Let G be a finite group and Irr(G) be the set of all complex irreducible characters of G. The character-graph Δ(G) associated to G, is a graph whose vertex set is the set of primes which divide the degrees of some characters in Irr(G) and two distinct primes p and q are adjacent in Δ(G) if the product pq divides χ(1), for some χ∈Irr(G). Tong-Viet posed the conjecture that if Δ(G) is k-regular for some integer k⩾2, then Δ(G) is either a complete graph or a cocktail party graph. In this paper, we show that his conjecture is true for all regular character-graphs whose eigenvalues are in the interval [−2,∞).
Introduction: In the past two decades, there has been a growing line of research on the possible effects of psychological interventions on patients with inflammatory bowel disease (IBD). This study aimed to evaluate the qualitative validity of a lifestyle-based intervention in patients with ulcerative colitis (UC) by examining their experiences. Methods: This study employed a concurrent embedded mixed methods design. To this end, a qualitative study was conducted in the form of a clinical trial that applied a lifestyle-based intervention to patients with ulcerative colitis. The patients’ experiences were assessed twice through the focus group interviews. The data from both interviews (posttest and follow-up phases) were analyzed using thematic network analysis. Results: Based on the results of this study, 3 global themes, 10 organizing themes, and 21 basic themes were identified and summarized in three thematic networks of benefits, barriers, and disadvantages. Benefits included satisfaction with attending the meetings, knowledge acquisition, cognitive, emotional, and behavioral changes, and partial physical improvement; barriers included physical symptoms, need for retraining, giving importance to the topic, and restrictions in physical activities; and the disadvantages included the negative impact of the group and the negative impact of the training program. Conclusion: The results of this study confirmed the qualitative validity of a lifestyle-based intervention in a group of patients with ulcerative colitis by demonstrating the educational and therapeutic effects of the intervention and its acceptability.
Background The most frequent malignancy in women is breast cancer (BC). Gastric cancer (GC) is also the leading cause of cancer-related mortality. Long non-coding RNAs (lncRNAs) are thought to be important neurotic regulators in malignant tumors. In this study, we aimed to evaluate the expression level of NEAT1 and the interaction of this non-coding RNA with correlated microRNAs, lncRNAs, and mRNAs or protein coding genes, experimentally and bioinformatically. Methods For the bioinformatics analyses, we performed RNA-RNA and protein–protein interaction analyses, using ENCORI and STRING. The expression analyses were performed by five tools: Microarray data analysis, TCGA data analysis (RNA-seq, R Studio), GEPIA2, ENCORI, and real-time PCR experiment. qRT-PCR experiment was performed on 50 GC samples and 50 BC samples, compared to adjacent control tissue. Results Based on bioinformatics and experimental analyses, lncRNA NEAT1 have a significant down-regulation in the breast cancer samples with tumor size lower than 2 cm. Also, it has a significant high expression in the gastric cancer patients. Furthermore, NEAT1 have a significant interaction with XIST, hsa-miR-612 and MTRNR2L8. High expression of NEAT1 have a correlation with the lower survival rate of breast cancer samples and higher survival rate of gastric cancer patients. Conclusion This integrated computational and experimental investigation revealed some new aspects of the lncRNA NEAT1 as a potential prognostic biomarker for the breast cancer and gastric cancer samples. Further investigations about NEA1 and correlated mRNAs, lncRNAs, and microRNAs – specially the mentioned RNAs in this study – can lead the researchers to more clear information about the role of NEAT1 in the breast cancer and gastric cancer.
The photoelectrochemical degradation of methylene blue (MB) dye through the stabilization of BiOBr/Bi2S3/TiO2/GO nanocatalyst was examined. The nanocatalysts were synthesized using the solvothermal method and the spin coating method was applied for the coating. In photoelectrochemical processes, the coated-FTO was used as the anode and the graphite as the cathode. An initial dye concentration of 10 mg/L, the initial pH level of 6, photocatalyst dosage of 0.5 g/L, and irradiation time of 90 min, were the optimal conditions for the photocatalytic degradation. The experimental value of degradation in MB was conceived to be 97.9%. The optimal conditions for the photoelectrochemical degradation of MB include the initial concentration of the dye of 17 mg/L, the initial pH level of 6, the number of coatings of 3 times, and the irradiation time of 90 min, and the experimental value of 98.7% was obtained. The current density produced under optimal conditions was calculated and the effect of the light source and nitrogen gas on the efficiency was observed. The possibility of recycling the photocatalyst up to 4 times in both suspended and stabilized forms, confirms the stability, repeatability, and optimal activity of the photocatalyst. The economic performance was also investigated. According to the trapping tests, holes and hydroxyl radicals were the main active species in the degradation process.
One of the main concerns in development of metros in historical cities is adverse effects of train-induced vibrations on Cultural and Historical Structures (CHS). In this regard, several approaches have been developed in the literature to predict the level of railway-induced vibration received by CHSs. One of the main limitations of the proposed prediction approaches is a lack of consideration of the effect of variation of water table level on the railway-induced vibrations. To fill this gap, a comprehensive field measurement was carried out in this research in the historical city of Isfahan. Based on the data obtained from the field measurement, significant effects of variation of water table level on the Peak Particle Velocity (PPV) and Soil Transfer Function (STF) were shown. Using the result obtained from the measurement, an adjustment factor was derived to consider the effect of variation of water table level in the conventional train-induced vibration prediction approach. The accuracy and validity of the water table level adjustment factor derived in this study were evaluated through an independent comprehensive field measurement performed in a different historical city.
Non-healing wounds have long been the subject of scientific and clinical investigations. Despite breakthroughs in understanding the biology of delayed wound healing, only limited advances have been made in properly treating wounds. Recently, research into nucleic acids (NAs) such as small-interfering RNA (siRNA), microRNA (miRNA), plasmid DNA (pDNA), aptamers, and antisense oligonucleotides (ASOs) has resulted in the development of a latest therapeutic strategy for wound healing. In this regard, dendrimers, scaffolds, lipid nanoparticles, polymeric nanoparticles, hydrogels, and metal nanoparticles have all been explored as NA delivery techniques. However, the translational possibility of NA remains a substantial barrier. As a result, different NAs must be identified, and their distribution method must be optimized. This review explores the role of NA-based therapeutics in various stages of wound healing and provides an update on the most recent findings in the development of NA-based nanomedicine and biomaterials, which may offer the potential for the invention of novel therapies for this long-term condition. Further, the challenges and potential for miRNA-based techniques to be translated into clinical applications are also highlighted.
In this research, a data-driven adaptive model is developed to predict the variables indicating gasoline quality in the light naphtha isomerization process and determine the optimal conditions leading to improved gasoline quality. To this end, an integrated method based on double-level similarity criterion and support vector regression (DLS-SVR) is proposed. The variables that indicate gasoline quality are research octane number (RON), benzene volume percentage (BVP), and Reid vapor pressure (RVP). In addition to the influential operating variables of pressure, temperature, feed weight hourly space velocity (WHSV), and hydrogen to naphtha feed molar ratio, the model considers benzene’s feed concentration and cycloparaffin content. Experiments are conducted using commercial Pt/Al2O3-CCl4 catalyst in a pilot-scale packed-bed reactor. The developed model’s predictive performance and generalization ability are compared with the response surface methodology, support vector regression, and double-level locally weighted extreme learning machine through the fivefold cross-validation technique. The generalized DLS-SVR predicts gasoline’s RON, BVP, and RVP with R² = 0.901, 0.959, and 0.931 and RMSE = 0.055, 0.061, and 0.053, respectively, indicating that its performance is superior to alternative generalized models. The optimal conditions are computed using the DLS-SVR model and co-evolutionary particle swarm optimization algorithm (CPSO). The optimal operation of the reactor yielded a 6.78-unit increase in gasoline RON and a minimum BVP of 0.394 %. The results demonstrate that the proposed DLS-SVR model can accurately predict the variables indicating the quality of isomerate gasoline.
Low level current and similarity of High Impedance Faults (HIF) in respect of characteristics to other transient events have posed a critical challenge to the protection of distribution systems. In addition, the dependency of previous methods on large amounts of training data increases the simulation error rate, and preparing this amount of data is time-consuming. In this paper, a novel scheme based on conditional generative adversarial network (CGAN) and convolutional neural network (CNN) classifier techniques is proposed, that reduces this dependency and leads to acceptable classification accuracy. In the proposed method, a small amount of data is extracted from the under-study network as the real data. Then, the third harmonic angle of the current is extracted from the real data by an adaptive linear neuron (ADALINE) as an effective feature. The CGAN is performed to produce a large amount of pseudo data. At last, the fault data is separated from other transient network events via the CNN classifier. Five different scenarios are used to evaluate the proposed method on a 13-bus IEEE network. The simulation results show that the Precision and Recall of distinguishing HIFs from other transient events is greater than 98% in all the scenarios. These results verify that the proposed scheme is very accurate despite the low dependency on input training data.
Plants growing on quarry tailings at the Irankouh Pb/Zn mine encounter both drought stress and high levels of Pb. To better understand the role of drought and Pb in plant adaptation to Pb/Zn quarry tailings, we compared effects of drought stress (simulated by polyethylene glycol - PEG) and Pb, individually and in concert, to determine how these stressors affected two plant species: the metallicolous species Matthiola flavida (M) and the non-metallicolous congener, M. incana (NM). Plants were exposed to Pb (Pb(NO3)2) and three levels of PEG-6000 (0, -0.25, and -0.75 MPa) in a complete factorial design. Pb had non-significant effects on growth and oxidative stress but enhanced levels of osmoprotectants and phenol compounds in the M species M. flavida, whereas in M. incana the effect of Pb was non-significant on the same variables (except for anthocyanins and the osmoprotectants, proline and glycine betaine). Compared to M. incana, the M species M. flavida was tolerant of Pb, showing strongly reduced root-to-shoot translocation and enhanced Pb accumulation in the root (especially when under drought stress), which reduced toxic effects of Pb in the shoot. Tolerance of Pb by the M species M. flavida was aided by the accumulation of reducing sugars and phenolic compounds, as well as by greater catalase activity.
The operating speed of adaptive single-phase auto-reclosure (ASPAR) is of great importance to maintain power systems stability in high-voltage power transmission lines (PTLs). This paper proposes a two-step protection scheme using the long short-term memory (LSTM) network to enhance the ASPAR performance. The discrimination between transient and permanent faults is made by an LSTM with high accuracy in the first step. If transient faults are detected in the second step, another LSTM is applied to predict the secondary arc extinction time (SAET). To this end, the second LSTM accurately foresees the voltage waveform of the faulty phase a half-power cycle earlier, and predicts the SAET in order to get a successful reclosing. The results obtained from extensive simulations using EMTP-RV and MATLAB software environments indicate that the presented protection scheme outperforms other ones in terms of fault type classification, achieving an F-measure value of 98.90%. Moreover, the results verify that the LSTM can accurately estimate the voltage waveform and the SAET, that ensures a successful reclosing of the faulty phase.
Doxycycline and Naproxen are among the most widely used drugs in the therapy of CoVID 19 disease found in surface water. Water scarcity in recent years has led to research to treat polluted water. One of the easy and low-cost methods for treatment is adsorption. The utilize of Metal-Organic Frameworks (MOFs) to evacuate pharmaceutical contaminants from water sources has been considered by researchers in the last decade. In this research, HKUST-1/ZnO/SA composite with high adsorption capacity, chemical and water stability, recovery, and reuse properties has been synthesized and investigated. By adding 10 wt% of ZnO and 50 wt% of sodium alginate to HKUST-1, at 25 °C and pH = 7, the specific surface area is reduced by 60%. The parameters of drugs concentration C0 =(5,80) mg/L, time=(15,240) min, and pH= (2,12) were investigated, and the results showed that the HKUST-1/ZnO/SA is stable in water for 14 days and it can be used in 10 cycles with 80% removal efficiency. The maximum Adsorption loading of doxycycline and Naproxen upon HKUST-1/ZnO/SA is 97.58 and 80.04 mg/g, respectively. Based on the correlation coefficient (R²), the pseudo-second-order and the Langmuir isotherm models were selected for drug adsorption. The proposed mechanism of drug uptake is by MOFs, hydrogen bonding, electrostatic bonding, and acid-base interaction.
A considerable amount of industrial heat is wasted to the atmosphere. This valuable energy could be recovered through fixed bed systems by sensible or latent heat. Mathematical modeling and experimental study of a fixed bed thermal energy recovery system by sensible heat are presented in this paper. An experimental setup was constructed in which air and water were utilized as the charging and discharging process heat transfer fluids, respectively. Three different materials including silica-ceramic, alumina-ceramic, and metal with different sizes were used as the energy storage material. Mathematical modeling was performed at three different levels to analyze the system behavior. The difference among the various modeling levels was in their simplifying assumptions. The resulting equations in each level of modeling were numerically solved. Validation of the modeling results against the experimental data was performed to evaluate the capability of the developed models in prediction of the system actual behavior. Level I model with an average error of 24 % in the charging process and 11 % in discharging process showed unsuitable results, while level II and level III models showed approximately the same and acceptable results with 5 % and 9 % average errors in charging and discharging processes, respectively. Then level II model was selected for prediction of the system performance due to its less complexity compared to level III model. Finally, the influence of the packings material, packing size, and inlet air velocity and temperature on the system performance was predicted using level II model. The system yield had the highest value for all packings at the entering air highest temperature and lowest superficial velocity examined. Metal packings showed better performance and had an experimental system yield of 54 % and 67 % at the entering air highest temperature and lowest superficial velocity, respectively. It was also observed that metal packings outperformed other packings and had the highest experimental yield of 58 % at the same operating conditions. Besides, the highest experimental yield of 61 % was achieved by packings with the smallest size (13 mm) at the highest entering air temperature (140 °C). Based on the findings of this study it can be concluded that lower inlet air velocity and higher inlet air temperature enhanced the recovery efficiency. Besides, the smaller metal packings size showed higher recovery efficiency.
Liquid transportation biofuel production is a promising strategy to reduce greenhouse gas emissions. Hydrothermal gasification (HTG) has shown great potential as an effective method for valorizing wet biomass. The high-quality syngas produced using the HTG process can be chemically/biochemically converted to liquid biofuels. Therefore, this paper aims to comprehensively review and critically discuss syngas production from biomass using the HTG process and its conversion into liquid biofuels. The basics and mechanisms of biomass HTG processing are first detailed to provide a comprehensive and deep understanding of the process. Second, the effects of the main operating parameters on the performance of the HTG process are numerically analyzed and mechanistically discussed. The syngas cleaning/conditioning and Fischer-Tropsch (FT) synthesis are then detailed, aiming to produce liquid biofuels. The economic performance and environmental impacts of liquid biofuels using the HTG-FT route are evaluated. Finally, the challenges and prospects for future development in this field are presented. Overall, the maximum total gas yield in the HTG process is obtained at temperature, pressure, and residence time in the range of 450–500 °C, 28–30 MPa, and 30–60 min, respectively. The highest C5+ liquid hydrocarbon selectivity in the FT process is achieved at temperatures between 200 and 240 °C. Generally, effective conversion of biomass to syngas using the HTG process and its successful upgrading using the FT process can offer a viable route for producing liquid biofuels. Future studies should use HTG technology in the biorefinery context to maximize biomass valorization and minimize waste generation.
Community detection is one of the most essential issues in social networks analysis field. Among the available categories of algorithms, the label propagation algorithm-based (LPA-based) methods, due to their proper time complexity, are of high concern. As all the social networks explicitly or implicitly include signed relationships, the attempt here is to suggest an LPA-based approach for community detection in the directed signed social networks. The direction of edges is not addressed in available LPA-based community detection methods for signed social networks. In this respect, 1) a weighting method is suggested in order to utilize the direction information that converts the network into an undirected weighted signed social network, 2) this weight is combined with a second weight obtained from the sign information of the edges, and 3) the LPA is extended, where the combined weights are applied in label propagation. Moreover, the directed signed modularity and the directed signed flow-based capacity measures are proposed. The findings of the run experiments indicate that the proposed method as to the directed signed modularity, directed signed flow-based capacity, and frustration measures on real-world and synthetic data sets, outperforms its counterparts.
Nowadays, decentralized microgrids (DC-MGs) have become a popular topic due to the effectiveness and the less complexity. In fact, DC-MGs resist to share their internal information with the distribution system operator (DSO) to protect their privacy and compete in the electricity market. Further, lack of information sharing among MGs in normal operation conditions leads to form a competitive market. However, in emergency operation conditions, it results numerous challenges in managing network outages. Therefore, this paper presents a hierarchical model consisting of three stages to enhance the resilience of DC-MGs. In all stages, the network outage management is performed considering the reported data of MGs. In the first stage, proactive actions are performed with the aim of increasing the network readiness against the upcoming windstorm. In the second stage, generation scheduling, allocation of mobile units and distribution feeder reconfiguration (DFR) are operated by DSO to minimize operating costs. In the final stage, the repair crew is allocated to minimize the energy not served (ENS). Uncertainties of load demand, wind speed and solar radiation are considered, and the effectiveness of the proposed model is investigated by integrating to the 118-bus distribution network. Finally, the results of the simulation indicate that DFR and proactive actions decrease the ENS by 19,124 kWh and 4101 kWh, respectively. Further, the sharing of information among MGs leads to a 48.16% growth in the supply service level to critical loads, and consequently a 3.47% increase in the resilience index.
- Malek Abbasi
- Michel Théra
This paper aims to present some sufficient criteria under which a given function between two spaces that are either topological vector spaces whose topologies are generated by metrics or metrizable subsets of some topological vector spaces, satisfies the error bound property. Then, we discuss the Hoffman estimation and obtain some results for the estimate of the distance to the set of solutions to a system of linear equalities. The advantage of our estimate is that it allows to calculate the coefficient of the error bound. The applications of this presentation are illustrated by some examples.
- Behnam Ashrafian
- Afrouzossadat Hosseini-Abari
Pectin is one of the main structural components in fruits and an indigestible fiber made of d-galacturonic acid units with α (1-4) linkage. This study investigates the microbial degradation of pectin in apple waste and the production of bioactive compounds. Firstly, pectin-degrading bacteria were isolated and identified, then pectinolytic activity was assessed by DNS. The products were evaluated by TLC and LC–MS–ESI. The antioxidative effects were investigated using DPPH and anti-cancer effects and cytotoxicity were analyzed by MTT and flow cytometry. In this study two new bacterial isolates, Alcaligenes faecalis AGS3 and Paenibacillus polymyxa S4 with the pectinolytic enzyme were introduced. Structure analysis showed that the products of enzymatic degradation include unsaturated mono, di, tri, and penta galacturonic acids with 74% and 69% RSA at 40 mg/mL for A. faecalis and P. polymyxa S4, respectively. The results of anti-tumor properties on MCF-7 cells by MTT assay, for products of AGS3 and S4 at 40 mg/mL after 48 h, showed 7% and 9% survival, respectively. In the flow cytometric assessment, the compounds of AGS3 at 40 mg/mL were 100% lethal in 48 h and regarding S4 isolate caused 98% death. Cytotoxicity evaluation on L-929 cells showed no significant toxicity on living cells.
Finding a proper kernel for Support vector machine and adjusting the involved parameters for a better classification remain immense challenges. This paper addresses both challenges in two parts. In part one, a new kernel, called Frequency Component Kernel, is presented; and in the second part, a couple of techniques to form objective functions are introduced to estimate its shape parameter. In designing the FCK, a new Frequency-Based Regressor Matrix is designed based on data structure discovery through curve fitting. The inner product of this regressor matrix with itself produces an intermediary kernel. FCK is a smoothed version of this intermediary kernel. The FCK’s classification accuracy with a 95% confidence interval is compared to well-known kernels, namely Gaussian, Linear, Polynomial, and Sigmoid kernels, for fifteen sets of data. A grid search method is employed for parameter assignments in all kernels. This comparison shows the superiority of FCK in most cases. In part two, the first technique to form an objective function is based on variances of data groups, distances between the centers of data groups, and upper bound classification errors; and the second technique is based on distances between all data, SVM margin, and distance between the centers of data groups. Both techniques take advantage of the FCK development so that all data are converted to the new space via the FBRM. Then, the data distances in this space are calculated. The comparative results show that both suggested techniques to form objective functions outperform the current state-of-the-art parameter estimation methods. The inclusive results show that the combination of our FCK with our two automatic shape parameter estimation methods, could be used as a superlative choice in many related SVM usages and applications.
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