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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]

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]

With the unprecedented growth in recent voltage source converter-based high voltage direct current (VSC-HVDC) integrated offshore wind power plants, frequency regulation of onshore systems and their stability has been crucial. Offshore wind farms cannot directly provide frequency support to the onshore AC grid as back-to-back converters in variable speed wind turbines and HVDC grid decouple the two ending terminals. It prevents the kinetic and electrostatic energy from mitigating the frequency transients in the onshore AC grid. Low inertia contributes to an increase in the rate of change of frequency with a further decrease in frequency nadir that undermines the onshore AC system's reliability. However, variable speed wind turbines and voltage source converter stations with appropriate control strategies can efficiently participate in the onshore AC grid's frequency regulation. This paper provides an in-depth review of the frequency support strategies for offshore wind power plants and the voltage source converter stations in a multi-terminal HVDC system, including a comparison of the related approaches and the potential gaps present in the existing literature. This paper also provides guidelines and serves as a reference for researchers focused on the frequency support strategies for such an integrated system. • A comprehensive review on power system frequency regulation through VSWTs and VSC stations is presented. • SFD occurs during rotor speed recovery if no other power is available to compensate for power shortage at that moment. • Use of ESS could be a viable alternative to compensate for the energy shortage during rotor speed recovery. • AFC-based coordinated control in VSC-HVDC is the preferred practicing technology due to its simplicity and robustness. • New protocols need to be defined to cyber secure the offshore wind power plant-based power systems. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak created immense disruptions in our daily lives. Travel is one of the major areas severely impacted by the pandemic. Bangladesh experienced two waves of COVID-19 between March 2020 and July 2021. Although several studies focused on significant changes in the travel behavior of urban road users before and during the pandemic, hardly any research examined the differences between the two waves. Hence, it was important to study the differences in travel behavior between the two waves of the COVID-19 pandemic to understand how the effects of travel restrictions, health concerns, and the severity of the pandemic on people's travel decisions change over time. This study intends to investigate the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka and the factors that influence these changes. Data were collected through online questionnaire surveys using the Google form. Voluntary response and convenience sampling techniques were used to collect responses from 447 people in Dhaka. Descriptive statistics and non-parametric tests were conducted to analyze the data. In addition, two multinomial logistic models were developed to identify the factors behind the changes in travel behavior. This study reveals that work and shopping trips increased, whereas work-from-home and online shopping somewhat decreased during the second pandemic wave compared to the first pandemic wave. Most non-car owners who reduced their use of public transportation during the first pandemic increased their use during the second wave. There was no significant increase in the use of active transport modes for work and shopping trips in Dhaka. The perceived risk of COVID-19 infection was relatively lower during the second wave, influencing the respondents to travel more frequently for work and shopping purposes. Ensuring more adaptive public transportation, flexibility to work from home, and creating supportive infrastructures for active transport modes might help to provide a safe, affordable, and efficient transportation system for all during the future waves of the pandemic and other unprecedented events. • Both work and shopping trip frequencies increased from the first to the second wave of the pandemic. • People perceived COVID-19 infection as less risky compared to the first wave, which influenced them to travel more frequently during the second wave. • Non-car owners reverted to public transportation during the second wave. • No significant increase in the use of active transport modes for work and shopping trips in the second wave compared to the first. [ABSTRACT FROM AUTHOR]

Background: Improving access to medications for opioid use disorder (MOUD) is a national priority; however, these efforts commonly focus on the provider. Access to buprenorphine through retail pharmacies and stigma associated with filling prescriptions for MOUD pose additional barriers for patients when embarking on their road to recovery.Methods: This study performed a pre-post retrospective chart review to evaluate the potential positive impact on patient retention when providing buprenorphine at office visits instead of at pharmacies. Study staff reviewed electronic medical records to document patient retention in treatment at 6 months as the primary outcome. The study evaluated as secondary outcomes missed office visits, medication adherence, illicit drug use (that drug testing results identified), and drug-related emergency department (ED) utilization. Study staff documented outcomes for patients given their buprenorphine medication at their office visit (n = 154) compared with randomly selected patients prescribed buprenorphine from the same office-based opioid treatment clinic who had to go to retail pharmacies to fill their prescriptions (n = 154).Results: Patients receiving buprenorphine at their office visit demonstrated a 52.2% higher retention rate after 6 months compared to the control group (p = .005). Patients were more likely to attend scheduled office visits (p = .046), less likely to test positive for nonprescribed/illicit drugs (p < .001), and less likely to utilize the ED for drug-related reasons (p = .018) when the program alleviated the need to fill buprenorphine prescriptions at retail pharmacies and began to offer the pharmacy services at office visits.Conclusions: Provision of buprenorphine to patients at their treatment visit was associated with higher patient retention rates and better health outcomes compared with patients who filled their buprenorphine at pharmacies prior to the program's integration of medication provision at patient office visits. Understanding how alleviating barriers to medication access impacts retention in care has meaningful implications for opioid use disorder patients and treatment providers. [ABSTRACT FROM AUTHOR]

• Classification of Normal, Pneumonia-bacterial, Pneumonia-viral and Covid-19 chest x-ray images. • Presented a deep convolutional neural network model based on Xception architecture. • Used a transfer learning method to initialize model by weight parameters learned on large-scale datasets. • Trained the model on a dataset prepared by collecting x-ray images from publically available databases. • Achieved an overall accuracy of 89.6% and precision and recall rate for Covid-19 cases are 93% and 98.2%. The results obtained by our proposed model are superior compared to other studies in the literature. • Promising results indicate that this model can be very helpful to doctors around the world in their fight against Covid-19 Pandemic. The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays. In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases. CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available. CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases. [ABSTRACT FROM AUTHOR]

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]

• Constant ice density assumption produces significant bias. • Glaciers in the Indus are losing mass similar to the rest of Asia. • The western and central Karakoram glaciers are losing negligible mass. • Indus Basin contributes about 0.014 mm a−1 to global sea-level rise. Glaciers in the upper Indus supply more than half of the river water, are experiencing significant melting with a debated fate. The recent melting rate is still contained by considerable uncertainties, hindering to estimate precise glacier mass change. Here we present geodetic mass balance results for the whole Indus Basin using SRTM and ALOS 30 m elevation data, improved glacier inventory, optimized glacier surface density, and validation through in-situ differential GPS and ICESat data. Our glacier inventory and derived by improving RGI6.0 boundaries and separated into debris cover and debris free parts. The derived surface elevation changes were converted into annual mass balances using separated density assumptions (four criteria) for debris-covered ice (900 ± 60 kg m−3), debris-free ice (below 20° and 25° slopes (850 ± 60 kg m−3) and above 20° and 25° slopes (600 ± 60 kg m−3)), respectively. The resulting mass balance biased between −0.20 and 0.09 m water equivalent (w.e.) a-1 using an average (850 ± 60 kg m−3) density assumption throughout the Indus Basin. In the western Himalaya and Hindu Kush, the glacier mass losses are less affected by the average density assumption compared to the Karakoram. The western (Hunza) and central (Shigar) Karakoram glaciers show negligible mass losses of −0.02 ± 0.12 and −0.01 ± 0.13 m w.e. a−1 in contrast to the relatively more negative mass balance (−0.26 ± 0.21 m w.e. a−1) in the eastern (Shyok) Karakoram. All the sub-basins exhibit negative mass balances, with the most negative values ranging from −0.34 ± 0.31 to 0.44 ± 0.27 m w.e. a−1 in the Ravi, Chenab and Jhelum sub-basins of the Himalaya. The whole Indus Basin contributes approximately +0.014 ± 0.016 mm a−1 to the global mean sea-level equivalent. [ABSTRACT FROM AUTHOR]

Abstract In the present paper, two different modifications are proposed for (p, q)-Szász-Mirakjan-Kantorovich operators which preserve some test functions. Some approximation results with the help of better-known Korovkin's theorem and weighted Korovkin's theorem for these operators are presented. Furthermore, convergence properties in terms of modulus of continuity and class of Lipschitz functions are studied. It has been shown that for a given absolute error bound, King type modified (p, q)-Szász-Mirakjan-Kantorovich operators require lesser value of m and elapsed time within some subintervals. Further for comparisons, some graphics and error estimation tables are presented using MATLAB(R2018a). [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]

In this paper, we introduce a new analogue of Bernstein operators and we call it as ( p, q )-Bernstein operators which is a generalization of q -Bernstein operators. We also study approximation properties based on Korovkin’s type approximation theorem of ( p, q )-Bernstein operators and establish some direct theorems. Furthermore, we show comparisons and some illustrative graphics for the convergence of operators to a function. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new analogue of Bernstein–Stancu operators based on ( p, q )-integers which we call as ( p, q )-Bernstein–Stancu operators. We study approximation properties for these operators based on Korovkin’s type approximation theorem and also study some direct theorems. Furthermore, we give comparisons and some illustrative graphics for the convergence of operators to some function. [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]

Orientation estimation is considered as an important task in many subsequent pattern recognition and image enhancement systems. In a noisy environment, the gradient-based estimator provides poor results. A pre-smoothing Gaussian function with an appropriate scale is conventionally used to get improved gradients. Later on, a family of pre-smoothing Gaussian functions with a range of scales is employed for estimation, this is referred to as multi-scale orientation estimator. To provide groundwork for comparison, a more formal framework of multi-scale orientation estimation, based on scale-space axioms, in spatial domain is presented. Then for improvement purposes a Fourier domain approach, where directional filter bank (DFB) structure is embedded in multi-scale orientation estimation framework, is proposed. This is referred to as multi-scale DFB approach. The paper presents the comparison work for estimation of local orientations using multi-scale approaches both in spatial and Fourier domain. In the Fourier-domain approach, two linear combinations are deployed, one across the directional image, and the other across the scales. This is opposed to only one linear combination across the scales, used in simple spatial domain techniques. Further more, the DFB-based Fourier domain approach extracts the best local orientation by comparing and contrasting all possible orientations with their respective strength measures. The strength measure used in Fourier method is based on local variance, free from inaccurate gradient calculation. Simulations are conducted over noisy test images as well as real fingerprints. Our objective results indicate that multi-scale Fourier domain approach always yields better estimates at variable level of noise as compared to stand alone multi-scale spatial domain approaches. The improvements made by Fourier domain estimate can largely be attributed to the use of double linear combination both across the directional bands and across the scales. [ABSTRACT FROM AUTHOR]

Corrosion rates in marine structural steels differ significantly with the varying compositions of seawater particularly near harbours or coastal regions primarily due to the presence of untreated chemically active species from various sources. The reviewed literature reports accelerated steel corrosion losses in coastal seawater exposure conditions, which has widely been attributed to the presence of aggressive chemical compounds e.g., dissolved inorganic nitrogenous (DINs) compounds, sulphur containing compounds, in combination with various other environmental factors and their interdependent complex relationships. This paper aims to investigate the influence of nitrates, a DIN compound, on the corrosion behaviour of a low carbon ship structural steel, by exposing surface the cleaned coupons to an artificial seawater solution in a controlled laboratory environment. The uniform and localised corrosion damages were measured on steel coupons by using the standard weight loss and the dimensional metrology methods. A significant increase in corrosion losses was observed on coupons exposed to the nitrate-added artificial seawater than those exposed to similar seawater compositions with no additional nitrate content. Elemental compositions of corrosion deposits and corrosion morphologies investigated using various analytical tools such as SEM, EDS and Raman scattering techniques have shown different types of corrosion products in both exposure conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

In the present paper, we have discussed about Theorem 3.1, Remark 3.1 and Theorem 4.1 studied in our article “Some approximation results by ( p, q )-analogue of Bernstein–Stancu operators” [Appl. Math. Comput. 264 (2015) 392–402]. We give here the correct version of the said Theorem 3.1, Remark 3.1 and Theorem 4.1. [ABSTRACT FROM AUTHOR]

[Display omitted] • bapA gene knockout to study role of type 3 effector BapA in B. pseudomallei intracellular lifecycle. • Mutant showed attenuation in attachment and entry into A549 cells with unaffected intracellular replication. • Mutant demonstrated impaired cell-to-cell spreading with no evident defect in actin tail formation. • Mutant displayed reduced phagocytosis and intracellular replication rates in macrophage cells. • BapA may be important in the early stages of B.pseudomallei intracellular lifecycle. The etiological agent of melioidosis, Burkholderia pseudomallei , utilises a type III secretion system cluster 3 (T3SS3) to deliver proteins termed type III effectors (T3SEs) into the host cytoplasm in order to establish an intracellular lifecycle in phagocytic and non-phagocytic cells, thus playing an important role in pathogenesis. To gain insight into possible functional roles for BapA, a putative T3SE with unknown function, in the intracellular lifecycle of B.pseudomallei , bapA gene knockout mutant was constructed. The effect of the knockout on virulence to the otherwise isogenic parental strain, K96243, was studied by cellular infection assays and Caenorhabditis elegans killing assay. The attachment and subsequent entry into A549 cells was significantly (P < 0.05) attenuated in the Δ bapA compared to K96243. However, intracellular replication was not affected. Furthermore, the cell-to-cell spreading capacity of Δ bapA was impaired although the mutant exhibited no evident defect in its actin tail formation. Additionally, phagocytosis and intracellular replication rates of Δ bapA in U937 macrophage cells were significantly reduced relative to K96243 without phagosomal escape being affected. Based on these observations, we conclude that the BapA T3SE could play an important role in B.pseudomallei intracellular lifecycle, especially, in the early stages of attachment and entry into the host cell. [ABSTRACT FROM AUTHOR]

• Reassembly of the soil environmental characteristics along the soil-eroding catena altered prokaryotic communities. • Less extensive prokaryotic interactions and lower community stability potential in the depositional zone. • The higher SOC increased copiotrophic group abundance in the depositional zone. Soil prokaryotes composition and diversity are the key to uncovering the mechanisms that drive variations in soil biogeochemical processes. Soil erosion is a primary factor that affects the spatial distribution of the soil prokaryotic community, but how soil prokaryotes in soil-eroding catena respond to environmental factors related to topography remains largely unclear. In this study, topsoils were sampled from three typical erosion geomorphic units (autonomous, transitional and depositional zones) in 2018 to identify the soil prokaryotic community and interactions among the species on the Chinese Loess Plateau. The alpha-diversity was greater but the beta-diversity was lower in the autonomous and transitional zones than in the depositional zone. Gammaproteobacteria and Bacteroidetes were 73% and 68% lower in the autonomous and transitional zones than in the depositional zone. In addition, Deltaproteobacteria, Acidobacteria, Firmicutes, Actinobacteria, Chloroflexi, Gemmatimonadetes and Nitrospirae were significantly higher in the autonomous and transitional zones. A less clustered network and fewer co-occurrences within the prokaryotic community and functional groups of processes were found in the depositional zone than in the autonomous and transitional zones. The alpha-diversity index was significantly negatively correlated with clay particles, soil water content, soil organic carbon (SOC), and ratio of SOC and nitrogen (C/N) but positively correlated with total nitrogen (TN). The higher relative abundances of copiotrophic groups (including Gammaproteobacteria, Bacteroidetes, etc.) in the depositional zone was mainly due to the increased SOC caused by the deposition of SOC-rich clay. Reassembly of the soil physico-chemistry characteristics among the topographic units significantly altered the soil prokaryotic community along the soil-eroding catena. [ABSTRACT FROM AUTHOR]

In the present study, novel in-situ oxidation precipitation of ferrous hydroxide method is used to synthesized Fe 3 O 4 nanoparticles and Polyethylene glycol 200 is used for covalent functionalization. Various characterizations tests (FESEM, EDX, XRD, FTIR, HRTEM) were performed, and the Polyethylene glycol treated Iron oxides functionalization was confirmed. Thermal conductivity, density, specific heat, and viscosity were experimentally assessed and validated using the correlations that were available. The corresponding improvements in thermophysical characteristics were 13.35%, 0.06%, 0.37%, and 20.9% at 0.1 wt% and 35°C. UV–vis spectroscopy and Zeta protentional was used to check the stability of nanofluids and found that nanofluid was stable for 30 days. A flat plate solar collector was experimentally installed, and the thermal performance of collector was calculated using ferrofluid under ASHRAE standard 93–2003 at different heat flux intensities (597,775,988 W/m2), mass flow rates ((0.0133, 0.0200, 0.0266 kg/s), inlet fluid temperature (30–50°C) and weight concentrations (0.025,0.05,0.075,0.1%). The highest thermal efficiency enhancement of 13.83% was noticed. Moreover, the ANSYS-CFD model was used for the numerical analysis of the thermal performance of flat plate collector at the same environmental conditions, and a maximum difference of 8.33% at 0.1 wt% was noticed in comparison with experimental data. • Nanofluid was synthesized using in-situ oxidation method and confirmed with different characterization technique. • Thermophysical properties of Nanofluids with different weight concentrations were measured experimentally. • Thermal performance of FPSC was evaluated using ferrofluid and found 13.83% enhancement. • A maximum difference of 8.33% at 0.1 wt% was noticed in comparison with experimental data when ANSYS-CFD model was used. [ABSTRACT FROM AUTHOR]

• Melatonin harmoniously coordinates plant responses to abiotic stressors. • Melatonin-induced ROS-RNS interplay activates plant antioxidant system. • Melatonin crosstalk with phytohormones and nitric oxide is pivotal for plant growth and stress tolerance. • The effect of melatonin on epigenetic pathways reveals crucial insights into cellular systems. In the face of escalating environmental challenges, plants encounter complex combinations of abiotic stresses that pose severe threats to their growth and survival. Recent advancements have shed light on the multifaceted role of melatonin, revealing its intricate interplay with redox regulation and epigenetic signaling in plant stress responses. As a versatile ally, melatonin orchestrates a cascade of redox regulatory events that counteract the oxidative damage caused by abiotic stress synergies. Melatonin strengthens plant defenses by fine-tuning antioxidant systems and directly scavenging reactive oxygen species (ROS), mitigating oxidative stress, and preserving cellular homeostasis. Moreover, melatonin interacts with epigenetic mechanisms, exerting control over gene expression through DNA methylation, histone modifications, and miRNA regulation. These epigenetic modifications have far-reaching implications in shaping stress-responsive gene networks and enabling plants to adapt to complex stress scenarios. However, understanding the intricate crosstalk by which melatonin coordinates redox regulation and epigenetic signaling to develop novel strategies to enhance plant resilience against a wide range of abiotic stresses remains unclear. This review delves into the current advancements, knowledge gaps, and charting future directions of melatonin-induced redox regulation and epigenetic signaling as the key mechanisms underlying abiotic stress resilience in plants. This study will pave the way for the future development of sustainable agricultural production of major horticulture crops, offering a beacon of hope amid mounting environmental challenges. [Display omitted] [ABSTRACT FROM AUTHOR]

Recent progress in nanotechnology has advanced the development of magnetic nanoparticle (MNP) hyperthermia as a potential therapeutic platform for treating diseases. Due to the challenges in reliably predicting the spatiotemporal distribution of temperature in the living tissue during the therapy of MNP hyperthermia, critical for ensuring the safety as well as efficacy of the therapy, the development of effective and reliable numerical models is warranted. This article provides a comprehensive review on the various mathematical methods for determining specific loss power (SLP), a parameter used to quantify the heat generation capability of MNPs, as well as bio-heat models for predicting heat transfer phenomena and temperature distribution in living tissue upon the application of MNP hyperthermia. This article also discusses potential applications of the bio-heat models of MNP hyperthermia for therapeutic purposes, particularly for cancer treatment, along with their limitations that could be overcome. Image 1 • Mathematical models of MNP (Magnetic nanoparticles) hyperthermia are reviewed. • Various methods are discussed for evaluating the SLP (Specific Loss Power). • Different factors that optimizing the SLP are reviewed for hyperthermia applications. • Bio-heat models and their applications are appraised for cancerous tumors. • Limitations and challenges for the modeling of hyperthermia are addressed. [ABSTRACT FROM AUTHOR]

Color is an important quality attribute of fungi, and a useful marker for classification, genetic, and molecular research. However, there is much debate over which enzymes play key regulatory roles in pigment synthesis pathways among different fungi and even within the same species. Auricularia cornea is the most widely cultivated mushroom in the genus Auricularia ; 1.834 million tons of this mushroom were produced in 2016 in China. Thus, systematic studies on its color inheritance and the genes encoding key enzymes for pigment synthesis have high scientific and economic value. In this study, the white strain ACW001 and the purple strain ACP004 of A. cornea were used as dikaryotic parents. Selfing populations of ACW001 and ACP004 were constructed with their monokaryotic strains. The fruiting body color of the two populations was consistent with that of their parents, confirming that the two parents were color homozygotes. All strains in the hybrid population of the two parents produced purple fruiting bodies. A robust hybrid strain (ACW001-33×ACP004-33) was selected from the hybrid population, and 87 monokaryotic strains of ACW001-33×ACP004-33 were obtained as a mapping population. Finally, a testcross population was constructed by crossing the mapping population with the test strain ACW001-9. The color genotype of each monokaryotic strain in the mapping population was identified by a fruiting test. The genomes of the two monokaryotic strains ACW001-33 and ACP004-33 were sequenced, and then simple sequence repeat (SSR) and sequence-related amplified polymorphism (SRAP) molecular marker primers were developed. Then, 88 pairs of primers that could distinguish the genotypes of the mapping population were used to construct a genetic linkage map. The genetic linkage map consisted of 12 linkage groups (LGs) spanning 1315.2 cM. The color control locus was preliminarily located at 24.5 cM of the 11th LG. Fine-mapping primers were designed based on sequence differences between ACW001-33 and ACP004-33 in the primary location region. Four color control candidate genes were located in an 8.2-kb region of ACW001-33_contig733 and a 9.2-kb region of ACP004-33_contig802. Homologous alignment and prediction of conserved domain analyses indicated that two of the color control candidate genes encoded proteins with unknown function, and the other two, ACP004_g11815 and ACP004_g11816, encoded glutamyl aminotransferases. These two genes were consecutively arranged on ACP004-33_contig802, and were likely to encode key enzymes in the γ-glutamine-4-hydroxy-benzoate (GHB) pigment synthesis pathway. Primers were designed from the flanking sequences of the two genes and used to analyze the testcross population. Products were amplified only from the 30 testcross strains with purple fruiting bodies, confirming the accuracy of the localization results. We discuss the deficiencies and advantages of map-based cloning in fungi vs. plants, and summarize the steps and requirements of the map-based cloning method for fungi. This study has provided novel ideas and methods for locating functional genes in fungi. • Classical genetic and molecular methods were combined to study the color inheritance and pigment synthesis of A. cornea. • The parental selfing populations were constructed to verify that the two parents were homozygous for color traits. • The color control candidate genes were located in the 8.2–9.2 kb region through preliminary location and fine mapping. • The steps and requirements of the mapping and cloning of fungal specific genes by map-based cloning were summarized. [ABSTRACT FROM AUTHOR]

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]

We propose density independent hydrodynamics model (DIHM) which is a novel and automatic method for coherency detection in crowded scenes. One of the major advantages of the DIHM is its capability to handle changing density over time. Moreover, the DIHM avoids oversegmentation and thus achieves refined coherency detection. In the proposed DIHM, we first extract a motion flow field from the input video through particle initialization and dense optical flow. The particles of interest are then collected to retain only the most motile and informative particles. To represent each particle, we accumulate the contribution of each particle in a weighted form, based on a kernel function. Next, the smoothed particle hydrodynamics (SPH) is adopted to detect coherent regions. Finally, the detected coherent regions are refined to remove the effects of oversegmentation. We perform extensive experiments on three benchmark datasets and compare the results with 10 state-of-the-art coherency detection methods. Our results show that DIHM achieves superior coherency detection and outperforms the compared methods in both pixel level and coherent region level average particle error rates (PERs), average coherent number error (CNE) and F-score. [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]

[Display omitted] • GA/Fe 3 O 4 composite hydrogel Synthesis via co-precipitation method. • GA/Fe 3 O 4 was characterized by FTIR, XRD, EDX, VSM, SEM, and BET techniques. • Adsorption followed the pseudo second-order kinetic and freundlich isotherm model. • Adsorption reactions are favourable, spontaneous, and exothermic nature. • The composite hydrogel exhibited excellent desorption and recyclability. Release of industrial effluents comprising dyes in water bodies is one of the foremost causes of water pollution. Therefore, the proper and proficient treatment of these dyes contaminated left-over material before their release is crucial. Herein, an eco-friendly biological macromolecule Gum-Acacia (GA) integrated Fe 3 O 4 nanoparticles composite hydrogel was manufactured via co-precipitation technique for effective adsorption of Congo red (CR) dye existing in water bodies. The as-prepared magnetic GA/Fe 3 O 4 composite hydrogel was characterized by FTIR, XRD, EDX, VSM, SEM, and BET techniques. These studies discovered the fruitful fabrication of biodegradable magnetic GA/Fe 3 O 4 composite hydrogel possessing porous structure with large surface area (21.78 m2/g) and enough saturated magnetization value (24.5 emu/g) for the efficient elimination of CR. The magnetic GA/Fe 3 O 4 composite hydrogel showed higher adsorption (96.3 %) of CR as compared to pristine Fe 3 O 4 (45 %) in 50 min at pH 6 with 0.02 g of adsorbent and 100 ppm CR. The data obtained from the adsorption of CR over GA/Fe 3 O 4 composite hydrogel have a linear relationship, following the pseudo second-order kinetic model (R2 = 0.990) and Freundlich adsorption isotherm model (R2 = 0.983). The evaluated factors of thermodynamic recommended that the experiments were favourable, spontaneous, and exothermic in nature. In addition, the biodegradable magnetic GA/Fe 3 O 4 composite hydrogel also exhibited excellent desorption as well as recyclability owing to its amazing robust magnetic nature. What is more, the GA/Fe 3 O 4 composite hydrogel reserved the same % adsorption proficiency (>96.3 %) up to several repeated rounds, proving its potential applicability for water purgative at domestic plus industrial level. [ABSTRACT FROM AUTHOR]

[Display omitted] • Simultaneous extraction and determination of Ni (II) and Zn (II) in food samples. • Minimization of solvents consumption. • Selective extraction based on hydrophobic deep eutectic solvents. • Highly sensitive method at ppb (µg/Kg) level. • Universal sample preparation with easier and faster extraction procedure. A new approach was developed for the simultaneous pre-concentration and determination of Ni (II) and Zn (II) in food samples. This method is based on ultrasound-assisted liquid–liquid micro extraction using hydrophobic deep eutectic solvent (DES) and 1,10-phenanthroline as chelating agent. The effect of several parameters, such as pH, selection and volume of DES, amount of chelating agent, time of sonication and centrifugation, was studied. Under optimized conditions, the developed procedure offered exceptional sensitivity and linearity. The limit of detection was approximately 0.029 µg/Kg and 1.5 µg/Kg for Ni (II) and Zn (II), respectively. The proposed method was applied for the pre-concentration and determination of Ni (II) and Zn (II) in hydrogenated edible oils, fishes, and milk samples. The results of this study were compared with reported methods in the literature revealing its advantages. [ABSTRACT FROM AUTHOR]