Miracle-dz/UniFAR-FTUs huggingface dataset:
The mechanism of phosphate bacteria in increasing the solubility of phosphorus in Indonesian Andisols

The purpose of this research was to determine: 1) phosphate bacteria (PB) mechanisms responsible for enhancing the soil’s soluble phosphorus (P), using the processes of inorganic P solubilisation, organic P mineralization, and blocking of soil colloidal adsorption site, and 2) to investigate the factors contributing to this increase. Phosphate bacteria (PB) was inoculated into sterile Andisols in three separate compositions, termed 1 kg P∙kg (2.82 g phosphate rock, 0.5 g Ca3(PO4)2, 0.4 g Al3(PO4)2, or 0.4 g Fe3(PO4)2), organic P (0.5 cm para-Nitrophenylphosphate (pNPP) or 0.5 g Na-phytate), and 1 kg P (KH2PO4)∙kg, in order to analyse inorganic P solubilisation, organic P mineralization, and evaluate blocking soil colloidal site for adsorption P, respectively. Furthermore, spectrophotometry technique was applied to determine the amount of dissolved P. The PB showed an improvement in inorganic P solubilisation from 147.66 to 194.61 mg P∙kg, and also in organic P from 63.6 to 91.7 mg P∙kg, compared to control, (31.06 mg P∙kg) and (23.7 mg P∙kg), respectively. Meanwhile, the micro-organisms were known to decrease P adsorption by 13.43%, beyond the restraint set at 85.34%. Therefore, increased soluble P in Andisols is possibly expressed, using the equation as follows: soluble P (mg P∙kg) = 1201.96 + 1.18 inorganic P solubilisation (mg P∙kg) + 1.09 organic P mineralization (mg P∙kg) – 0.92 adsorption P (mg P∙kg) (R = 0.99).
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation

Database management systems (DBMSs) are notoriously difficult to deploy and administer. Self-driving DBMSs seek to remove these impediments by managing themselves automatically. Despite decades of DBMS auto-tuning research, a truly autonomous, self-driving DBMS is yet to come. But recent advancements in artificial intelligence and machine learning (ML) have moved this goal closer. Given this, we present a system implementation treatise towards achieving a self-driving DBMS. We first provide an overview of the NoisePage self-driving DBMS that uses ML to predict the DBMS’s behavior and optimize itself without human support or guidance. The system’s architecture has three main ML-based components: (1) workload forecasting, (2) behavior modeling, and (3) action planning. We then describe the system design principles to facilitate holistic autonomous operations. Such prescripts reduce the complexity of the problem, thereby enabling a DBMS to converge to a better and more stable configuration more quickly.
IMPACT OF MOTIVATION ON THE PRODUCTIVITY OF EMPLOYEE: A CASE STUDY OF CENTRAL BANK OF SOMALIA

The study examined that impact of motivation on the productivity of employee using Central Bank of Somalia. Quantitative method was used and the employed descriptive analysis, factor analysis, reliability test, correlation test and regression analysis. The findings revealed that incentive and salary have positive connection to influence employee productivity, monetary reward and incentive have vibrant connection to impact on employee productivity, while non-incentive and employee productivity do not have significant connection to each other. It was concluded that workers will not perform well when their salary is being delayed and favoritism on the part of the management cannot contribute to productivity. Motivating staff by the chief executive officer is an essential concept in an organization and staff do not put in their best when they are placed on little or no-incentive package. It was also concluded that positive connection exists between incentive and salary of the employee.
Dual Color -Chromogenic in Situ Hybridization Approaches to Evaluate HER2/Neu Gene Amplification in Breast Carcinomas

Breast cancer continue to be the most frequent malignant tumor in women, constituting the second most common fatal malignant tumor in women. The validated prognostic HER/2neu. Aim of the study:Evaluating HER-2 by immunohistochemical (IHC) for protein expression and in situ hybridization (ISH) by dual-color chromogenic insitu hybridization (DC-CISH) technique for HER-2 gene copy amplification and chromosome 17/CEN (chr.17 CEN) aneuploidy in breast cancer. Methods: This is a prospective study, where by fifty-pairs of fresh tissues from both breast cancer of invasive ductal carcinoma and apparently normal adjacent tissues (NATs) which considered as healthy normal internal control. we select only positive cases of HER-2 /neu by IHC (16 cases which included score+2 and score +3 HER-2 by IHC) for gene amplification by DC-CISH were done in CPHL/Baghdad. Results: Out of 6 cases with her2(score +2) in breast cancer by IHC expression, 2 (33.33%) show no amplification, while all cases4(66.67%), show low amplification. All cases with (score +3) by IHC, showed gene amplification. Out of 10 cases with (score +3) by IHC, 2 cases (20%) showed low amplification while majority of cases,8 (80%) showed high amplification. Conclusion: The DC-CISH is confirmatory technique for the detection of both HER-2 gene amplification and multiplication of chromosome 17/CEN in breast cancer.
Dynamic change of gut microbiota in the male bee of Bombus terrestris (Hymenoptera: Apidae)

BackgroundThe gut microbiota play a key role in the development and health of bumble bees. Male bees are important for the reproductive activity of a colony, yet there are few studies on their gut microbiota. ResultsBy using qPCR, we found there are significant changes in total bacteria and six important bacteria genera from different developmental stages compared to workers bees. The results indicate that Gilliamella , Snodgrassella , and Lactobacillus are the dominant gut bacteria in male bees, which is consistent with the previous studies in worker bees, however, there are more total bacteria in male bees. Another gut bacteria genus, Bacillus may be a probiotic bacteria for reproduction in male bees, although the possible function of these bacteria require further study. ConclusionsThis research can provide insight into the relationship between the bacterial community and the physiological health and reproductive capacity of male bumble bees. #Authors contributed equally to this work.
IN SILICO CHARACTERIZATION OF Β-1,3-GLUCANASE PROMOTER FROM DROSERA BINATA

The promoter region of the β-1,3-glucanase gene (DbGluc) was isolated from carnivorous plant species Drosera binata and sequenced. Subsequently, a DNA fragment of 1154 bp upstream of the transcription start site was searched for the presence of motifs identical to cis-element motifs previously reported in the PLACE database. The analysis revealed that the DbGluc promoter contains a TATA-box sequence 35 nucleotides upstream of the transcription start site. Moreover, three functional groups of light-, tissue- and stress-responsive putative cis-elements were identified within the investigated DNA sequence. The group of stress-responsive elements (MYBCORE, ACGTATATERD1, CURECORECR, WRKY-box, WBOXATNPR1, GT1) is very characteristic for promoters of genes encoding pathogenesis-related proteins, including β-1,3-glucanases. These hydrolases may play a role not only in reaction to biotic and abiotic stress but also during the digestive processes in carnivorous species.
AutoExecutor: Predictive Parallelism for Spark SQL Queries

Right-sizing resources for query execution is important for cost-efficient performance, but estimating how performance is affected by resource allocations, upfront, before query execution is difficult. We demonstrate AutoExecutor , a predictive system that uses machine learning models to predict query run times as a function of the number of allocated executors, that limits the maximum allowed parallelism, for Spark SQL queries running on Azure Synapse.
ISLAMIC ACCOUNTING STANDARDS VS INTERNATIONAL FINANCIAL REPORTING STANDARDS

Proposed paper aimed to express the uniqueness of both Islamic Accounting and Financial Reporting Standards. The study aimed to make a significant comparative work by considering key aspects. Islamic Accounting Standards is backed by Auditing Organization for Islamic Financial Institutions (AAOIFI). The presented research work was intended to mainly focus to identify which system is recommended for user in the UAE. The survey was carried out by considering detailed information from the respondent’s cross various research questionnaire.The strategy involved for gathering the data was by means of complete professional documents and there by preforming a well-defined set of questionnaires. These contents helped to make better survey by integrating financial institutions in the UAE.This work can be of significant importance which relates to (IFRS International Financial Reporting Standards), since it signifies few of the critical issues of the society such Riba and Zakat with respect to interest. The concept here it to inductee the communication with each other, due to the prolong era of new generation, improved accounting system has developed variety of ideological and mid set. In addition, these reporting standards promote the global implications which are much ahead of jurisdictions.
Modeling Health Stage Development of Patients With Dynamic Attributed Graphs in Online Health Communities

In this paper, we propose a novel DynAttGraph2Seq framework to model complex dynamic transitions of an individual user's activities and the textual information of the posts over time in online health forums and learning how these correspond to his/her health stage. To achieve this, we first formulate the transition of user activities as a dynamic attributed graph with multi-attributed nodes that evolves over time, then formalize the health stage inference task as a dynamic attributed graph to sequence learning problem. Our proposed model consists of a novel dynamic graph encoder along with a two-level sequential encoder to capture the semantic features from user posts and an interpretable sequence decoder that learn the mapping between a sequence of time-evolving user activity graphs as well as user posts to a sequence of target health stages. We go on to propose new dynamic graph regularization and dynamic graph hierarchical attention mechanisms to facilitate the necessary multi-level interpretability. A comprehensive experimental analysis of its use for a health stage prediction task demonstrates both the effectiveness and the interpretability of the proposed models.
TQEL: Framework for Query-Driven Linking of Top-K Entities in Social Media Blogs

Social media analysis over blogs (such as tweets) often requires determining top-k mentions of a certain category (e.g., movies) in a collection (e.g., tweets collected over a given day). Such queries require entity linking (EL) function to be executed that is often expensive. We propose TQEL, a framework that minimizes the joint cost of EL calls and top-k query processing. The paper presents two variants TQEL-exact and TQEL-approximate that retrieve the exact / approximate top-k results. TQEL-approximate, using a weaker stopping condition, achieves significantly improved performance (with the fraction of the cost of TQEL-exact) while providing strong probabilistic guarantees (over 2 orders of magnitude lower EL calls with 95% confidence threshold compared to TQEL-exact). TQEL-exact itself is orders of magnitude better compared to a naive approach that calls EL functions on the entire dataset. PVLDB Reference Format: Abdulrahman Alsaudi, Yasser Altowim, Sharad Mehrotra, and Yaming Yu. TQEL: Framework for Query-Driven Linking of Top-K Entities in Social Media Blogs. PVLDB, 14(11): 2642 2654, 2021. doi:10.14778/3476249.3476309
A Stochastic Volatility Model with Mean-reverting Volatility Risk Premium

The volatility risk premium (VRP) has long been the core issue in option pricing and risk management. The VRP is usually defined as a linear function of volatility which ignores the time-varying property of VRP and limits the degree of freedom of the model. In this paper, we adopt a CIR process in the stochastic volatility model (VRP-CIR-SV) to incorporate the mean-reverting and time-varying properties of VRP. We show that the decomposition of VRP is consistent to investor’s behaviour. Our Monte Carlo simulation results show that, compared with the traditional linear VRP model, the VRP-CIR-SV model can better depict the rich shapes of implied volatility curve. Our paper innovatively models the time-varying VRP with mean-reverting property, which may provide new thoughts for VRP estimation.
Evaluation of heat resistance of carbon fiber reinforced plastics based on organosilicon compounds

The paper presents composite materials based on elastomeric matrices in combination with phenol-formaldehyde resin, reinforced with carbon fiber fabric. Technology of obtaining composites is described. Polymer binder has been modified by introducing inorganic additives (SiC, ZrB2, glass microspheres). Experimental data of thermogravimetric analysis of the obtained carbon plastics are presented. Comparative tensile strength tests of composite materials have been carried out before and after plasma treatment. Materials described in the paper are intended for use in high-temperature heat shielding.
Toward the Gravitational Redshift Detection in NGC 4258 and the Estimation of Its Black Hole Mass-to-distance Ratio

We construct from first principles a general relativistic approach to study Schwarzschild black hole (BH) rotation curves and estimate the mass-to-distance ratio of the active galactic nucleus of NGC 4258 in terms of astrophysical observable quantities. The presented method allows one to clearly distinguish and quantify the general and special relativistic contributions to the total redshift expression. The total relativistic redshift/blueshift comprises three components: the gravitational redshift due to the spacetime curvature generated by the mass of the BH in its vicinity, the kinematic shift, originated by the photons’ local Doppler effect, and the redshift due to a special relativistic boost that describes the motion of a galaxy from a distant observer. We apply our method to the largest data set of highly redshifted water megamaser measurements on the accretion disk of the NGC 4258 active galaxy and use this general relativistic method to estimate its BH mass-to-distance ratio: M/D = (0.5326 ± 0.00022) × 107 M ⊙ Mpc−1.
Method of Indoor Navigation and Safety Improvement by SE-Dense-DQN

Since the increasing requirement of indoor navigation with the assurance of safety, Deep Learning was been used in this area by numerous scholars. In order to help unmanned aerial vehicle navigate in various environment without prior experience, this paper proposed the method of Deep Q Learning with the Squeeze-and-Excitation Networks and Dense blocks. The structure of the Q-Network also amended with the information of LIDAR processed by Long Short-Term Memory to make sure the accuracy. The structure of the network shows in figure 6. The experiment conducted in simulated environment. Experimental results indicated that this method excelled in efficiency and accuracy than other classic Deep Q Networks like classic Deep Q Network and Double Deep Q Network. The results displaying in figure 8 and figure 9 shows unmanned aerial vehicle can arrive at target place more accurately than other networks.
An Image Retrieving Method Based on Strong Constrained Manifold-embedded Hashing

In recent years hashing methods have attracted considerable attention. The main procedure of manifold hashing methods is to convert the high dimension features into binary codes and then use manifold distance to approximate the original Euclidean distance. However, there still is a problem that how to directly preserve the manifold structure by hashing. Besides, because of information loss, the effect of using existing hashing methods to retrieve images is not ideal when the length of binary code is very short. In order to solve these problems, we proposed a strong constrained manifold-embedded hash (SCDMH), which can not only explore the nonlinear manifold structure of data, but also make full use of the semantic information in the data. Additionally, the objective function we proposed reconstructs the original features of the datasets and the hash representation mutually to preserve the semantic information. Our experimental results on three large benchmark datasets demonstrate that SCDMH outperforms other state-of-the-art baselines.
Single Image Super-resolution Reconstruction Algorithm Based on Deep Learning

Recover the high-resolution image from a single low-resolution image is known as single image super-resolution reconstruction (SISR). SSIR is currently a research hotspot in computer vision. In this paper, we study the feature learning module of the magnification-arbitrary network for Super-Resolution(Meta-SR), and propose an improved method of image feature learning. Its idea is multi-scale residual dense block (MRDB). We integrate it into the existing structure of the Meta-SR network. As a result, the same network has increased the receptive field, and it can obtain more detailed features of the image. This structure can be extended to other existing SISR network models. In the published and standard test set Set14, compared with the interpolation method, the average PSNR value of the algorithm in this paper increases by 3.79dB, 3.01dB and 2.85dB at amplification factors 2, 3 and 4, respectively. Through the analysis of SISR results, it can be obtained that the improved method proposed in this paper has a significant improvement in all indicators, and can more fully extract the image detail features, thereby improving the quality of the reconstructed image.
A Data Compress Algorithm for Daisy Chain Communication System

In the era of big data, traditional data compression methods cannot complete data compression with features of large volume, rapid growth and complex structure, especially in the processing of real-time data compression. The compression algorithm proposed in this paper is improved on the basis of a lossless bit-swap compression coding algorithm. The coding algorithm is based on VAE model and uses classical ANS compression technology to code multiple potential variables, so as to realize the rapid compression and decompression of data. The experimental results show that the algorithm proposed in this paper can compress data with a high compression rate(47.65%), which is higher than LZAM algorithm and Bit-Swap algorithm.
Image Denoising Based on Dual-tree Complex Wavelet Transform and Convolutional Neural Network

In view of the problem that the noise in the image will have a negative impact on the subsequent image processing, this paper proposes a noise reduction algorithm based on the dual-tree complex wavelet transform and convolutional neural network by using the characteristics of the DTCWT and CNN. Firstly, the two-dimensional image is decomposed into two parallel wavelet trees by double tree complex wavelet transform, which forms six high-frequency detail parts and two same low-frequency parts in different directions. Then, it is put into the designed deep convolution neural network for training, Finally, the six high-frequency components and one of the low-frequency components are recombined into the image after noise reduction by using the inverse transform of double tree complex wavelet. The simulation results show that PSNR and MS-SSIM of the image are better than other noise reduction algorithms, and the details of the original image can be restored well while noise reduction. It has practical application value in the case that the collected image has a large noise due to various reasons.
Problem of State Filtering in Case of Partially Known System Matrices

The linear state-space model with uniform innovations (LU model) proposed in previous author’s work is extended here. The states and parameters of LU model are estimated under hard physical bounds. The estimation of the innovation boundaries is also included. Maximum a posteriori probability estimation reduces to the linear programming. The on-line estimation is running within a sliding window. Compared to the original model, we consider that model matrices can be time-variant. Also, offset terms are included. We present the problem of the joint parameter and state estimation, i.e., the state filtering with unknown model matrices. The ambiguity in the state estimates can be substantially decreased by partial knowledge of some entries in the model matrices. The simple example illustrates this approach.
Recognition and Classification of Concrete Cracks under Strong Interference Based on Convolutional Neural Network

This paper proposes the UmNet model based on convolutional neutral network (CNN), aiming to improve the ability to recognize and classify concrete cracks in a background complicated by construction seams and seepage traces. The model was derived from the famous CNN AlexNet. Without changing the receptive field, large convolutional kernels were replaced with small ones to reduce the parameters, deepen the network, and increase nonlinear transforms. Next, convolutional block attention module (CBAM) was introduced to highlight the key information in images and focus on high-weight channels. Finally, Bayesian network (BN) layer and L2 regularization were added, and the number of nodes in fully connected layer were reduced. A series of comparative experiments were carried out on three datasets D, P, and W. The results show that the proposed UmNet surpassed AlexNet in the recognition accuracy on D, P, and W by 3.74%, 3.17%, and 5.74%, respectively, and reduced the number of parameters by 75.04%. Therefore, our model is an effective means to recognize and classify of concrete cracks under strong interference.
An Extensible and Reusable Pipeline for Automated Utterance Paraphrases

In this demonstration paper we showcase an extensible and reusable pipeline for automatic paraphrase generation , i.e., reformulating sentences using different words. Capturing the nuances of human language is fundamental to the effectiveness of Conversational AI systems, as it allows them to deal with the different ways users can utter their requests in natural language. Traditional approaches to utterance paraphrasing acquisition, such as hiring experts or crowdsourcing, involve processes that are often costly or time consuming, and with their own trade-offs in terms of quality. Automatic paraphrasing is emerging as an attractive alternative that promises a fast, scalable and cost-effective process. In this paper we showcase how our extensible and reusable pipeline for automated utterance paraphrasing can support the development of Conversational AI systems by integrating and extending existing techniques under an unified and configurable framework.