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It is crucial to comprehend how fertilization and cultivation management alter the composition of dissolved organic carbon (DOC) and microbial communities to regulate the biogeochemical cycling of soil nutrients and mitigate adverse impacts on soil–water quality. Based on 15 years of long-term field trials conducted in purple soil on farmland with a slope of 15° in southwestern China, the following five treatments were examined: CK (no fertilizer was applied), T1 (NPK plus organic manure and downslope cultivation), T2 (NPK and downslope cultivation), T3 (1.5-fold NPK and downslope cultivation), and T4 (NPK and contour cultivation). Soil samples were obtained from summer maize at two soil depths (0–10 and 10–20 cm) and from rhizospheric soil, and the changes in the DOC content, UV–visible (UV–Vis) absorptivity, and phospholipid fatty acids (PLFAs) were assessed. Our results revealed a significant change in the DOC content following fertilization, especially in T1, as it was 136.0%, 179.4%, and 132.2% higher, respectively, than that in CK at the 0–10 and 10–20 cm depths and rhizospheric soil. Fertilization decreased the UV–Vis absorptivity variables of DOC (i.e., SUVA254, SUVA260, SUVA400, SUVA465, SUVA665, and C:C ratio) and raised the E4:E6 ratio (fulvic acid to humic acid in DOC), regardless of T2 and T3 at the 10–20 cm depth and in the rhizospheric soil compared with those in CK, respectively. Fertilization significantly increased the total PLFA content and selected microbial groups relative to CK. Among the treatments, T1 significantly increased the total PLFA content by 50.6%, 59.0%, and 46.2%, respectively, relative to CK, at the 0–10 and 10–20 cm depths and in the rhizospheric soil. The microbial community structure in contour cultivation (T4) was significantly greater than in downslope cultivation (T2). Random forest analysis (RFA) revealed that SOC and DOC were likely the primary variables for regulating the total PLFAs in the examined soil. Partial least squares path modeling (PLS-PM) further indicated that the DOC content and the ratio of E4:E6 among DOC compositions had greater effects on the soil microbial community structure in the examined soil. These observations suggested that long-term fertilization and cultivation management are effective approaches to regulating the soil microbial community structure by altering the composition of DOC in sloping farmland. [ABSTRACT FROM AUTHOR]

Contrasting fertilization modifies soil phosphorus (P) transformation and bioavailability, which impact crop P uptake and P migration in the soil profile. A long-term (25-year) fertilizer experiment was employed to investigate crop yield, P uptake and changes in sequentially extracted P fractions in the soil profile, and their relationships on a calcareous soil derived from loess material under a winter wheat and summer maize double-cropping system. The experiment involved seven nutrient management treatments: control (CK, no nutrient input), N, NK, NP, and NPK, representing various combinations of synthetic nitrogen (N), phosphate (P), and potassium (K) applications, as well as combinations of NPK fertilizers with either crop residues (SNPK, where S refers to maize stalk or wheat straw) or manure (MNPK, where M refers to dairy manure). Wheat and maize yields were significantly higher with P input fertilizer relative to the P-omitted treatments. Long-term application of P-containing fertilizers markedly raised the contents of inorganic (Pi) and organic (Po) P fractions at 0–20 cm depth compared with the P-omitted treatments. Moreover, both Pi and Po fractions were markedly higher under MNPK than under NPK and SNPK treatments. For achieving high yield for wheat and maize, the critical contents of labile P were 54 and 63 mg kg−1, and those of moderately labile P were 48 and 49 mg kg−1, respectively, defined by the linear plateau model. In addition, the change points of labile P and moderately labile P were 99 and 70 mg kg−1, above which CaCl2-P content significantly increased. Moreover, long-term P input significantly accumulated different P fractions in the deeper soil layers up to 100 cm, with large portions of organic P being a composite of labile and moderately labile P, especially in MNPK treatment. Our results suggest that excessive P supply with organic manure resulted in massive P accumulation in the topsoil and promoted soil P fraction transformation and availability in the deep soil layers, especially in an organic P form that has often been neglected. [ABSTRACT FROM AUTHOR]

Traffic flow analysis is essential to develop smart urban mobility solutions. Although numerous tools have been proposed, they employ only a small number of parameters. To overcome this limitation, an edge computing solution is proposed based on nine traffic parameters, namely, vehicle count, direction, speed, and type, flow, peak hour factor, density, time headway, and distance headway. The proposed low-cost solution is easy to deploy and maintain. The sensor node is comprised of a Raspberry Pi 4, Pi camera, Intel Movidius Neural Compute Stick 2, Xiaomi MI Power Bank, and Zong 4G Bolt+. Pre-trained models from the OpenVINO Toolkit are employed for vehicle detection and classification, and a centroid tracking algorithm is used to estimate vehicle speed. The measured traffic parameters are transmitted to the ThingSpeak cloud platform via 4G. The proposed solution was field-tested for one week (7 h/day), with approximately 10,000 vehicles per day. The count, classification, and speed accuracies obtained were 79.8%, 93.2%, and 82.9%, respectively. The sensor node can operate for approximately 8 h with a 10,000 mAh power bank and the required data bandwidth is 1.5 MB/h. The proposed edge computing solution overcomes the limitations of existing traffic monitoring systems and can work in hostile environments. [ABSTRACT FROM AUTHOR]

The significance of nanomaterials in biomedicines served as the inspiration for the design of this study. In this particular investigation, we carried out the biosynthesis of calcium oxide nanoparticles (CaONPs) by employing a green-chemistry strategy and making use of an extract of Ficus carica (an edible fruit) as a capping and reducing agent. There is a dire need for new antimicrobial agents due to the alarming rise in antibiotic resistance. Nanoparticles' diverse antibacterial properties suggest that they might be standard alternatives to antimicrobial drugs in the future. We describe herein the use of a Ficus carica extract as a capping and reducing agent in the phyto-mediated synthesis of CaONPs for the evaluation of their antimicrobial properties. The phyto-mediated synthesis of NPs is considered a reliable approach due to its high yield, stability, non-toxicity, cost-effectiveness and eco-friendliness. The CaONPs were physiochemically characterized by UV-visible spectroscopy, energy-dispersive X-ray (EDX), scanning-electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). The biological synthesis of the calcium oxide nanoparticles revealed a characteristic surface plasmon resonance peak (SPR) at 360 nm in UV-Vis spectroscopy, which clearly revealed the successful reduction of the Ca2+ ions to Ca0 nanoparticles. The characteristic FTIR peak seen at 767 cm−1 corresponded to Ca-O bond stretching and, thus, confirmed the biosynthesis of the CaONPs, while the scanning-electron micrographs revealed near-CaO aggregates with an average diameter of 84.87 ± 2.0 nm. The antibacterial and anti-biofilm analysis of the CaONPs showed inhibition of bacteria in the following order: P. aeruginosa (28 ± 1.0) > S. aureus (23 ± 0.3) > K. pneumoniae (18 ± 0.9) > P. vulgaris (13 ± 1.6) > E. coli (11 ± 0.5) mm. The CaONPs were shown to considerably inhibit biofilm formation, providing strong evidence for their major antibacterial activity. It is concluded that this straightforward environmentally friendly method is capable of synthesizing stable and effective CaONPs. The therapeutic value of CaONPs is indicated by their potential as a antibacterial and antibiofilm agents in future medications. [ABSTRACT FROM AUTHOR]

Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version. Most of the existing blind SR techniques use a degradation estimator network to explicitly estimate the blur kernel to guide the SR network with the supervision of ground truth (GT) kernels. To solve this issue, it is necessary to design an implicit estimator network that can extract discriminative blur kernel representation without relying on the supervision of ground-truth blur kernels. We design a lightweight approach for blind super-resolution (Blind-SR) that estimates the blur kernel and restores the HR image based on a deep convolutional neural network (CNN) and a deep super-resolution residual convolutional generative adversarial network. Since the blur kernel for blind image SR is unknown, following the image formation model of blind super-resolution problem, we firstly introduce a neural network-based model to estimate the blur kernel. This is achieved by (i) a Super Resolver that, from a low-resolution input, generates the corresponding SR image; and (ii) an Estimator Network generating the blur kernel from the input datum. The output of both models is used in a novel loss formulation. The proposed network is end-to-end trainable. The methodology proposed is substantiated by both quantitative and qualitative experiments. Results on benchmarks demonstrate that our computationally efficient approach (12x fewer parameters than the state-of-the-art models) performs favorably with respect to existing approaches and can be used on devices with limited computational capabilities. [ABSTRACT FROM AUTHOR]

Date palm (Phoenix dactylifera L.) is a considerably beneficial and economically profitable fruit crop. Female date palm plants produce fruit that is rich in fiber and sugar. Date palm is propagated by two means: suckers and seed. The propagation of date palm through seeds is very necessary for germplasm conservation and breeding. The late reproductive age (4–5 years) and dioecious nature of date palm make genetic improvement and breeding difficult. Early sex determination is the only way to improve breeding by selecting experimental male and female plants at the seedling stage. The primers for Tapetum Determinant 1 (TPD1-like) were designed using Amplify software. The DNA amplification of selected date palm suckers of three genotypes (Ajwa, Amber, and Medjool) was observed through PCR. Expression profiling of selected genotypes was carried out through semi-q PCR and RT-PCR by using the cDNA of suckers and unknown seedlings. Different in silico analyses were performed for the gene and protein characterization and identification of cis-acting elements in the promoter region. The promoter was identified along with the protein's properties and functionality. The expression of TPD1-like gene was found in the leaves of three selected genotypes of male sucker and in some plants of selected unknown seedlings that are considered male plants, and no expression was observed in female suckers and unknown seedlings that are considered female plants. The findings suggested that the TPD1-like gene has the potential for sex differentiation at the seedling stage, as the TPD1-like gene is essential to the specialization of tapetal cells and plays a critical role in plant reproduction. [ABSTRACT FROM AUTHOR]

The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals' access to cloud-based patient-sensitive data more securely. The experiment's findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective. [ABSTRACT FROM AUTHOR]

Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. However, continual use of multiple sensors and actuators of the ADAS can lead to failure of AV sensors. Thus, prognostic health management (PHM) of ADAS is important for smooth and continuous operation of AVs. The PHM of AVs has recently been introduced and is still progressing. There is a lack of surveys available related to sensor-based PHM of AVs in the literature. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field. [ABSTRACT FROM AUTHOR]

In this paper, we obtain new Ostrowski type inequalities by using the extended version of Montgomery identity and Green's functions. We also give estimations of the difference between two integral means. [ABSTRACT FROM AUTHOR]

The gut microbiota is important in regulating host metabolism, maintaining physiology, and protecting immune homeostasis. Gut microbiota dysbiosis affects the development of the gut microenvironment, as well as the onset of various external systemic diseases and metabolic syndromes. Cyclophosphamide (CTX) is a commonly used chemotherapeutic drug that suppresses the host immune system, intestinal mucosa inflammation, and dysbiosis of the intestinal flora. Immunomodulators are necessary to enhance the immune system and prevent homeostasis disbalance and cytotoxicity caused by CTX. In this study, shrimp peptide hydrolysate (SPH) was evaluated for immunomodulation, intestinal integration, and microbiota in CTX-induced immunosuppressed mice. It was observed that SPH would significantly restore goblet cells and intestinal mucosa integrity, modulate the immune system, and increase relative expression of mRNA and tight-junction associated proteins (Occludin, Zo-1, Claudin-1, and Mucin-2). It also improved gut flora and restored the intestinal microbiota ecological balance by removing harmful microbes of various taxonomic groups. This would also increase the immune organs index, serum levels of cytokines (IFN-γ, IL1β, TNF-α, IL-6), and immunoglobin levels (IgA, IgM). The Firmicutes/Bacteroidetes proportion was decreased in CTX-induced mice. Finally, SPH would be recommended as a functional food source with a modulatory effect not only on intestinal microbiota, but also as a potential health-promoting immune function regulator. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) and Deep Neural Network (DNN) is designed. In the proposed PCO-DNN framework, DNN is used to predict cardiac disease, and the PCO is utilized to optimize the DNN parameters, thereby maximizing the prediction performances. The proposed framework aims to predict and classify cardiovascular diseases accurately. Further, an intensive comparative analysis is performed to validate the obtained results with the existing classification models. The results show that the proposed framework achieves an accuracy of 96.6665%, a precision of 97.5256%, a recall of 97.0953%, and an F1-measure of 96.4242% and can outperform the existing CVD predictors. [ABSTRACT FROM AUTHOR]

Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%. [ABSTRACT FROM AUTHOR]

We report on the recent progress and development of research into cellulose-based electro-active paper for bending actuators, bioelectronics devices, and electromechanical transducers. The cellulose electro-active paper is characterized in terms of its biodegradability, chirality, ample chemically modifying capacity, light weight, actuation capability, and ability to form hybrid nanocomposites. The mechanical, electrical, and chemical characterizations of the cellulose-based electro-active paper and its hybrid composites such as blends or coatings with synthetic polymers, biopolymers, carbon nanotubes, chitosan, and metal oxides, are explained. In addition, the integration of cellulose electro-active paper is highlighted to form various functional devices including but not limited to bending actuators, flexible speaker, strain sensors, energy harvesting transducers, biosensors, chemical sensors and transistors for electronic applications. The frontiers in cellulose paper devices are reviewed together with the strategies and perspectives of cellulose electro-active paper and cellulose nanocomposite research and applications. [ABSTRACT FROM AUTHOR]

A simulation model can provide insight into the characteristic behaviors of different health states of an actual system; however, such a simulation cannot account for all complexities in the system. This work proposes a transfer learning strategy that employs simple computer simulations for fault diagnosis in an actual system. A simple shaft-disk system was used to generate a substantial set of source data for three health states of a rotor system, and that data was used to train, validate, and test a customized deep neural network. The deep learning model, pretrained on simulation data, was used as a domain and class invariant generalized feature extractor, and the extracted features were processed with traditional machine learning algorithms. The experimental data sets of an RK4 rotor kit and a machinery fault simulator (MFS) were employed to assess the effectiveness of the proposed approach. The proposed method was also validated by comparing its performance with the pre-existing deep learning models of GoogleNet, VGG16, ResNet18, AlexNet, and SqueezeNet in terms of feature extraction, generalizability, computational cost, and size and parameters of the networks. [ABSTRACT FROM AUTHOR]

Expert systems, a form of artificial intelligence (AI), are typically designed to solve many real-world problems by reasoning through knowledge, which is primarily represented as IF–THEN rules, with the information acquired from humans or domain experts. However, to assume such rules for personalized decision-making in an intelligent, context-aware mobile application is a challenging issue. The reason is that different mobile users may behave differently in various day-to-day situations, i.e., not identical, and thus the rules for personalized services must be reflected according to their symmetrical or asymmetrical behavioral activities. Therefore, our key focus is to solve this issue through adding personalized decision-making intelligence to develop powerful mobile applications to assist the end-users. To achieve our goal, in this paper, we explore on "Mobile Expert System", where we take into account machine-learning rules as knowledge-base rather than traditional handcrafted static rules. Thus, the concept of a mobile expert system enables the computing and decision-making processes more actionable and intelligent than traditional ones in the domain of mobile analytics and applications. Our experiment section shows that the context-aware machine learning rules discovered from users' mobile phone data can contribute in building a mobile expert system to solve a particular problem, through making personalized decisions in various context-aware test cases. [ABSTRACT FROM AUTHOR]

As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter's data clusters are more distinct than the former's. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy. [ABSTRACT FROM AUTHOR]

Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data. [ABSTRACT FROM AUTHOR]

In this paper, binomial convolution in the frame of quantum calculus is studied for the set A q of q-Appell sequences. It has been shown that the set A q of q-Appell sequences forms an Abelian group under the operation of binomial convolution. Several properties for this Abelian group structure A q have been studied. A new definition of the q-Appell polynomials associated with a random variable is proposed. Scale transformation as well as transformation based on expectation with respect to a random variable is used to present the determinantal form of q-Appell sequences. [ABSTRACT FROM AUTHOR]

A two-year experiment was carried out at Shenyang Agricultural University's research field area in China to evaluate the impact of the combined application of straw mulch (0 and 8 t ha−1) and biochar (0, 4, 12, and 36 t ha−1) on the morphological traits and grain development of rainfed maize during 2018 and 2019. The results showed that straw mulch and different biochar application rates significantly impacted the maize growth index. Compared to non-biochar-treated soils, the introduction of straw mulch improved plant height, stem diameter, leaf area index (LAI), leaves, stem, root, and crop growth rate (CGR), and dry weight of rainfed maize crop. The highest plant height, stem diameter, LAI, leaves, stem, root growth rate, CGR, and dry weight of rainfed maize crop were reported when soil was treated with a higher rate of biochar (36 t ha−1). Biochar increased grain filling rate while decreasing grain filling duration in rainfed maize crops. Our results indicate that straw mulch and biochar-based soil management strategies can improve the rainfed maize growth with the environmental benefits of global warming mitigation. However, due to the wide range of biochar properties, the interactions between straw mulch and biochar should be given special consideration in the maize cropping system. [ABSTRACT FROM AUTHOR]

The calculus in the absence of limits is known as quantum calculus. With a difference operator, it substitutes the classical derivative, which permits dealing with sets of functions that are non-differentiations. The theory of integral inequality in quantum calculus is a field of mathematics that has been gaining considerable attention recently. Despite the fact of its application in discrete calculus, it can be applied in fractional calculus as well. In this paper, some new Anderson type q-integral and h-integral inequalities are given using a Feng Qi integral inequality in quantum calculus. These findings are highly beneficial for basic frontier theories, and the techniques offered by technology are extremely useful for those who can stimulate research interest in exploring mathematical applications. Due to the interesting properties in the field of mathematics, integral inequalities have a tied correlation with symmetric convex and convex functions. There exist strong correlations and expansive properties between the different fields of convexity and symmetric function, including probability theory, convex functions, and the geometry of convex functions on convex sets. The main advantage of these essential inequalities is that they can be converted into time-scale calculus. This kind of inevitable inequality can be very helpful in various fields where coordination plays an important role. [ABSTRACT FROM AUTHOR]

Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination. [ABSTRACT FROM AUTHOR]

In this paper, the active vibration control of a piezo-bonded laminated composite is investigated in the presence of sensor partial debonding and structural delamination. Improved layerwise theory, higher-order electric potential field, and the finite-element method were employed to develop an electromechanically coupled model for the two types of damage (i.e., sensor partial debonding and delamination). The developed model was numerically implemented on a single-input-multi-output (SIMO) system to demonstrate the effects of sensor partial debonding and structural delamination on the ability of a constant gain velocity feedback (CGVF) controller to attenuate vibration. The two types of damage were assessed in terms of controlled outputs of the sensors, nodal displacements, and control input signals being applied to the actuator to suppress vibrations. The obtained results showed that the sensor partial debonding and structural delamination have opposite effects on the vibration-attenuation characteristics of the CGVF controller. The presence of partial debonding in the sensor made the controller less able to suppress vibrations because of a spurious sensing signal, whereas structural delamination increased the control authority of the controller because of the loss of structural stiffness that results from structural delamination. [ABSTRACT FROM AUTHOR]

Electro-active paper (EAPap) is a cellulose-based smart material that has shown promising results in a variety of smart applications (e.g., vibration sensor, piezo-speaker, bending actuator) with the merits of being flexible, lightweight, fracture tolerant, biodegradable, naturally abundant, cheap, biocompatible, and with the ability to form hybrid nanocomposites. This paper presents a review of the characterization and application of EAPap as a flexible mechanical vibration/strain sensor, bending actuator, and vibration energy harvester. The working mechanism of EAPap is explained along with the various parameters and factors that influence the sensing, actuation, and energy harvesting capabilities of EAPap. Although the piezoelectricity of EAPap is comparable to that of commercially available polyvinylidene fluoride (PVDF), EAPap has the preferable merits in terms of natural abundance and ample capacity of chemical modification. The article would provide guidelines for the characterization and application of EAPap in mechanical sensing, actuation, and vibration energy scavenging, along with the possible limitations and future research prospects. [ABSTRACT FROM AUTHOR]

Meibomian gland dysfunction (MGD) is a highly prevalent condition and the most common cause of evaporative dry eye disease. Studying the proteome of MGD can result in important advances in the management of the condition. Here, we collected tear film samples from treatment naïve patients with MGD (n = 10) and age-matched controls (n = 11) with Schirmer filtration paper. The samples were analyzed with label-free quantification nano liquid chromatography—tandem mass spectrometry. The proteins were considered differentially expressed if p < 0.05. A total of 88 proteins were significantly regulated. The largest change was observed in cystatin-SN, which was downregulated in MGD and correlated negatively with tear meniscus height. The downregulation of cystatin-SN was confirmed with targeted mass spectrometry by single reaction monitoring (SRM). Eighteen immunoglobulin components involved in B cell activation, phagocytosis, and complement activation were downregulated in MGD including Ig alpha-1 chain C region, immunoglobulin J chain, immunoglobulin heavy variable 3–15, and Ig mu chain C region. The changes in cystatin-SN and immunoglobulin chains are likely to result from the inflammatory changes related to tear film evaporation, and future studies may assess their association with the meibum quality. [ABSTRACT FROM AUTHOR]

COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods. [ABSTRACT FROM AUTHOR]

Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate model. Most of the existing solutions rely solely on content-based features that are insufficient and overlapping. Moreover, most of the models used for classification are constructed with the concept of a dense features vector unsuitable for short news sentences. To address this problem, this study proposed a Web-Informed-Augmented Fake News Detection Model using Stacked Layers of Convolutional Neural Network and Deep Autoencoder called ICNN-AEN-DM. The augmented information is gathered from web searches from trusted sources to either support or reject the claims in the news content. Then staked layers of CNN with a deep autoencoder were constructed to train a probabilistic deep learning-base classifier. The probabilistic outputs of the stacked layers were used to train decision-making by staking multilayer perceptron (MLP) layers to the probabilistic deep learning layers. The results based on extensive experiments challenging datasets show that the proposed model performs better than the related work models. It achieves 26.6% and 8% improvement in detection accuracy and overall detection performance, respectively. Such achievements are promising for reducing the negative impacts of fake news on communities. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma poses an inevitable threat to patients despite aggressive therapy regimes. It displays a great level of molecular heterogeneity and numerous substitutions in several genes have been documented. Next-generation sequencing techniques have identified various molecular signatures that have led to a better understanding of the molecular pathogenesis of glioblastoma. In this limited study, we sought to identify genetic variants in a small number of rare patients with aggressive glioblastoma. Methods: Five tumor tissue samples were isolated from four patients with rapidly growing glioblastoma. Genomic DNA was isolated and whole exome sequencing was used to study protein-coding regions. Generated FASTQ files were analyzed and variants were called for each sample. Variants were prioritized with different approaches and functional annotation was applied for the detrimental variants. Results: A total of 49,780 somatic variants were identified in the five glioblastoma samples studied, with the majority as missense substitutions. The top ten genes with the highest number of substitutions were MUC3A, MUC4, MUC6, OR4C5, PDE4DIP, AHNAK2, OR4C3, ZNF806, TTN, and RP1L1. Notably, variant prioritization after annotation indicated that the MTCH2 (Chr11: 47647265 A>G) gene sequence change was putative deleterious in all of the aggressive tumor samples. Conclusion: The MTCH2 (Chr11: 47647265 A>G) gene substitution was identified as putative deleterious in highly aggressive glioblastomas, which merits further investigation. Moreover, a high tumor mutation burden was observed, with a signature of the highest substitutions in MUC3A, MUC4, MUC6, OR4C5, PDE4DIP, AHNAK2, OR4C3, ZNF806, TTN, and RP1L1 genes. The findings provide critical, initial data for the further rational design of genetic screening and diagnostic approaches against aggressive glioblastoma. [ABSTRACT FROM AUTHOR]

As an essential micronutrient, copper is vital for normal growth and development of plants, however, its accumulation in soil exerts a severe negative impact on the agronomic characteristics and yield of the crop plants. Phytoextraction is a low-cost method for restoring soil fertility and avoiding losses due to heavy metal contamination. We found that using EDTA and IAA together improved sunflower hyperaccumulation capacity. Sunflowers were cultivated under various levels of Cu (0 (control), 25, 50, and 75 mg/kg of soil) and treated with EDTA alone or combined with IAA. The results revealed that the amended treatment significantly enhanced the absorption and accumulation of Cu in the sunflowers. Furthermore, the various doses of Cu significantly reduced the root and shoot growth of sunflowers in a concentration-dependent manner by impairing the chlorophyll content, hormones (indole 3-acetic acid, salicylic acid, and gibberellic acid), flavonoids, phenolics, and antioxidant response. The injurious effect of Cu was reduced by the addition of EDTA alone, and the supplementation of IAA led to a significant restoration of shoot growth (~70%) and root growth (~13%) as compared to the plant treated with Cu alone. Moreover, significantly higher levels of chlorophyll content, GA3, endogenous IAA, and flavonoids were recorded, indicating the effectiveness of the treatment in ameliorating plant health. The results also showed considerable restoration of the catalase and ascorbate peroxidase activities in plants treated with EDTA and IAA. These results are suggestive that application of EDTA and IAA enhances the Cu absorption potential of sunflower and increases its tolerance to copper, which may not only serve as a better technique for phytoextraction of Cu, but also to bring Cu contaminated soil under cultivation. [ABSTRACT FROM AUTHOR]

Several environmental stresses, including biotic and abiotic factors, adversely affect the growth and development of crops, thereby lowering their yield. However, abiotic factors, e.g., drought, salinity, cold, heat, ultraviolet radiations (UVr), reactive oxygen species (ROS), trace metals (TM), and soil pH, are extremely destructive and decrease crop yield worldwide. It is expected that more than 50% of crop production losses are due to abiotic stresses. Moreover, these factors are responsible for physiological and biochemical changes in plants. The response of different plant species to such stresses is a complex phenomenon with individual features for several species. In addition, it has been shown that abiotic factors stimulate multi-gene responses by making modifications in the accumulation of the primary and secondary metabolites. Metabolomics is a promising way to interpret biotic and abiotic stress tolerance in plants. The study of metabolic profiling revealed different types of metabolites, e.g., amino acids, carbohydrates, phenols, polyamines, terpenes, etc, which are accumulated in plants. Among all, primary metabolites, such as amino acids, carbohydrates, lipids polyamines, and glycine betaine, are considered the major contributing factors that work as osmolytes and osmoprotectants for plants from various environmental stress factors. In contrast, plant-derived secondary metabolites, e.g., phenolics, terpenoids, and nitrogen-containing compounds (alkaloids), have no direct role in the growth and development of plants. Nevertheless, such metabolites could play a significant role as a defense by protecting plants from biotic factors such as herbivores, insects, and pathogens. In addition, they can enhance the resistance against abiotic factors. Therefore, metabolomics practices are becoming essential and influential in plants by identifying different phytochemicals that are part of the acclimation responses to various stimuli. Hence, an accurate metabolome analysis is important to understand the basics of stress physiology and biochemistry. This review provides insight into the current information related to the impact of biotic and abiotic factors on variations of various sets of metabolite levels and explores how primary and secondary metabolites help plants in response to these stresses. [ABSTRACT FROM AUTHOR]

Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community's behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques. [ABSTRACT FROM AUTHOR]

Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simulations. In contrast, Machine Learning Algorithms (MLA) are (1) free of such limitations, (2) can incorporate information from multiple sources and therefore emerge with increasing interest in real-time resource estimation and automation. However, MLAs need to be explored for robust learning of phenomena, better accuracy, and computational efficiency. This paper compares MLAs, i.e., Multiple Linear Regression (MLR) and Random Forest (RF), with Ordinary Kriging (OK). The techniques were applied to the publicly available Walkerlake dataset, while the exhaustive Walker Lake dataset was validated. The results of MLR were significant (p < 10 × 10−5), with correlation coefficients of 0.81 (R-square = 0.65) compared to 0.79 (R-square = 0.62) from the RF and OK methods. Additionally, MLR was automated (free from an intermediary step of variogram modelling as in OK), produced unbiased estimates, identified key samples representing different zones, and had higher computational efficiency. [ABSTRACT FROM AUTHOR]

Clustering approaches are widely used to group similar objects and facilitate problem analysis and decision-making in many fields. During short-term planning of open-pit mines, clustering aims to aggregate similar blocks based on their attributes (e.g., geochemical grades, rock types, geometallurgical parameters) while honoring various constraints: i.e., cluster shapes, size, alignment with mining direction, destination, and rock type homogeneity. This approach helps to reduce the computational cost of optimizing short-term mine plans. Previous studies have presented ways to perform clustering without honoring constraints specific to mining. This paper presents a novel block clustering heuristic capable of considering and honoring a set of mining block aggregation requirements and constraints. Constraints can relate to the clustering adjacent blocks, achieving higher destination homogeneities, controlled cluster size, consistency with mining direction, and achieving clusters with mineable shapes and rock types' homogeneity. The proposed algorithm's application on two different datasets demonstrates its efficiency and capability in generating reasonable block clusters while meeting different predefined aggregation requirements and constraints. [ABSTRACT FROM AUTHOR]

Hempcrete is a sustainable biocomposite that can reduce buildings' embodied energy while improving energy performance and indoor environmental quality. This research aims to develop novel insulating hemp-lime composites using innovative binder mixes made of recycled and low-embodied energy pozzolans. The characterization of composites' mechanical and hygrothermal properties includes measuring compressive strength, splitting tensile strength, thermal conductivity, specific heat capacity, and moisture buffer capacities. This study also investigates the impact of sample densities and water content on compressive strength at different ages. The findings suggest that mixes with a 1:1 binder to hemp ratio and 300−400 kg/m3 density have hygrothermal and mechanical properties suitable for insulating infill wall applications. Hence, compressive strengths, thermal conductivity, and specific heat capacity values range from 0.09 to 0.57 MPa, 0.087 to 0.10 W/m K, and 1250 to 1557 J/kg K, respectively. The average moisture buffer value for all hempcrete samples of 2.78 (gm/m2 RH%) indicates excellent moisture buffering capacity. Recycled crushed brick pozzolan can enhance the hygrothermal performance of the hemp-lime composites. Thus, samples with 10% crushed brick have the lowest thermal conductivity considering their density and the highest moisture buffer capacity. The new formulas of hydrated lime and crushed brick have mechanical properties comparable to metakaolin and hydraulic lime formulas. [ABSTRACT FROM AUTHOR]

This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5   kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to establish summation-integral type generalized Lupaş operators with weights of Beta basis functions which depends on μ having some properties. Primarily, for these new operators, we calculate moments and central moments, weighted approximation is discussed. Further, Voronovskaya type asymptotic theorem is proved. Finally, quantitative estimates for the local approximation is taken into consideration. [ABSTRACT FROM AUTHOR]

This paper mainly formulates the problem of predicting context-aware smartphone apps usage based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the user-centric context and device-centric context. In the area of artificial intelligence and machine learning, decision tree model is one of the most popular approaches for predicting context-aware smartphone usage. However, real-life smartphone apps usage data may contain higher dimensions of contexts, which may cause several issues such as increases model complexity, may arise over-fitting problem, and consequently decreases the prediction accuracy of the context-aware model. In order to address these issues, in this paper, we present an effective principal component analysis (PCA) based context-aware smartphone apps prediction model, "ContextPCA" using decision tree machine learning classification technique. PCA is an unsupervised machine learning technique that can be used to separate symmetric and asymmetric components, and has been adopted in our "ContextPCA" model, in order to reduce the context dimensions of the original data set. The experimental results on smartphone apps usage datasets show that "ContextPCA" model effectively predicts context-aware smartphone apps in terms of precision, recall, f-score and ROC values in various test cases. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce q-analogues of generalized Lupaş operators, whose construction depends on a continuously differentiable, increasing, and unbounded function ρ. Depending on the selection of q, these operators provide more flexibility in approximation and the convergence is at least as fast as the generalized Lupaş operators, while retaining their approximation properties. For these operators, we give weighted approximations, Voronovskaja-type theorems, and quantitative estimates for the local approximation. [ABSTRACT FROM AUTHOR]

Hydrogels evolved as an outstanding carrier material for local and controlled drug delivery that tend to overcome the shortcomings of old conventional dosage forms for small drugs (NSAIDS) and large peptides and proteins. The aqueous swellable and crosslinked polymeric network structure of hydrogels is composed of various natural, synthetic and semisynthetic biodegradable polymers. Hydrogels have remarkable properties of functionality, reversibility, sterilizability, and biocompatibility. All these dynamic properties of hydrogels have increased the interest in their use as a carrier for peptides and proteins to be released slowly in a sustained manner. Peptide and proteins are remarkable therapeutic agents in today's world that allow the treatment of severe, chronic and life-threatening diseases, such as diabetes, rheumatoid arthritis, hepatitis. Despite few limitations, hydrogels provide fine tuning of proteins and peptides delivery with enormous impact in clinical medicine. Novels drug delivery systems composed of smart peptides and molecules have the ability to drive self-assembly and form hydrogels at physiological pH. These hydrogels are significantly important for biological and medical fields. The primary objective of this article is to review current issues concerned with the therapeutic peptides and proteins and impact of remarkable properties of hydrogels on these therapeutic agents. Different routes for pharmaceutical peptides and proteins and superiority over other drugs candidates are presented. Recent advances based on various approaches like self-assembly of peptides and small molecules to form novel hydrogels are also discussed. The article will also review the literature concerning the classification of hydrogels on a different basis, polymers used, "release mechanisms" their physical and chemical characteristics and diverse applications. [ABSTRACT FROM AUTHOR]

Author Contributions Conceptualization, S.R., W.K.K. and B.M.; data curation, S.R.; formal analysis, S.R., N.A. and M.K.O.; funding acquisition, M.K.O.; investigation, S.R.; methodology, S.R.; project administration, B.M.; software, A.K.; supervision, B.M.; validation, I.A.A.; visualization, R.M. and S.A.A., writing - original draft, S.R. and W.K.K.; writing - review and editing, Y.A.A. and B.M. All authors have read and agreed to the published version of the manuscript. The authors of this paper [[1]] have agreed that they would like to add Waqas Khan Kayani as a co-author, as he contributed to the generation of whole plants from transgenic hairy roots of I Ajuga bracteosa i and to the writing the original manuscript. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. [Extracted from the article]

In recent decades, Pakistan has suffered a decline in cotton production due to several factors, including insect pests, cotton leaf curl disease (CLCuD), and multiple abiotic stresses. CLCuD is a highly damaging plant disease that seriously limits cotton production in Pakistan. Recently, genome editing through CRISPR/Cas9 has revolutionized plant biology, especially to develop immunity in plants against viral diseases. Here we demonstrate multiplex CRISPR/Cas-mediated genome editing against CLCuD using transient transformation in N. benthamiana plants and cotton seedlings. The genomic sequences of cotton leaf curl viruses (CLCuVs) were obtained from NCBI and the guide RNA (gRNA) were designed to target three regions in the viral genome using CRISPR MultiTargeter. The gRNAs were cloned in pHSE401/pKSE401 containing Cas9 and confirmed through colony PCR, restriction analysis, and sequencing. Confirmed constructs were moved into Agrobacterium and subsequently used for transformation. Agroinfilteration in N. benthamiana revealed delayed symptoms (3–5 days) with improved resistance against CLCuD. In addition, viral titer was also low (20–40%) in infected plants co-infiltrated with Cas9-gRNA, compared to control plants (infected with virus only). Similar results were obtained in cotton seedlings. The results of transient expression in N. benthamiana and cotton seedlings demonstrate the potential of multiplex CRISPR/Cas to develop resistance against CLCuD. Five transgenic plants developed from three experiments showed resistance (60−70%) to CLCuV, out of which two were selected best during evaluation and screening. The technology will help breeding CLCuD-resistant cotton varieties for sustainable cotton production. [ABSTRACT FROM AUTHOR]

Simple Summary: Viral sequence variation can expand the host repertoire, enhance the infection ability, and/or prevent the build-up of a long-term specific immunity by the host. The study of viral diversity is, thus, critical to understand sequence change and its implications for intervention strategies. Typically, these studies are performed using alignment-dependent approaches. However, such an approach becomes limited with increase in sequence diversity. Herein, we present an alignment-free algorithm, implemented as a publicly available tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. UNIQmin enables the generation of a minimal set for a given sequence dataset of interest and is applicable to big data, with a reasonable time performance. The minimal set is the smallest possible number of unique sequences required to represent a given peptidome diversity (pool of distinct peptides of a specific length) exhibited by a non-redundant dataset. This compression is possible through the removal of unique sequences that do not contribute effectively to the peptidome diversity pool. The utility of UNIQmin was demonstrated for the species Dengue virus, genus Flavivirus, family Flaviviridae, and superkingdom Viruses. The concept of a minimal set is generic and thus possibly applicable to both genomic and proteomic data of non-viral, pathogenic microorganisms. The study of viral diversity is imperative in understanding sequence change and its implications for intervention strategies. The widely used alignment-dependent approaches to study viral diversity are limited in their utility as sequence dissimilarity increases, particularly when expanded to the genus or higher ranks of viral species lineage. Herein, we present an alignment-independent algorithm, implemented as a tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. This is done by performing an exhaustive search to generate the minimal set of sequences for a given viral non-redundant sequence dataset. The minimal set is comprised of the smallest possible number of unique sequences required to capture the diversity inherent in the complete set of overlapping k-mers encoded by all the unique sequences in the given dataset. Such dataset compression is possible through the removal of unique sequences, whose entire repertoire of overlapping k-mers can be represented by other sequences, thus rendering them redundant to the collective pool of sequence diversity. A significant reduction, namely ~44%, ~45%, and ~53%, was observed for all reported unique sequences of species Dengue virus, genus Flavivirus, and family Flaviviridae, respectively, while still capturing the entire repertoire of nonamer (9-mer) viral peptidome diversity present in the initial input dataset. The algorithm is scalable for big data as it was applied to ~2.2 million non-redundant sequences of all reported viruses. UNIQmin is open source and publicly available on GitHub. The concept of a minimal set is generic and, thus, potentially applicable to other pathogenic microorganisms of non-viral origin, such as bacteria. [ABSTRACT FROM AUTHOR]

Ajuga bracteosa Wall. ex Benth. is an endangered medicinal herb traditionally used against different ailments. The present study aimed to create new insight into the fundamental mechanisms of genetic transformation and the biological activities of this plant. We transformed the A. bracteosa plant with rol genes of Agrobacterium rhizogenes and raised the regenerants from the hairy roots. These transgenic regenerants were screened for in vitro antioxidant activities, a range of in vivo assays, elemental analysis, polyphenol content, and different phytochemicals found through HPLC. Among 18 polyphenolic standards, kaempferol was most abundant in all transgenic lines. Furthermore, transgenic line 3 (ABRL3) showed maximum phenolics and flavonoids content among all tested plant extracts. ABRL3 also demonstrated the highest total antioxidant capacity (8.16 ± 1 μg AAE/mg), total reducing power, (6.60 ± 1.17 μg AAE/mg), DPPH activity (IC50 = 59.5 ± 0.8 μg/mL), hydroxyl ion scavenging (IC50 = 122.5 ± 0.90 μg/mL), and iron-chelating power (IC50 = 154.8 ± 2 μg/mL). Moreover, transformed plant extracts produced significant analgesic, anti-inflammatory, anticoagulant, and antidepressant activities in BALB/c mice models. In conclusion, transgenic regenerants of A. bracteosa pose better antioxidant and pharmacological properties under the effect of rol genes as compared to wild-type plants. [ABSTRACT FROM AUTHOR]

Avian influenza virus A (H7N9), after circulating in avian hosts for decades, was identified as a human pathogen in 2013. Herein, amino acid substitutions possibly essential for human adaptation were identified by comparing the 4706 aligned overlapping nonamer position sequences (1–9, 2–10, etc.) of the reported 2014 and 2017 avian and human H7N9 datasets. The initial set of virus sequences (as of year 2014) exhibited a total of 109 avian-to-human (A2H) signature amino acid substitutions. Each represented the most prevalent substitution at a given avian virus nonamer position that was selectively adapted as the corresponding index (most prevalent sequence) of the human viruses. The majority of these avian substitutions were long-standing in the evolution of H7N9, and only 17 were first detected in 2013 as possibly essential for the initial human adaptation. Strikingly, continued evolution of the avian H7N9 virus has resulted in avian and human protein sequences that are almost identical. This rapid and continued adaptation of the avian H7N9 virus to the human host, with near identity of the avian and human viruses, is associated with increased human infection and a predicted greater risk of human-to-human transmission. [ABSTRACT FROM AUTHOR]

Submicron-sized carbon fibres have been attracting research interest due to their outstanding mechanical and electrical properties. However, the non-renewable resources and their complex fabrication processes limit the scalability and pose difficulties for the utilisation of these materials. Here, we investigate the use of plasma arc technology to convert renewable electrospun lignin fibres into a new kind of carbon fibre with a globular and porous microstructure. The influence of arc currents (up to 60 A) on the structural and morphological properties of as-prepared carbon fibres is discussed. Owing to the catalyst-free synthesis, high purity micro-structured carbon fibres with nanocrystalline graphitic domains are produced. Furthermore, the humidity sensing characteristics of the treated fibres at room temperature (23 °C) are demonstrated. Sensors produced from these carbon fibres exhibit good humidity response and repeatability in the range of 30% to 80% relative humidity (RH) and an excellent sensitivity (0.81/%RH) in the high RH regime (60–80%). These results demonstrate that the plasma arc technology has great potential for the development of sustainable, lignin-based carbon fibres for a broad range of application in electronics, sensors and energy storage. [ABSTRACT FROM AUTHOR]

Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets. [ABSTRACT FROM AUTHOR]

Cyber security has recently received enormous attention in today's security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today's security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree ("IntruDTree") machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model. [ABSTRACT FROM AUTHOR]

A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives. [ABSTRACT FROM AUTHOR]

Sea buckthorn (Hippophae rhamnoides L.) is a dioecious, wind-pollinated shrub growing in Eurasia including the Karakoram Mountains of Pakistan (Gilgit-Baltistan territory). Contrary to the situation in other countries, in Pakistan this species is heavily underutilized. Moreover, a striking diversity of berry colors and shapes in Pakistan raises the question: which varieties might be more suitable for different national and international markets? Therefore, both morphological and genetic diversity of sea buckthorn were studied to characterize and evaluate the present variability, including hypothetically ongoing domestication processes. Overall, 300 sea buckthorn individuals were sampled from eight different populations and classified as wild and supposedly domesticated stands. Dendrometric, fruit and leaf morphometric traits were recorded. Twelve EST-SSRs (expressed sequence tags-simple sequence repeats) markers were used for genotyping. Significant differences in morphological traits were found across populations and between wild and village stands. A significant correlation was found between leaf area and altitude. Twenty-two color shades of berries and 20 dorsal and 15 ventral color shades of leaves were distinguished. Mean genetic diversity was comparatively high (He = 0.699). In total, three distinct genetic clusters were observed that corresponded to the populations' geographic locations. Considering high allelic richness and genetic diversity, the Gilgit-Baltistan territory seems to be a promising source for selection of improved germplasm in sea buckthorn. [ABSTRACT FROM AUTHOR]

Devastating floods adversely affect human life and infrastructure. Various regions of the Hindukush-Karakoram-Himalayas receive intense monsoon rainfall, which, together with snow and glacier melt, produce intense floods. The Kabul river basin originates from the Hindukush Mountains and is frequently hit by such floods. We analyses flood frequency and intensity in Kabul basin for a contemporary period (1981–2015) and two future periods (i.e., 2031–2050 and 2081–2100) using the RCP4.5 and RCP8.5 scenarios based on four bias-corrected downscaled climate models (INM-CM4, IPSL-CM5A, EC-EARTH, and MIROC5). Future floods are modelled with the SWAT hydrological model. The model results suggest an increasing trend due to an increasing precipitation and higher temperatures (based on all climate models except INM-CM4), which accelerates snow and glacier-melt. All of the scenario results show that the current flow with a 1 in 50 year return period is likely to occur more frequently (i.e., 1 in every 9–10 years and 2–3 years, respectively) during the near and far future periods. Such increases in intensity and frequency are likely to adversely affect downstream population and infrastructures. This, therefore, urges for appropriate early precautionary mitigation measures. This study can assist water managers and policy makers in their preparation to adequately plan for and manage flood protection. Its findings are also relevant for other basins in the Hindukush-Karakoram-Himalayas region. [ABSTRACT FROM AUTHOR]