Explore the vast range of titles available.

In this study, several simple equations are suggested to investigate the effects of size and density on the number, surface area, stiffening efficiency, and specific surface area of nanoparticles in polymer nanocomposites. In addition, the roles of nanoparticle size and interphase thickness in the interfacial/interphase properties and tensile strength of nanocomposites are explained by various equations. The aggregates/agglomerates of nanoparticles are also assumed as large particles in nanocomposites, and their influences on the nanoparticle characteristics, interface/interphase properties, and tensile strength are discussed. The small size advantageously affects the number, surface area, stiffening efficiency, and specific surface area of nanoparticles. Only 2 g of isolated and well-dispersed nanoparticles with radius of 10 nm ( R  = 10 nm) and density of 2 g/cm 3 produce the significant interfacial area of 250 m 2 with polymer matrix. Moreover, only a thick interphase cannot produce high interfacial/interphase parameters and significant mechanical properties in nanocomposites because the filler size and aggregates/agglomerates also control these terms. It is found that a thick interphase ( t  = 25 nm) surrounding the big nanoparticles ( R  = 50 nm) only improves the B interphase parameter to about 4, while B  = 13 is obtained by the smallest nanoparticles and the thickest interphase.

Silver nanoparticles are potent antimicrobials and could be used as a promising alternative of conventional antibiotics. The aim of this study was to isolate bacteria from soil that have ability to produce AgNPs by secondary metabolite activity and their elucidation against human pathogens. These strains Escherichia coli , Exiguobacterium aurantiacumm , and Brevundimonas diminuta with NCBI accession number MF754138, MF754139, and MF754140 respectively were grown for secondary metabolite production. The nanoparticles were confirmed and characterized by UV-Vis spectroscopy and transmission electron microscopy. The optimization study was also carried out to obtain the maximum production of silver nanoparticles. Three parameters, temperature, pH, and AgNO3 concentration, were used to optimize the production of silver nanoparticles. Antimicrobial potential of these nanoparticles was addressed on the Muller-Hinton Agar, and their zones of inhibitions were measured. TEM analysis revealed the size and shape of the silver nanoparticles. All types of AgNPs were spherical in shape; their size range is from 5 to 50 nm. The findings of optimization study showed the maximum production of silver nanoparticles at the pH 9, temperature 37 °C, and 1 mM AgNO3 concentration. All the strains exhibited the great potential as antimicrobial agents against MRSA and several other MDR bacteria with minimum 10 mm to maximum 28 mm zone of inhibition. It was concluded that the present study is an eco-friendly approach for the synthesis of AgNPs that will be beneficial to control the nosocomial infections triggered by MRSA and other human pathogens.

To ward off pathogens and pests, plants use a sophisticated immune system. They use pattern-recognition receptors (PRRs), as well as nucleotide-binding and leucine-rich repeat (NB-LRR) domains, for detecting nonindigenous molecular signatures from pathogens. Plant PRRs induce local and systemic immunity. Plasma-membrane-localized PRRs are the main components of multiprotein complexes having additional transmembrane and cytosolic kinases. Topical research involving proteins and their interactive partners, along with transcriptional and posttranscriptional regulation, has extended our understanding of R-gene-mediated plant immunity. The unique LRR domain conformation helps in the best utilization of a surface area and essentially mediates protein–protein interactions. Genome-wide analyses of inter- and intraspecies PRRs and NB-LRRs offer innovative information about their working and evolution. We reviewed plant immune responses with relevance to PRRs and NB-LRRs. This article focuses on the significant functional diversity, pathogen-recognition mechanisms, and subcellular compartmentalization of plant PRRs and NB-LRRs. We highlight the potential biotechnological application of PRRs and NB-LRRs to enhance broad-spectrum disease resistance in crops.

Nitrogen is the main limiting nutrient after carbon, hydrogen and oxygen for photosynthetic process, phyto-hormonal, proteomic changes and growth-development of plants to complete its lifecycle. Excessive and inefficient use of N fertilizer results in enhanced crop production costs and atmospheric pollution. Atmospheric nitrogen (71%) in the molecular form is not available for the plants. For world’s sustainable food production and atmospheric benefits, there is an urgent need to up-grade nitrogen use efficiency in agricultural farming system. The nitrogen use efficiency is the product of nitrogen uptake efficiency and nitrogen utilization efficiency, it varies from 30.2 to 53.2%. Nitrogen losses are too high, due to excess amount, low plant population, poor application methods etc., which can go up to 70% of total available nitrogen. These losses can be minimized up to 15–30% by adopting improved agronomic approaches such as optimal dosage of nitrogen, application of N by using canopy sensors, maintaining plant population, drip fertigation and legume based intercropping. A few transgenic studies have shown improvement in nitrogen uptake and even increase in biomass. Nitrate reductase, nitrite reductase, glutamine synthetase, glutamine oxoglutarate aminotransferase and asparagine synthetase enzyme have a great role in nitrogen metabolism. However, further studies on carbon–nitrogen metabolism and molecular changes at omic levels are required by using “whole genome sequencing technology” to improve nitrogen use efficiency. This review focus on nitrogen use efficiency that is the major concern of modern days to save economic resources without sacrificing farm yield as well as safety of global environment, i.e. greenhouse gas emissions, ammonium volatilization and nitrate leaching.

Contamination of soils with heavy metals (HMs) caused serious problems because plants tend to absorb HMs from the soil. In view of HM hazards to plants as well as agro-ecosystems, we executed this study to assess metal toxicity to mung bean ( Vigna radiata ) plants cultivated in soil with six treatment levels of cadmium (Cd) and nickel (Ni) and to find metal tolerant variety, i.e., M-93 (V 1 ) and M-1(V 2 ) with multifarious plant biochemical and physiological attributes. Increasing doses of Cd and Ni inhibited plant growth and photosynthesis and both varieties showed highly significant differences in the morpho-physiological attributes. V 2 showed sensitivity to Cd and Ni treatments alone or in combination. Tolerance indices for attributes presented a declined growth of Vigna plants under HM stress accompanied by highly significant suppression in gas exchange characteristics. Of single element applications, the adverse effects on mung bean were more pronounced in Cd treatments. V 1 showed much reduction in photosynthesis attributes except sub-stomatal CO 2 concentration in all treatments compared to V 2 . The yield attributes, i.e., seed yield/plant and 100-seed weight, were progressively reduced in T 5 for both varieties. In combination, we have observed increased mobility of Cd and Ni in both varieties. The results showed that water use efficiency (WUE) generally increased in all the treatments for both varieties compared to control. V 2 exhibited less soluble sugars and free amino acids compared to V 1 in all the treatments. Similarly, we recorded an enhanced total free amino acid contents in both varieties among all the metal treatments against control plants. We conclude that combinatorial treatment proved much lethal for Vigna plants, but V 1 performed better than V 2 in counteracting the adverse effects of Cd and Ni.

Over the last decade, the demand for early diagnosis of breast cancer has resulted in new research avenues. According to the world health organization (WHO), a successful treatment plan can be provided to individuals suffering from breast cancer once the non-communicable disease is diagnosed at an early stage. An early diagnosis of cure disease can reduce mortality all over the world. Computer-Aided Diagnosis (CAD) tools are widely implemented to diagnose and detect different kinds of abnormalities. In the last few years, the use of the CAD system has become common to increase the accuracy in different research areas. The CAD systems have minimum human intervention and producing accurate results. In this study, we proposed a CAD technique for the diagnosis of breast cancer using a Deep Convolutional Neural Network followed by Softmax classifier. The proposed technique was tested on the Wisconsin Breast Cancer Datasets (WBCD). The proposed classifier produced an accuracy of 100% and 99.1% for two different datasets, which indicates effective diagnostic capabilities and promising results. Moreover, we test our proposed architecture with different train-test partitions.

Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds.

In the aftermath of the 2022 Pakistan flooding, disaster management faced critical challenges, particularly in mental health support. This study analyzed an incident where eighteen internally displaced individuals lost their lives in a bus fire. The current approach involves a comprehensive analysis of the incident, exploring the difficulties encountered in managing relief efforts, and providing mental health support. The study aims were to evaluate existing mental health support mechanisms, to identify challenges in disaster management, and to propose recommendations for future preparedness. Recommendations include enhancing disaster response training, integrating mental health services into primary health care, and prioritizing community resilience. These insights contribute to a deeper understanding of disaster management in resource-constrained regions.

In this work, we present a simple, environmentally friendly and economical route for the preparation of a novel copper-Schiff-base organometallic complex on Fe 3 O 4 nanoparticles (Fe 3 O 4 @Schiff-base-Cu) using an inexpensive and simple method and available materials. This magnetic nanocatalyst was comprehensively characterized using Fourier transform infrared spectroscopy (FT-IR), X-Ray Diffractometer (XRD), inductively coupled plasma atomic emission spectroscopy (ICP), energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), X-ray mapping, thermogravimetric analysis (TGA) and vibrating sample magnetometer (VSM) analysis. In the second stage, the catalytic activity of this catalyst was studied in the synthesis of polyhydroquinoline derivatives via Hantzsch reaction in water as a green solvent. In this sense, simple preparation of the catalyst from the commercially available materials, high catalytic activity, simple operation, short reaction times, high yields and use of green solvent can be regarded as some advantages of this protocol. In addition, it is worth mentioning that this nanocatalyst was easily recovered using external magnet and reused for several times without significant loss of its catalytic efficiency. Finally, the leaching, heterogeneity and stability of Fe 3 O 4 @Schiff-base-Cu were studied by hot filtration test and ICP technique. Graphic Abstract A green and novel Fe 3 O 4 @Schiff-base-Cu catalyst sucssesfully was prepared and characterized. This catalyst can be used for the Synthesis of polyhydroquinolines in water as the green solvent. This catalyst could be recovered easily and reused many times without important decrease in efficiency.