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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]

Opioids are the mainstay therapy in burned adults. Little data in the pediatric burn population exists that elucidates opioid prescribing practices. The primary purpose of this report is to quantify opioid and non-opioid analgesic use in pediatric burn patients admitted to a tertiary referral burn center. A retrospective audit of hospital charts and discharge records for patients <18 years old from March 2016 to March 2017 was performed. Opioid amounts were converted to either oral morphine miligram equivalents (MME) or oral MME per day and subsequently adjusted for age in kilograms (kg). Of the 226 patients, 223 (98.7%) were administered an opioid during admission. The median total opioid amount administered during admission was 0.4 (IQR: 0.3-0.6) mg oral MME per kilogram per day. Anecdotally, doses above 1 mg/kg/day are considered high risk for opioid tolerance. The median total opioid amount prescribed upon discharge was high at 3.9 (IQR: 2.3, 5.6) mg of oral MME per kilogram. Hydrocodone (96.0%) was the most common opioid administered, followed by morphine (88.1%). The most commonly prescribed discharge opioid was hydrocodone (95.4%). Non-opoioid analgesia during admission was used in 112 patients (49.6%). This study provides novel insight into the opioid practices at a tertiary burn center for pediatric patients, with our analysis showcasing high usage of opioids during admission and discharge for burn analgesia. It emphasizes the need to expand beyond opioids for burn analgesia and the importance of promoting non-opioid, multimodal analgesia in the pediatric burn population. [ABSTRACT FROM AUTHOR]

[Display omitted] • The suggested approach BC-AI is utilized to determine the prediction of breast cancer using computer vision. • A data-driven computer-aided diagnostic (CAD) method for identifying patients as malignant, non-cancerous, or neither. • Feature extraction using the GLCM and HOG-based approach. • Self-constructing ensemble learning fuzzy algorithm (S-ELFA) blends a fuzzy methodology with an advanced neural network for optimum breast cancer illness detection and diagnosis. Breast cancer is a leading cause of mortality among women. Early detection will increase the chances of successful treatment and minimize the death rate. Even though many studies have been conducted to detect breast cancer, medical experts still face difficulty distinguishing between malignant and benign tumors. Hence, a technique enabling medical practitioners to effectively identify breast cancer was developed in this study. A computer-aided diagnostic (CAD) tool is used for classifying and diagnosing patients. The input images are pre-processed at the initial stage and a algorithm based on histogram of oriented gradients (HOG) and gray level co-occurrences matrix (GLCM) is applied to extract key features from pre-processed images. Then, shuffle shepherded optimization (SSO) selects the best features from the extracted features. Finally, the proposed self-constructing ensemble learning fuzzy algorithm (S-ELFA) identifies benign and malignant tumors. ROC, sensitivity, specificity, precision, and accuracy metrics were used to evaluate the developed method. [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 Very few qualitative studies focusing on teaching and learning practices at the tertiary level have emerged from Pakistan. The present study, which applied a qualitative approach, examined those contextual and institutional factors that have a direct link with teaching/learning practices at the tertiary level in Pakistan. While exploring the said phenomenon, the study exclusively focused on three factors: 1) reading material, 2) course syllabi, and 3) teaching practices. The study noted that although there was a consensus among the university students and instructors regarding the satisfactory level of teaching and learning practices, some fundamental elements related to teaching and learning, were either ignored or missing. [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]

Abstract Polyindole–tin (IV) molybdophosphate (PIn-SMP), an organic-inorganic nanocomposite ion exchanger, was synthesized by a sol-gel process and exhibited excellent ion exchange capacity (2.1 meq g−1). A heterogeneous ion exchange membrane of PIn-SMP (ion exchange capacity 0.95 meq g−1) was also prepared by a solution casting method. The materials were characterized by various instrumental methods, such as scanning electron microscopy, transmission electron microscopy, Fourier transform IR spectroscopy, and thermogravimetric analysis. PIn-SMP exhibits high electrical conductivity (6.1 × 10−2 S/cm) and is stable up to 120 °C under ambient conditions. A Cu2+-selective membrane electrode was fabricated, and its linear working range (1.8 × 10−7–1.0 × 10−1 M), response time (20 s), Nernstian slope (24.07 mV dec−1), and working pH range (4–7) were calculated. Moreover, it was used as an indicator electrode in the potentiometric titration of Cu2+. Graphical abstract Image 1 Highlights • Polyindole–tin (IV) molybdophosphate nanocomposite ion exchanger (IEC-2.1 meq g−1), was synthesized by a sol-gel process. • The nanocomposite displayed high DC electrical conductivity (6.1 × 10−2 S cm−1) at room temperature. • A heterogeneous ion exchange membrane (IEC-0.95 meq g−1) was prepared by a solution casting method. • The materials were characterized by various instrumental methods, such as SEM, TEM, FT-IR, and TGA analysis. • A Cu2+-selective membrane electrode was fabricated and exhibited a wide linear response range (1.8 × 10−7–1.0 × 10−1 M). [ABSTRACT FROM AUTHOR]

Wise phosphorus (P) management is paramount to ensure food and environmental security and prolong the life of P resources. Long-term experiments under winter wheat–summer maize (with irrigation) and winter wheat–summer fallow (rain-fed) cropping systems were undertaken to investigate P efficiency, soil P availability and P downward movement in the deep profile of a loess soil in China. The four nutrient management regimes in each experiment were: a control without any nutrient input; P and K combined (PK); N, P and K combined (NPK), and organic manure (M) combined with NPK (MNPK). The P efficiency ranged from 20 to 54% under the double cropping system, and from 13 to 33% in the single cropping system over the 25 years studied, and the highest P efficiency was in the NPK treatment in both systems. Application of P significantly increased soil total-P, Olsen-P and CaCl 2 -P over the years, especially in the MNPK treatment. Two segment regression analyses indicated that Olsen-P was about 14 mg kg −1 or 16 mg kg −1 for optimal crop yield under double cropping or single cropping. In addition, Olsen-P content above 37 mg kg −1 or 57 mg kg −1 led to a significant increase in CaCl 2 -P content under double cropping or single cropping, which might indicate risk of P leaching. After 24 years of P applications, total P accumulated at various depths depending on treatment under double cropping, but there was no accumulation under single cropping. While Olsen-P leached down to a depth of 300 cm in the MNPK treatments under both cropping systems, and higher concentrations were recorded in the deeper layers in single cropping than in double cropping systems. The Olsen-P leached to a shallower depth in the PK and NPK treatments under single cropping compared to double cropping. We conclude that P supply in excess of the crop’s requirement (e.g. MNPK) or an unbalanced nutrient supply (e.g. PK) resulted in not only low P efficiency and massive accumulation of P in the topsoil but also leaching out of root zone under both irrigated and rain-fed conditions in the loess soil. Thus, P fertilization recommendations need to be adjusted based on changes in soil P over time. [ABSTRACT FROM AUTHOR]

System identification is an inverse algorithm of developing/improving mathematical representation of a physical system from input-output responses. This paper assessed a mathematical model of smart composite laminate identified with spurious output data due to sensor debonding failure. Improved layerwise theory and higher-order electric potential field were incorporated to develop electromechanically coupled finite element based mathematical model of the smart composite laminate with or without sensor debonding failure. The input-output data of the developed model were fed into the direct system identification algorithm to identify the state-space model of the smart structure. The developed theory was numerically implemented on a 16-layer cross-ply laminate with surface bonded piezoelectric sensor and actuator. Results showed that system identification algorithm misapprehended the true dynamic behavior of the smart structure in the presence of sensor debonding failure. In addition, principal component analysis was used to detect the presence and severity of partial sensor debonding in the identified state-space model. [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]

• Automated oral cytology-based assays mandate single epithelial cell segmentation. • Presence of debris, blood cells, and cell clusters form the major challenge. • Two-step method proposed for diagnosis; semantic segmentation and classification. • Multichannel, fluorescent-labeled microscopic cell images were used. • Modified U-Net provided high accuracy for segmentation (IoU:0.79). • New CNN model, artefact-net classified the segmented cells with a high F1 score (0.91). Cytology is a proven, minimally-invasive cancer screening and surveillance strategy. Given the high incidence of oral cancer globally, there is a need to develop a point-of-care, automated, cytology-based screening tool. Oral cytology image analysis has multiple challenges such as, presence of debris, blood cells, artefacts, and clustered cells, which necessitate a skilled expertise for single-cell detection of atypical cells for diagnosis. The main objective of this study is to develop a semantic segmentation model for Single Epithelial Cell (SEC) separation from fluorescent, multichannel, microscopic oral cytology images and classify the segmented images. We have used multi-channel, fluorescent, microscopic images (number of images; n = 2730), which were stained differentially for cytoplasm and nucleus. The cytoplasmic and cell membrane markers used in the study were Mackia Amurensis Agglutinin (MAA; n: 2364) and Sambucus Nigra Agglutinin-1 (SNA-1; n: 366) with a nuclear stain DAPI. The cytology images were labelled for SECs, cluster of cells, artefacts, and blood cells. In this study, we used encoder-decoder models based on the well-established U-Net architecture, modified U-Net and ResNet-34 for multi-class segmentation. The experiments were performed with different class combinations of data to reduce imbalance. The derived MAA dataset (n: 14,706) of SEC, cluster, and artefacts/blood cells were used for developing a classification model. InceptionV3 model and a new custom Convolutional-Neural-Network (CNN) model (Artefact-Net) were trained to classify SNA-1 marker stained segmented images (n:6101). For segmentation models, Intersection Over Union (IoU) and F1 score were used as the evaluation matrices, while the classification models were evaluated using the conventional classification metrics like precision, recall and F1-Score. The U-Net and the modified U-Net models gave the best IoU overall (0.73–0.76) as well as for SEC segmentation (079). The images segmented using the modified U-Net model were classified by Artefact-Net and Inception V3 model with F1 scores of 0.96 and 0.95 respectively. The Artefact-Net, when compared to InceptionV3, provided a better precision and F1 score in classifying clusters (Precision: 0.91 vs 0.80; F1: 0.91 vs 0.86). This study establishes a pipeline for SEC segmentation with the segmented component containing only single cells. The pipline will enable automated, cytology-based early detection with reduced bias. [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]

Summary The hydrograph separation method, previously proposed to quantify base flow, seasonal snowmelt, and glacial melt components in river flows within Upper Indus basin underestimates glacial melt component. This is particularly limiting for highly glacierized watersheds. The limitation has been corrected by a further refinement of the method. The results with the refined procedure are highly consistent with the physical characteristics such as hypsometry and glacier extents of the watershed even though the method itself is completely independent of the physical characteristics of the watershed where it is applied. Glacial melt far outweigh snowmelt in the rivers draining the Karakoram and Zanskar ranges. In the Karakoram, on an annualized basis, glacial melt proportion varies from 43% to 50% whereas snowmelt varies from 27% to 31%. On the other hand, snowmelt dominates over glacial melt in the rivers draining the western Greater Himalayas and the Hindu Kush. Here snowmelt percentage in river discharge varies from 31% to 53% whereas that of glacial melt ranges from 16% to 30%. In the main stem of Upper Indus River, snowmelt fraction in most cases is slightly greater than the glacial melt fraction. In the main stem, snowmelt percentage ranges from 35% to 44% whereas glacial melt percentage ranges from 25% to 36%. Upper Indus River just upstream of Tarbela Reservoir carries annual flows constituted of 70% melt water of which 26% is contributed by glacial melts and 44% by snowmelts. We also show that during the later part of twentieth century and continuing into the early part of twenty first century glacial melt contributions to river discharge has decreased compared to the previous decades. This phenomenon can be ascribed to either basin wide loss of glacial mass in the recent decades in the elevation range from where most of the glacial melt originates or glacier growth and stability due to either reduction in energy inputs or increase in precipitation or both at the high altitude bands wherefrom glacial melt water originates. [ABSTRACT FROM AUTHOR]

• A suitable size expert-annotated apple leaf disease dataset has been prepared. • Presented a two-stage apple disease detection system based on Xception and Faster-RCNN. • Used a transfer learning method to initialize model by weight parameters learned on large-scale datasets. • Achieved an overall 88% of classification accuracy and our best detection model achieved mAP of 42%. • Promising results indicate that this system can be very helpful for farmers and Apple growers. Diseases and pests are one of the major reasons for low productivity of apples which in turn results in huge economic loss to the apple industry every year. Early detection of apple diseases can help in controlling the spread of infections and ensure better productivity. However, early diagnosis and identification of diseases is challenging due to many factors like, presence of multiple symptoms on same leaf, non-homogeneous background, differences in leaf colour due to age of infected cells, varying disease spot sizes etc. In this study, we first constructed an expert-annotated apple disease dataset of suitable size consisting around 9000 high quality RGB images covering all the main foliar diseases and symptoms. Next, we propose a deep learning based apple disease detection system which can efficiently and accurately identify the symptoms. The proposed system works in two stages, first stage is a tailor-made light weight classification model which classifies the input images into diseased, healthy or damaged categories and the second stage (detection stage) processing starts only if any disease is detected in first stage. Detection stage performs the actual detection and localization of each symptom from diseased leaf images. The proposed approach obtained encouraging results, reaching around 88% of classification accuracy and our best detection model achieved mAP of 42%. The preliminary results of this study look promising even on small or tiny spots. The qualitative results validate that the proposed system is effective in detecting various types of apple diseases and can be used as a practical tool by farmers and apple growers to aid them in diagnosis, quantification and follow-up of infections. Furthermore, in future, the work can be extended to other fruits and vegetables as well. [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]

The susceptibility of the aging brain to neurodegenerative disease may in part be attributed to cellular aging of the microglial cells that survey it. We investigated the effect of cellular aging induced by telomere shortening on microglia by the use of mice lacking the telomerase RNA component (TERC) and design-based stereology. TERC knockout (KO) mice had a significantly reduced number of CD11b + microglia in the dentate gyrus. Because of an even greater reduction in dentate gyrus volume, microglial density was, however, increased. Microglia in TERC KO mice maintained a homogenous distribution and normal expression of CD45 and CD68 and the aging marker, ferritin, but were morphologically distinct from microglia in both adult and old wild-type mice. TERC KO mice also showed increased cellular apoptosis and impaired spatial learning. Our results suggest that individual microglia are relatively resistant to telomerase deficiency during steady state conditions, despite an overall reduction in microglial numbers. Furthermore, telomerase deficiency and aging may provide disparate cues leading to distinct changes in microglial morphology and phenotype. [ABSTRACT FROM AUTHOR]

Electrically conductive Polypyrrole-zirconium(IV)selenoiodate (PPy/ZSI) cation exchange nanocomposite have been synthesized by chemical oxidative polymerization of polypyrrole (PPy) in the presence of zirconium(IV)selenoiodate (ZSI) by sol-gel method. The formation of PPy/ZSI nanocomposite was characterized by fourier transform infra-red spectroscopy (FTIR), x-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive x-ray (EDX) and thermogravimetric analysis (TGA). The ion exchange capacity (IEC) and electrical conductivity of the nanocomposite was found to be 2.49 meq/g and 0.436 S/cm respectively. The nanocomposite showed appreciable isothermal stability in terms of DC electrical conductivity retention under ambient condition up to 130 °C. In addition, the cation exchange nanocomposite based sensor for detection of formaldehyde vapors was fabricated at room temperature. It was revealed that the resistivity of the nanocomposite increases on exposure to higher percent concentration of formaldehyde at room temperature (25 °C), also the sensor exhibited good reversible response towards formaldehyde vapors ranging from 5 to 7%. The present study may serve as a basis for designing other smart materials for formaldehyde sensors. [ABSTRACT FROM AUTHOR]

Summary The Hindukush Karakoram Himalayan mountains contain some of the largest glaciers of the world, and supply melt water from perennial snow and glaciers to the Upper Indus Basin (UIB) upstream of Tarbela dam, which constitutes greater than 80% of the annual flows, and caters to the needs of millions of people in the Indus Basin. It is therefore important to study the response of perennial snow and glaciers in the UIB under changing climatic conditions, using improved hydrological modeling, glacier mass balance, and observations of glacier responses. However, the available glacier inventories and datasets only provide total perennial-snow and glacier cover areas, despite the fact that snow, clean ice and debris covered ice have different melt rates and densities. This distinction is vital for improved hydrological modeling and mass balance studies. This study, therefore, presents a separated perennial snow and glacier inventory (perennial snow-cover on steep slopes, perennial snow-covered ice, clean and debris covered ice) based on a semi-automated method that combines Landsat images and surface slope information in a supervised maximum likelihood classification to map distinct glacier zones, followed by manual post processing. The accuracy of the presented inventory falls well within the accuracy limits of available snow and glacier inventory products. For the entire UIB, estimates of perennial and/or seasonal snow on steep slopes, snow-covered ice, clean and debris covered ice zones are 7238 ± 724, 5226 ± 522, 4695 ± 469 and 2126 ± 212 km 2 respectively. Thus total snow and glacier cover is 19,285 ± 1928 km 2 , out of which 12,075 ± 1207 km 2 is glacier cover (excluding steep slope snow-cover). Equilibrium Line Altitude (ELA) estimates based on the Snow Line Elevation (SLE) in various watersheds range between 4800 and 5500 m, while the Accumulation Area Ratio (AAR) ranges between 7% and 80%. 0 °C isotherms during peak ablation months (July and August) range between ∼ 5500 and 6200 m in various watersheds. These outputs can be used as input to hydrological models, to estimate spatially-variable degree day factors for hydrological modeling, to separate glacier and snow-melt contributions in river flows, and to study glacier mass balance, and glacier responses to changing climate. [ABSTRACT FROM AUTHOR]

Highlights: [•] Summer river flows in central Karakoram conspicuously exhibit rising trends. [•] Rate of loss of glacial storage is approximately 0.04–0.05mm/day/year. [•] Increasing summer precipitation is responsible for nonnegative glacial mass balance. [•] Rising river flows accompany nonnegative glacial mass balance in central Karakoram. [ABSTRACT FROM AUTHOR]

Highlights: [•] Kinetics studies envisage the mechanism of ion exchange on the surface of nanocomposite PANI-ZTP. [•] Exchange process governed by particle controlled diffusion phenomenon which is faster than the film diffusion. [•] Self-diffusion co-efficient of Ba2+ is highest because of greater ionic size. [•] Activation energy is calculated by using verified and validated Arrhenius equation. [•] Negative values of ΔS° indicate the presence of more active exchangeable sites in the proposed nanocomposite. [ABSTRACT FROM AUTHOR]

Highlights: [•] Two cropping systems (common and drought adopted) were compared for fertilizer and irrigation effects on forage quality. [•] Irrigation water use efficiencies for crude protein (CP) and metabolizeable energy (ME) were also studied. [•] Fertilizer and irrigation did not affect the quality parameters significantly. [•] Drought adopted system proved more water efficient for CP and ME production. [•] Nutritional quality of both systems was in acceptable range. [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]

Highlights: [•] Auto pit-filling applied to DEMs can result in significant errors in catchment areas. [•] Past estimates of the Upper Indus Basin area are ranging between 160,000 and 266,000km2. [•] The best estimate of the Upper Indus Basin (UIB) area is 164,867km2, using SRTM DEM. [•] The Pangong Tso catchment is not part of the UIB, for at least past thousand years. [•] Past studies based on the over-estimated watershed areas of the UIB need revision. [Copyright &y& Elsevier]

Highlights: [•] Glacio-hydrometeorological conditions with four flow regimes in Upper Indus Basin. [•] Highest flow regime from July to September carry 53–62% annual flow volumes (m3). [•] Three most glaciated watersheds contribute 48–54% flow to the highest flow regime. [•] Melt water from altitude >3500m constitutes 41–54% of annual flow volumes. [•] Near the outlet, glacial and snow melt components respectively constitute 21% and 49% of annual flows. [Copyright &y& Elsevier]

Highlights: [•] Application of PAZMP membrane to fabricate Ni(II) ion selective electrode. [•] Preparation and characterization have also been done of ion exchange membrane. [•] Determination of liner potential response with concentration range of 1×10−7 M–1×10−1 M. [•] Chronopotentiometric studies of membrane were evaluated by Nernst-Planck equation. [•] Some physical parameters such as D 0, E a and ΔS° were calculated. [ABSTRACT FROM AUTHOR]

Highlights: [•] PU–Si gel is new anion exchanger material synthesized and characterized. [•] This material used as anion exchange membrane is applied for electroanalytical studies. [•] The method for detection and determination of AsO4 3− in traces amounts discussed. [•] The results are also verified from 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]

Cancer is the top death causing disease in the world, due to its occurrence through various mechanism and form. Medicinal plants have been extensively used for the purifications and isolations of phytochemicals for the treatment and prevention of cancer. Consequently, this research was designed to document the traditional practices of anti-cancer plants and its phytochemical essay across the districts of KP, Pakistan. Semi-structured interviews were conducted in 24 districts from the informants mostly the traditional herbalists (key informants). The information were compared with the publish data using various authentic search engines including, google, researchgate, google scholar and NCBI. One hundred and fifty-four (154) anti-cancer plants were recognized belonging to 69 families among all, Lamiaceae (13 sp.), Asteraceae (12 sp.) and Solanaceae (9 sp.) were the preferred families. The local inhabitants in the area typically prepare ethnomedicinal recipes from leaves (33.70%) and whole plants (23.37%) in the form of decoction and powder (24.67%), respectively. Herbs stayed the most preferred life form (61.68%) followed by shrub (21.4%). Similarly, breast (29.22%) and lung cancer (14.83%) was the common disease type. Literature study also authorize that, the medicinal plants of the research area were rich in phytochemical like quercetin, coumarine, kaempferol, apigenin, colchicine, alliin, rutin, lupeol, allicin, berbarine, lutolin, vanilic acid, urocilic acid and solamargine have revealed significant activates concerning the cancer diseases, that replicating the efficacy of these plants as medicines. The Khyber Pakhtunkhwa is rural area and the local inhabitants have very strong traditional knowledge about the medicinal plants for different diseases like cancer. The medicinal plants for significant ranked disorder might be pharmacologically and phtyochemicaly explored to demonstrate their efficacy. Moreover, the local flora especially medicinal plants facing overgrazing, overexploitation and inappropriate way of collection, however, proper management strategies like reforestation, controlled grazing, proper permission from concerned department and rangeland strategies among others may be assumed to enhance the proper usage of medicinal plants. [Display omitted] [ABSTRACT FROM AUTHOR]

Polymethylmethacrylate–silica gel (PMMA–silica gel) organic–inorganic anion-exchange composite fibers were prepared by simple stirring with magnetic bar in different stoichiometric ratios of polymethylmethacrylate (PMMA) and silica gel. The structure and morphology of the prepared composite fibers were ascertained by Fourier transform spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and elemental analysis (CHN/O). The material was found to be highly desirable for electroanalytical application as ionic sensor, its membrane electrode has been developed for the detection of As(V) in solutions. The membrane electrode shows the best result with linear potential response in the concentration range of 1×10−1 molL−1 to 1×10−8 molL−1 of As(V) ion with a slope of 30.78mV/decade. The useful pH of the electrode is 5.5–10.0. The selectivity coefficient values for interfering ions indicate good selectivity for As(V) over interfering anions. The accuracy of the procedure has been tested on arsenic-free drinking water samples spiked with known amount of arsenate and results were comparable to those generated by Portable Coulometric Analyzer (PCA-Arsenomat). [ABSTRACT FROM AUTHOR]

Abstract: In this paper we construct some positive linear operators by means of q-Lagrange polynomials and prove some approximation results via A-statistical convergence. We also define and study the rate of A-statistical approximation of these operators by using the notion of modulus of continuity and Lipschitz class. [Copyright &y& Elsevier]

Abstract: A novel organic–inorganic nano composite cation exchanger poly-o-toluidine Sn(IV) tungstate has been synthesized by incorporation of a polymer material into inorganic precipitate. The material is a class of hybrid ion-exchanger with good ion-exchange properties, reproducibility, stability and good selectivity for heavy metals. The physico-chemical properties of this nano composite material were characterized by using XRD, TGA, FTIR, SEM and TEM. The ion-exchange capacity, pH titrations, elution behavior and chemical stability were also carried out to study ion-exchange properties of the material. Distribution studies for various metal ions revealed that the nano composite is highly selective for Cd(II). An ion-selective membrane electrode was fabricated using this material for the determination of Cd(II) ions in solutions. The analytical utility of this electrode was established by employing it as an indicator electrode in electrometric titrations. [Copyright &y& Elsevier]

Abstract: Electrically conductive poly(3-methythiophene)-titanium(IV)molybdophosphate (P3MTh–TMP) cation exchange nanocomposites have been synthesized for the first time by in situ chemical oxidative polymerization of 3-methythiophene (3MTh) in the presence of titanium(IV)molybdophosphate (TMP). Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) analysis, thermogravimetric analysis (TGA) and elemental analysis were used to characterize P3MTh–TMP cation exchange nanocomposites. The characterization results confirmed that there is a strong interaction between P3MTh and TMP particles and the nanocomposites showed higher thermal stability than pure P3MTh. The composite showed good ion-exchange capacity, electrical conductivity and isothermal stability in terms of DC electrical conductivity retention under ambient condition below 100°C. A cation exchange nanocomposite based sensor was fabricated for the detection of aqueous ammonia, it was found that the resistivity of the nanocomposites increases on exposure to ammonia at room temperature (25°C) and there showed a linear relationship between the responses and the concentration of ammonia. [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: Electrically conductive Poly-o-toluidine–titanium(IV)phosphate (POT–TiP) cation exchange nanocomposite was synthesized by sol–gel mixing of organic polymer (Poly-o-toluidine) into inorganic precipitate of titanium(IV)phosphate (TiP). The material was characterized by using transmission electron microscopy (TEM), X-ray diffraction analysis (XRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). The humidity sensing response of the nanocomposite was examined. It was found that the conductivity varied according to low humidity to high humidity. The composite showed good humidity response, ion-exchange capacity and electrical conductivity. Poly-o-toludine–TiP nanocomposite was also used to study electrical conductivity under isothermal conditions in the temperature range 50–130°C. The composite was found stable under ambient conditions below 90°C in terms of DC electrical conductivity retention. These studies suggest that the cation exchange nanocomposite could be a good sensing material for humidity. [Copyright &y& Elsevier]

Abstract: Newly developed organic–inorganic cation-exchange materials poly-o-anisidine and poly-o-toluidine Sn(IV) tungstate composites were synthesized by a modified sol–gel technique by incorporating Sn(IV) tungstate precipitate with the matrix of electrical conducting poly-o-anisidine and poly-o-toluidine. The materials showed good ion-exchange behavior and were used successfully in the separation of metal ions. The conductivity of the composites was measured by 4-in-line-probe DC electrical conductivity measuring device and was found within the range of 10−4–10−2 S/cm. The DC electrical conductivity of both composite materials was found stable up to 140°C under ambient conditions. The conductivity was at the border of metallic and semiconductor region. [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]

Abstract: This study investigated the instructional management of a private and a government secondary school principal in the Gigit-Baltistan region of Northern Pakistan. The study used the following lenses to examine the instructional behavior of the two principals: supervisory techniques, professional development activities, curriculum enrichment, and the availability of instructional resources. A comparison between the levels of engagement of the private and public school principals for the promotion of instructional matter suggests that the private school principal spent more time than the government principal on the development of instructional practices in his school. The study maintains that variables, such as structural processes, personal disposition of principals, and staff characteristics, led the two subjects to deal with their instructional responsibility differently. [Copyright &y& Elsevier]

Abstract: 2,4,5-Trichlorophenoxy acetic acid (2,4,5-T) is used in forestry and in agriculture as a systemic herbicide (for eradication of undergrowth, etc). Adsorption behaviour of 2,4,5-T on the surface of poly-o-toluidine Zr(IV)phosphate was studied and some thermodynamic parameters such as Freundlich constants and Langmuir constants were determined on the surface of cation-exchanger. In this work, the adsorption of 2,4,5-T on cation-exchanger from aqueous medium was studied with respect to pH and initial 2,4,5-T concentration. The experimental data were analyzed by the Freundlich isotherm, and the Langmuir isotherm. The results indicated that the cation-exchanger is very effective for the adsorption of 2,4,5-T from aqueous solutions. Pesticide sensitive membrane electrode was fabricated by using an electro-active composite material poly-o-toluidine Zr(IV) phosphate. The sensitivity towards the 2,4,5-T was monitored. [Copyright &y& Elsevier]

Abstract: The objective of the present research was to synthesize, characterize and to investigate the efficiency of an advanced class of hybrid nano-composite cation exchanger for the detection of mercury(II) ion in aqueous solution. In the present study the cation exchanger ployaniline–zirconium titanium phosphate (PANI–ZTP) nanocomposite, was synthesized via sol–gel mixing of organic polymer polyaniline (PANI) into the matrices of the inorganic precipitate of zirconium titanium phosphate (ZTP), having extraordinary ion exchange capacity, thus used as electroactive component for the construction of an ion-selective membrane electrode. The proposed electrode shows fairly good discrimination of mercury ion over several other inorganic ions. The membrane electrode was mechanically stable, having wide dynamic range of 10−10 mol/m3 to 10−1 mol/m3 of Hg2+ ions, with quick response time and could be operated for at least 4months without any considerable divergence in the potential response characteristics. The electrode was successfully applied for direct determination of mercury ions in some real sample with satisfactory results and acts as indicator electrode in complexation titrations. [Copyright &y& Elsevier]

Abstract: Composites are 21st century material used to meet the demand of improved materials and possess a combination of several desirable properties. Present study focussed on the conducting behavior of ‘polymeric–inorganic’ nanocomposite of conducting polymer polyaniline and polypyrrole. ‘Polymeric–inorganic’ nanocomposite cation-exchangers, i.e., polyaniline zirconium titanium phosphate (PANI-ZTP) and polypyrrole zirconium titanium phosphate (PPy-ZTP), were synthesized via sol–gel mixing of electrical conducting polymers into the matrices of inorganic precipitate of zirconium titanium phosphate (ZTP) having excellent ion exchange properties. The proposed nanocomposite possessed DC electrical conductivity in the semi-conducting range, i.e., 10−5–10−3 Scm−1. The stability in terms of DC electrical conductivity retention was also studied in an oxidative environment by two slightly different techniques viz. isothermal and cyclic techniques. The DC electrical conductivity of composite material was found stable upto 110°C under ambient conditions. [Copyright &y& Elsevier]

Abstract: An organic-inorganic composite, poly-o-toluidine Ce(IV) phosphate was chemically synthesized by mixing ortho-toluidine into the gel of Ce(IV) phosphate in different mixing volume ratios. Effect of eluant concentration, elution behavior and pH-titration studies were carried out to understand the ion-exchange capabilities. The physico-chemical properties of the material were determined using AAS, CHN elemental analysis, UV–VIS spectrophotometry, FTIR, SEM/EDX, TGA–DTA, TEM (Transmission electron microscopy), XRD and SEM studies. The distribution studies revealed that the cation-exchange material is highly selective for Cd(II). Due to selective nature of the cation-exchanger, ion selective membrane electrode was fabricated for the determination of Cd(ІІ) ions in solutions. The analytical utility of this electrode was established by employing it as an indicator electrode in electrometric titrations. [Copyright &y& Elsevier]

Abstract: In this study, the electrically conducting polyaniline–titanium(IV)phosphate (PANI–TiP) cation exchange nanocomposite was synthesized by sol–gel method. The cation exchange nanocomposite based sensor for detection of ammonia vapors was developed at room temperature. It was revealed that the sensor showed good reversible response towards ammonia vapors ranging from 3 to 6%. It was found that the sensor with p-toluene sulphonic acid (p-TSA) doped exhibited higher sensing response than hydrochloric acid doped. This sensor has detection limit ≤1% ammonia. The response of resistivity changes of the cation exchange nanocomposite on exposure to different concentrations of ammonia vapors shows its utility as a sensing material. These studies suggest that the cation exchange nanocomposite could be a good material for ammonia sensor at room temperature. [Copyright &y& Elsevier]

Abstract: A ‘polymeric-inorganic’ advanced nano-composite cation-exchanger has been synthesized via sol–gel technique by incorporating polyaniline into inorganic ion-exchanger, zirconium titanium phosphate, a class of mixed material of tetravalent bimetallic acid salts (TMA). The composite shows extraordinary high ion-exchange capacity of 4.52meqg−1 as well as good chemical and thermal stability than other polyaniline composites prepared so far. The physico-chemical property of the composite, polyaniline zirconium titanium phosphate (PAZTP) was determined using AAS, TEM, SEM, FTIR and TGA-DTA studies. Ion-exchange behavior was also observed to characterize the material. On the basis of distribution studies, the material was found to be highly selective for toxic heavy metal ions Hg(II) and Pb(II). The analytical applications of the material have been explored by achieving some binary as well as ternary separations of heavy metal ions. A comparative study of the ion-exchange properties of PAZTP with other polyaniline composites has been done. [ABSTRACT FROM AUTHOR]

Abstract: Electrically conductive composite of polyaniline–titanium(IV)phosphate (PANI–TiP) was synthesized by sol–gel method. The obtained composite was characterized by using Fourier transform infra red spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). The composite showed good ion-exchange capacity and isothermal stability in terms of DC electrical conductivity retention at an ambient conditions below 100°C. The composite material was investigated as alcohol vapors sensor and compared with blank polyaniline. The results showed that the composite was more selective and sensitive towards methanol vapors. [ABSTRACT FROM AUTHOR]

Abstract: Conductive fibers based on polyaniline (PANI) and polyacrylonitrile (PAN) were obtained by stirring with magnetic bar. This research was conducted to investigate conducting fibers of polyaniline:polyacrylonitrile (PANI:PAN) composite with different weight ratios of aniline in PAN matrix. The fibers were prepared by stirring process. The best conductivity behavior of the fibers was obtained with 5mL of aniline. The fibers obtained were characterized using Fourier-transform infrared spectra (FTIR), scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). The variation of electrical conductivity with different type doping agents (HCl, H2SO4 and HClO4) and the stability in terms of DC electrical conductivity retention was studied in an oxidative environment by isothermal characteristics. [Copyright &y& Elsevier]

Abstract: A polymeric-inorganic composite cation-exchanger, i.e. Nylon-6,6 Sn(IV) phosphate was synthesized via mixing of polymer Nylon-6,6 into the matrices of inorganic precipitate of Sn(IV) phosphate to form composite cation-exchanger. Ion-exchange capacity (IEC), ion-exchange properties, thermal stability and distribution behavior, etc. were also carried out to understand the cation-exchange behavior of the material. The physico-chemical properties of the material were determined using AAS, CHN elemental analysis, FTIR, TGA-DTA, XRD, and SEM studies. On the basis of distribution studies, the material was found to be highly selective for Hg(II), a highly toxic environmental pollutant. Using this electroactive composite material, a new heterogeneous precipitate based selective membrane electrode was fabricated for the determination of Hg(II) ions in solutions. The membrane electrode is mechanically stable, with a quick response time, and can be operated within a wide pH range. The selectivity coefficients for different cations determined by mixed solution method were found to be less than unity. The electrode was also found to be satisfactory in potentiometric titrations. [Copyright &y& Elsevier]