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Model-free predictive control for PMSM based on a nonlinear autoregressive exogenous model and an adaptive recursive least squares algorithm
This paper proposes a novel nonlinear autoregressive with exogenous input-based adaptive recursive least squares model-free predictive control (NARX-ARLS-MFPC) strategy for permanent magnet synchronous motor (PMSM) drives. The core challenge addressed is the performance degradation of conventional model predictive control (MPC) under inevitable motor parameter mismatches. The proposed method integrates the NARX model with an ARLS algorithm featuring a variable forgetting factor to dynamically track system changes. Comprehensive simulation studies validate the superior robustness of the strategy. Under significant inductance and flux linkage mismatches, the proposed method reduces current total harmonic distortion (THD) by 28.3% and 12.3% compared to conventional finite-control-set model predictive control (FCS-MPC) and a baseline control method, respectively. It maintains stable performance under moderate sensor noise with appropriate tuning. During load transients combined with resistance and inductance mismatches, it achieves THD reductions of 24.1% and 15.7% versus the two benchmark methods, respectively. Statistical analysis under parameter perturbations confirms its overall superior performance across key dynamic and steady-state metrics. The results demonstrate that the synergistic integration of the nonlinear model and adaptive identification effectively suppresses current harmonics caused by model inaccuracies while enhancing dynamic performance.
Dual-channel graph neural network for dental 3D point cloud model segmentation
Chiral Phosphoric Acid-Catalyzed Asymmetric Hydrogenolysis of C–O Bonds
Experimental emergence of conventions in human dyads
Conventions can be defined as arbitrary and self-sustaining practices that emerge in a population and facilitate solving coordination problems. A recent study traced the formation of simple conventions in captive baboons in a touch-screen-based color-matching ‘game’. We replicated this task with human pairs under different conditions (with/without visual access to the partner’s screen; with/without prior information on the task structure) to assess their effects on the formation and stability of conventions. We found that more information delayed the formation of conventions (arbitrary rankings of colors that determined choices in any given color pairing). Analysis of self-reported strategies did not reveal a clear effect of condition on levels of elicited strategic behavior. Interestingly, pairs maintained their conventions even when given visual access to their partner’s screen, despite the availability of an alternative, potentially simpler, cognitive strategy. In a follow-up variant of the task that paired up experienced subjects with a naïve partner, conventions emerged faster but did not replicate the color hierarchy convention of the experienced player, demonstrating the transmission of “know-how” but not “know-what” information. We discuss the implications of our results for understanding the cognitive mechanisms necessary to support the formation, maintenance, and transmission of conventions.
VARUNA: verifiable adaptive resilient unified framework against byzantine attacks in federated learning-based IDS
Abstract The integration of Federated Learning (FL)-based Intrusion Detection Systems (IDS) in Internet of Things (IoT) faces significant challenges due to the statistical heterogeneity of distributed datasets. The IDS datasets are often highly imbalanced and biased toward majority classes, leading to degraded IDS performance. To mitigate this, Generative Adversarial Network (GAN) models are deployed at central server nodes to balance datasets and maintain system heterogeneity. However, the security of FL systems remains vulnerable to adversarial attacks, which can manipulate model updates and compromise system integrity. This research introduces the Multi-Agent GAN Network Exploitation Tactic (MAGNET), leveraging actor-critic-based reinforcement learning to manipulate GAN models. MAGNET distributes these compromised models to selected clients, enabling the simulation of complex adaptive attacks. Additionally, we propose the Coordinated Model Attack Network System (CMANS), which disrupts global model convergence through malicious gradient manipulation techniques such as Gaussian noise injection, gradient scaling, and inversion. These attacks significantly impact the performance of FL environments applied to IDS datasets. We present a robust Verifiable Adaptive Resilient Unified (VARUNA) Framework for trustworthy IDS in resilient FL systems to address these vulnerabilities. This approach employs an adaptive trust score to detect and eliminate Byzantine activities, effectively neutralizing over 99% of malicious clients in scalable FL environments. VARUNA achieves consistent error rate reductions of up to 9.4% across all attack classes, restoring model performance from an average error increase of 0.03 under CMANS and MAGNET attacks back to the pre-attack baseline across all four aggregation methods (FedAvg, Krum, Trimmed Mean, and Median). Our solution ensures a secure, efficient, and trustworthy IDS, outperforming undefended FL baselines by a statistically significant margin on all three evaluated benchmark IDS datasets.
Trimethylammonium-Substituted Amphiphilic Pillar[ <i>n</i> ]arene Channels for Selective Water Permeation
In vivo changes in zebrafish anesthetic sensitivity in response to the loss of kif5Aa are associated with the alteration of mitochondrial motility
Anesthetic and sedative drugs are small compounds known to bind to hundreds of proteins. One intriguing binding partner of propofol is the motor domain of a neuronal mitochondrial transport kinesin, kif5A. Here, we used zebrafish wild type (WT) and kif5Aa knockout (KO) larval behavioral assays to assess anesthetic sensitivity and combined that with zebrafish primary neuronal cell culture to probe for alterations in mitochondrial motility. We found that the loss of kif5Aa increases behavioral sensitivity to propofol and etomidate, with etomidate hypersensitivity greater than propofol. In contrast, kif5Aa KO animals were resistant to the behavioral effects of dexmedetomidine. Finally, WT and kif5Aa KO larvae responded similarly to the behavioral effects of ketamine. Propofol inhibited the anterograde motility of mitochondria in WT zebrafish neurons, while etomidate inhibited mitochondrial motility in both anterograde and retrograde directions; neither drug altered mitochondrial motility in the kif5Aa knockout (KO) neurons. In contrast, dexmedetomidine enhanced retrograde mitochondrial motility in both WT and kif5Aa KO animals. Finally, ketamine had little significant effect on mitochondrial motility in either mutant or WT animals. These data demonstrate that each anesthetic/sedative drug affects the motor protein machinery uniquely and is associated with unique changes in behavior. Understanding how different anesthetic compounds alter neuron motor proteins will be important in defining how anesthetics alter neuronal signaling and energetic dynamics.
A predictive maintenance model for sustainable urban transit: the Riyadh Metro case study
Directed Evolution of Nonheme Fe Enzymes for Enantioselective and Regiodivergent 1,3- and 1,4-Nitrogen Migration: Biocatalytic Asymmetric Synthesis of Noncanonical α- and β-Amino Acids
Integrative characterization of the HER2-TLS maturation axis reveals immune heterogeneity and prognostic subtypes in gastric cancer
Dynamic Coordination-Enabled Metal-Dependent Configurational Switching in a Covalent Organic Framework
Seasonality of acute kidney injury incidence in Japanese outpatients
Abstract Acute kidney injury (AKI) is a clinically important condition associated with adverse outcomes. While community-acquired AKI is common, its seasonal patterns remain poorly understood, particularly in outpatient settings. This study aimed to examine the seasonality and community-acquired AKI in Japan. Of 4,401,387 individuals with at least 1 month of data in the Japanese health insurance claims database between August 2016 and July 2018, 4,282,805 were included in the analysis as outpatients. The monthly incidence of community-acquired AKI was evaluated using the incidence rate ratio (IRR) calculated by generalized estimating equations. We constructed multivariable models adjusted for sex, age, comorbidities and medications. There were 20,634 new-onset community-acquired AKI cases. The frequency of new-onset community-acquired AKI gradually increased from June to July, with the highest adjusted IRR in July (1.19 [95% confidence intervals: 1.11–1.27]). Subgroup analyses showed that IRRs for community-acquired AKI were consistently higher in July than in February across all categories. Notably, a significant interaction was observed between seasonality and age ( P = 0.029), with the highest IRR observed in the youngest age group (< 20 years). These results suggest the potential relevance of seasonal and environmental factors in AKI prevention in outpatient settings.
Patchy Membrane-Directed Multiphase Complex Coacervation
Association of urinary diversion type and surgical approach with long-term renal function after radical cystectomy: a nationwide cohort study
Bigger Molecules Enter Smaller Channels of Zeolites via Inward-Coil-Alike Adaptation
A global–local hybrid transformer framework for automatic Navarasa facial expression recognition
Discovery of Noninhibitory Macrocyclic Ligands for Protein Tyrosine Phosphatase 1B Using a Function-Based, Iterative Screening Strategy
Prevalence and associated factors of pre-cancerous cervical lesions among HIV-positive women on ART in Northwest Ethiopia: a retrospective cross-sectional study
Abstract Cervical cancer is a major public health concern in Ethiopia with high mortality rates, especially in women living with HIV/AIDS. While its screening service has been available in Ethiopia since 2009, the updated prevalence of precancerous cervical lesions among HIV-positive women is not well understood. Therefore, this study aimed to determine the prevalence of precancerous cervical lesions and its associated factors among women living with HIV receiving ART in the Northwest Ethiopia. An institution-based retrospective cross-sectional study was conducted from February 28 to April 1, 2024 at the University of Gondar Comprehensive Specialized Hospital. A total of 419 eligible participants were selected by using simple random sampling technique. The data were collected through patient chart review using a pre-tested and structured checklist. The data were entered into Epi-info version 7, cleaned, and analyzed using SPSS version 20. Binary logistic regression model was used to identify factors associated with the outcome variable, and odds ratios with 95% confidence intervals and its corresponding p-values were computed to identify significant variables in the multivariable analysis. In this study, the prevalence of precancerous cervical lesions was found to be 11.5% (95% CI: 8.2–14.7). History of STI (AOR = 3.7, 95% CI: 1.79–7.62), age at first marriage less than 15 years (AOR = 3.02, 95% CI: (1.00-9.24), monthly income below 3000 Ethiopian Birr (AOR = 6.3, 95% CI: 1.20-32.82), and being widowed (AOR = 0.36, 95% CI: 0.13–0.95) were factors significantly associated with the occurrence of precancerous cervical lesions. The present study revealed 11.5% of precancerous cervical lesions among women living with HIV. History of sexually transmitted infections (STIs), early marriage, low income level, and being widowed were significantly associated with the outcome variable. Therefore, targeted interventions such as delaying early marriage and strengthening strategies that promote protection against sexually transmitted infections and poverty are recommended to reduce the burden of the disease in this highly vulnerable population.