Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-operative Scheduled Energy Aware Load-Balancing technique for an Efficient Computational Cloud

Cloud Computing in the recent years has been taking its evolution from the scientific to the non scientific and commercial applications. Power consumption and Load balancing are very important and complex problem in computational Cloud. A computational Cloud differs from traditional high-performance computing systems in the heterogeneity of the computing nodes, as well as the communication links that connect the different nodes together. Load Balancing is a very important component in the commodity services based cloud computing. There is a need to develop algorithms that can capture this complexity yet can be easily implemented and used to solve a wide range of loadbalancing scenarios in a Data and Computing intensive applications. In this paper, we propose to find the best EFFICIENT cloud resource by Co-operative Power aware Scheduled Load Balancing solution to the Cloud loadbalancing problem. The algorithm developed combines the inherent efficiency of the centralized approach, energy efficient and the fault-tolerant nature of the distributed environment like Cloud.
Decision trees based knowledge discovery in databases for high-rise structures intelligent from selection

First, the settlement course, tactics, and technological key for KDD based on decision tree are addressed. The method of optimize structure that from make use of weighting entropy is Put forward. And with characteristic of structure from selection, the basic algorithm method of ID3 is improved. Secondly, based on 20 High-rise structure cases, the cases of High-rise structures intelligent from selection assorted case-mining and part of selection intelligent from which are addressed. Finally, 1008 high-rise building cases are collected. The KDD system which based on case-base and improve the method of decision tree is set up. And it will help the methods and measures sustain for mining structure form-selection from building cases. The given examples proved that the decision tree methods can exploit cryptic structure form-selection knowledge from building cases effectively, it has significant meaning for utilizing abundant case resources fully, improving the design of quality and efficiency structure form-selection, and offering a new approach for solve the problem of "bottle-neck" structure intelligent form selection.
Comparative genomics of Staphylococcus capitis reveals species determinants

Staphylococcus capitis is primarily described as a human skin commensal but is now emergent as an opportunistic pathogen isolated from the bloodstream and prosthetic joint infections, and neonatal intensive care unit (NICU)-associated sepsis. We used comparative genomic analyses of S. capitis to provide new insights into commensal scalp isolates from varying skin states (healthy, dandruff lesional, and non-lesional), and to expand our current knowledge of the species populations (scalp isolates, n = 59; other skin isolates, n = 7; publicly available isolates, n = 120). A highly recombinogenic population structure was revealed, with genomes including the presence of a range of previously described staphylococcal virulence factors, cell wall-associated proteins, and two-component systems. Genomic differences between the two described S. capitis subspecies were explored, which revealed the determinants associated exclusively with each subspecies. The subspecies ureolyticus was distinguished from subspecies capitis based on the differences in antimicrobial resistance genes, β-lactam resistance genes, and β-class phenol soluble modulins and gene clusters linked to biofilm formation and survival on skin. This study will aid further research into the classification of S. capitis and virulence-linked phylogroups to monitor the spread and evolution of S. capitis.
Crosstalk of necroptosis and pyroptosis defines tumor microenvironment characterization and predicts prognosis in clear cell renal carcinoma

Pyroptosis and necroptosis are two recently identified forms of immunogenic cell death in the tumor microenvironment (TME), indicating a crucial involvement in tumor metastasis. However, the characteristics of necroptosis and pyroptosis that define tumor microenvironment and prognosis in ccRCC patients remain unknown. We systematically investigated the transcriptional variation and expression patterns of Necroptosis and Pyroptosis related genes (NPRGs). After screening the necroptosis-pyroptosis clusters, the potential functional annotation for clusters was explored by GSVA enrichment analysis. The Necroptosis-Pyroptosis Genes (NPG) scores were used for the prognosis model construction and validation. Then, the correlations of NPG score with clinical features, cancer stem cell (CSC) index, tumor mutation burden (TMB), TME, and Immune Checkpoint Genes (ICGs) were also individually explored to evaluate the prognosis predictive values in ccRCC. Microarray screenings identified 27 upregulated and 1 downregulated NPRGs. Ten overall survival associated NPRGs were filtered to construct the NPG prognostic model indicating a better prognostic signature for ccRCC patients with lower NPG scores (P< 0.001), which was verified using the external cohort. Univariate and multivariate analyses along with Kaplan-Meier survival analysis demonstrated that NPG score prognostic model could be applied as an independent prognostic factor, and AUC values of nomogram from 1- to 5- year overall survival with good agreement in calibration plots suggested that the proposed prognostic signature possessed good predictive capabilities in ccRCC. A high-/sNPG score is proven to be connected with tumor growth and immune-related biological processes, according to enriched GO, KEGG, and GSEA analyses. Comparing patients with a high-NPG score to those with a low-NPG score revealed significant differences in clinical characteristics, growth and recurrence of malignancies (CSC index), TME cell infiltration, and immunotherapeutic response (P< 0.005), potentially making the NPG score multifunctional in the clinical therapeutic setting. Furthermore, AIM2, CASP4, GSDMB, NOD2, and RBCK1 were also found to be highly expressed in ccRCC cell lines and tumor tissues, and GASP4 and GSDMB promote ccRCC cells’ proliferation, migration, and invasion. This study firstly suggests that targeting the NPG score feature for TME characterization may lend novel insights into its clinical applications in the prognostic prediction of ccRCC.
Zinc enhances autophagic flux and lysosomal function through transcription factor EB activation and V-ATPase assembly

The stimulation of autophagy or lysosomes has been considered therapeutic for neurodegenerative disorders because the accumulation of misfolded proteins is commonly observed in the brains of individuals with these diseases. Although zinc is known to play critical roles in the functions of lysosomes and autophagy, the mechanism behind this regulatory relationship remains unclear. Therefore, in this study, we examined which mechanism is involved in zinc-mediated activation of autophagy and lysosome. Exposure to zinc at a sub-lethal concentration activated autophagy in a concentration-dependent manner in mRFP-GFP-LC3-expressing H4 glioma cells. Zinc also rescued the blocking of autophagic flux arrested by pharmaceutical de-acidification. Co-treatment with zinc attenuated the chloroquine (CQ)-induced increase in the number and size of mRFP-GFP-LC3 puncta in H4 cells and accumulation of p62 by CQ or ammonium chloride in both H4 and mouse cerebrocortical cultures. Zinc rapidly induced the expression of cathepsin B (CTSB) and cathepsin D (CTSD), representative lysosomal proteases in neurons, which appeared likely to be mediated by transcription factor EB (TFEB). We observed the translocation of TFEB from neurite to nucleus and the dephosphorylation of TFEB by zinc. The addition of cycloheximide, a chemical inhibitor of protein synthesis, inhibited the activity of CTSB and CTSD at 8 h after zinc exposure but not at 1 h, indicating that only late lysosomal activation was dependent on the synthesis of CTSB and CTSD proteins. At the very early time point, the activation of cathepsins was mediated by an increased assembly of V-ATPase on lysosomes and resultant lysosomal acidification. Finally, considering that P301L mutation in tau protein causes frontotemporal dementia through aggressive tau accumulation, we investigated whether zinc reduces the accumulation of protein aggregates in SK-N-BE(2)-C neuroblastoma cells expressing wild-type tau or mutant P301L-tau. Zinc markedly attenuated the levels of phosphorylated tau and total tau as well as p62 in both wild-type and mutant tau-overexpressing cells. We also observed that zinc was more effective than rapamycin at inducing TFEB-dependent CTSB and CTSD expression and V-ATPase-dependent lysosomal acidification and CTSB/CTSD activation. These results suggest that the regulation of zinc homeostasis could be a new approach for developing treatments for neurodegenerative diseases, including Alzheimer’s and Parkinson’s.
Molecular docking study of various Enterovirus—A71 3C protease proteins and their potential inhibitors

Hand, foot, and mouth disease (HFMD) is a common infection that primarily affects children in preschool and kindergarten; however, there is yet no vaccination or therapy available. Despite the fact that current research is only focused on numerous strains of Enterovirus—A71 (EV-A71) 3C protease (3Cpro), these investigations are entirely separate and unrelated. Antiviral agents must therefore be tested on several EV strains or mutations. In total, 21 previously reported inhibitors were evaluated for inhibitory effects on eight EV-A71 3Cpro, including wild-type and mutant proteins in this study, and another 29 powerful candidates with inhibitory effects on EV-A71 were investigated using the molecular docking approach. This method is to determine the broad-spectrum of the antiviral agents on a range of strains or mutants because the virus frequently has mutations. Even though Rupintrivir is reported to pass phase I clinical trial, 4-iminooxazolidin-2-one moiety (FIOMC) was shown to have a broader anti-3Cpro spectrum than Rupintrivir. Meanwhile, Hesperidin possessed a better 3Cpro inhibitory capability than FIOMC. Thus, it could be considered the most promising candidate for inhibiting various strains of EV-A71 3Cpro proteins in the newly anti-EV compounds group. Furthermore, the mutation at E71A has the most significant impact on the docking results of all ligands evaluated. Future in vitro experiments on Hesperidin’s ability to inhibit 3Cpro activity should be conducted to compare with FIOMC’s in vitro results and validate the current in silico work.
Algorithmic Approach to Data Mining and Classification Techniques

Objective/Background: This paper highlights the extension of access data to data mining from passing year to recent. Main aim of this paper is comparative study of tools/techniques/algorithms which are used for analysis of huge amount of data. Methods/Statistical Analysis: Different methods of data mining has been studied and discussed which include decision tree, neural network, regression, clustering techniques are implemented on different tools for fraud detection. Different algorithms Adaboost, page rank, K-means used for data mining are also discussed. For generate relevant information from data streams, frequent pattern generation tree algorithm is also implemented and discussed. Findings: Out of so many available algorithms decision tree has been found out to be the most suitable for mining data provided the data is restricted to some thousand of entries. The most prominent feature as its advantage lies in its clear illustration in the form of graphical tree with inherent tree structure capability. However the concern about ambiguity should be carefully dealt with maintains consistency. Applications: For the extraction of the relevant data, data mining is helpful in various ways. The various areas where data mining is being used have also been discussed in the paper. Future Scope: The scope of the paper extends from an exhaustive survey and analysis of all available empirical and conceptual techniques and tools in the area of data mining.
uyghur semantic similarity computation based on contextual information in web documents

In many research fields such as Linguistics, Natural Language Processing, and Artificial Intelligence, semantic similarity computation between words is an important issue. In this paper, semantic similarity metrics are firstly introduced and analyzed in order to determine their advantages and limitations, then a new unsupervised Uyghur context-based semantic similarity metric is proposed combining the feature characteristics of Uyghur. The proposed metric is automatic, do not require any annotated knowledge resources, and can be applied to other languages. In this work, the proposed metric is evaluated on the Miller & Charles data set, the metric is evaluated for different feature weighting schemes and as a function of the number of Web documents used. 50 Uyghur speakers are chosen to take the experiments, it is shown that the correlation scores between context-based similarity metric and human judgments are significantly higher than that of the co-occurrence-based metrics. 1553-9105/Copyright © 2012 Binary Information Press.
Dutch compound splitting for bilingual terminology extraction

Compounds pose a problem for applications that rely on precise word alignments such as bilingual terminology extraction. We therefore developed a state-of-the-art hybrid compound splitter for Dutch that makes use of corpus frequency information and linguistic knowledge. Domain-adaptation techniques are used to combine large out-of-domain and dynamically compiled in-domain frequency lists. We perform an extensive intrinsic evaluation on a Gold Standard set of 50,000 Dutch compounds and a set of 5,000 Dutch compounds belonging to the automotive domain. We also propose a novel methodology for word alignment that makes use of the compound splitter. As compounds are not always translated compositionally, we train the word alignment models twice: a first time on the original data set and a second time on the data set in which the compounds are split into their component parts. The obtained word alignment points are then combined.
Efficient xml stream processing and searching

In this dissertation, I present a table-driven streaming XML (Extensible Markup Language) parsing and searching technique, called TDX, and investigate related techniques. TDX expedites XML parsing, validation and searching by pre-recording the states of an XML parser in tabular forms and by utilizing an efficient runtime streaming parsing engine based on a two-stack push-down automaton. The parsing tables are automatically produced from the XML schemas or from the WSDL (Web Services Description Language) service descriptions. Because the schema constraints and XPath expressions are pre-encoded in a parsing table, the approach effectively implements a schema-specific XML parser and/or query processor that combines parsing, validation and search into a single pass. Moreover, the runtime parsing engine is independent of XML schemas and XPath query expressions, parsing can be populated on-the-fly to the runtime engine, thus TDX efficiently eliminates the recompilation and redeployment requirements of schema-specific parsers to address the schema changes. Similarly, different XPath queries can also be preprocessed at compile time and populated on-the-fly to the TDX searching engine without runtime overhead. To construct the parsing tables, we developed a set of mapping rules that translate XML schemas to augmented grammars. The augmented grammars support the full expressive power of the W3C XML Schema by introducing permutation phrase grammars and multi-occurrence phrase grammars. The augmented grammars are suitable to construct a predicative parsing table. The predictive parsing table constructed from the augmented grammars can be integrated into the parser at any time to maximize the performance or be populated on-the-fly at runtime and address schema changes efficiently. Because parsing tables or searching tables are pre-processed at compile time, and looking up the tables at runtime is deterministic and takes constant time, TDX efficiently implements a single pass, predictive validating parser without backtracking or function calling overheads. Our experimental results show a significant performance improvement compared to widely used XML parsers, either validating and non-validating, and to XML query processors.
Therapeutic potential of exosomes from adipose-derived stem cells in chronic wound healing

Chronic wound healing remains a challenging medical problem affecting society, which urgently requires anatomical and functional solutions. Adipose-derived stem cells (ADSCs), mesenchymal stem cells with self-renewal and multiple differentiation ability, play essential roles in wound healing and tissue regeneration. The exosomes from ADSCs (ADSC-EXOs) are extracellular vesicles that are essential for communication between cells. ADSC-EXOs release various bioactive molecules and subsequently restore tissue homeostasis and accelerate wound healing, by promoting various stages of wound repair, including regulating the inflammatory response, promoting wound angiogenesis, accelerating cell proliferation, and modulating wound remodeling. Compared with ADSCs, ADSC-EXOs have the advantages of avoiding ethical issues, being easily stored, and having high stability. In this review, a literature search of PubMed, Medline, and Google Scholar was performed for articles before August 1, 2022 focusing on exosomes from ADSCs, chronic wound repair, and therapeutic potential. This review aimed to provide new therapeutic strategies to help investigators explore how ADSC-EXOs regulate intercellular communication in chronic wounds.
Biocomposite based electrode for effective removal of Cr (VI) heavy metal via capacitive deionization

Abstract This study focuses on the fabrication of biocomposite electrode and removal of Cr (VI) ions from wastewater using a capacitive deionization (CDI) method. The activated carbon (AC) was synthesized from Bael fruit shell (BS). The synthesized AC surface has a macroporous and mesoporous structure with the large specific surface area (617.72 m2 g−1) and high adsorption capacity. The cyclic voltammetry and CDI were performed for the detection and for the removal of chromium (VI) ions, respectively. The lower level of detection of Cr (VI) by a modified electrode was found to be 10 ppt. SEM, BET, and FTIR analyses were performed to explore the surface properties of electrode materials. The removal efficiency was achieved 100% by using biocomposite electrode with an applied potential of 15 V. The highest percent removal mechanism consists of electrosorption and electroreduction due to the affinity between polyvinyl alcohol modified electrode and Cr (VI) ions, under electrochemically faradic process.
Flux Density Variations at 3.6 cm in the Massive Star-forming Region W49A

A number of ultracompact H ii regions in Galactic star-forming environments have been observed to vary significantly in radio flux density on timescales of 10–20 years. Theory predicted that such variations should occur when the accretion flow that feeds a young massive star becomes unstable and clumpy. We have targeted the massive star-forming region W49A with the Karl G. Jansky Very Large Array for observations at 3.6 cm with the B-configuration at ∼0. ″8 resolution, to compare to nearly identical observations taken almost 21 years earlier (2015 February and 1994 August). Most of the sources in the crowded field of ultracompact and hypercompact H ii regions exhibit no significant changes over this time period. However, one source, W49A/G2, decreased by 20% in peak intensity (from 71 ± 4 mJy/beam to 57 ± 3 mJy/beam), and 40% in integrated flux (from 0.109 ± 0.011 Jy to 0.067 ± 0.007 Jy), where we cite 5σ errors in peak intensity, and 10% errors in integrated flux. We present the radio images of the W49A region at the two epochs, the difference image that indicates the location of the flux density decrease, and discuss explanations for the flux density decrease near the position of W49A/G2.
A hypergraph grammar with an opportunistic control scheme for conceptual design automation with application to mechanism synthesis

This thesis addresses the conceptual design stage, the stage that contributes the most to the cost and quality of a product. The understanding of this stage is necessary to realize the goal of automating and integrating this stage with the other stages of the product life cycle and in providing valuable tools to the design engineer. The model developed here, which is based on the well-established paradigms for problem solving, "constraint satisfaction", "transformation", and "decomposition" (top-down refinement), contributes to the development of design theory and methodology as it provides formal, rigorous representation and reasoning schemes. A Hypergraph grammar with opportunistic control scheme has been developed as a computational model of the conceptual design process. The production rules defining the transformations and decompositions, along with the underlying hierarchical constraint network, represent the design product knowledge while the interpreter of the grammar (generator and parser), along with the opportunistic controller, represents the design process knowledge. The transformational and decompositional knowledge defines the syntax of the design and the underlying constraints define the semantics of the design. This approach enables one to handle many problems that fall between strongly connected systems (for which the transformational approach is generally appropriate) and weakly connected systems (for which the decompositional approach is generally appropriate) and handles both transformations and decompositions and the underlying constraints with a common language. Linkage synthesis is one domain in this middle ground. These problems are decomposable, yet highly coupled. The design strategies proposed here could be broadly classified as opportunistic, least-commitment and deterministic. A classification of constraints, that includes classes such as generators and filters, required and preferred, has been proposed to implement the design strategies and to exploit the different types of constraints. The grammar was implemented in CLP(R) language, a Prolog-based constraint-logic programming language for real number domain. Many features of CLP(R) make it an ideal candidate as a low level implementation language for conceptual design automation. Two prototype applications, HyperDesigner-PT, for power transmission domain, and HyperDesigner-DM, for dwell mechanism domain, have been developed to validate the model. The validity was tested using many test cases.
Research on Fast High-frequency Strings Extracting and Statistics Algorithm with no Thesaurus

In this paper we describe a fast high frequency strings extracting algorithm. Our approach uses HASH technology to avoid relying on corpus and word segmentation. To extract the high frequency strings, we only use statistics information. After processing the prefixes and suffixes, the high frequency strings we get can be the supplement knowledge for the un-login words processing, word disambiguation and word weighting. The experimental results show that it has a high speed and can work on arbitrary texts. Our method has good effect when processing novels and other real texts.