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Impact of heavy rainfall on ambulance response time in Japan
Suboptimal immunity of hepatitis B vaccination and seroprotection among healthcare professionals in a tertiary hospital in Ghana
The Chinese finance whizz whose DeepSeek AI model stunned the world
Ensemble deep learning with advanced feature engineering for embryo evaluation on in-vitro fertilisation procedures using biomedical images
The structural, electronic, and magnetic properties of substitutional transition metal doping in CrSi2N4 monolayer
Abstract After the synthesis of two-dimensional (2D) structures, Mo(W)Si 2 N 4 (Hong et al., 2020; Science 369 670), proclaimed the dawn of a new family of 2D materials, including CrSi 2 N 4 (CrSiN) semiconductor, which has a band gap of 0.49 eV and boasts remarkable properties. In this regard, we conducted density functional theory to investigate the structural, magnetic, and electronic properties of mono cation doped CrSiN (TM-CrSiN) with substitutional 3d/4d transition metal (TM) (at the Cr site). The bond lengths of TM-N increase as the atomic size of TM increases, and the formation/binding energy can be explained by the atomic size and electronegativity of TM as compared to the corresponding value of Cr. The doped structures with Ti/Zr, V/Nb, Mo, and Mn/Tc are nonmagnetic monolayers. In contrast, the other doped monolayers are magnetic. The doped monolayers with Ti/Zr and Mo are semiconductors with a band gap of 0.20/0.25 eV and 0.52 eV, respectively. It is noteworthy that the structures with Sc/Y, Fe, Co/Rh, Ni/Pd, and Zn/Cd dopants are half-metallic, which makes them suitable monolayers for spintronic applications. We find Cu- and Ag-CrSiN sheets behave as metallic structures and could be utilized in spin-filter devices.
Reservoir computing-driven inverse dynamics for autonomous vehicle trajectory tracking control
Abstract The strong coupling between lateral and longitudinal dynamics in autonomous vehicles presents a significant challenge for trajectory tracking control, especially under high-dynamic and complex conditions. To address this, this paper proposes a real-time optimal control method driven by a Reservoir Computing (RC)-based vehicle inverse dynamics model. The approach first involves training an RC network on a comprehensive vehicle dynamics dataset, covering multiple operating conditions, to learn the inverse mapping from accelerations to control commands. Second, an online correction mechanism incorporating Proportional-Derivative (PD) feedback is designed to dynamically adjust the desired acceleration inputs based on trajectory tracking errors. Finally, these corrected accelerations are fed into the trained RC network to rapidly compute high-precision control commands, completing the closed-loop tracking. Comprehensive simulations on double-lane-change, figure-eight, and Rössler chaotic trajectories demonstrate that the proposed method achieves high-precision tracking with remarkable computational efficiency and excellent robustness against control disturbances and sensor noise. Notably, moderate sensor noise exhibits trajectory-dependent performance enhancement, with system failure boundaries under combined disturbances clearly characterized.
This scientist found a new trick of the immune system by digging through cellular rubbish
Ensemble agent based machine learning approach for energy efficient attack detection and prevention in MANETs
Abstract Mobile Ad hoc Networks (MANETs) represent a decentralized and self-tuning network paradigm that relies on routing protocols to transmit data from source to destination. However, the absence of a fixed infrastructure makes MANETs vulnerable to various security threats, including blackhole and gray hole attacks. Addressing these vulnerabilities is critical to ensuring the reliability and security of MANETs. The paper proposes an agent-based approach for effectively identifying and preventing such attacks within the MANET environment. Unlike existing static or centralized models, agent-based approach deploys dedicated agent nodes in each cluster for real-time monitoring and classification of malicious behaviour. Furthermore, the paper introduces an energy-efficient optimum clustering method, leveraging ensemble clustering-based optimization techniques, to select cluster heads responsible for data aggregation. The combination of optimal clustering and agent-based attack detection enhances the overall security and performance of the MANET. This also significantly improving energy efficiency and data aggregation reliability. Each cluster in the proposed model is equipped with an attack detection agent node, which plays a critical role in identifying suspicious, blackhole, wormhole, and normal nodes within the incoming traffic. This proactive detection mechanism ensures timely response and mitigation of potential security threats. The development of an ensemble-based clustering optimization technique to enhance energy efficiency and improve data aggregation. In addition to the detection mechanism, the study performs a comprehensive comparison of multiple machine learning algorithms. This comparison aims to determine the most effective models for accurate attack identification and trust score generation for network nodes. This determines the most effective algorithms based on accuracy and computational cost by enabling more accurate threat identification and trust-based routing decision. By combining agent-based attack detection, energy-efficient clustering, and intelligent machine learning models, this research work offers a comprehensive and robust solution to enhance the security and reliability of MANETs. Experimental results on simulated MANET environments demonstrate that the proposed approach significantly improves detection accuracy and enhances network lifetime and throughput compared to existing methods. Specifically, proposed approach achieved a throughput of 93 Kbps. This shows approx. 8% improvement over existing approach. The results demonstrate the effectiveness of the proposed approach in providing valuable insights for future research in securing MANETs.
‘Giant step forward’ for Huntington’s — the scientist behind the first gene therapy
Optimizing salinity and stocking density for red tilapia in zero-water-exchange biofloc system: integrated performance, physiological, and economic assessment
Abstract This study investigated the interactive effects of salinity levels (0‰, 18‰, and 36‰) and stocking densities (50, 100, 150, and 200 fish/m 3 ) on water quality, growth performance, physiological responses, and economic returns of red tilapia ( Oreochromis spp., initial weight of 12.33 ± 2.51 g/fish) reared in a biofloc technology (BFT) system using saline groundwater. A 3 × 4 factorial design with 36 fiberglass tanks (1 m 3 each) was employed for 6 months. Key water quality indicators, fish growth indices, hematological and biochemical markers, antioxidant enzymes, immune parameters, and economic performance metrics were assessed. Results showed that increasing salinity and density significantly reduced dissolved oxygen (DO) levels and increased total ammonia nitrogen (TAN), NH 3 , NO 2 , and NO 3 concentrations ( p < 0.001). Biofloc volume (BFV) increased with stocking density across salinities, peaking at 44.4 ± 1.06 mL/L at 0‰ and 200 fish/m 3 , while higher salinity (36‰) generally reduced BFV. Variations in biofloc composition (protein 22–33%) and fish muscle composition (protein and lipid reduction at 36‰ and 200 fish/m 3 ) indicated metabolic adjustments under stress. The highest final weight (261 ± 1.69 g/fish) was observed at 36‰ salinity with low stocking density (50 fish/m 3 ), whereas the most favorable combination of growth rate, feed conversion ratio, and protein efficiency ratio occurred at 18‰ salinity and moderate stocking densities (100–150 fish/m 3 ). Growth performance and feed utilization declined markedly at 36‰ with high density (200 fish/m 3 ). Hematological indicators (RBC, Hb, Hct) and immune biomarkers (lysozyme, IgM, complement C3) were suppressed at extreme salinity-density combinations, while oxidative stress (high MDA) and hepatic dysfunction (elevated AST and ALT) were evident. Economic analysis confirmed that 18‰ salinity with 200 fish/m 3 yielded the highest profit (1000 ± 54.8 EGP/treatment) and lowest operating ratio, while 150 fish/m 3 at the same salinity provided slightly lower profit but better fish welfare indicators and immune responses, whereas high-density and hypersaline conditions reduced profitability due to poor growth and increased feed costs. In conclusion, 18‰ salinity combined with 100–150 fish/m 3 provides the optimal balance between biological performance, fish welfare, and economic viability in red tilapia BFT systems. These findings offer evidence-based guidelines for sustainable inland saline aquaculture, supporting enhanced production efficiency and profitability in arid and saline-prone regions.