Cyanobacterial community patterns as water quality bioindicators

The main goal of this study was to examine the use of cyanobacteria for evaluating the quality of running water. Accordingly epilithic cyanobacterial communities were collected in Dez River and Ojeyreb drain in south of Iran. Samples were collected in two seasons: autumn and spring. Effective physical and chemical factors on the structure of cyanobacterial communities and the dispersion of the species in relation with them were determined using PCA and CCA analyses. The Shannon-Wiener biodiversity index was used to define the species diversity. The concentration of nitrate as main nutrient had significant increase in Drain stations. A decline in species richness was observed associated with these increases in nutrient load in both seasons in different cyanobacterial community structure. The results indicated that order Oscillatoriales had higher proportion of cyanobacteria species at Drain. The species Oscillatoria chlorina, Chroococcus minor, Phormidium tenue and Lyngbya kuetzingii S had the most positive correlation with nutrient factor. Species Lyngbya infixa and Lyngbya mesotrichia had the most negative correlation with nitrate. Our results confirm the using of cyanobacteria species as indicators for monitoring eutrophication in rivers and define them as water eutrophication bioindicators

Damage characterization of composite plates under low velocity impact using ultrasonic guided waves

In this work, two numerical procedures based on Finite Elements Method (FEM) have been developed in order to simulate the Lamb wave propagation in Low Velocity Impact (LVI) damaged CFRP (Carbon Fibre Reinforced Polymer) laminate. The former (softening representation), usually adopted in literature, consists of modelling LVI damages by lowering the elastic material properties which allowed investigating the Lamb wave propagation at different stages of LVI damages evolution. The latter, proposed in this paper, conversely to the first one and the most of techniques presented in literature, consists of simulating Lamb wave propagation in a plate characterized by an initial stress-strain state and the related failures carried out by a previous impact simulation involving the same model. Such technique allows a better damage modelling and, consequently, overcoming the damage modelling approximations introduced by the former strategy; the lowering of the elastic material properties leads to a bad damage modelling which does not allow reproducing accurately what happens in the reality. Such procedure allowed investigating the Lamb wave propagation at different impact energy levels. The interaction between Lamb waves and damages has been investigated under three central frequencies of the actuation signal: 150 kHz, 200 kHz and 250 kHz which resulted in interesting observations to minimize the effect of the first lamina's fibres orientation on the wave propagation velocity. It is well known that different wave propagation velocities along fibres and matrix lead to different RMSD (Root Mean Square Deviation) damage index values, even if the sensors are mounted at the same distance from the damage location, resulting in wrong or less accurate information about the identification of both damage size and location during the post-processing phase. Moreover, the relationships between the RMSD damage index values, recorded at different instants of time of the impact history, and the impactor phases has been achieved. Finally a comparison between the results achieved by the two investigated strategies has been carried out and presented here

Design of a wireless passive sensing system for impact detection of aerospace composite structures

In this paper, the design and implementation of a novel on-board wireless passive sensing system for impact detection of composite airframe is presented for the first time. Several modules, including filtering, impact detection, local processing and wireless transmission are designed and evaluated for detecting rare, random and transitory impact events. An event-triggered mechanism with high responsiveness is adopted to reduce the system power dissipation and to maintain the detection effectiveness. This design allows the system to be adaptive, energy-efficient and highly responsive to impacts. The whole system was implemented in an experimental study, and the effectiveness was evaluated and illustrated. The system was woken up by impact events in around 12 µs, and the impact data were recorded at 200 kHz (up to 5.33 MHz). This work provides a guideline for low-power, high-responsiveness passive on-board sensing system design. This system can also be adapted to other sensing applications in aerospace engineering

Editorial: Climate change mitigation and adaptation in power and energy systems

\ua9 2024. This editorial summarizes the papers selected for publication in the Special Issue on Climate Change Mitigation and Adaptation in Power and Energy Systems (CMAP). After a rigorous review of 86 submitted manuscripts, 23 papers were accepted for publication. These accepted papers cover various aspects of climate change mitigation and adaptation and are classified as follows: boosting renewable energy efficiency (two papers), climate resilience strategies (four papers), decarbonization strategies (four papers), renewable energy integration (five papers), policies, incentives, and science communications (four papers), and the role of energy markets (four papers). The Guest Editorial Board is optimistic that this Special Issue will serve as a rich resource, offering invaluable insights to propel future research and advancements in climate change mitigation and adaptation

Direct carboxylation of aromatic compounds using the sodium hydrogen carbonate/triphenylphosphine ditriflate system

A new procedure was developed for the highly regioselective synthesis of aromatic carboxylic acids using the sodium hydrogen carbonate/triphenylphosphine ditriflate system in ethanol at room temperature. This metal-free system was used for the carboxylation of thiophenol with unexpected products in terms of selectivity. The simplicity of the procedure, readily available aromatic compounds, short reaction times, and mild reaction conditions are other advantages of this protocol.We have developed a novel approach for direct carboxylation of a wide range of aromatic compounds in moderate to good yields with high regioselectivity using NaHCO3/TPPD system in EtOH. This metal-free protocol does not need to use carbon monoxide or carbon dioxide gases

Thefromdominancepatients andof healthypilus isletindividuals1 in pneumococcal isolates collected

Background: Pili in Streptococcus pneumoniae have been shown to be one of the adherence factors for epithelial cells in the human upper respiratory tract. Two types of pilus-like structures (pilus islet-1 and pilus islet-2) have been distinguished in S. pneumoniae. Objectives: To investigate the presence of pilus islet-1 (PI-1) in S. pneumoniae and the correlation between our isolates. Materials and Methods: In this study, 162 S. pneumoniae isolates were collected fromclinical specimens, and normal flora were also examined for the distribution of PI-1 using the presence of the rlrA and rrgC genes as markers for this islet and sipA as an indicator of pilus islet-2 (PI-2). BOX-PCR analyses were performed to determine the genetic relationship between isolates. Results: The results confirmed the presence of rlrA and rrgC genes in both clinical (n = 39) and normal flora (n = 26) isolates. Theminimal inhibitory concentration results revealed that the rate of resistance of these isolates to the three antibiotics tested ranged from 26 for penicillin to 46 for erythromycin and tetracycline. Furthermore, 12 of the isolates were resistant to all three antibiotics. Strain typing using repetitive element BOX-PCR analysis among the 65 isolates identified 8 different band patterns. Conclusions: Our results indicated that the dissemination of PI-1 was widespread in S. pneumoniae isolates, although no PI-2 isolates were detected. Furthermore, the frequency of rlrA and rrgC of clinical isolates was significantly more than that of normal flora isolates. © 2016, Ahvaz Jundishapur University of Medical Sciences

Study the Response of Physiological Traits and Grain Yield to Integrated Use of Chemical Nitrogen Fertilizer with Sugarcane Residue Compost in Heat Stress Conditions

IntroductionIn the pursuit of a resilient and progressive agricultural system, the incorporation of diverse fertilizers is deemed essential. This practice not only enhances product quality but also aids in cost reduction. However, over-reliance on a specific type of input can inadvertently lead to unintended repercussions. The unrestricted utilization of chemical fertilizers, for instance, can precipitate adverse outcomes such as imbalanced pH levels, the accumulation of heavy elements, soil structure deterioration, and environmental contamination. Conversely, organic fertilizers, while environmentally friendly, often release nutrients at a slower rate, potentially disrupting optimal plant growth. To attain a balanced and sustainable agricultural approach, the combined application of organic and chemical fertilizers is advocated. Moreover, harnessing the biological potential inherent in soil ecosystems, including beneficial microbial communities encompassing bacteria and fungi, emerges as a promising avenue in cultivating sustainable agriculture. Acknowledging the adverse impact of late-season heat stress on wheat production in Khuzestan and recognizing the significance of reducing chemical fertilizer usage while augmenting organic and biological fertilizers to foster ecological health, this experiment undertakes the exploration of the effects of a synergistic approach. Specifically, it delves into the combined utilization of nitrogen and compost fertilizers, complemented by the incorporation of plant growth-promoting rhizobacteria. This endeavor aims to shed light on how this combined strategy operates within the context of terminal heat stress, assessing its influence on the physiological attributes and yield of the wheat cultivar Chamran 2.Materials and MethodsThis experiment was carried out as split-split plots based on a randomized complete block design with three replications in the crop year of 2021-2022 in the research farm of Agricultural Sciences and Natural Resources University of Khuzestan. The experimental factors include three planting dates: December 1st, December 20th, and December 10th in the main plots; Six levels of combined use of nitrogen fertilizer with compost fertilizer include control (without nitrogen and organic), 100% nitrogen, 75% nitrogen+ 25% compost, 50% nitrogen+ 50% compost, 25% nitrogen+ 75% compost and 100% compost in sub-plots and two levels of application and non-application of plant growth promoting rhizobacteria in sub-plots. Each sub-plot was 3 meters long and 2 meters wide (with an area of 6 square meters) and included 10 crop lines at a distance of 20 cm from each other. The distance between the main and secondary plots was considered to be half a meter and the distance between the blocks was two meters. After physiological maturity, the plants were harvested and the physiological traits and grain yield were measured.Results and DiscussionVariance analysis showed that the interaction effect of planting date, combined use of nitrogen with compost, and plant growth promoting rhizobacteria, on the traits of relative leaf water content, planting to flowering, and grain yield were significant at the 1% probability level. Also, the interaction effect of planting date and the combined use of nitrogen with compost on all traits except the length of the grain filling period and the length of sowing to physiological maturity was significant at the probability level of 1%. The mean comparison showed that the highest relative leaf water content, cell membrane thermostability, and canopy temperature depression were obtained from the treatment of 100% compost, and the highest traits of the length of sowing to flowering and length of sowing to physiological maturity were obtained in the use of 100% nitrogen. Also, the longest grain filling period, grain filling rate, and grain yield were obtained in the combined use of 50% nitrogen+ 50% compost and plant growth-promoting rhizobacteria, and the lowest value was obtained in the control of not using nitrogen and compost. In general, the delay in planting and the occurrence of terminal heat stress caused a decrease in grain yield, but on different planting dates, the combined use of 50% nitrogen+ 50% compost compared to the treatment of 100% nitrogen increased wheat grain yield.ConclusionAccording to the obtained results, in areas with terminal heat stress, the combined use of 50% nitrogen+ 50% compost and plant growth-promoting rhizobacteria can be considered to increase the growth and yield of wheat

Structural Health Monitoring Impact Classification Method Based on Bayesian Neural Network

This paper proposes a novel method for multi-class classification and uncertainty quantification of impact events on a flat composite plate with a structural health monitoring (SHM) system by using a Bayesian neural network (BNN). Most of the existing research in passive sensing has focused on deterministic approaches for impact detection and characterization. However, there are variability in impact location, angle and energy in real operational conditions which results in uncertainty in the diagnosis. Therefore, this paper proposes a reliability-based impact characterization method based on BNN for the first time. Impact data are acquired by a passive sensing system of piezoelectric (PZT) sensors. Features extracted from the sensor signals, such as their transferred energy, frequency at maximum amplitude and time interval of the largest peak, are used to develop a BNN for impact classification (i.e., energy level). To test the robustness and reliability of the proposed model to impact variability, it is trained with perpendicular impacts and tested by variable angle impacts. The same dataset is further applied in a method called multi-artificial neural network (multi-ANN) to compare its ability in uncertainty quantification and its computational efficiency against the BNN for validation of the developed meta-model. It is demonstrated that both the BNN and multi-ANN can measure the uncertainty and confidence of the diagnosis from the prediction results. Both have very high performance in classifying impact energies when the networks are trained and tested with perpendicular impacts of different energy and location, with 94% and 98% reliable predictions for BNN and multi-ANN, respectively. However, both metamodels struggled to detect new impact scenarios (angled impacts) when the data set was not used in the development stage and only used for testing. Including additional features improved the performance of the networks in regularization; however, not to the acceptable accuracy. The BNN significantly outperforms the multi-ANN in computational time and resources. For perpendicular impacts, both methods can reach a reliable accuracy, while for angled impacts, the accuracy decreases but the uncertainty provides additional information that can be further used to improve the classification.This research received no external funding