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Sumac (Rhus coriaria L.) is one of the medicinal plants of Anacardiaceae family and widely used as a spice in Iran and Arab countries. Rhus coriaria var. zebaria is a small tree or large shrub, wildly growing in Iraq and described as a new variety with special characteristics. These increase the importance of studying sumac in these areas. Here, the phytochemical variations and the antibacterial activity of 50 accessions of this variety from five different climatic conditions was evaluated in order to identify the best accession to use and the best area for its cultivation. This is the most comprehensive study on this plant. Essential oil compounds were identified using GC-MS method and according to the results, Z, E-2,13-octadecadien, caryophyllene oxide, 2,4-decadienal, E-caryophyllene and nonanoic acid were among the main compounds. Also, the variety is a rich source of minerals including K, Ca, Mg, Na, P, and N. Sumac fruit extract from Akre Xerds had the highest anthocyanin and the lowest amount was from Kavilca region. The radical scavenging effect of extract from Dostic area in the concentration of 400 µg/mL is closer to the effect of ascorbic acid. The largest inhibition was found in the sumac extracted oil of Xasto Zhere area against S. aureus in compared with penicillin and amoxicillin and enrofloxacin antibiotics., (© 2024. The Author(s).)

Growing evidence demonstrates that cancer-associated fibroblasts (CAF) are responsible for tumor genesis, growth, metastasis, and treatment response. Therefore, targeting these cells may contribute to tumor control. It has been proposed that targeting key molecules and pathways of proliferative functions can be more effective than killing CAFs. In this regard, multicellular aggregates, like spheroids, can be used as human tumor models. Spheroids closely resemble human tumors and mimic many of their features. Microfluidic systems are ideal for cultivation and study of spheroids. These systems can be designed with different biological and synthetic matrices in order to have a more realistic simulation of the tumor microenvironment (TME). In this study, we investigated the effect of all-trans retinoic acid (ATRA) on 3D spheroid invasion of MDA-MB cells exposed to hydrogel matrix derived from CAFs. The number of invasive cells significantly decreased in CAF-ECM hydrogel treated with ATRA (p < 0.05), which indicates that ATRA could be effective for CAFs normalization. This experiment was done using an agarose-alginate microfluidic chip. As compared with common methods, such hydrogel casting is an easier method for chip fabrication and can even reduce costs., Supplementary Information: The online version contains supplementary material available at 10.1007/s10616-023-00578-y., Competing Interests: Conflict of interestThe authors have no competing interests as defined by the journal, or other interests that might be perceived to influence the results and/or discussion reported in this paper., (© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Artemisia is one of the most diverse genera in the Asteraceae family. The genus is wildly distributed in Irano-Turanian habitats and includes 34 species in Iran. Here, for the first time the essential oil variability, antioxidants and anti-cholinesterase and anti-tyrosinase activities of extracts of three Artemisia species (A. tournefortiana, A. khorassanica, A. haussknechtii), from different regions of Iran were evaluated. Based on GC-MS analyses, 81.84% to 98.70% of the total oils were identified. Cluster analysis grouped the studied populations in three different chemotypes. The highest and the lowest essential oil contents were observed in A. khorassanica and A. haussknechtii species, respectively. Camphor, en-in-dicycloether, 1,8-cineole and (Z)-β-farnesene were the dominant components of essential oil in investigated ecotypes. The results revealed that the total phenol content was higher in A. tournefortiana collected from Kerman and A. haussknechtii collected from Chaharmahal and Bakhtiari. However, the lowest phenol content was recorded for A. haussknechtii collected from Isfahan province. The highest flavonoids content was found in A. tournefortiana collected from West Azerbaijan and A. khorassanica collected from North Khorasan. The highest FRAP antioxidant activity was observed in A. tournefortiana (Kerman) and the lower amount was in A. haussknechtii collected from Kohgiluyeh and Boyer-Ahmad. The highest antioxidant activity by DPPH method was in A. khorassanica collected from South Khorasan and the lowest activity was in Isfahan's A. haussknechtii. The acetycholine esterase inhibitory activity was higher in A. tournefortiana collected from West Azerbaijan; and the lowest activity was in A. haussknechtii collected from Chaharmahal and Bakhtiari province. The highest tyrosinase inhibitory activity was in A. khorassanica collected from North Khorasan; and the lowest was in A. haussknechtii collected from Chaharmahal and Bakhtiari., (© 2022. The Author(s).)

In this work, a rapid, selective naked eyes colorimetric chemical probe for the detection of Cr 3+ was developed based on functionalization of gold nanoparticles. For this purpose, surface of Au NPs was functionalized using 4-amino-5-methyl-4H-1,2,4-triazole-3-thiol (AMTT). Through colorimetric studies, it was found that in the presence of Cr 3+ ions, AMTT-Au NPs instantly aggregated and resulted in a color change of the solution from red to blue. The color change of AMTT-Au NPs due to the aggregation induced by Cr 3+ can be seen with even naked eyes and also by UV-Vis spectroscopy with a detection limit of 1.8μM and 0.1μM, respectively. AMTT-Au NPs showed excellent selectivity toward Cr 3+ compared to other cations tested, including K + , Na + , Cs + , Fe 3+ , Ni 2+ , Cu 2+ , Co 2+ , Zn 2+ , Ba 2+ , Ca 2+ , Mg 2+ , Cd 2+ , Pb 2+ , Hg 2+ ions and especially all trivalent lanthanide ions. The absorbance ratio (A 650 /A 525 ) was linear toward Cr 3+ concentrations in the range of 0.6-6.1μM (R 2 =0.996). The best response was achieved over a pH range of 3-5. Furthermore, the proposed colorimetric method based on AMTT-Au NPs was successfully used for Cr 3+ ion detection in plasma sample and some water samples., (Copyright © 2017 Elsevier B.V. All rights reserved.)

This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally. The ANN model achieves a prediction error of MAE test = (0.04 ± 0.06)   MPa for the instantaneous mechanical stress predictions, evaluated over the entire range of stress values (0 to 5.32 MPa) across the component structure. The virtual sensor is capable of producing a quasi-instantaneous, detailed full stress map of the component in just 0.13 s using the ROM, for any combination of 4-load inputs, compared to the 6 min and 31 s required by the FEM. Thus, the approach significantly reduces computational complexity while maintaining a high degree of precision, enabling efficient real-time monitoring. The proposed method's effectiveness is demonstrated through rigorous ROM validation, underscoring its potential for stress control. This precise AI-driven procedure opens new horizons for predictive maintenance strategies centered on stress cycle monitoring. [ABSTRACT FROM AUTHOR]

This study presents an asynchronous parallel strategy coordinating central processing unit (CPU) and graphic processing unit (GPU) to accelerate neighborhood operation (NO). Specifically, we propose a data partitioning method called multi-anchor task queuing and a task scheduling method called bi-direction task scheduling, which can support CPU and GPU to find the responsible data blocks rapidly and concurrently handle their tasks via a bi-direction merge. Moreover, we optimize the organization of threads distributed among the CPU and GPU. Experimental results show that when a 1.7 GB raster dataset is processed, the speedup ratio achieved by the proposed parallel algorithm reaches 29.63, which is 19% and 18% higher than those of the GPU and standard asynchronous parallel algorithm, respectively. Additionally, the load balance index is below 0.085, which is significantly better than the value achieved by a conventional algorithm. Thus, the strategy achieves a higher speedup ratio and more adaptable load balance, thereby accelerating the NO more efficiently. Further, the impacts of the data volume, computational intensity, organization mode of the GPU threads, and granularity of the GPU stream on the parallel efficiency are evaluated and discussed. We also test the efficiency of four other common NOs with our strategy. [ABSTRACT FROM AUTHOR]

Despite the known medicinal properties of cajuput oils, there is a lack of comprehensive research on their chemical composition and bioactivity across different Malaysian districts. Thus, this study examined the chemical composition, antibacterial, antioxidant, and toxicity profiles of cajuput oils from different districts in Terengganu and Kelantan, Malaysia. Gas chromatography-flame ionization detection (GC-FID) and mass spectrometry were used to identify the chemical constituents. Antibacterial effectiveness was measured via broth microdilution assays, while antioxidant properties were tested using the DPPH assay. The toxicity profiles were determined using the brine shrimp lethality assay (BSLA). Major compounds in Besut essential oil (BTEO) were 4-(methoxycarbonyl) phenol (17.27%), cyclopentasiloxane decamethyl (13.43%), and methyl-lathodoratin (13.03%). Pasir Mas essential oil (PMEO) was characterized by 3-phenylbutyrophenone (18.30%), 2-isopropyl-10-methylphenanthrene (15.44%), and octanoic acid hex-4-yn-3-yl ester (14.03%). Meanwhile, the major components of Pasir Puteh essential oil (PPEO) were octanoic acid hex-4-yn-3-yl ester (32.89%), N1-(44-dimethyl-13-thiazolan-2-yliden) cyclohexan-1-amine (15.10%), and methyl-lathodoratin (9.44%). BTEO exhibited substantial antibacterial effects, with minimum inhibitory concentration (MIC) values between 0.125 and 0.5 mg/mL. Furthermore, PPEO exhibited significant antioxidant activity, with an IC50 value of 6.06 mg/mL. BSLA demonstrated non-toxicity for PMEO (LC50 1047.13 µg/mL) and PPEO (LC50 13182.57 µg/mL), while BTEO exhibited moderate toxicity (LC50 630.96 µg/mL). These findings suggest that cajuput oil has potential as a natural antioxidant and antibacterial agent. [ABSTRACT FROM AUTHOR]

Given a multiset of colors as the query and a list-colored graph, i.e., an undirected graph with a set of colors assigned to each of its vertices, in the NP-hard list-colored graph motif problem the goal is to find the largest connected subgraph such that one can select a color from the set of colors assigned to each of its vertices to obtain a subset of the query. This problem was introduced to find functional motifs in biological networks. We present a branch-and-bound algorithm named RANGI for finding and enumerating list-colored graph motifs. As our experimental results show, RANGI's pruning methods and heuristics make it quite fast in practice compared to the algorithms presented in the literature. We also present a parallel version of RANGI that achieves acceptable scalability.

Nanotechnology is becoming a promise for scientific advancement nowadays in areas like medicine, consumer products, energy, materials, and manufacturing. Copper oxide nanoparticles (CuO NPs) were synthesized using Ocimum lamiifolium Hochst. ex Benth and Withana somnifera (L) Dunal leaf extract via green synthetic pathway. The leaf of O. lamiifolium and W. somnifera were known to have strong antibiotic and antioxidant properties arising due to the presence of various secondary metabolites, including, flavonoids, alkaloids, saponins, tannins, cardiac glycosides, and phenolic compounds which serve as reducing, stabilizing, and capping agents for the CuO-Nanoparticles (NPs) synthesized. The biosynthesized CuO NPs were characterized based on Fourier transform infrared spectroscopy, X-ray diffraction spectroscopy, and scanning electron microscopy. O. lamiifolium and W. somnifera leaf extract mediated synthesis could produce CuO NPs with average crystallite size of 15 nm and 19 nm, respectively. The biosynthesized CuO-NPs were further examined for antibacterial activity with Gram-positive (S. aureus) and Gram-negative bacteria (E. coli and P. aeruginosa). The GZDK-CuO NPs synthesized using W. somnifera leaf extract inhibited the growth of E. coli. and P. aeruginosa largely in comparison to S. aureus. Whereas the DMAZ-CuO NPs synthesized with the help of O. lamiifolium leaf extract showed higher bacterial inhibition on E. coli compared to S. aureus and P. aeruginosa. The minimum inhibitory concentration (MIC) values of both types of NPs are also assessed on all three pathogens. The newly biosynthesized nanoparticles, thus, were found to be optional materials for inhibiting the growth of drug- resistant bacterial pathogens. [ABSTRACT FROM AUTHOR]

There is increasing demand for natural food preservative in food manufacturing industry as it is the key to meet consumers' preferences toward healthier food choice. Plant is listed among the most important resources of bioactive components to be utilized as the green and natural food preservatives. There are more than 10,000 kinds of bioactive components in plants that possess antioxidant and antimicrobial properties. Artemisia with potential antimicrobial and antioxidant attributes, as well as functional and medicinal properties, is one of the most important plant species. The manuscript presents a comprehensive review of the potential of the Artemisia species as natural food preservatives. The current challenges and ways forward in using Artemisia EOs and extracts as food preservatives are also discussed. This topic is timely and important considering the natural preservatives used to replace chemical ingredients, sustaining quality, healthy properties, and shelf life of food products as well as efficient and novel extraction techniques. [ABSTRACT FROM AUTHOR]

Lithium in coal, as a new type of associated mineral resource, has considerable potential for exploration. Exploration of high-lithium coal seams is essential for developing and using the associated lithium resources. To explore the distribution of lithium resources in the early stages of development in coal seams, the relationship between coal seam logging data and lithium content was analyzed by taking Guojiadi Coal Mine (China) as example. By analyzing the correlation between the different logging curves and the lithium content in coal and combining the K-means algorithm to identify the logging characteristics of different lithium-containing coal seams, we finally obtained the logging identification characteristics of high-lithium coal seams. The results reveal differences in the logging curves of coal seams with different lithium contents. The natural gamma and lateral resistivity of high-lithium coal seams are approximately 80 API and 100 Ω.M, respectively. Our study shows that the early identification of high-lithium coal seams can be evaluated from a logging perspective. We propose a preliminary identification method of high-lithium coal seam based on logging curve parameters by clustering analysis of borehole logging data to achieve accurate prediction. [ABSTRACT FROM AUTHOR]

With its historical roots, Iranian traditional medicine has played a significant role in addressing liver‐related disorders and providing alternative approaches to synthetic drugs. Liver‐related disorders, such as hepatitis, liver cirrhosis, and nonalcoholic fatty liver disease, pose significant health challenges worldwide. From traditional practices and indigenous knowledge, Iranian traditional medicine offers a holistic approach to liver health. It emphasizes the importance of lifestyle modifications, including dietary adjustment, physical activity, and stress reduction, to support liver function and restore balance within the body. This review collects from different databases, mainly Google Scholar, ScienceDirect, and SID. It focused on medicinal plants that are recommended in Iranian traditional medicine and scientifically proved to have liver protection properties as well as summarized our 10 years of experience in this field. This comprehensive article is an effort to study the integration of traditional knowledge with modern evidence‐based practices that can contribute to a comprehensive understanding of Iranian medicine's potential in managing liver‐related disorders. Iranian traditional medicine incorporates many natural remedies derived from medicinal plants, minerals, and animal products. These remedies are often used in the form of herbal preparations, decoctions, and dietary supplements. Specific plants include Zataria multiflora, Satureja spp., Heracleum persicum, Carum carvi, Ferula spp., Hypericum scabrum, and Archillae spp. They are known for their hepatoprotective properties and are commonly employed in the management of liver disorders in Iranian traditional medicine. This traditional treatment provides a unique perspective by offering natural approaches to liver health. Traditional remedies aim to minimize potential side effects associated with synthetic drugs while addressing the root causes of liver disorders. [ABSTRACT FROM AUTHOR]

Introduction: The anti-inflammatory and immunomodulatory properties of tarragon have been noted. Here, we examined the effects of an aqueous extract of the tarragon plant in a rat model of ulcerative colitis. Methods: Ulcerative colitis was induced in Wistar rats using a 2 ml acetic acid (4%) intrarectal enema. Experimental groups received sulfasalazine (2 mg/kg) or tarragon aqueous extract (100 mg/kg) orally for ten consecutive days. After ten days, the animals were euthanized and evaluated for disease activity index (DAI), production of inflammatory and pro-inflammatory mediators in the intestinal tissue. Results: Both the tarragon aqueous extract and sulfasalazine treatments were effective in reducing the disease severity index in experimental ulcerative colitis. Malondialdehyde intensity, nitric oxide level, and myeloperoxidase activity regressed in the colon of animals treated with the tarragon aqueous extract more than in the group treated with sulfasalazine. However, sulfasalazine significantly reduced TNF-α and IL-1 levels compared to the tarragon aqueous extract. There was no statistical difference in IL-6 and PGE2 reduction between the two groups. Conclusion: These findings suggested that the aqueous extract of tarragon may be applied as a natural resource to control ulcerative colitis. [ABSTRACT FROM AUTHOR]

Malignant gliomas are one of the most common and lethal brain tumors with poor prognosis. Most patients with glioblastoma (GBM) die within 2 years of diagnosis, even after receiving standard treatments including surgery combined with concomitant radiotherapy and chemotherapy. Temozolomide (TMZ) is the first-line chemotherapeutic agent for gliomas, but the frequent acquisition of chemoresistance generally leads to its treatment failure. Thus, it’s urgent to investigate the strategies for overcoming glioma chemoresistance. Currently, many studies have elucidated that cancer chemoresistance is not only associated with the high expression of drug-resistance genes in glioma cells but also can be induced by the alterations of the tumor microenvironment (TME). Numerous studies have explored the use of antifibrosis drugs to sensitize chemotherapy in solid tumors, and surprisingly, these preclinical and clinical attempts have exhibited promising efficacy in treating certain types of cancer. However, it remains unclear how tumor-associated fibrotic alterations in the glioma microenvironment (GME) mediate chemoresistance. Furthermore, the possible mechanisms behind this phenomenon are yet to be determined. In this review, we have summarized the molecular mechanisms by which tumor-associated fibrotic reactions drive glioma transformation from a chemosensitive to a chemoresistant state. Additionally, we have outlined antitumor drugs with antifibrosis functions, suggesting that antifibrosis strategies may be effective in overcoming glioma chemoresistance through TME normalization. [ABSTRACT FROM AUTHOR]

The diversity and scarcity of resources across devices in heterogeneous computing environments can impact their ability to meet users' quality-of-service (QoS) requirements, especially in open real-time environments where computational loads are unpredictable. Despite this uncertainty, timely responses to events remain essential to ensure desired performance levels. To address this challenge, this paper introduces collaborative service execution, enabling resource-constrained IoT devices to collaboratively execute services with more powerful neighbors at the edge, thus meeting non-functional requirements that might be unattainable through individual execution. Nodes dynamically form clusters, allocating resources to each service and establishing initial configurations that maximize QoS satisfaction while minimizing global QoS impact. However, the complexity of open real-time environments may hinder the computation of optimal local and global resource allocations within reasonable timeframes. Thus, we reformulate the QoS optimization problem as a heuristic-based anytime optimization problem, capable of interrupting and quickly adapting to environmental changes. Extensive simulations demonstrate that our anytime algorithms rapidly yield satisfactory initial service solutions and effectively optimize the solution quality over iterations, with negligible overhead compared to the benefits gained. [ABSTRACT FROM AUTHOR]

Acute and chronic wounds present a significant healthcare challenge, requiring innovative solutions for effective treatment. The exploitation of natural by-products with advanced cell regeneration potential and plant-based materials, which possess bioactive properties, is an innovative topic in wound management. This study investigates the potential of donkey gelatin and keratin for blending with natural bioactive extracts such as sumac, curcumin, and oak acorn to fabricate antioxidant and antimicrobial nanofibers with accelerated wound healing processes. The fabricated nanofibers possess good in vitro biocompatibility, except for the sumac-based donkey nanofibers, where cell viability significantly dropped to 56.25% (p < 0.05 compared to non-treated cells). The nanofiber dimensions showed structural similarities to human extracellular matrix components, providing an ideal microenvironment for tissue regeneration. The donkey nanofiber-based sumac and curcumin extracts presented a higher dissolution in the first 10 min (74% and 72%). Curcumin extract showed similar antimicrobial and antifungal performances to rivanol, while acorn and sumac extracts demonstrated similar values to each other. In vitro tests performed on murine fibroblast cells demonstrated high migration rates of 89% and 85% after 24 h in the case of acorn and curcumin nanofibers, respectively, underscoring the potential of these nanofibers as versatile platforms for advanced wound care applications. [ABSTRACT FROM AUTHOR]

The aim of this review is to present the potential application of camphor—a bicyclic monoterpene ketone—in the prevention of skin infections. Skin diseases represent a heterogeneous group of disorders characterized by prolonged symptoms that significantly diminish the quality of life. They affect the dermis, the epidermis, and even subcutaneous tissue. They very often have a bacterial or fungal background. Therapy for dermatological skin disorders is difficult and long-term. Therefore, it is important to find a compound, preferably of natural origin, that (i) prevents the initiation of this infection and (ii) supports the skin's repair process. Based on its documented anti-inflammatory, antibacterial, antifungal, anti-acne, anesthetic, strengthening, and warming properties, camphor can be used as a preventative measure in dermatological infectious diseases and as a component in medical and cosmetic products. This work discusses the structure and physicochemical properties of camphor, its occurrence, and methods of obtaining it from natural sources as well as through chemical synthesis. The use of camphor in industrial preparations is also presented. Additionally, after a detailed review of the literature, the metabolism of camphor, its interactions with other medicinal substances, and its antimicrobial properties against bacteria and fungi involved in skin diseases are discussed with regard to their resistance. [ABSTRACT FROM AUTHOR]

Cell cultivation has been one of the most popular methods in research for decades. Currently, scientists routinely use two-dimensional (2D) and three-dimensional (3D) cell cultures of commercially available cell lines and primary cultures to study cellular behaviour, responses to stimuli, and interactions with their environment in a controlled laboratory setting. In recent years, 3D cultivation has gained more attention in modern biomedical research, mainly due to its numerous advantages compared to 2D cultures. One of the main goals where 3D culture models are used is the investigation of tumour diseases, in both animals and humans. The ability to simulate the tumour microenvironment and design 3D masses allows us to monitor all the processes that take place in tumour tissue created not only from cell lines but directly from the patient's tumour cells. One of the tumour types for which 3D culture methods are often used in research is the canine mammary gland tumour (CMT). The clinically similar profile of the CMT and breast tumours in humans makes the CMT a suitable model for studying the issue not only in animals but also in women. [ABSTRACT FROM AUTHOR]

Clustering algorithms are usually iterative procedures. In particular, when the clustering algorithm aims to optimise an objective function like in k-means clustering or Gaussian mixture models, iterative heuristics are required due to the high non-linearity of the objective function. This implies higher computational costs and the risk of finding only a local optimum and not the global optimum of the objective function. In this paper, we demonstrate that in the case of one-dimensional clustering with one main and one noise cluster, one can formulate an objective function, which permits a closed-form solution with no need for an iteration scheme and the guarantee of finding the global optimum. We demonstrate how such an algorithm can be applied in the context of laboratory medicine as a method to estimate reference intervals that represent the range of "normal" values. [ABSTRACT FROM AUTHOR]

Infectious diseases are a global challenge today. One preventive measure is to maintain and improve humans’ immunity. Many people consume sungkai, a medicinal plant that is believed to cut down the exposure of severe covid-19 because it is related to antioxidant and antibacterial activity. This research investigated the antioxidant and antimicrobe agents of fungal endophyte extracts from the stem bark of sungkai. Endophytic fungal was isolated from the fresh tissue of the stem bark of sungkai and identified morphologically. The antioxidant of the endophytic fungi extract was tested with the DPPH, and antibacterial agent was tested by using the paper disc diffusion method. Molecular identification of endophytic fungi was done to extract that showed the most potential antioxidant and antibacterial activity. The pure compounds were isolated by chromatographic techniques. Structural determinations of the compounds were accomplished using a spectroscopic method, comprising 1D and 2D NMR. Twenty isolates of fungal endophyte were found residing in the stem bark of sungkai, specifically RB1-RB20. Isolates RB4 and RB6 showed the most potential antioxidant and antibacterial activity and were identified molecularly. The results of molecular identification showed that RB4 and RB6 were Curvularia intermedia and Colletotrichum cliviicola. Based on the spectroscopic analysis, the compounds identified from the two fungi were the different compound, specifically 3-hydroxy-4(hydroxy(4-hydroxyphenyl)methyl)-ɣ-butyrolactone (1) and 5-hydroxy-4-(hydroxymethyl)-2H-pyran-2-on (2). Compound 1 and 2 showed antibacterial and antioxidant in strong to moderate. These compounds and the endophytic fungi extract can be used as a new ingredient for medicine because it has antioxidant and antibacterial activity with further research. [ABSTRACT FROM AUTHOR]

Lemongrass is a fragrant herb with lengthy, thin leaves that contains myrcene (an aromatic compound) as well as citral and geraniol (antimicrobial compounds). Therefore, identifying an appropriate drying method for this plant is crucial for maintaining aromatic and antimicrobial compounds and enhancing the shelf life of the product. This investigation seeks to assess the influence of various drying tactics involving hot air at temperatures of 40, 50, and 60 °C, infrared radiation at intensities of 0.5, 0.6, and 0.8 W / cm 2 , sequential hot-air/infrared, as well as simultaneous hot air-infrared, on the drying mechanism, color, appearance, yield, and essential oil constituents of lemongrass leaves, with the objective of enhancing the marketability of the product. The essential oils of lemongrass were extracted through the process of hydro-distillation, and subsequently, the volatile compounds present were analyzed using Gas Chromatography—Mass Spectrometry (GC–MS). The findings indicated: (a) The most appropriate technique for preserving optimal color quality of lemongrass leaves was through the application of hot air drying solely at a temperature of 60 °C; (b) To optimize the retention and amplification of the essential oil content in lemongrass, our study recommends the employment of a simultaneous hybrid drying technique involving hot air drying at a temperature of 50 °C in conjunction with infrared drying set at a radiation intensity level of 0.6 W / cm 2 ; and (c) The data analysis demonstrated that in order to achieve elevated levels of volatile compounds, specifically neral and geranial, infrared drying with a radiation intensity of 0.6 and 0.8 W / cm 2 , respectively, was found to be optimal. [ABSTRACT FROM AUTHOR]

Many traditional clustering algorithms are incapable of processing mixed-type datasets in parallel, limiting their applications in big data. In this paper, we propose a CF tree clustering algorithm based on MapReduce to handle mixed-type datasets. Mapper phase and reducer phase are the two primary phases of MR-CF. In the mapper phase, the original CF tree algorithm is modified to collect intermediate CF entries, and in the reducer phase, k-prototypes is extended to cluster CF entries. To avoid the high costs associated with I/O overheads and data serialization, MR-CF loads a dataset from HDFS only once. We first analyze the time complexity, space complexity, and I/O complexity of MR-CF. We also compare it with sklearn BIRCH, Apache Mahout k-means, k-prototypes, and mrk-prototypes on several real-world datasets and synthetic datasets. Experiments on two mixed-type big datasets reveal that MR-CF reduces execution time by 45.4% and 61.3% when compared to k-prototypes, and it reduces execution time by 73.8% and 55.0% when compared to mrk-prototypes. [ABSTRACT FROM AUTHOR]

Big social data and user-generated content have emerged as important sources of timely and rich knowledge to detect customers' behavioral patterns. Revealing customer satisfaction through the use of user-generated content has been a significant issue in business, especially in the tourism and hospitality context. There have been many studies on customer satisfaction that take quantitative survey approaches. However, revealing customer satisfaction using big social data in the form of eWOM (electronic word of mouth) can be an effective way to better understand customers' demands. In this study, we aim to develop a hybrid methodology based on supervised learning, text mining, and segmentation machine learning approaches to analyze big social data on travelers' decision-making regarding hotels in Mecca, Saudi Arabia. To do so, we use support vector regression with sequential minimal optimization (SMO), latent Dirichlet allocation (LDA), and k-means approaches to develop the hybrid method. We collect data from travelers' online reviews of Mecca hotels on TripAdvisor. The data are segmented, and travelers' satisfaction is revealed for each segment based on their online reviews of hotels. The results show that the method is effective for big social data analysis and traveler segmentation in Mecca hotels. The results are discussed, and several recommendations and strategies for hotel managers are provided to enhance their service quality and improve customer satisfaction. [ABSTRACT FROM AUTHOR]

The essential oil content and composition of medicinal plants may be influenced by eco-friendly products for nutrient availability under abiotic stresses. This research was conducted to determine the effects of biochar (30 g kg−1 soil) and biochar-based nanocomposites (BNCs) of iron (30 g BNC-FeO kg−1 soil), zinc (30 g BNC-ZnO kg−1 soil), and their combined form (15 + 15 g) on dill (Anethum graveolens L.) under salinity levels (non-saline, 6 and 12 dS m−1). Application of biochar, particularly BNCs increased iron and zinc content and decreased sodium accumulation in leaf tissues. The seed essential oil content increased under high salinity. Salinity changed the values of major compounds in essential oil and induced the formation of compounds such as alpha,2-dimethylstyrene, cuminyl alcohol, p-cymene, and linalool. Biochar treatments especially BNCs with a higher production of monoterpenes increased the levels of limonene, carvone, apiol, and dillapioll. All extracts showed a considerable DPPH-inhibitory effect with application of BNCs under salinity. The maximum antioxidant activity was observed under high level of salinity with application of the combined form. Therefore, the combined form of nanocomposite was the best treatment to improve the content of basic commercial monoterpenes and consequently antioxidant activity of essential oil in salt-stressed dill plants. [ABSTRACT FROM AUTHOR]

In many recent applications, a graph is used to simulate many complex systems, such as social networks, traffic models or bioinformatics, and the underlying graphs for these systems are very large. Algorithms for mining all frequent subgraphs from a single large graph have attracted much attention and been studied in more detail lately. Mining frequent subgraphs is important, and defined as finding all subgraphs whose occurrences in a dataset are greater than or equal to a given frequency threshold. Among frequent subgraph algorithms, GraMi is considered as the state-of-the-art approach. However, GraMi has a huge search space, and therefore still needs a lot of time and memory to process a large graph. In this paper, we propose two effective strategies to balance and reduce the search space of GraMi, which can decrease the number of candidate subgraphs generated, with early pruning of a large portion of the domain for each candidate. Our experiments were performed on four real datasets and the results show that the performance of our balancing GraMi is better than those of the original algorithm GraMi and the optimized version SoGraMi. [ABSTRACT FROM AUTHOR]

Discovery of frequent subgraphs of a network is a challenging and time-consuming process. Several heuristics and improvements have been proposed before. However, when the size of subgraphs or the size of network is big, the process cannot be done in feasible time on a single machine. One of the promising solutions is using the processing power of available parallel and distributed systems. In this paper, we present a distributed solution for discovery of frequent subgraphs of a network using the MapReduce framework. The solution is named MRSUB and is developed to run over the Hadoop framework. MRSUB uses a novel and load-balanced parallel subgraph enumeration algorithm and fits it into the MapReduce framework. Also, a fast subgraph isomorphism detection heuristic is used which accelerates the whole process further. We executed MRSUB on a private cloud infrastructure with 40 machines and performed several experiments with different networks. Experimental results show that MRSUB scales well and offers an effective solution for discovery of frequent subgraphs of networks which are not possible on a single machine in feasible time. [ABSTRACT FROM AUTHOR]

Discovery of frequent subgraphs of an input network is one of the most important facilities for mining and analyzing complex networks. The most accurate solution to frequent subgraph discovery is to enumerate all subgraphs of size k and then count the frequency of each isomorphic class. However, the process is much time consuming because the number of subgraphs grows exponentially with the growth of the input network, or by increasing the size of the subgraphs. Also, there is no known polynomial-time algorithm for subgraph isomorphism detection, and this issue makes the problem harder. Hence, the available solutions can just mine small input networks and small subgraph sizes. A parallel and load-balanced solution named Subdigger is proposed which is faster and more efficient compared to available solutions. Subdigger efficiently executes on current multicore and multiprocessor machines, and incorporates a fast heuristic with a high-performance concurrent data structure which significantly accelerates detection and counting of isomorphic subgraphs. Subdigger can also handle large networks and subgraph sizes using external memory and external sorting. We performed several experiments using real-world input networks. Compared to the available solutions, Subdigger can extract frequent subgraphs much faster and the performance scales almost linearly using additional processor cores. The experimental results show that Subdigger can be more than 100 times faster than other solutions on a 4-core Intel i7 machine. Besides performance, Subdigger can process larger subgraphs using external memory while other tools crash due to memory limitation. [ABSTRACT FROM AUTHOR]

An embedded obstacle-type micromixer-concentration gradient generator based on capillary self-driven is proposed and studied. Herringbone structure (HS) for mixing and palisade-shape small channels at the outlet are designed in the device (named HS). Simulation and experimentation are done to study the liquid mixing efficiency in the small channels and concentration gradient at the outlet, and the experimental results agree with the simulation results. For three cases of liquid dripping (sequential, reverse, and delayed drippings), mixing analysis shows that the mixing efficiency increases along both mixing channel and palisade length, and is high in the middle small channel of the palisade-shape area and low on both sides. An obvious concentration gradient at the outlet can form compared with the device without the palisade-shape area. Finally, water pH value detection is done as one of the applications of HS. This study can provide guidance for the application of HS in biochemical detection, cell research, drug screening, etc. based on the capillary-driven effect., (© 2023 Wiley-VCH GmbH.)

Conducting polymers are considered as reactive gels which can sense the working ambient through their unique electrochemical reaction. Copolymers of aniline with o-toluidine for three different monomer compositions were synthesized chemically and were characterized using FTIR and UV-Vis spectroscopy, SEM, TGA, and cyclic voltammetry. The electrochemical reactive sensing characteristics with respect to the electrical and chemical working conditions of the copolymers were verified and compared through Chronopotentiometric responses in aqueous solutions of HCl by changing the reaction variables: applied current and chemical environment (electrolyte concentration) at a fixed constant charge. The consumed electrical energy during the electrochemical reaction of the copolymers varies linearly with the driving current and follows a logarithmic dependence on the electrolyte concentration. The consumed electrical energy during the reaction was found to act as the sensing parameter. At the same experimental condition, the sensitivity was associated with the conductivity of the copolymers. The highly conducting composition gives the highest sensitivity. These experimental results were fitted with the theoretical equation. [ABSTRACT FROM AUTHOR]

The current studies were focused on the phytochemical profiling of two local wild Artemisia species, Artemisia scoparia and Artemisia absinthium leaves' essential oils, extracted via the hydro distillation method along with evaluation of their antioxidant as well as antimicrobial effects. The constituents of EOs were identified using a combined gas chromatography-mass spectrometric (GC-MS) technique. A total of 25 compounds in A. scoparia essential oil (EOAS) were identified, and 14 compounds with percentage abundance of >1% were tabulated, the major being tocopherol derivatives (47.55%). A total of nine compounds in Artemisia absinthium essential oil (EOAA) were enlisted (% age > 1%), the majority being oleic acid derivatives (41.45%). Strong antioxidant effects were pronounced by the EOAS in DPPH (IC50 = 285 ± 0.82 µg/mL) and in ABTS (IC50 = 295 ± 0.32 µg/mL) free radical scavenging assays. Both the EOs remained potent in inhibiting the growth of bacterial species; Escherichia coli (55–70%) and Shigella flexneri (60–75%) however remained moderately effective against Bacillus subtilis as well as Staphylococcus aureus. Both EOAS and EOAA strongly inhibited the growth of the tested fungal species, especially Aspergillus species (up to 70%). The oils showed anti-cholinesterase potential by inhibiting both Acetylcholinesterase (AChE; IC50 = 30 ± 0.04 µg/mL (EOAS), 32 ± 0.05 µg/mL (EOAA) and Butyrylcholinesterase (BChE; IC50 = 34 ± 0.07 µg/mL (EOAS), 36 ± 0.03 µg/mL (EOAA). In conclusion, the essential oils of A. scoparia and A. absinthium are promising antioxidant, antimicrobial and anticholinergic agents with a different phytochemical composition herein reported for the first time. [ABSTRACT FROM AUTHOR]

A new Al3+ carbon paste ion selective electrode based on N,N'-bis(salicylidene)-1,3-propanediamine (SB-Salpr) was prepared for the quantification of trace Aluminum. The proposed sensor has significant selectivity and sensitivity toward Al3+ ions in the presence of a wide range of cations including alkaline, alkaline earth, intermediate and heavy metal ions. The use of multi-walled carbon nanotubes (MWCNTs) and nanosilica in the composition of carbon paste sensors caused improvements in their characteristics; these materials have suitable conductivity and can help in conducting signals through the carbon paste sensors. The electrode consisted of 3% of MWCNTs, 0.5% of nanosilica, 25% of paraffin oil, 66.5% of graphite powder and 5% of SB-Salpr ligand. The sensor shows a Nernstian response to Al3+ ions with a Nernstian gradient of 20.2 ± 0.1 in the concentration range of 1.0 × 10–6 to 1.0 × 10–2 M with the detection limit of 2.1 × 10–7 M. The sensor response is independent of pH in the range of 24 and possesses the advantages of fast response time less than 5 s. According to the obtained results, Al3+ nanocomposite carbon paste sensor exhibited good selectivity in the presence of a few metal ions. The sensor was effectively used as an indicator electrode in the potentiometric titration of Al3+ ions with ethylenediaminetetraacetic acid. [ABSTRACT FROM AUTHOR]

Following significant advances in image acquisition, synapse detection, and neuronal segmentation in connectomics, researchers have extracted an increasingly diverse set of wiring diagrams from brain tissue. Neuroscientists frequently represent these wiring diagrams as graphs with nodes corresponding to a single neuron and edges indicating synaptic connectivity. The edges can contain "colors" or "labels", indicating excitatory versus inhibitory connections, among other things. By representing the wiring diagram as a graph, we can begin to identify motifs, the frequently occurring subgraphs that correspond to specific biological functions. Most analyses on these wiring diagrams have focused on hypothesized motifs—those we expect to find. However, one of the goals of connectomics is to identify biologically-significant motifs that we did not previously hypothesize. To identify these structures, we need large-scale subgraph enumeration to find the frequencies of all unique motifs. Exact subgraph enumeration is a computationally expensive task, particularly in the edge-dense wiring diagrams. Furthermore, most existing methods do not differentiate between types of edges which can significantly affect the function of a motif. We propose a parallel, general-purpose subgraph enumeration strategy to count motifs in the connectome. Next, we introduce a divide-and-conquer community-based subgraph enumeration strategy that allows for enumeration per brain region. Lastly, we allow for differentiation of edges by types to better reflect the underlying biological properties of the graph. We demonstrate our results on eleven connectomes and publish for future analyses extensive overviews for the 26 trillion subgraphs enumerated that required approximately 9.25 years of computation time. [ABSTRACT FROM AUTHOR]

To achieve urban sustainability, it is critical to enhance the environment, economy, and society simultaneously. This study adopted the revised genuine progress indicator (GPI) and ecological footprint (EF) to evaluate the ecological efficiency and economic sustainability of the Yangtze River Delta from 2000 to 2018. Spatial analysis was utilized to identify spatial autocorrelation. A total of 27 cities were then partitioned through k-means cluster analysis. The results showed that GPI and ecological efficiency improved rapidly, but economic sustainability showed a downward trend. GPI and GDP had a high degree of spatial correlation, especially in Suzhou-Wuxi-Changzhou Metropolitan Area. However, no spatial correlation existed between GPI and EF. The city with high GEE can reach 3000 $/gha, indicating the city consumed 1 global hectare to create $3000 of genuine economic growth. Shanghai, Hangzhou, and Taizhou were cities with the highest level of economic sustainability and ecological efficiency. The spatiotemporal characteristics of economic sustainability and ecological efficiency revealed in this study will provide theoretical guidance for alleviating ecological pressure and promoting economic sustainable development. [ABSTRACT FROM AUTHOR]

Large graphs are often used to simulate and model complex systems in various research and application fields. Because of its importance, frequent subgraph mining (FSM) in single large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes. FSM is aimed at finding all subgraphs whose number of appearances in a large graph is greater than or equal to a given frequency threshold. In most recent applications, the underlying graphs are very large, such as social networks, and therefore algorithms for FSM from a single large graph have been rapidly developed, but all of them have NP‐hard (nondeterministic polynomial time) complexity with huge search spaces, and therefore still need a lot of time and memory to restore and process. In this article, we present an overview of problems of FSM, important phases in FSM, main groups of FSM, as well as surveying many modern applied algorithms. This includes many practical applications and is a fundamental premise for many studies in the future. This article is categorized under:Algorithmic Development > Association RulesAlgorithmic Development > Structure Discovery [ABSTRACT FROM AUTHOR]

Composites of polyaniline and polyvinyl alcohol were prepared through an in-situ polymerisation of aniline using ethanol as a coagulating agent and characterised for their applicability as supercapacitor electrode material. The thermally stable composites afford moderately high electrical conductivity. Composite with 0.03 mol aniline exhibited a high electrical conductivity of 0.371 × 10−2 S/cm. The variation in electrical conductivity with an increase in aniline content is consistent with the band gap energies estimated from electronic spectra and IR drop. SEM analysis confirms coral-like nanowires of polyaniline (PANI) grown within the polyvinyl alcohol matrix. Cyclic voltametric studies revealed that the composites are highly electroactive as they allow easy diffusion of ions through the nanowires of PANI for efficient electrochemical reaction. The composite with 0.03 mol aniline has a superior specific capacitance of 302 Fg−1 at a scan rate of 5 mV/s. The composite exhibited a maximum energy density of 41.94 Whkg−1 and excellent stability even after storage of 18 months in ambient condition with 83.3% capacitance retention. [ABSTRACT FROM AUTHOR]

This paper presents a peach surface defect recognition method based on GWOABC-KM (Gray Wolf algorithm and K-means optimized by improved bee swarm algorithm). Firstly, aiming at the problem of poor sharpness and noise in peach images taken in natural environment, the peach images collected by IDCNN (DCNN model combined with Inception structure) are denoised. Then, aiming at the problem that the complex background interferes greatly and affects the accurate segmentation of defective areas, the denoising is carried out by using threshold and edge detection fusion algorithm. The background information of the processed image is removed to get the complete peach image. Finally, the GWOABC algorithm is used to determine the type of peach defect. The method was validated on the peach dataset collected, and the detection rate was 93.7% and 97.4% respectively. Compared with the traditional fruit surface defect detection, this algorithm can better complete the surface defect recognition. [ABSTRACT FROM AUTHOR]

Biologically active secondary metabolites, essential oils, and volatile compounds derived from medicinal and aromatic plants play a crucial role in promoting human health. Within the large family Asteraceae, the genus Artemisia consists of approximately 500 species. Artemisia species have a rich history in traditional medicine worldwide, offering remedies for a wide range of ailments, such as malaria, jaundice, toothache, gastrointestinal problems, wounds, inflammatory diseases, diarrhoea, menstrual pains, skin disorders, headache, and intestinal parasites. The therapeutic potential of Artemisia species is derived from a multitude of phytoconstituents, including terpenoids, phenols, flavonoids, coumarins, sesquiterpene lactones, lignans, and alkaloids that serve as active pharmaceutical ingredients (API). The remarkable antimalarial, antimicrobial, anthelmintic, antidiabetic, anti-inflammatory, anticancer, antispasmodic, antioxidative and insecticidal properties possessed by the species are attributed to these APIs. Interestingly, several commercially utilized pharmaceutical drugs, including arglabin, artemisinin, artemether, artesunate, santonin, and tarralin have also been derived from different Artemisia species. However, despite the vast medicinal potential, only a limited number of Artemisia species have been exploited commercially. Further, the available literature on traditional and pharmacological uses of Artemisia lacks comprehensive reviews. Therefore, there is an urgent need to bridge the existing knowledge gaps and provide a scientific foundation for future Artemisia research endeavours. It is in this context, the present review aims to provide a comprehensive account of the traditional uses, phytochemistry, documented biological properties and toxicity of all the species of Artemisia and offers useful insights for practitioners and researchers into underutilized species and their potential applications. This review aims to stimulate further exploration, experimentation and collaboration to fully realize the therapeutic potential of Artemisia in augmenting human health and well-being., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Cloud computing has become a well-known platform for solving big data and complex problems such as workflow applications. Infrastructure as a Service (IaaS) from the cloud is a suitable platform to solve these problems as it can potentially provide a nearly unlimited amount of resources using virtualization technology with a pay-per-use cost model. Various Quality of Service (QoS) objectives, such as cost and time, have been considered individually for workflow scheduling. In this paper, we proposed two energy-efficient heuristic algorithms with budget-deadline constraints that are appropriate for resources with Dynamic Voltage and Frequency Scaling (DVFS) enabled, as well as those that do not support DVFS. They are Budget Deadline Constrained Energy-aware (BDCE) and Budget Deadline DVFS-enabled energy-aware (BDD) algorithms for the cloud. Furthermore, they acquire affordable cost, faster scheduling length, and higher energy-saving ratio. Various evaluation metrics like success rate, cost and time ratios, energy consumption, utilization rate, and energy-saving ratio are utilized to evaluate the performance of the proposed algorithms. The obtained results are compared with budget-deadline constraints methods, such as BDSD, DBCS, and BDHEFT, as well as two other energy-efficient deadline-constrained algorithms, namely, ERES and Safari's algorithm in various scenarios on scientific workflow applications. [ABSTRACT FROM AUTHOR]

A new mixed-ligand Cu(II) complex, [Cu(pyr)(bpy)(OH2)]ClO4 (1), was synthesized from 4-acetyl-3-methyl-1-phenyl-2-pyrazoline-5-one (H-pyr) and 2,2-bipyridine (bpy). Single-crystal X-ray diffraction, elemental analysis, and FTIR and UV–Vis spectroscopy were used to confirm its structure. The pyr ligand is the monoanionic deprotonated form of the H-pyr enol tautomer. Additionally, the complex's battery-like capabilities were investigated. The newly synthesized complex possessed a high specific capacity and provided sufficient energy density for SC devices. Electrochemical evaluations, such as cyclic voltammetry, galvanostatic charge/discharge, and electrochemical impedance spectroscopy (EIS), indicated that (1) with redox of metal-center holds satisfactory capacity and delivered energy when constructed in three- or two-electrode configuration (KOH 3 M). (1) displayed battery-like qualities (b-value ~ 0.88), as determined by the diffusion contribution at various scan rates. In an alkaline environment, the EIS measurements revealed a low interfacial charge resistance. The maximum specific capacity measured in the three-electrode system was 483 C g−1 (413.6 C g−1 at 2 mV s−1) at a current density of 1 A g−1. These pyrazolone-based compounds would be promising candidates for further energy storage. [ABSTRACT FROM AUTHOR]

This paper uses data mining technology to analyze students' English scores. In view of the influence of many factors on students' English performance, the analysis is realized by using the association rule algorithm. The thesis analyzes and applies students' English scores based on association rules and mainly does the following work: (1) at present, the problem of the CARMA algorithm is low operating efficiency. The combination of the genetic algorithm's crossover, mutation, and the CARMA algorithm realizes the fast search of the algorithm. The simulation results show that the operation performance of the algorithm is greatly improved after the crossover and mutation operations in the genetic algorithm are applied to the CARMA algorithm. The simulation results show that the mining accuracy of the improved algorithm is 97.985%, and the mining accuracy before the improvement is 92.221%, indicating that the improved algorithm can improve the accuracy of mining. (2) By comparing the mining time of the improved CARMA algorithm, the traditional CARMA algorithm, the FP-Growth algorithm, and the Apriori algorithm, the results show that when the number is 6,500, the mining efficiency of the improved CARMA algorithm is twice that of the other three algorithms. As the amount of data increases, the effect of improving mining efficiency gradually increases. (3) By using the improved CARMA algorithm to analyze students' English performance, it is found that the quality of student performance is strongly related to the quality of daily homework, and if it is related to the teacher's gender, professional title, etc., it is recommended that schools should pay more attention to homework during the teaching process. [ABSTRACT FROM AUTHOR]

Presents the 2021 subject/author index for this publication. [ABSTRACT FROM AUTHOR]

Although the use of conductive polymers has invaded the electronics industry, the insolubility parameter presents a major problem against their direct application on the surfaces of materials. Attachment of cyclic compounds to polymer chains is recommended to increase the solubility of conductive polymers. The aim of this study is to synthesize a new type of intrinsic conductive polymer, soluble in common solvents. The technique is based on the copolymerization of thiophene with a synthesized monomer phenylazepane-2-one. The reaction was catalyzed by a solid catalyst, prepared by the acid treatment of natural clay, generating active sites responsible for the adhesion of the thiophene and benzene rings. Proton and carbon nuclear magnetic resonances (1H NMR/13C NMR), ultraviolet spectroscopy (UV–Visible), infrared spectroscopy (IR) and differential scanning calorimetry (DSC) were used to identify the material obtained poly [(phenylazepane-2-one)-co-(thiophene)] abbreviated poly (PAT). Thermogravimetric analysis (TGA) showed the thermal stability of poly (PAT) before 200 °C. The solubility of poly (PAT) has been tested and confirmed in various common solvents. The indirect bandgap was calculated at 1.12 eV using Tauc formula. Ac electrical conductivity and dielectric permittivity have been studied versus frequency and temperature, showing the semiconductor character of poly (PAT). The results obtained showed that this novel polymer can play a pioneering role to replace conventional insoluble conducting polymers. [ABSTRACT FROM AUTHOR]

Generally, big interaction networks keep the interaction records of actors over a certain period. With the rapid increase of these networks users, the demand for frequent subgraph mining on a large database is more and more intense. However, most of the existing studies of frequent subgraphs have not considered the temporal information of the graph. To fill this research gap, this article presents a novel temporal frequent subgraph-based mining algorithm (TFSBMA) using spark. TFSBMA employs frequent subgraph mining with a minimum threshold in a spark environment. The proposed algorithm attempts to analyze the temporal frequent subgraph (TFS) using a Frequent Subgraph Mining Based Using Spark (FSMBUS) method with a minimum support threshold and evaluate its frequency in a temporal manner. Furthermore, based on the FSMBUS results, the study also tries to compute TFS using an incremental update strategy. Experimental results show that the proposed algorithm can accurately and efficiently compute all the TFS with corresponding frequencies. In addition, we also apply the proposed algorithm on a real-world dataset having artificial time information that confirms the practical usability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Big data storage and processing are among the most important challenges now. Among data mining algorithms, DBSCAN is a common clustering method. One of the most important drawbacks of this algorithm is its low execution speed. This study aims to accelerate the DBSCAN execution speed so that the algorithm can respond to big datasets in an acceptable period of time. To overcome the problem, an initial grouping was applied to the data in this article through the K-means++ algorithm. DBSCAN was then employed to perform clustering in each group separately. As a result, the computational burden of DBSCAN execution reduced and the clustering execution speed increased significantly. Finally, border clusters were merged if necessary. According to the results of executing the proposed algorithm, it managed to greatly reduce the DBSCAN execution time (98% in the best-case scenario) with no significant changes in the qualitative evaluation criteria for clustering. [ABSTRACT FROM AUTHOR]

High energy consumption (EC) is one of the leading and interesting issue in the cloud environment. The optimization of EC is generally related to scheduling problem. Optimum scheduling strategy is used to select the resources or tasks in such a way that system performance is not violated while minimizing EC and maximizing resource utilization (RU). This paper presents a task scheduling model for scheduling the tasks on virtual machines (VMs). The objective of the proposed model is to minimize EC, maximize RU, and minimize workflow makespan while preserving the task's deadline and dependency constraints. An energy and resource efficient workflow scheduling algorithm (ERES) is proposed to schedule the workflow tasks to the VMs and dynamically deploy/un-deploy the VMs based on the workflow task's requirements. An energy model is presented to compute the EC of the servers. Double threshold policy is used to perceive the server' status i.e. overloaded/underloaded or normal. To balance the workload on the overloaded/underloaded servers, live VM migration strategy is used. To check the effectiveness of the proposed algorithm, exhaustive simulation experiments are conducted. The proposed algorithm is compared with power efficient scheduling and VM consolidation (PESVMC) algorithm on the accounts of RU, energy efficiency and task makespan. Further, the results are also verified in the real cloud environment. The results demonstrate the effectiveness of the proposed ERES algorithm. [ABSTRACT FROM AUTHOR]

Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithms. These traditional clustering algorithms present some serious issues in connection with the speed-up, the throughput, and the scalability. Thus, they can no longer be directly used in the context of Big Data, where data are mainly characterized by their volume, velocity, and variety. In order to overcome their limitations, the research today is heading to the parallel computing concept by giving rise to the so-called parallel clustering algorithms. This paper presents an overview of the latest parallel clustering algorithms categorized according to the computing platforms used to handle the Big Data, namely, the horizontal and vertical scaling platforms. The former category includes peer-to-peer networks, MapReduce, and Spark platforms, while the latter category includes Multi-core processors, Graphics Processing Unit, and Field Programmable Gate Arrays platforms. In addition, it includes a comparison of the performance of the reviewed algorithms based on some common criteria of clustering validation in the Big Data context. Therefore, it provides the reader with an overall vision of the current parallel clustering techniques. [ABSTRACT FROM AUTHOR]