Optimized and efficient image-based IoT malware detection method

With the widespread use of IoT applications, malware has become a difficult and sophisticated threat. Without robust security measures, a massive volume of confidential and classified data could be exposed to vulnerabilities through which hackers could do various illicit acts. As a result, improved network security mechanisms that can analyse network traffic and detect malicious traffic in real-time are required. In this paper, a novel optimized machine learning image-based IoT malware detection method is proposed using visual representation (i.e., images) of the network traffic. In this method, the ant colony optimizer (ACO)-based feature selection method was proposed to get a minimum number of features while improving the support vector machines (SVMs) classifier’s results (i.e., the malware detection results). Further, the PSO algorithm tuned the SVM parameters of the different kernel functions. Using a public dataset, the experimental results showed that the SVM linear function kernel is the best with an accuracy of 95.56%, recall of 96.43%, precision of 94.12%, and F1_score of 95.26%. Comparing with the literature, it was concluded that bio-inspired techniques, i.e., ACO and PSO, could be used to build an effective and lightweight machine-learning-based malware detection system for the IoT environment

Structural behavior of Lightweight and High strength Layered Hollow Core Slabs

A new technique of Layered Hollow Core Slab (LHCS) has been used to obtain a slab with an optimum weight-to-strength ratio. Specimens with a 90 mm top layer of High Strength Concrete (HSC) and a 90 mm bottom layer of Lightweight Aggregate Concrete (LWAC) were examined. Nine full-scale slabs with dimensions of 1600* 450* 180 mm were tested under a 4-point loading test. The %core, a/d, RFT ratio, and connection method were the different studied parameters. A push-out test was conducted on triplet specimens to study the bond strength at the interface between HSC jacket and LWAC cubes using bond agent material or shear dowels, or without treatment, to determine which method of them is suitable for connecting the two layers of the tested slabs. Load, deflection, ductility, strain, crack pattern, and mode of failure were studied. The results indicate that ultimate strength is enhanced with decreasing a/d and %core and with an increasing RFT ratio of the LHCS specimens. Using shear dowels ensures an efficient bond between the two layers of the tested slabs. ANSYS program used for modelling the slab. The numerical study accepted the experimental data with a variation of less than 10% for all slabs

Effect of fulvic acids on lead-induced oxidative stress to metal sensitive Vicia faba L. plant

Lead (Pb) is a ubiquitous environmental pollutant capable to induce various morphological, physiological, and biochemical functions in plants. Only few publications focus on the influence of Pb speciation both on its phytoavailability and phytotoxicity. Therefore, Pb toxicity (in terms of lipid peroxidation, hydrogen peroxide induction, and photosynthetic pigments contents) was studied in Vicia faba plants in relation with Pb uptake and speciation. V. faba seedlings were exposed to Pb supplied as Pb(NO3)2 or complexed by two fulvic acids (FAs), i.e. Suwannee River fulvic acid (SRFA) and Elliott Soil fulvic acid (ESFA), for 1, 12, and 24 h under controlled hydroponic conditions. For both FAs, Pb uptake and translocation by Vicia faba increased at low level (5 mg l−1), whereas decreased at high level of application (25 mg l−1). Despite the increased Pb uptake with FAs at low concentrations, there was no influence on the Pb toxicity to the plants. However, at high concentrations, FAs reduced Pb toxicity by reducing its uptake. These results highlighted the role of the dilution factor for FAs reactivity in relation with structure; SRFA was more effective than ESFA in reducing Pb uptake and alleviating Pb toxicity to V. faba due to comparatively strong binding affinity for the heavy metal

Physical complexity to model morphological changes at a natural channel bend

This study developed a two‐dimensional (2‐D) depth‐averaged model for morphological changes at natural bends by including a secondary flow correction. The model was tested in two laboratory‐scale events. A field study was further adopted to demonstrate the capability of the model in predicting bed deformation at natural bends. Further, a series of scenarios with different setups of sediment‐related parameters were tested to explore the possibility of a 2‐D model to simulate morphological changes at a natural bend, and to investigate how much physical complexity is needed for reliable modeling. The results suggest that a 2‐D depth‐averaged model can reconstruct the hydrodynamic and morphological features at a bend reasonably provided that the model addresses a secondary flow correction, and reasonably parameterize grain‐sizes within a channel in a pragmatic way. The factors, such as sediment transport formula and roughness height, have relatively less significance on the bed change pattern at a bend. The study reveals that the secondary flow effect and grain‐size parameterization should be given a first priority among other parameters when modeling bed deformation at a natural bend using a 2‐D model

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

MAXIMIZING USE EFFICIENCY OF MINERAL N-FERTILIZATION FOR SPINACH AND ROCKET PLANTS

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