rise sharply as the average temperature rising from 1988 to 1998.
Use of a very low carbohydrate diet for prediabetes and type 2 diabetes: An audit

insulin, reduced to 16 patients at 6 months, with average dose of those remaining on insulin reduced by 72 % . Conclusion: Very low carbohydrate ketogenic diet is effective in enhancing glucose control, weight loss and cardiovascular risk factors in T2D. Most patients achieved insulin independence, with others significantly reducing insulin dosage. The study underscores the potential of integrating a VLCKD with medication management in comprehensive T2D treatment. Contribution: The audit shows the application of a KD in patients with long-standing diabetes.
Rheological Scaling of Ionic-Liquid-Based Polyelectrolytes in Ionic Liquid Solutions

Polymerized ionic liquids (PILs) are a special class of polyelectrolytes with ionic liquid (IL) species being covalently attached to the repeating unit. The rheological properties of PIL in IL solutions are strongly influenced by the electrostatic screening between IL and PIL chains. However, the effect of IL electrostatic screening on the rheology of PIL in IL solutions remains elusive. To address this challenging yet important question, we conduct detailed rheological characterization of a model system containing a PIL [PC4-TFSI: poly(1-butyl-3-vinylimidazolium bis(trifluoromethanesulfonyl)imide)] in a mixture of a salt-free solvent (DMF: dimethylformamide) and an IL [Bmim-TFSI: 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide] solution, with low to high IL concentrations, while spanning dilute and semidilute polymer regimes. We compare the specific viscosity ηsp and the longest relaxation time λ of PILs measured at various Bmim-TFSI concentrations from 0 M (pure DMF) to 3.42 M (pure Bmim-.
Method for calculating the fractal dimension of the pore structure of low permeability reservoirs:A case study on the Xujiahe formation reservoir in central Sichuan basin

In order to find out whether the method based on water or the one based on mercury saturation is better for calculating the fractal dimension of the pore structure of low permeability reservoirs,this paper takes the 41core plunger sample of Upper Triassic Xujiahe Formation reservoir as an example to calculate the fractal dimension of the pore structure by two methods separately.The results show that the fractal dimension calculated by mercury saturation corresponds with the displacement pressure,sorting coefficient and other pore structure parameters well,the displacement pressure increases while the sorting coefficient decreases with the increase of fractal dimension.So the fractal dimension calculated by this method can quantitatively describe the parameters and the complexity of reservoir pore structure.While on the other hand,the fractal dimension calculated by water saturation corresponds with the displacement pressure,with poor sorting of coefficient and other pore structure parameters,to analyze this phenomenon.It is hoped that the mercury injection data obtained by conventional mercury injection techniques can be used to calculate the fractal dimension of reservoir pore structure if possible.
Concurrent-Validity and Reliability of Photocells in Sport: A Systematic Review

Specific physical qualities as sprint running, change-of-direction or jump height are determinants of sports performance. The photocells systems are practical and easy to use systems to assess the time from point A to point B, in addition, these photoelectric systems are also used to obtain the time of vertically displaced movements. Knowing the accuracy and precision of photocells timing can be determinant for ensuring a higher-quality interpretation of results and for selecting the most appropriate devices for specific objectives. This systematic review aimed to: (1) identify and summarize the studies that have examined the validity and reliability of photocells in sport sciences. A systematic review of PubMed, SPORTDiscus, and Web of Science databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 164 studies initially identified, 16 were fully reviewed, and their outcome measures were extracted and analyzed. Photocells appears to have a strong agreement with the force platform (gold standard), but not interchangeable with force plate to measure the vertical jump. For monitoring horizontal displacement, double beam systems, compared to single beam systems, are more valid and reliable when it comes to avoiding false triggers caused by swinging arms or legs.
Understory Changes in Fraxinus excelsior Stands in Response to Dieback in Latvia

Abstract Intense dieback of Fraxinus excelsior L. has been causing rapid changes in advance growth of trees and understory shrub growth of the affected stands. In this study, changes in composition and density of understory were studied in 15 permanent plots (each 235.6 m2), repeatedly sampled in 2005, 2010, and 2015. Within each plot, the number and average height of understory individuals were determined. The successional changes in understory were assessed by Detrended Correspondence Analysis. In total, 11 advance growth and 20 undergrowth species were recorded. A significant increase in the density of understory was observed only in 2015, mainly due to understorey growth of Corylus avellana L. , Padus avium Mill. , and Lonicera xylosteum L. Regarding advanced growth, the highest density was observed for Ulmus glabra Huds. , F. excelsior and Acer platanoides L. ; the density of A. platanoides and F. excelsior increased particularly in the period from 2010–2015. The observed successional changes suggested individuality of development of the affected stands according to the composition of the remaining and neighbouring canopy trees.