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Silicon nanoparticles (Si-NPs) have shown their potential for use in farming under water-deficient conditions. Thus, the experiment was accomplished to explore the impacts of seed priming of Si-NPs on wheat ( Triticum aestivum L.) growth and yield under different drought levels. The plants were grown in pots under natural ecological environmental conditions and were harvested on 25th of April, 2020. The results revealed that seed priming of Si-NPs (0, 300, 600, and 900 mg/L) suggestively improved, the spike length, grains per spike, 1000 grains weight, plant height, grain yield, and biological yield by 12–42%, 14–54%, 5–49%, 5–41%, 17–62%, and 21–64%, respectively, relative to the control. The Si-NPs improved the leaf gas trade ascribes and chlorophyll a and b concentrations, though decreased the oxidative pressure in leaves which was demonstrated by the diminished electrolyte leakage and upgrade in superoxide dismutase and peroxidase activities in leaf under Si-NPs remedies over the control. The outcomes proposed that Si-NPs could improve the yield of wheat under a dry spell. In this manner, the utilization of Si-NPs by seed priming technique is a practical methodology for controlling the drought stress in wheat. These findings will provide the basis for future research and helpful to improve the food security under drought and heat related challenges.

In recent years, the Internet of Things (IoT) has had a big impact on both industry and academia. Its profound impact is particularly felt in the industrial sector, where the Industrial Internet of Things (IIoT), also known as Industry 4.0, is revolutionizing manufacturing and production through the fusion of cutting-edge technologies and network-embedded sensing devices. The IIoT revolutionizes several industries, including crucial ones such as oil and gas, water purification and distribution, energy, and chemicals, by integrating information technology (IT) with industrial control and automation systems. Water, a vital resource for life, is a symbol of the advancement of technology, yet knowledge of potential cyberattacks and their catastrophic effects on water treatment facilities is still insufficient. Even seemingly insignificant errors can have serious consequences, such as aberrant pH values or fluctuations in the concentration of hydrochloric acid (HCI) in water, which can result in fatalities or serious diseases. The water purification and distribution industry has been the target of numerous hostile cyber security attacks, some of which have been identified, revealed, and documented in this paper. Our goal is to understand the range of security threats that are present in this industry. Through the lens of IIoT, the survey provides a technical investigation that covers attack models, actual cases of cyber intrusions in the water sector, a range of security difficulties encountered, and preventative security solutions. We also explore upcoming perspectives, illuminating the predicted advancements and orientations in this dynamic subject. For industrial practitioners and aspiring scholars alike, our work is a useful, enlightening, and current resource. We want to promote a thorough grasp of the cybersecurity landscape in the water industry by combining key insights and igniting group efforts toward a safe and dependable digital future.

Social media has become a powerful tool for public discourse, shaping opinions and the emotional landscape of communities. The extensive use of social media has led to a massive influx of online content. This content includes instances where negativity is amplified through hateful speech but also a significant number of posts that provide support and encouragement, commonly known as hope speech. In recent years, researchers have focused on the automatic detection of hope speech in languages such as Russian, English, Hindi, Spanish, and Bengali. However, to the best of our knowledge, detection of hope speech in Urdu and English, particularly using translation-based techniques, remains unexplored. To contribute to this area we have created a multilingual dataset in English and Urdu and applied a translation-based approach to handle multilingual challenges and utilized several state-of-the-art machine learning, deep learning, and transfer learning based methods to benchmark our dataset. Our observations indicate that a rigorous process for annotator selection, along with detailed annotation guidelines, significantly improved the quality of the dataset. Through extensive experimentation, our proposed methodology, based on the Bert transformer model, achieved benchmark performance, surpassing traditional machine learning models with accuracies of 87% for English and 79% for Urdu. These results show improvements of 8.75% in English and 1.87% in Urdu over baseline models (SVM 80% English and 78% in Urdu).

The present study aimed to prepare solid lipid-based nanoparticles (SLNs) using Precirol ® ATO 5 as solid lipid and Poloxamer 188 and Tween 80 as surfactant and co-surfactant respectively, and SLNs-derived gel for sustained delivery, enhanced in-vitro cytotoxicity, enhanced cellular uptake of 5-FU and enhanced permeation of 5-FU across the skin. The 5-FU-loaded SLNs were prepared by the hot melt encapsulation method and converted into SLN-derived gel using a gelling agent (Carbopol 940). The 5-FU-loaded SLNs had a particle size in the range of 76.82±1.48 to 327±4.46 nm, zeta potential between -11.3±2.11 and -28.4±2.40 mV, and entrapment efficiency (%) in range of 63.46±1.13 and 76.08±2.42. The FTIR analysis depicted that there was no chemical interaction between 5-FU and formulation components. Differential scanning calorimetric analysis showed thermal stability of 5-FU in the nanoparticles and powdered X-ray diffraction analysis revealed successful incorporation of 5-FU in nanoparticles. The in-vitro release study of 5-FU-loaded SLNs showed biphasic release behavior with initial burst release followed by sustained release over 48 hr. The 5-FU-loaded SLNs showed a greater cytotoxic effect on skin melanoma (B16F10 cells) and squamous cell carcinoma (A-431 cells) as compared to free 5-FU drug solution after 48 hr. Flow cytometry and fluorescence microscopy displayed enhanced quantitative and qualitative cellular uptake of SLNs. The SLNs formulation showed acceptable safety and biocompatible profile after an acute toxicity study in Wistar rats. Moreover, ex-vivo permeation studies depicted 2.13±0.076 folds enhanced flux of 5-FU-loaded SLN derived gel compared to 5-FU plain gel, and skin retention studies revealed target efficiency (%) 2.54±0.03 of 5-FU-loaded SLN derived gel compared to 5-FU plain gel.

Lipid polymer hybrid nanoparticles (LPHNPs) for the controlled delivery of hydrophilic doxorubicin hydrochloride (DOX.HCl) and lipophilic DOX base have been fabricated by the single step modified nanoprecipitation method. Poly (D, L-lactide-co-glicolide) (PLGA), lecithin, and 1,2-distearoyl-Sn-glycero-3-phosphoethanolamine-N-[methoxy (polyethylene glycol)-2000 (DSPE-PEG 2000) were selected as structural components. The mean particle size was 173-208 nm, with an encapsulation efficiency of 17.8±1.9 to 43.8±4.4% and 40.3±0.6 to 59. 8±1.4% for DOX.HCl and DOX base, respectively. The drug release profile was in the range 33-57% in 24 hours and followed the Higuchi model (R =0.9867-0.9450) and Fickian diffusion (n<0.5). However, the release of DOX base was slower than DOX.HCl. The in vitro cytotoxicity studies and confocal imaging showed safety, good biocompatibility, and a higher degree of particle internalization. The higher internalization of DOX base was attributed to higher permeability of lipophilic component and better hydrophobic interaction of particles with cell membranes. Compared to the free DOX, the DOX.HCl and DOX base loaded LPHNPs showed higher antiproliferation effects in MDA-MB231 and PC3 cells. Therefore, LPHNPs have provided a potential drug delivery strategy for safe, controlled delivery of both hydrophilic and lipophilic form of DOX in cancer cells.

Sparse matrix–vector multiplication (SpMV) plays a significant role in the computational costs of many scientific applications such as 2D/3D robotics, power network problems, and computer vision. Numerous implementations using different sparse matrix formats have been introduced to optimize this kernel on CPUs and GPUs. However, due to the sparsity patterns of matrices and the diverse configurations of hardware, accurately modeling the performance of SpMV remains a complex challenge. SpMV computation is often a time-consuming process because of its sparse matrix structure. To address this, we propose a machine learning-based tool, namely Elegante+, that predicts optimal scheduling policies by analyzing matrix structures. This approach eliminates the need for repetitive trial and error, minimizes errors, and finds the best solution of the SpMV kernel, which enables users to make informed decisions about scheduling policies that maximize computational efficiency. For this purpose, we collected 1000+ sparse matrices from the SuiteSparse matrix market collection and converted them into the compressed sparse row (CSR) format, and SpMV computation was performed by extracting 14 key sparse matrix features. After creating a comprehensive dataset, we trained various machine learning models to predict the optimal scheduling policy, significantly enhancing the computational efficiency and reducing the overhead in high-performance computing environments. Our proposed tool, Elegante+ (XGB with all SpMV features), achieved the highest cross-validation score of 79% and performed five times faster than the default scheduling policy during SpMV in a high-performance computing (HPC) environment.

Ensuring reliable Internet connectivity in Industrial Control Systems is critical for real-time monitoring and anomaly detection. Existing methods, however, struggle with high computational complexity, limited applicability to specific datasets, and elevated false-positive rates. This paper presents a novel collaborative data processing framework that enhances anomaly detection in ICS by integrating the Grey Wolf Optimizer with Autoencoders. The proposed approach optimizes GWO by improving prey selection, encircling mechanisms, and initial population generation, while enhancing AE dropout functionality for improved model generalization. The method operates in two stages: (1) Optimizing GWO for feature selection to identify relevant features and reduce feature errors, and (2) Utilizing AE for efficient anomaly detection. Experimental validation on the SWaT and WADI benchmark datasets demonstrates the superior performance of the proposed model, achieving significant improvements in accuracy, precision, recall, and F1-score over existing state-of-the-art approaches. These results highlight the potential of the proposed approach in addressing the limitations of current anomaly detection systems in ICS.

In the current digital era, the Google Play Store and the App Store are major platforms for the distribution of mobile applications and games. Billions of users regularly download mobile games and provide reviews, which serve as a valuable resource for game vendors and developers, offering insights into bug reports, feature suggestions, and documentation of existing functionalities. This study showcases an innovative application of fine-tuned RoBERTa for detecting bugs in mobile phone games, highlighting advanced classification capabilities. This approach will increase player satisfaction, lead to higher ratings, and improve brand reputation for game developers, while also reducing development costs and saving time in creating high-quality games. To achieve this goal, a new bug detection dataset was created. Initially, data were sourced from four top-rated mobile games from multiple domains on the Google Play Store and the App Store, focusing on bugs, using the Google Play API and App Store API. Subsequently, the data were categorized into two classes: binary and multi-class. The Logistic Regression, Convolutional Neural Network (CNN), and pre-trained Robustly Optimized BERT Approach (RoBERTa) algorithms were used to compare the results. We explored the strength of pre-trained RoBERTa, which demonstrated its ability to capture both semantic nuances and contextual information within textual content. The results showed that pre-trained RoBERTa significantly outperformed the baseline models (Logistic Regression), achieving superior performance with a 5.49% improvement in binary classification and an 8.24% improvement in multi-class classification, resulting in cross-validation scores of 96% and 92%, respectively.

PurposeWhen bending a large diameter thin-walled tube, the thickn ess of outer side wall will reduce greatly, which leads to a decrease of structural strength of the tube. To solve this problem, this paper investigated the deformation principles of an eccentric tube in the rotary draw bending process, trying to find a way to reduce the wall thickness difference between inner and outer diameters.Design/methodology/approachAn finite element model is established for analyzing the deformation of an eccentric tube in rotary draw bending process. The wall thickness distribution of the formed pipe was analyzed along the axis and diameter, respectively.FindingsIt is found that there exists an optimal eccentricity between the inner and outer circle center of the tube cross-section. If the eccentricity of the tube is chosen properly, it is possible to get a bent tube with equal thickness of inner and outer side walls. In addition, it is also found the optimal eccentricity on the cross-section can be influenced by bending radius, wall thickness, diameter and bending angle. The optimal eccentricity increases greatly with the decreasing of bending radius, the increase of outer diameter and the increase of wall thickness. The influence of bending angle on the optimal eccentricity can be divided into two situations. When the bending angle is small, the optimal eccentricity increases with the increase of bending angle. When the bending angle exceeds a certain value, the pipe enters a stable forming state. The optimal eccentricity of the stable forming region does not change with the bending angle.Originality/valueSuch a research is beneficial for reducing the thickness difference between inner and outer side walls in the rotary draw bending process.

Cyanobacteria, being a prominent category of phototrophic organism, exhibit substantial potential as a valuable source of bioactive compounds and phytonutrients, including liposomes, amino derivatives, proteins, and carotenoids. In this investigation, a polyphasic approach was employed to isolate and characterize a newly discovered cyanobacterial strain from a rice field in the Garh Moor district of Jhang. Desertifilum sp. TN-15, a unique and less explored cyanobacterial strain, holds significant promise as a novel candidate for the synthesis of nanoparticles. This noticeable research gap underscores the novelty and untapped potential of Desertifilum sp. TN-15 in the field of nanomedicine. The characterization of the biogenically synthesized ZnO–NPs involved the application of diverse analytical techniques. Ultraviolet–visible spectroscopy revealed a surface plasmon resonance peak at 298 nm. Fourier transform infrared spectral analysis was utilized to confirm the involvement of biomolecules in the biogenic synthesis and stability. Scanning electron microscopy was employed to probe the surface morphology of the biogenic ZnO–NPs unveiling their size of 94.80 nm and star-shaped. Furthermore, X-ray diffraction analysis substantiated the crystalline nature of ZnO–NPs, with a crystalline size measuring 46 nm. To assess the physical stability of ZnO–NPs, zeta potential and dynamic light scattering measurements were conducted, yielding values of + 31.6 mV, and 94.80 nm, respectively, indicative of favorable stability. The antibacterial capabilities of Desertifilum sp. TN-15 are attributed to its abundance of bioactive components, including proteins, liposomes, amino derivatives, and carotenoids. Through the synthesis of zinc oxide nanoparticles (ZnO–NPs) with this strain, we have effectively used these chemicals to generate nanoparticles that exhibit noteworthy antibacterial activity against Staphylococcus aureus (MIC: 30.05 ± 0.003 µg/ml). Additionally, the ZnO–NPs displayed potent antifungal activity and antioxidant properties, as well as significant antihemolytic effects on red blood cells (IC50: 4.8 µg/ml). Cytotoxicity assessment using brine shrimps revealed an IC50 value of 3.1 µg/ml. The multifaceted actions of the biogenically synthesized ZnO–NPs underscore their potential applications in pharmacological and therapeutic fields. This study proposes a novel method for ZnO–NPs production utilizing the recently identified cyanobacterial strain Desertifilum sp. TN-15, highlighting the growing significance of biological systems in the environmentally friendly fabrication of metallic oxide nanomaterials.