As the world second largest economy, China aims at achieving high-quality and innovation-driven development. This paper investigates the effects of different technological innovations on green ...productivity in 261 Chinese cities from 2004 to 2017. We employ the super-efficiency Epsilon-based measure (EBM) model with undesirable outputs under meta frontier and a global Malmquist-Luenberger (GML) index. We decompose the GML index into efficiency change (GEC) index and technology change (GTC) index to measure the dynamic changes of China's urban green development. Results showed that the average GML index is 0.924 thereby, indicating China's urban green development is at a decreasing trend. The eastern region had the highest GML index among three regions followed by the western region. Moreover, we explored the determinants of urban green productivity through panel quantile regression and found the heterogeneous impacts of different technological innovations on green productivity. Invention patents and design patents positively affected the urban green productivity while utility patents exerted a significantly negative influence. Invention patents promoted the GTC and GEC index while utility patents impeded at some quantiles. FDI yielded both “Pollution Halo Hypothesis” and “Pollution Heaven Hypothesis” effects. Invention, utility and design patents all have threshold effects on China's green productivity.
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•Fabrication of PVA/Nanocellulose/Ag nanocomposite films by solution casting method.•Synthesis of starch-capped silver nanoparticlres (Ag NPs).•Good antibacterial activity against ...Gram-positive and Gram-negative bacteria.•The addition of NC and Ag NPs enhanced the mechancial and thermal properties.•Water vapor transmission rate was decreased with the incorporation of NC and Ag NPs.
Antimicrobial packaging is an area of emerging interest and is rapidly expanding with application of nanotechnology. The present work investigates the effect of nanocellulose (NC) and Ag NPs on the physical, mechanical and thermal properties of PVA nanocomposite films. The tensile strength of PVA was improved from 5.52 ± 0.27 MPa to 12.32 ± 0.61 MPa when filled with 8 wt% of NC. Nanocomposite films exhibited strong antibacterial activity against both Staphylococcus aureus (MRSA) and Escherichia coli (DH5-alpha). The maximum inhibition zone at 0.5 g Ag NPs with 12 wt% NC against DH5-alpha was 14 ± 0.70 mm. While, the maximum inhibition zone at 0.3 g Ag NPs for 16 wt% NC was 13.6 ± 0.68 mm against MRSA. Moreover, nanocomposites films have no cytotoxicity effect on HepG2 and cell viability was more than 90%. Based on mechanical properties and antibacterial potential of the developed nanocomposite films, it can be envisaged to use these films for packaging applications.
Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the ...healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
•The effects of various CXR enhancement techniques were extensively studied on plain and segmented CXR image classification.•This is the largest CXR and lung segmented image dataset comprising COVID-19, normal, and Non-COVID images.•A modified version of the U-Net model is proposed, it outperforms standard U-Net for the lung segmentation of CXR images.•The outcome of this study was verified by image visualization technique to confirm the findings of the deep networks.•Enhancement techniques, transfer learning, and lung segmentation resulted in superior results of COVID-19 detection.
•Application of self-healing polymers in packaging is introduced.•SSMD policy is proved to provide with more profit.•Effect of variable emissions and transportation is studied.•Carbon reduction ...policy for environmental protection is suggested.
Recent advances in the product packaging materials have enabled the supply chain management systems to adopt returnable transport packaging policies to achieve economic and environmental sustainability. The application of the advanced self-healing polymers in packaging material has enabled the packaging to withstand fatigue associated failures with the increased mechanical strength. In this perspective, this paper develops a multi-attribute closed-loop supply chain model for self-healing polymers based returnable transport packaging with single supplier, single manufacturer, and multi-retailers under budget and storage constraints. A single-setup-multi-delivery (SSMD) policy is recommended for the centralized decision making of the supplier and manufacturer in a proposed supply chain management to improve the economic sustainability. To depict the real world situations for environmental protection, the effect of the variable aspects of transportation and carbon emissions are minimized through the optimal production delivery strategies. Multi-objectives of the proposed supply chain model include profit maximization and carbon emissions minimization of the system. A weighted goal programming technique along with three distinct metaheuristic approaches are applied to obtain the efficient trade-off among model objectives. The experimental analysis is carried out to illustrate the practical implication of the proposed supply chain management model and numerical results are analyzed for their robustness. The experimental outcomes for the application of SSMD policy are compared with single-setup-single-delivery (SSSD) policy, which proves that the SSMD policy improves the total profit of the whole system by devising the optimal number of shipments. The sensitivity analysis is carried out to study the behavior of the key parameters involved in the proposed supply chain management for varying decision maker preferences and significant managerial insights are obtained.
Among different abiotic stresses, drought stress is the leading cause of impaired plant growth and low productivity worldwide. It is therefore essential to understand the process of drought tolerance ...in plants and thus to enhance drought resistance. Accumulating evidence indicates that phytohormones are essential signaling molecules that regulate diverse processes of plant growth and development under drought stress. Plants can often respond to drought stress through a cascade of phytohormones signaling as a means of plant growth regulation. Understanding biosynthesis pathways and regulatory crosstalk involved in these vital compounds could pave the way for improving plant drought tolerance while maintaining overall plant health. In recent years, the identification of phytohormones related key regulatory genes and their manipulation through state‐of‐the‐art genome engineering tools have helped to improve drought tolerance plants. To date, several genes linked to phytohormones signaling networks, biosynthesis, and metabolism have been described as a promising contender for engineering drought tolerance. Recent advances in functional genomics have shown that enhanced expression of positive regulators involved in hormone biosynthesis could better equip plants against drought stress. Similarly, knocking down negative regulators of phytohormone biosynthesis can also be very effective to negate the negative effects of drought on plants. This review explained how manipulating positive and negative regulators of phytohormone signaling could be improvised to develop future crop varieties exhibiting higher drought tolerance. In addition, we also discuss the role of a promising genome editing tool, CRISPR/Cas9, on phytohormone mediated plant growth regulation for tackling drought stress.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid ...screening of the COVID-19 infection with high accuracy can be crucially helpful to the healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis. This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.
To optimize the mechanical performance of fused deposition modelling (FDM) fabricated parts, it is necessary to evaluate the influence of process parameters on the resulting mechanical performance. ...The main focus of the study was to characterize the influence of the initial process parameters on the mechanical performance of thermoplastic polyurethane under a quasi-static and high strain rate (~2500 s−1). The effects of infill percentage, layer height, and raster orientation on the mechanical properties of an FDM-fabricated part were evaluated. At a quasi-static rate of loading, layer height was found to be the most significant factor (36.5% enhancement in tensile strength). As the layer height of the sample increased from 0.1 to 0.4 mm, the resulting tensile strength sample was decreased by 36.5%. At a high-strain rate of loading, infill percentage was found to be the most critical factor influencing the mechanical strength of the sample (12.4% enhancement of compressive strength at 100% as compared to 80% infill). Furthermore, statistical analysis revealed the presence of significant interactions between the input parameters. Finally, using an artificial neural networking approach, we evaluated a regression model that related the process parameters (input factors) to the resulting strength of the samples.
Plastering is a commonly used construction technique that traditionally relies on the use of conventional sand, which is becoming scarcer and hence has become environmentally unsustainable. This ...study aims to assess the technical feasibility and performance of marine sand, a residue of off-shore dredging operations, in plastering applications. The particle size distribution of the marine sand demonstrated its unsuitability in concreting applications due to the presence of finer materials in large quantities. The plastering mixture was evaluated for its mechanical, rheological, durability and hygroscopic properties. A comparative analysis between marine sand plaster and contemporary crushed stone sand plaster was also carried out. Workable plasters with optimal rebound losses and adequate open porosity, water absorption, water tightness and reasonable indoor comfort were categorised based on the available standards. The results indicated that plaster compositions incorporating marine sand, with cement-to-sand ratios of 1:3 and 1:4, demonstrated suitability for external plastering tasks. Additionally, a ratio of 1:5 was identified to be suitable for internal plastering applications, aligning with all the applicable codal provisions.
Abiotic stresses are the primary sources of crop losses globally. The identification of key mechanisms deployed and established by plants in response to abiotic stresses is necessary for the ...maintenance of their growth and persistence. Recent discoveries have revealed that phytohormones or plant growth regulators (PGRs), mainly jasmonic acid (JA), have increased our knowledge of hormonal signaling of plants under stressful environments. Jasmonic acid is involved in various physiological and biochemical processes associated with plant growth and development as well as plant defense mechanism against wounding by pathogen and insect attacks. Recent findings suggest that JA can mediate the effect of abiotic stresses and help plants to acclimatize under unfavorable conditions. As a vital PGR, JA contributes in many signal transduction pathways, i.e., gene network, regulatory protein, signaling intermediates and enzymes, proteins, and other molecules that act to defend cells from the harmful effects of various environmental stresses. However, JA does not work as an independent regulator, but acts in a complex signaling pathway along other PGRs. Further, JA can protect and maintain the integrity of plant cells under several stresses by up-regulating the antioxidant defense. In this review, we have documented the biosynthesis and metabolism of JA and its protective role against different abiotic stresses. Further, JA-mediated antioxidant potential and its crosstalk with other PGRs have also been discussed.
Sahiwal cattle is an indigenous cattle breed of Pakistan and mastitis is one of the major problems faced by Sahiwal cattle which hinders its production potential. The study was designed to ...investigate the milk microbiota of healthy and mastitic Sahiwal cattle as part of a multistep project to develop probiotics for the mitigation and control of mastitis. Milk samples of Sahiwal cattle (healthy clinical mastitis and subclinical mastitis) reared under similar husbandry and management practices were processed for 16S rRNA gene base metagenomics analysis. It is concluded that the milk microbiota of healthy sahiwal cattle has higher diversity and dominant taxa in the different groups may be used as signature microbes for mastitis susceptibility. Akkermansia muciniphila is one of candidate specie that was identified and may be used for development of probiotics.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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