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Advanced fermentation techniques enhance dioxolanone type biopesticide production from Phyllosticta capitalensis
Constrained search space selection based optimization approach for enhanced reduced order approximation of interconnected power system models
An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment
Abstract In an era of increasing sophistication and frequency of cyber threats, securing Internet of Things (IoT) networks has become a paramount concern. IoT networks, with their diverse and interconnected devices, face unique security challenges that traditional methods often fail to address effectively. To tackle these challenges, an Intrusion Detection System (IDS) is specifically designed for IoT environments. This system integrates a multi-faceted approach to enhance security against emerging threats. The proposed IDS encompasses three critical subsystems: data pre-processing, feature selection and detection. The data pre-processing subsystem ensures high-quality data by addressing missing values, removing duplicates, applying one-hot encoding, and normalizing features using min-max scaling. A robust feature selection subsystem, employing Synergistic Dual-Layer Feature Selection (SDFC) algorithm, combines statistical methods, such as mutual information and variance thresholding, with advanced model-based techniques, including Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) are employed to identify the most relevant features. The classification subsystem employ two stage classifier namely LightGBM and XGBoost for efficient classification of the network traffic as normal or malicious. The proposed IDS is implemented in MATLAB by using TON-IoT dataset with various performance metrics. The experimental results demonstrate that the proposed SDFC method significantly enhances classifier performance, consistently achieving higher accuracy, precision, recall, and F1 scores compared to other existing methods.
Potential of roughening geometric elements as bridge pier local scour countermeasures
Role of genetic diversity and salicylic acid in drought stress memory of tall fescue
Efficient red-NIR laser of Er3+/Yb3+, Er3+/Nd3+, and Er3+/Ce3+ co-doped in oxyfluorophosphate glass
3D virtual histology of rodent and primate cochleae with multi-scale phase-contrast X-ray tomography
Abstract Multi-scale X-ray phase contrast tomography (XPCT) enables three-dimensional (3D), non-destructive imaging of intact small animal cochlea and apical cochlear turns. Here we report on post-mortem imaging of excised non-human primate and rodent cochleae at different $${\upmu }$$ -CT and nano-CT synchrotron instruments. We explore different sample embeddings, stainings and imaging regimes. Under optimized conditions of sample preparation, instrumentation, imaging protocol, and phase retrieval, high image quality and detail level can be achieved in 3D reconstructions. The showcased instrumentation and imaging protocols along with the reconstucted volumes can serve as benchmarks and reference for multi-scale microanatomy and 3D histology. The provided benchmarks and imaging protocols of this work cover a wide range of scales and are intended as augmented imaging tools for auditory research.
Author Correction: Generalized robust regression techniques and adaptive cluster sampling for efficient estimation of population mean in case of rare and clustered populations
APBench and benchmarking large language model performance in fundamental astrodynamics problems for space engineering
Continuous nursing symptom management in cancer chemotherapy patients using deep learning
Enhancement of power quality in grid-connected systems using a predictive direct power controlled based PV-interfaced with multilevel inverter shunt active power filter
Abstract The integration of Nonlinear Loads (NLs) in industrial, commercial and residential settings over the past two decades has significantly worsened power quality issues in modern electrical distribution networks. In today’s modern era, the growing use of sensitive and expensive electronic devices makes it crucial to ensure power quality for the reliable and secure functioning of the power system. Shunt Active Power Filters (SAPF) are necessary to prevent current distortions caused by NLs from entering the grid. Otherwise, system effectiveness and power transmission capabilities would be diminished. In this work, we introduce a novel Predictive Direct Power Control (PDPC) strategy incorporating generating reference signals for SAPF model of a Three-level (3 L) Neutral-Point Clamped (NPC) inverter. This innovative system serves as a SAPF, specifically designed to attenuate the harmonics emerging from abrupt increments in NLs. Moreover, it proactively addresses the challenge of reactive power within distribution systems. Utilizing an Enhanced Incremental Conductance (EINC) Maximum Power Point Tracking (MPPT) algorithm, the Photovoltaic (PV) module effectively optimizes power extraction, thereby augmenting the efficiency of the SAPF integration. This system is adept at satisfying the reactive power demands of the load by mitigating harmonics induced by the NLs while concurrently supplying active power harnessed from the PV arrays. The incorporation of the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm facilitates the stabilization of the DC link voltage, further contributing to the system’s capability to meet reactive power requirements and elevate grid Power Quality (PQ) through the elimination of harmonics. The proposed strategy effectively reduces harmonics and maintains stable DC link voltage in variable linear and NLs load conditions. This system has been systematically designed, simulated, and experimentally validated, with results across various phases demonstrating its superior performance and enhanced efficiency in improving power quality.
Research on the extension of respiratory interaction modalities in virtual reality technology and innovative methods for healing anxiety disorders
Diurnal variations in gait parameters among older adults with early-stage knee osteoarthritis: insights from wearable sensor technology
Biomarkers of cell cycle arrest, microcirculation dysfunction, and inflammation in the prediction of SA-AKI
An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children
1,25(OH)₂D₃ inhibits ferroptosis in nucleus pulposus cells via VDR signaling to mitigate lumbar intervertebral disc degeneration
Numerical investigation on the penetration process of steel casings in riprap environment of estuarine mudflats
Investigating whether smoking and alcohol behaviours influence risk of type 2 diabetes using a Mendelian randomisation study
Abstract Previous studies suggest that smoking and higher alcohol consumption are associated with greater type 2 diabetes (T2D) risk. However, studies examining whether this reflects causal relationships are limited and often do not consider continuous glycaemic traits. We conducted both two-sample and one-sample Mendelian randomisation (MR), using publicly available GWAS data and UK Biobank data, respectively, to examine the potential causal effects of lifetime smoking index (LSI) and alcoholic drinks per week (DPW) on T2D and continuous traits (fasting glucose, fasting insulin and glycated haemoglobin, HbA1c). Two-sample MR results suggested possible causal effects of higher LSI on T2D risk (OR per 1SD higher LSI: 1.42, 95% CI 1.22 to 1.64); however, sensitivity analyses did not consistently support this finding. There was no robust evidence that higher DPW influenced T2D risk (OR per 1 SD higher log-transformed DPW: 1.04, 95% CI 0.40 to 2.65). There was evidence of a potential causal effect on higher fasting glucose (difference in mean fasting glucose in mmol/l per 1SD higher log-transformed DPW: 0.34, 95% CI 0.09 to 0.59), though, this was attenuated when accounting for body mass index (BMI), suggesting BMI confounding might explain the potential effect. One-sample MR results suggested a possible causal effect of higher DPW on T2D risk (OR per 1 SD higher log-transformed DPW: 1.71, 95% CI 1.24 to 2.36), but lower HbA1c levels (difference in mean SD of log transformed HbA1c (mmol/mol) per 1 SD higher log-transformed DPW: −0.07, 95% CI −0.11 to −0.02). Our results suggest effective public health interventions to prevent and/or reduce smoking and alcohol consumption are unlikely to reduce T2D prevalence.