Search

Abstract Food crops are attacked by microbial pathogens and insect pests, leading to significant yield reductions and economic losses. Conventional disease diagnosis and management approaches often fail to provide rapid and eco-friendly solutions. In the current situation, nanomaterials (NMs) serve a valuable role in both managing emerging pathogens and monitoring overall plant health. Nanotechnology has transformed the biotechnology industry including agriculture with specific applications such as nano-fungicides, nano-bactericides, and nano-pesticides. This review focuses on the use of various nanomaterials, including inorganic materials such as Ag, ZnO, CuO, and CeO, as well as carbon-based nanoparticles, nanotubes, nanowires, and nano-capsules. The application of NMs holds the potential to address various challenges in food security through novel applications like advanced nano-biosensors for rapid pathogen detection and targeted disease management strategies. This includes the potential to minimize reliance on chemical inputs and contribute to more sustainable agricultural practices. Nanomaterials (NMs) promise to deliver plant hormones and signaling molecules to plants, enhancing resistance inducers against major crop pathogens. NMs against newly arising pathogens through reactive oxygen generation, membrane damage, and biochemical interference are also reviewed. However, challenges regarding the stability, toxicity, and environmental impacts of NMs are discussed, along with recommendations on green synthesis and functionalization approaches. This article aims to investigate the role of nanomaterials (NMs) in managing emerging pathogens and monitoring overall crop health offering an insightful outlook for future generations. Further biosafety aspects and larger-scale validation of NM-based applications could enable their commercialization for improving global food security. Graphical Abstract

Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective.

Abstract Hybrid Nano fluid has emerged to be an important field of study due to its better thermal performance compared to other Nano fluids. The problem of carbon nanotubes rotating between two stretchable discs while suspended in water is investigated in this research. Due to numerous uses of this problem, such as metal mining, drawing plastic films, and cooling continuous filaments, this problem is essential to industry. Considerations here include suction/injection, heat radiation, and the Darcy-Forchheimer scheme with convective boundary conditions. The partial differential equations are reduced to ordinary differential equations by using appropriate transformation. To examine the approximate solution validation, training and testing procedures are interpreted and the performance is verified through error histogram and mean square error results. To describe the behavior of flow quantities, several tabular and graphical representations of a variety of physical characteristics of importance are presented and discussed in detail. The basic aim of this research is to examine the behaviour of carbon nanotubes (nanoparticles) between stretchable disks while considering the heat generation/absorption parameter by using the Levenberg–Marquardt technique of artificial neural network. Heat transfer rate is accelerated by a decrease in velocity and temperature and an increase in the nanoparticle volume fraction parameter which is a significant finding of the current study.

Purpose – This study aims to investigate the effects of XR technologies on the behavioral intentions of disabled tourists using a modified Technology Acceptance Model (TAM). Design – The model includes perceived trust, perceived control, perceived efficacy, and perceived enjoyment as determinants of technology acceptance by disabled tourists. The hypotheses derived from the literature were empirically tested. Methodology/Approach – The proposed model was tested by collecting data from 640 disabled tourists in Iran over a six-month period. An online survey was answered after watching two videos, representing the application of VR and AR. A quantitative method was applied, the PLS-SEM method was conducted to analyze the data. Findings – The model was significantly supported by the results on the determinants of attitudes of people with disabilities toward XR technology. Other than the effect of perceived efficiency on PEOU, all the hypotheses were supported, demonstrating the positive effects of each of factors on the attitudes of disabled tourists and their behavioral intentions towards XR technology. Originality of the research – This study significantly expands the academic knowledge on the fundamental factors affecting behavioural intentions of disabled tourists, as 15% of the world’s population. This is the first study to investigate these factors in relation to the disabled tourists and XR technologies. This study will provide insight to marketers and stakeholders on the behavioral intentions of disabled tourists.

Hyperspectral remote sensing has been an important technical means to obtain more refined information and provide rich, accurate, and reasonable data for the quantitative analysis and delicacy management of a ''smart city''. To better understand and use the hyperspectral data to help the construction of a digital city, the study of the feature and characteristics of hyperspectral remote sensing images is introduced in this paper. Then how to collect the hyperspectral information of urban ground objects through the unmanned aerial vehicle (UAV) and hyperspectral imager was described, which greatly improves the efficiency of urban data acquisition. Finally, various application cases of UAV-based hyperspectral remote sensing and deep information mining of urban ground objects were analyzed and discussed in detail, such as terrain classification, urban greening analysis, etc. The research result shows that airborne hyperspectral imagery (HIS) has unique advantages over color photography and multispectral remote sensing, with a richer and higher level of spectral details and physical & chemical properties.

Recent advances in graphene research have enabled the utilization of its nanocomposites for numerous energy-based and environmental applications. Recently, the advancement in graphene-based polymer nanocomposites has received much attention with special emphasis on synthesis and application. Graphene-based nanocomposites show astonishing electrical, mechanical, chemical, and thermal characteristics. Graphene nanocomposites (GNCs) are synthesized using a variety of methods, including covalent and non-covalent methods, a chemical-based deposition approach, hydrothermal growth, electrophoresis deposition, and physical deposition. Chemical methods are the most viable route for producing graphene in small quantities at low temperatures. The technique can also produce graphene films on a variety of substrate materials. The use of artificial intelligence (AI) for the synthesis of AI-created nanoparticles has recently received a lot of attention. These nanocomposite materials have excellent applications in the environmental, energy, and agricultural sectors. Due to high carrier mobility, graphene-based materials enhance the photocatalytic performance of semiconductor materials. Similarly, these materials have high potential for pollutant removal, especially heavy metals, due to their high surface area. This article highlights the synthesis of graphene-based nanocomposites with special reference to harnessing the power of modern AI tools to better understand GNC material properties and the way this knowledge can be used for its better applications in the development of a sustainable future.

Citrus canker (CC), caused by one of the most destructive subfamilies of the bacterial phytopathogen Xanthomonas citri subsp. Citri (Xcc), poses a serious threat to the significantly important citrus fruit crop grown worldwide. This has been the subject of ongoing epidemiological and disease management research. Currently, five different forms have been identified of CC, in which Canker A (Xanthomonas citri subsp. citri) being the most harmful and infecting the majority of citrus cultivars. Severe infection symptoms include leaf loss, premature fruit drop, dieback, severe fruit blemishing or discoloration, and a decrease in fruit quality. The infection spreads rapidly through wind, rain splash, and warm and humid climates. The study of the chromosomal and plasmid DNA of bacterium has revealed the evolutionary pattern among the pathovars, and research on the Xcc genome has advanced our understanding of how the bacteria specifically recognize and infect plants, spread within the host, and propagates itself. Quarantine or exclusion programs, which prohibit the introduction of infected citrus plant material into existing stock, are still in use. Other measures include eliminating sources of inoculum, using resistant hosts, applying copper spray for protection, and implementing windbreak systems. The main focus of this study is to highlight the most recent developments in the fields of Xcc pathogenesis, epidemiology, symptoms, detection and identification, host range, spread, susceptibility, and management. Additionally, it presents an analysis of the economic impact of this disease on the citrus industry and suggests strategies to reduce its spread, including the need for international collaboration and research to reduce the impact of this disease on the global citrus industry.

Business process models are the conceptual models to depict the workflow of an organization. Process model matching (PMM) refers to the automatic identification of corresponding activities between a pair of process models that show similar or the same behavior. During the last few years, PMM has received much of the researchers' attention due to its wide range of applications, such as clone detection and harmonization of process models. Consequently, a plethora of PMM techniques has been developed. In order to evaluate the effectiveness of these techniques, experts have developed three benchmark datasets, formally called PMMC'15 datasets. Furthermore, the process models in the datasets have been converted into OAEI'17 ontologies. These resources are a valuable asset for the PMM community to evaluate process model matching techniques. However, these resources (PMMC'15 and OAEI'17) are limited to fewer models and a handful collection of corresponding activities among these models that may not be sufficient to rigorously evaluate the PMM techniques. To fill this gap, this paper provides a large, diverse, and a carefully handcrafted collection of process models, along with their benchmark correspondences. The process model collection and benchmark correspondences between these models are freely available for the community [1]. Our newly developed dataset, together with the existing resources, can be used for a thorough evaluation of PMM techniques, especially in the context of the vocabulary mismatch problem. At last, we have evaluated the characteristics of our dataset by a series of experiments while involving widely used similarity measures in PMM research. The results reveal that our dataset is larger, diverse, and challenging as compared to existing datasets in the PMM domain.

Refeeding syndrome is a rare but preventable complication of malnutrition management. This is the second case of refeeding syndrome being reported from Pakistan. Although many cases have been reported worldwide. It is a clear indication of lack of knowledge of the entity (Refeeding syndrome) in our doctor’s community. Refeeding syndrome presents with a mix picture of clinical manifestations and thus it is necessary to keep a regular check and balance of electrolyte disturbance especially serum phosphate levels before and after initiating nutrition irrespective of the route nutrition are given through. We are reporting a case of refeeding syndrome in a severely malnourished 5-year-old child which unfortunately died despite taking strict lifesaving actions.

Electrophoretic deposition (EPD) of graphene oxide (GO) was carried out on Neodymium iron boron (NdFeB) magnets to study its potential as corrosion resistant coating. GO was produced from powder graphite by Hummers method. The GO was obtained by sonicating graphite oxide/water suspension and GO coatings were deposited on NdFeB magnets by making magnets anode in EPD process and by keeping voltage 10 V and time of deposition 1-2 min. GO and GO coating morphology and structure were studied by atomic force microscopy and x-ray diffraction, respectively. Electrophoretically deposited GO coatings on NdFeB were fairly uniform with some porosity. Potentiodynamic polarization and electrochemical impedance spectroscopy were used to study corrosion behavior of the GO coatings. GO coating reduced the corrosion rate of NdFeB by three times than the bare metal. The results show that GO coatings offer excellent corrosion resistant to NdFeB magnets in 3.5 % NaCl solution.

Acrocallosal syndrome is a rare autosomal recessive disorder. The prevalence of the disease is not known but fewer than 55 cases have been published since 1979. ACS is characterized by the total or partial absence of the corpus callosum, Minor craniofacial anomalies (prominent forehead, hypertelorism, short nose with anteverted nostrils and large anterior fontanel) moderate to severe psychomotor retardation (with hypotonia), polydactyly or polysyndactyly. We are reporting a case of 6 month old infant child who was admitted to the hospital with pneumonia, measles, episodes of fits, large anterior fontanelle, prominent forehead, high arched palate, fused tongue, micrognathism , hypotonia, motor developmental delay , polysyndactyly and absent corpus callosum. His clinical diagnosis was confirmed by CT-Brain. The patient was managed for superimposed pneumonia and measles. He was provided with other supportive treatment as well as parental counseling, physiotherapy sessions, and multiple disciplinary approaches was undertaken for further management.

The research investigates the Workplace Environment in Karachi Textile Industry towards Emotional Health. The paper utilizes Multiple Regression model to observe the internal environment of textile industry as well as for the implementation of healthful environment for the workers of textile at this time in Karachi. Primary data was collected by questionnaires circulated in four different textile companies, two local and two multinational. This paper also studies the impacts of light, temperature, noise and emotional effects on employees and on their daily life. The result revealed that among all factors health safety is the core element that is affecting on Job Satisfaction and required more deliberate.

The article presents a comprehensive and conceptual framework on key factors of intercultural interaction influencing customer satisfaction, which is one of the most important indicators of tourism industry in Malaysia. The theory-based path model of constructs and their possible interrelationships will be proposed such that the future analysis would be based on structural equation modeling approach or related statistical methods. In other words, this study anticipates to propose an applicable technique to empirically examine the inter relationships of intercultural communication competence, interaction comfort, inter-role congruence, perceived cultural distance, previous experience and satisfaction. It is basically a literature reviews on the related constructs and how the relationships’ assessment of those constructs should be carried out. Although numerous researchers have studied the extensive topics of cultural distance and customer satisfaction, none of those studies has explored on the critical role of intercultural communication competence and previous experience on customer satisfaction in the industry.

Abstract In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrombin to thrombin. Contrariwise, 4-carboxy-glutamate is also found to be involved in diseases including plaque atherosclerosis, osteoporosis, mineralized heart valves, bone resorption and serves as biomarker for onset of these diseases. Owing to the pathophysiological significance of 4-carboxyglutamate, its identification is important to better understand pathophysiological systems. The wet lab identification of prospective 4-carboxyglutamate sites is costly, laborious and time consuming due to inherent difficulties of in-vivo, ex-vivo and in vitro experiments. To supplement these experiments, we proposed, implemented, and evaluated a different approach to develop 4-carboxyglutamate site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Our approach does not require any feature extraction and employs deep neural networks to learn feature representation of peptide sequences and performing classification thereof. Proposed approach is validated using standard performance evaluation metrics. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 94.7%, AuC score of 0.91 and F1-score of 0.874 which shows the promise of proposed approach. The iCarboxE-Deep server is deployed at https://share.streamlit.io/sheraz-n/carboxyglutamate/app.py .

In the biological systems, Acetylation is a crucial post-translational modification, prevalent in various physiological functions and pathological conditions like carcinoma and malignancies. To better understand serine acetylation, the first step is the efficient identification of the same. Although multiple large-scale in-vivo, ex-vivo, and in-vitro methods have been applied to detect serine acetylation biomarkers, these experimental methods are time-consuming and labor-intensive. This research aims to develop an in-silico solution to supplement wetlab experiments for efficient detection of serine acetylation sites by combining Chou’s Pseudo Amino Acid Composition (PseAAC) with deep neural networks (DNNs). By employing well-known DNNs for feature learning and classification of peptide sequences, our approach obsoletes the need to separately perform costly and cumbersome feature learning process. Based on performance evaluation using standard evaluation metrics, CNN and FCN based models, for AcetylSerine site identification, surpassed previously reported predictors which shows the efficacy of proposed approach.