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With increased penetration of the converter dominated renewables, the overall inertial response and frequency stability of the power grid has been reduced. Low inertia contributes to maximum frequency deviations with higher rate of change of frequency that undermines the reliability of the power system. In the case of type-4 grid connected wind turbines, the kinetic energy of the rotor cannot be directly used to mitigate frequency excursions in the AC grid since fully-rated converters decouple the grid from the turbine rotor. Nonetheless, by designing an adequate control system for full converters, emulated inertia of the rotor can be propagated to the grid. In this paper, we present inertia emulation control strategy based on second order differential equation analogous to the swing equation of classical synchronous machines employed in machine side converter of the permanent magnet synchronous generator wind turbines. The proposed control allows the management of virtual inertia, damping and reactance. The performance of the proposed controller is verified through time-domain simulations and eigenvalue analysis performed in MATLAB Simulink. • The proposed control emulates SMs' behaviour and renders inertia support to the grid. • Unlike typical inertia controllers, proposed scheme does not rely on derivative term. • Enables power oscillation damping without affecting the steady state of the frequency. • Offers better RoCoF mitigation, and nadir improvement over PD-based controllers. • Its features include simple control design, easily scalable, efficient, and reliable. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a Convolutional Neural Network (CNN) based approach for the classification and prediction of various types of in-plane and through-the-thickness delamination in smart composite laminates using low-frequency structural vibration outputs. An electromechanically coupled mathematical model is developed for the healthy and delaminated smart composite laminates, and their structural vibration responses are obtained in the time domain. Short Time Fourier Transform (STFT) is employed to transform the transient responses into two-dimensional spectral frame representation. A convolutional neural network is incorporated to distinguish between the damaged and undamaged states, as well as various types of damage of the laminated composites, by automatically extracting discriminative features from the vibration-based spectrograms. The CNN showed a classification accuracy of 90.1% on one healthy and 12 delaminated cases. The study of the confusion matrix of CNN provided further insights into the physics of the problem. The predictive performance of a pre-trained CNN classifier was also evaluated on unseen cases of delamination, and physically consistent results were obtained. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes the synergetic integration of system identification and artificial intelligence for the detection and assessment of delamination damages in smart composite laminates. An electromechanically coupled mathematical model is developed for the healthy and delaminated smart composite laminates on the basis of improved layerwise theory, higher order electric potential field and finite element method. A discriminative feature space is constructed for the healthy and delaminated structures via system identification from their structural vibration responses. The discriminative features are used for the training and cross-validation of various supervised machine learning classifiers and an optimal classifier is identified. The optimal classifier is employed to make predictions on unseen test delamination cases, and its predictions are validated via a dimensionality reduction tool. The obtained results show that the proposed technique could be employed as a reliable tool for nondestructive evaluation of smart composite laminates. [ABSTRACT FROM AUTHOR]

As substantive components of intelligent structures, piezoelectric sensors need to perform their intended function without any spuriousness. In this work, a mathematical model has been developed to characterize the performance degradation of a piezoelectric sensor due to its debonding from a smart composite laminate. The electromechanically coupled governing equation of motion is developed by incorporating improved layerwise theory and higher electric potential field along with the finite element method and Extended Hamilton's principle. The problem governing equation is solved by Newmark time-integration algorithm to assess the performance of a piezoelectric sensor in the presence of partial debonding at the edge and inner side of the sensor. The partially debonded piezoelectric sensor is investigated in frequency domain via power spectral density of the sensor output. The degradative performance of the partially debonded sensor is generalized for random loadings via basic signal statistics. The numerical results show that the developed model recover the presence and extent of partial debonding between the piezoelectric sensor and the host laminate. [ABSTRACT FROM AUTHOR]

Polypyrrole-Sn(IV)arsenotungstate (Ppy-SnAT) nanocomposite cation exchanger membrane was prepared by solution casting method of Polypyrrole in the presence of Sn(II)arsenotungstate gel and polyvinyl chloride (PVC) as a binder. The formation of Polypyrrole-Sn(IV)arsenotungstate nanocomposite cation exchanger membrane was characterized by Fourier Transform Infra-Red spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), Scanning electron microscopy (SEM) and Energy dispersive X-ray (EDX). The membrane having a composition of 4.5:1 (composite:PVC) shows best results for water content, porosity, thickness and swelling. Our studies show that the present ion selective membrane electrode is selective for Ba(II) metal, having detection limit (1 × 10 − 8 M to 1 × 10 − 1 M), response time (35 s) and working pH range (3–5). The selectivity coefficient values for interfering ions indicate good selectivity for Ba(II) over interfering cations. The analytical utility of this electrode was established by employing it as an indicator electrode in potentiometric titration. The dielectrical constants (ε′, ε″), dielectric loss (tanδ) and AC conductivity (σ ac ) were studied as a function of frequency. [ABSTRACT FROM AUTHOR]

The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable energy resources in the upcoming years. Recycling sources from waste could efficiently ease the risk of world source strain. The waste classification was a good resolution for separating the waste from the recycled objects. It is inefficient and expensive to rely solely on manual classification of garbage and recycling sources. Convolutional neural networks (CNNs) have lately been used to classify recyclable waste, and this is the primary way for recycling the waste. This study presents a recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) model for bioenergy production. RWC-EPODL model focuses on recycling waste materials recognition and classification. When it comes to detecting and classifying trash, the RWC-EPODL model uses two stages. At the initial stage, the RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. In addition, Bayesian optimization (BO) algorithm is applied as hyperparameter optimizer of the AX-RetinaNet model. Following the EPO algorithm with a stacked auto-encoder (SAE) model, the EPO algorithm is used to fine-tune the parameters of the SAE technique for trash classification. The RWC-EPODL model's experimental validation is examined through a number of studies. The RWC-EPODL approach has a 98.96 percent success rate. The comparative result analysis reported the better performance of the RWC-EPODL model over recent approaches. [Display omitted] • Novel recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) for bioenergy production • The presented RWC-EPODL model majorly focuses on the recognition and classification of recycling waste materials. • The proposed RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. • The proposed model employs the EPO algorithm with stacked auto-encoder (SAE) model for waste classification. • To demonstrate the improved outcomes of the RWC-EPODL model, a series of experiments has been conducted to test the model. [ABSTRACT FROM AUTHOR]

Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods. [Display omitted] • Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. • RODL-HMSEP technique aims for predicting the sorption performance of HMs of various biochar features. • The RODL-HMSEP model uses density-based clustering (DBSCAN) technique for simulating the features of metal adsorption data. • Deep belief network (DBN) model performs the next phase of data clustering. • RODL-HMSEP technique ensured promising performance on the prediction of sorption efficiency onto biochar over other methods. [ABSTRACT FROM AUTHOR]

Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and secondary air pollutants, airborne particulate matter (APM) received considerable internet among research communities owing to the adversative impact on human health. Hence, size distribution details of airborne heavy metals are important in assessing the adverse health effects over the globe. Recently, deep learning models have gained significant interest over the mathematical and statistical prediction models. In this view, this paper presents a novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) model for predicting the size fractionated airborne particle bound metals. The proposed AOA-MABLSTM technique focuses on the forecasting of the size-fractionated airborne particle bound matter. The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals is used for determining temporal trend of heavy metal. The proposed model employs AOA based hyperparameter tuning process to optimally tune the hyperparameters included in the MABLSTM method. To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. The stimulation results emphasized the betterment of the presented model over the other methods. Aluminum metal had an RMSE of 73.200 for AOA-MABLSTM. On Cu metal, the AOA-MABLSTM approach had an RMSE of 6.747. On Zn metal, the AOA-MABLSTM system lowered the RMSE by 45.250. [Display omitted] • A novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) method. • The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. • The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals. • The proposed model employs AOA based hyperparameter tuning process to tune the hyperparameter included in the MABLSTM model. • To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. [ABSTRACT FROM AUTHOR]

In recent times, due to the growing global population and increased food demand, smart agriculture is becoming more vital. In this context, Internet of Things (IoT) technologies have emerged as a significant pathway to innovative agricultural techniques. Due to their low capacity, these IoT nodes have faced energy limits and complicated routing methods. As a result, in the sphere of IoT-based agriculture, transmitting data failure, energy consumption, network lifetime reduction, and delay occur. To overcome this problem, this study proposes a novel combination of optimized intelligent smart irrigation systems to improve the energy management performance of the system. Here, the optimal cluster head formation and selection is performed by Hierarchy Shuffled Shepherd Clustering (HSSC) method. Also, the finest energy regulation and routing path are provided by the proposed Emperor Penguin Jellyfish Optimizer (EPJO) method. The simulation of this work is performed on Network Simulator-2 (NS2) software. The simulation consequences from the proposed method are validated and compared with the conventional methods. Thus, the proposed approach results demonstrate that the developed model has much lesser energy consumption and improved network lifetime as compared to the traditional works. [Display omitted] [ABSTRACT FROM AUTHOR]

• Limited faulty data from different health states hinder the autonomous fault diagnosis via deep learning. • The inverse approach of system identification is employed to synthetically augment limited experimental data without the need for additional experiments. • Synthetic data generation bring out additional information about the system, and deep learning models can learn more robustly from the augmented data. • The comparison of deep learning and transfer learning results on the originally measured and augmented data show that data augmentation resulted in a more robust and rigorous autonomous delamination diagnosis framework for laminated composites. Deep learning models can autonomously learn discriminative features from the data; however, insufficient training data often limit their use. This paper proposes a synthetic data augmentation strategy to alleviate the issue of limited data and employ deep learning models for the autonomous assessment of delamination in laminated composites. Contrary to the existing techniques of image data augmentation (rotation, cropping) and time-series data augmentation (adding noise, windowing), the proposed approach brings out additional information during data augmentation through the variation of loading conditions without the need for further experiments. The approach was thoroughly validated and verified in time and frequency domains using various types of experimental vibration testing. The experimentally measured data of 45-time series was augmented to 4,545-time series, resulting in a more rigorous delamination assessment in laminated composites. The proposed approach is autonomous and does not require human-engineered statistical features while using a small amount of measured data. In addition, the approach would assist in tackling the issue of imbalanced data from the healthy and faulty states of laminated composite. [ABSTRACT FROM AUTHOR]

Abstract: Polyacrylonitrile–Aluminium hydroxide [PAN–Al(OH)3] composite anion-exchanger was prepared by solid–gel method. The physicochemical properties of the anion exchanger was determined using Fourier transform spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), X-ray diffraction (XRD) and elemental analysis (CHNS/O) studies. The analytical applications of the anion exchanger have been explored by making selective membrane electrode. Among different composition of membrane the composition of 5:1 (PAN:Al(OH)3) showed best results for water content, porosity, thickness and swelling. These studies showed that the ion selective membrane electrode was selective for having detection limit (1×10−7–1×10−1 M), response time (40s) and working pH range (5.0–9.0). The selectivity coefficients values for interfering ions indicate good selectivity for over interfering anions. The results were also compared by PCA-Arsenomat (Arsenic analyzer). [Copyright &y& Elsevier]

Abstract: Electrically conductive poly(3-methythiophene) cerium(IV)phosphate cation exchange nanocomposites have been synthesized for the first time by in situ chemical oxidative polymerization of 3-methythiophene in the presence of cerium(IV)phosphate. FTIR, SEM, TEM and XRD analysis were used to characterize P3MTh cerium(IV)phosphate cation exchange nanocomposites. The characterization results confirmed that there is a strong interaction between poly(3-methythiophene) and cerium(IV)phosphate particles. The thermal behavior of poly(3-methythiophene), cerium(IV)phosphate and poly(3-methythiophene) cerium(IV)phosphate nanocomposite were also investigated by thermogravimetric analysis (TGA), differential thermal analysis (DTA) and differential thermal gravimetry (DTG) at heating rate 10°Cmin−1 under nitrogen atmosphere and the P3MTh cerium(IV)phosphate nanocomposite was found thermally more stable than pure P3MTh. Isothermal stability of P3MTh and P3MTh cerium(IV)phosphate nanocomposite in terms of direct-current electrical conductivity retention was also studied in ambient air. The P3MTh cerium(IV)phosphate nanocomposite showed better ion-exchange capacity, electrical conductivity and isothermal stability in terms of direct-current electrical conductivity retention under ambient condition than pure poly(3-methythiophene). [Copyright &y& Elsevier]

Abstract: The separation of Hg(II) from aqueous solutions was studied using Poly-o-anisidine Sn(IV) tungstate nano-composite cation exchanger. This nano-composite material was synthesized via sol–gel mixing of Poly-o-anisidine with the inorganic precipitate of Sn(IV) tungstate, providing a new class of organic–inorganic nano-composite cation exchanger with good ion-exchange capacity, better chemical and thermal stability than most of the Poly-o-anisidine based exchanger reported so far. The ion-exchange capacity and ion-exchange behaviors of the nano-composite were also carried out to understand the ion-exchange capabilities. Its selectivity was examined by achieving some important binary and tertiary separations of metal cations on its column indicating its utility in environmental pollution control. This new exchanger was successfully applied for quantitative determination and separation of Hg(II) from a synthetic mixture of metal ions. [Copyright &y& Elsevier]

Abstract: An organic–inorganic nano-composite poly-o-anisidine Sn(IV) tungstate was chemically synthesized by sol–gel mixing of the incorporation of organic polymer o-anisidine into the matrices of inorganic ppt of Sn(IV) tungstate in different mixing volume ratios. This composite material has been characterized using various analytical techniques like XRD (X-ray diffraction), FTIR (Fourier transform infrared), SEM (Scanning electron microscopy), TEM (Transmission electron microscopy) and simultaneous TGA (Thermogravimetric analysis) studies. On the basis of distribution studies, the material was found to be highly selective for Hg(II). Using this nano-composite cation exchanger as electro-active material, a new heterogeneous precipitate based on ion-sensitive membrane electrode was developed for the determination of Hg(II) ions in solutions. The membrane electrode was mechanically stable, with a quick response time, and can be operated within a wide pH range. The electrode was also found to be satisfactory in electrometric titrations. [Copyright &y& Elsevier]

In this paper, Jaya algorithm is applied to find an optimal unit sizing of renewable energy resources, including photovoltaic (PV) panels, wind turbines (WTs), and fuel cells (FCs) with the objective of reducing the consumer's total annual cost in a stand-alone system. The system's reliability is considered using the maximum allowable loss of power supply probability (LPSP max) concept. The methodology is applied to real solar irradiation and wind speed data taken from Hawksbay, Pakistan. Simulation results achieved for low and high consumer's load profiles show that when LPSP max values are set to be 0% and 2%, the PV-FC is the most cost-effective system as compared to PV-WT-FC and WT-FC systems. Further, the results of Jaya algorithm are compared with a genetic algorithm, backtracking search algorithm, and particle swarm optimization. [ABSTRACT FROM AUTHOR]

Treatment methodologies and precautionary measures are the topmost priority for sustainable development. Mesoporous silicates MCM-41, SBA-15 and its hybrid SBA-PMDA were directly employed to remove the textile reactive dyes from water at room temperature and neutral pH. These nanostructured materials were prepared and characterized by using FTIR spectroscopy, Nitrogen adsorption/desorption, NMR spectroscopy, X-rays diffraction, Scanning electron microscopy (SEM), Thermogravimetric analysis (TGA) and Zeta potential. The cationic reactive dye brilliant green (BG) removal capacity of silicas SBA-15 and MCM-41 was found 119.0 mg g−1 133.20 mg g−1, respectively and zero affinity was observed for anionic dye reactive blue (RB 15) by these materials. In comparison, modified silica with PMDA bridged polysilsesquioxane SBA-PMDA, showed 25.66 mg g−1 of removal capacity for anionic dye RB 15. Kinetic studies showed that about 70% of the dye was removed within 4–5 h. To predict the system efficiency, the experimental data was treated with mathematical models such as Langmuir, Freundlich, Sips and Dubinin–Radushkevich isotherms, pseudo-first-order and pseudo-second-order. The obtained results suggest that nanostructured silicates could be simple, efficient, inexpensive and convenient adsorbents for the direct and selective removal of reactive dyes from water at room temperature. • Mesoporous silicas MCM-41, SBA-15 and its derivative SBA-PMDA with different pore sizes and surfaces were synthesized. • The synthesized materials showed high adsorption capacities. • These materials could be potential candidates for selective removal of cationic or anionic species from wastewater. [ABSTRACT FROM AUTHOR]

This paper reports the synthesis of polypyrrole–titanium(IV)phosphate (PPy–TiP) nanocomposite as a synthetic adsorbent for the adsorptive removal of Cr(VI) ions from aqueous solutions. Facile in situ chemical oxidative polymerization of pyrrole in presence of titanium(IV)phosphate (TiP) formed PPy–TiP nanocomposite. The prepared PPy–TiP nanocomposite was characterized by FT-IR, FE-SEM, TEM, EDAX and BET analysis. A strong interaction between PPy and TiP particles was observed, and the nanocomposite showed remarkable adsorption of Cr(VI) ions from aqueous solutions. In order to optimize the conditions for maximum adsorption of Cr(VI) ions, batch process was used and the effects of contact time, initial concentration, pH, point of zero charge measurement and adsorbent doses were investigated. The equilibrium isotherm data were fitted using Langmuir and Freundlich isotherms. The adsorption process based on the thermodynamic parameters was spontaneous and endothermic. Dubinin–Radushkevich (D–R) isotherm was used to calculate the mean free energy, which showed that the adsorption process involves chemical forces, hence chemical in nature. The kinetics data were evaluated using the pseudo-first-order and pseudo-second order kinetics model, and it was found that data was best fitted by the pseudo-second-order model for adsorption of Cr(VI) supporting that the adsorption mainly consisted of chemisorption. The desorption studies were also performed by batch process and ∼100% desorption of Cr(VI) ions was achieved using 0.5 M NaOH solution as eluent. [ABSTRACT FROM AUTHOR]

Microplastics are a significant environmental challenge, with growing concerns about their prevalence in soil ecosystems. It may affect the physicochemical characteristics of soil, cycling of nutrients, and a variety of microbial organisms. In addition, microplastics affect greenhouse gas (GHG) emissions, including carbon dioxide, nitrous oxide, and methane. The mechanism through which microplastic affects soil microbial communities and GHG emissions is complex and poorly understood. Nevertheless, new research indicates that microplastic pollution can have considerable and long-lasting impacts on the health and functioning of soil, which may have implications for ecosystem services and human well-being. Further investigation is required to comprehend the scope and methods of microplastic contamination in soil and its effects on soil functioning and to create efficient mitigation techniques that minimize its effects. This review highlights the urgent need for action to address this emerging environmental threat and protect soil health and ecosystem functioning. [Display omitted] • Understanding MPs impact on soil microbes, greenhouse gases crucial for sustainable soil and climate • Efficient strategies for biodegradable MPs are imperative to mitigate environmental risks. • Standardized detection, risk assessments are vital for microplastic pollution management. • MPs from farming and plastic waste harm soil quality, nutrient cycles and plant growth. • Analyzing MPs sphere and its ecological ramifications is crucial for long-term soil health. [ABSTRACT FROM AUTHOR]

Abstract: The concepts of equi-statistical convergence, statistical pointwise convergence and statistical uniform convergence for sequences of functions were introduced recently by Balcerzak et al. [M. Balcerzak, K. Dems, A. Komisarski, Statistical convergence and ideal convergence for sequences of functions, J. Math. Anal. Appl. 328 (2007) 715–729]. In this paper, we use the notion of -statistical convergence in order to generalize these concepts. We establish some inclusion relations between them. We apply our new notion of -equi-statistical convergence to prove a Korovkin type approximation theorem and we show that our theorem is a non-trivial extension of some well-known Korovkin type approximation theorems. Finally, we prove a Voronovskaja type approximation theorem via the concept of -equi-statistical convergence. Some interesting examples are also displayed here in support of our definitions and results. [Copyright &y& Elsevier]

Mesoporous silica based drug carriers were synthesized and characterized by FTIR, NMR spectroscopy, elemental analysis, nitrogen adsorption, thermogravimetry, scanning and transmission electron microscopy and X-rays diffraction. Zeta potential was measured with zetasizer. The synthesized materials were tested as a drug carrier for ibuprofen, ibuprofen sodium dihydrate and 5‑fluorouracil. Silica with long organic surface chains and ordered mesoporous network showed a high loading capacity for ibuprofen (22.0 wt%), ibuprofen sodium dehydrate (48.0 wt%) and 5‑fluorouracil (9.4 wt%). The long hydrophobic chains enhanced the drug adhesion to the silica surface which eventually delayed the release of the drug and hence presented a sustained and controlled release profiles. The study suggests that the synthesized materials have potential as controlled drug release systems where prolonged drug therapeutic effects are required. This study also suggests that the 5‑fluorouracil loaded nano-carriers in the form of films, mats or cream could be a potential candidate for skin cancer treatment formulations. [ABSTRACT FROM AUTHOR]

Several advantageous features like low density, large surface area, high porosity and tight pore size make the nanofiber suitable for a wide range of applications from medical to consumer products and industrial to high-tech applications. Present study concerns the synthesis of hexagonal boron nitride fibers (Ø=70–350 nm) from a mixture of Boron, Magnesium oxide and Iron oxide powders via a simple CVD technique. A relatively long annealing and growth duration of two hours at 500 °C and 1000 °C, respectively were utilized in this synthesis. The synthesized samples seem to have the BNFs of irregular curved human hair-like morphologies in lower resolution and solid cylinder-like structures in high resolution transmission electron microscopy. The presence of Boron and Nitrogen in the synthesized BNF's sample were confirmed via the B 1s peak at 190.7 eV and N 1s peak at 398.3 eV in the XPS survey whereas a major peak at 1370 (cm −1 ) in the Raman spectrum corresponds to the vibration of E 2g mode in h-BN. The sharp peaks in the XRD pattern verify the h-BN phase and highly crystalline nature of the synthesized BNFs. [ABSTRACT FROM AUTHOR]

The intraerythrocytic malaria parasite exerts tight control over its ionic composition. In this study, a combination of fluorescent ion indicators and 36Cl- flux measurements was used to investigate the transport of Cl- and the Cl--dependent transport of "H+-equivalents" in mature (trophozoite stage) parasites, isolated from their host erythrocytes. Removal of extracellular Cl-, resulting in an outward [Cl-] gradient, gave rise to a cytosolic alkalinization (i.e. a net efflux of H+ -equivalents). This was reversed on restoration of extracellular Cl-. The flux of H+-equivalents was inhibited by 4,4'-diisothiocyanostilbene-2,2'-disulfonic acid and, when measured in ATP-depleted parasites, showed a pronounced dependence on the pH of the parasite cytosol; the flux was low at cytosolic pH values < 7.2 but increased steeply with cytosolic pH at values > 7.2. 36Cl influx measurements revealed the presence of a Cl- uptake mechanism with characteristics similar to those of the Cl--dependent H+-equivalent flux. The intracellular concentration of Cl- in the parasite was estimated to be ~48 mM in situ. The data are consistent with the intraerythrocytic parasite having in its plasma membrane a 4,4'-diisothiocyanostilbene-2,2'-disulfonic acid-sensitive transporter that, under physiological conditions, imports Cl- together with H+-equivalents, resulting in an intracellular Cl- concentration well above that which would occur if Cl- ions were distributed passively in accordance with the parasite's large, inwardly negative membrane potential. [ABSTRACT FROM AUTHOR]

[Display omitted] • Green chemistry sample preparation concept by means of Deep Eutectic Solvents (DES). • Novel DES based approach for pre-concentration of Niclosamide. • Effective method dedicated to determination of Niclosamide in pharmaceutical and wastewater samples. • Step forward for trace analysis of Niclosamide in environmental samples. The paper presents a simple, but very effective and sensitive spectrophotometric method for trace analysis of Niclosamide based on liquid–liquid microextraction using deep eutectic solvents (DESs) prior to its quantification. Here, different DES systems, such as Choline chloride (ChCl) + Urea, ChCl + Citric acid, ChCl + Ethylene glycol and ChCl + Phenol, were synthesized and evaluated at different molar ratios, selecting ChCl + Phenol 1:2 as an extractive DES system. Optimization studies revealed that best performance were obtained at pH 8 with optimum volume of THF and DES as 0.3 mL and 0.4 mL, respectively. The developed method is characterized by good analytical performance, e.g., a recovery of 99.26% and precision described by RSD value as <2%. The inter-assay precision was 0.51% while intermediate precision was 0.0323%. The method was found linear from 4.8 to 48 µg/L. LOD and LOQ were found as 0.112 and 0.374 µg/L, respectively. The paper presents also examples of the application of the proposed method for the determination of Niclosamide in different pharmaceutical and wastewater samples. This alternative method reveals a better performance in respect to the British pharmacopoeia procedure, providing concurrently ease of operation and simplicity. [ABSTRACT FROM AUTHOR]

Affected by climate variability and human activities, the changes of basin environment have altered runoff, posing new challenges to water resources and land use management strategies, especially in areas with limited water resources. The purpose of this study is to quantify factors that drive the changes in runoff components. Annual runoff data from six hydrological stations in the Weihe River Basin, China and sixteen meteorological stations data from 1961 to 2015 were used. The Mann-Kendall test and Pettitt method were used to diagnose the runoff sequence and detect its abrupt-point. The period before the abrupt-point of annual runoff was taken as the base period (Period I), while the period after the abrupt-point was taken as the calculation period (Period II). The elasticity coefficient of runoff (Q) was deduced based on the Budyko hypothesis, including climate variabilities such as precipitation (P) and potential evapotranspiration (Ep), and the catchment characteristics representing land surface changes (n). The elasticity coefficient was the smallest for Ep ranging from −1.210 ~ −2.847, intermediate for n ranging from −1.602 ~ −2.964, and greatest for P ranging from 2.210–3.847. Runoff exhibited a significant downward trend throughout the Weihe River Basin, where the land surface change is the main cause of runoff reduction, accounting for 75% at the largest Linjiacun hydrological station, with the smallest being 40.36% at the Zhuangtou hydrological station. Adaptive water resource management measures are necessary to cope with future environmental change and water resource scarcity. • The analytical method based on coupling water balance and the Budyko hypothesis on the elasticity coefficient is applied. • Runoff showed a total reduction at six hydrological stations in the Weihe River Basin. • Overall, the change of land surface is the main factor of runoff decrease in the Weihe River Basin, followed by precipitation, and the potential evapotranspiration was less affected. [ABSTRACT FROM AUTHOR]