Search Results (1,105)

In this paper, an adaptive output feedback control scheme is proposed for a class of parametric strict feedback systems with asymmetric full-state constraints and unknown output gain. Firstly, an adaptive state observer is constructed to estimate the unmeasured system states. To compensate for the effect of the unknown output gain on the tracking performance, a new error signal incorporating an adaptive compensation coefficient is introduced into the backstepping design. Then, by combining the universal transformed function with a coordinate transformation, all system states are kept within time-varying asymmetric bounds, and the feasibility issues of conventional constrained control methods are avoided. Based on Lyapunov stability analysis, all signals in the closed-loop system are proven to be globally uniformly ultimately bounded. Finally, simulation results based on motor models demonstrate the effectiveness of the proposed scheme. Full article

This paper investigates the design and evaluation of compact convolutional neural networks (CNNs) for keyword spotting (KWS) and acoustic event detection under the stringent constraints of the TinyML paradigm. The research expands upon traditional binary classification approaches by addressing a multi-class acoustic scenario encompassing eight distinct categories: stop, no, go, yes, unknown, silence, noise_ambient, and noise_sudden. The primary objective is to evaluate the feasibility of deploying reliable acoustic detection systems on ultra-low-power microcontrollers for edge computing applications. To this end, five lightweight architectures were developed and benchmarked: AlexNet-Tiny, LeNet-Tiny, MobileNet-Tiny, VGG-Tiny, and CustomCNN-Tiny. The models were trained using Mel-spectrogram features and optimized through INT8 post-training quantization to facilitate embedded deployment. Hardware simulation was conducted targeting the XIAO nRF52840 Sense microcontroller (64 MHz, 256 KB RAM). Experimental results demonstrate that the Gold VGG-Tiny architecture achieves the highest classification accuracy ($89.81\%$), while Silver MobileNet-Tiny provides the superior operational efficiency with the lowest inference latency ($0.88$ ms) and minimal energy consumption ($14.4$ µJ). Furthermore, the Bronze CustomCNN-Tiny model achieves the most reduced memory footprint ($42.9$ KB), highlighting its suitability for memory-constrained environments. Statistical validation using Cohen’s Kappa, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC) confirms the robustness and reliability of the proposed models. The potential application of this system is motivated by acoustic monitoring for the early detection of high-risk situations, such as gender-based violence. Future work will focus on on-device physical validation and real-world deployment in wearable safety electronics. Full article

The use of bait in Vietnamese gillnet fisheries to improve fishing efficiency has recently increased, yet its influence on both target species and protected bycatch species is unknown. This study compared the catching performance of baited versus non-baited drift gillnets in a pelagic fishery off central Vietnam to evaluate whether bait can increase catch rates of commercially important species and protected species. Sea trials were conducted during the stewardship fishery using identical gillnets, differing only in the presence of bait bags containing round scad (Decapterus macrosoma). The results showed that baited gillnets significantly increased CPUE of most target species, including skipjack tuna (Katsuwonus pelamis) at 68.2%, frigate tuna (Auxis thazard thazard) at 55%, and bullet tuna (Auxis rochei) at 62.4%, compared to conventional gillnets. Wahoo (Acanthocybium solandri) and mahi mahi (Coryphaena hippurus) also increased by 50.6% and 51.6%, respectively; however, these increases were not statistically significant. Length-based analyses indicated that baited and non-baited gillnets differed in size-dependent catch efficiency, with certain length classes showing significantly higher or lower capture probabilities depending on the species. Notably, baited gillnets also showed a higher likelihood of capturing hammerhead sharks (Sphyrna spp.). These results show that baiting can improve the fishing efficiency in pelagic gillnet fisheries, potentially gaining economic performance. However, the increased interaction with protected species highlights a critical trade-off, underscoring the need for bycatch mitigation measures and management strategies to ensure that improvements in fishing efficiency remain aligned with the principles of sustainable fisheries and long-term ecosystem conservation. Full article

Class B scavenger receptor BI splice variants (SR-BI) and BII (SR-BII) internalize lipoproteins but also bind and internalize bacteria. Their individual roles in sepsis are unknown. We overexpressed human SR-BI or BII in transgenic mice, primarily in the liver, but also in the kidney and in bone marrow-derived macrophages, and then performed cecal ligation and puncture (CLP) surgery. SR-BI and BII transgenic mice had significantly worse survival compared to WT mice. Twenty-four hours after CLP, liver injury markers and histological damage were elevated in both SR-BI and BII transgenic mice, whereas kidney damage was similar. Systemic inflammatory cytokines were markedly increased in SR-BI and BII transgenic mice; parallel increases were seen in liver mRNA expression, but not in the kidney. The highest degree of neutrophil infiltration was observed in the liver of SR-BI. Human SR-BI and BII dramatically decreased bacterial accumulation in the liver. Green fluorescent protein-labeled E. coli were efficiently phagocytosed in hepatic macrophages of SR-BI and BII transgenic mice; phagocytosis was more prominent in SR-BII transgenic mice. Finally, human SR-BI overexpression reduced systemic HDL-C levels, eliminated adrenal cortex lipid droplets, and dampened the systemic increase of corticosterone after CLP. Supplementation with glucocorticoid and mineralocorticoid improved survival in SR-BI but not in SR-BII transgenic mice after CLP. In summary, our findings suggest human SR-BI and BII overexpression contributes to higher mortality after CLP by different mechanisms: excessive inflammatory response due to adrenal insufficiency (SR-BI) or hyperactive phagocytosis (SR-BII) in the liver. Full article

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) that impacts nearly 3 million people worldwide. While the etiology and pathogenesis of MS are not yet fully understood, current evidence suggests that it results from complex interactions between genetic and environmental conditions. Clarifying the autoimmune mechanisms underlying MS remains a central objective in the development of effective therapeutic strategies. Interferon-beta (IFN-$\beta$) is one of the most frequently prescribed disease-modifying treatments for individuals with MS. However, despite its established efficacy, recent studies report that approximately 30–50% of patients exhibit inadequate response to IFN-$\beta$, largely due to genetic variability. Machine learning (ML), a branch of artificial intelligence (AI), employs data-driven computational models to enhance predictive accuracy and classification. In recent MS research, unsupervised learning techniques such as hierarchical clustering and K-means have been applied for classification purposes. However, these methods often fail to yield optimal solutions because they require numerous arbitrary decisions and perform adequately only when datasets contain clusters of similar sizes and lack significant outliers. Fuzzy systems (FSs) are designed to model complex, ambiguous real-world phenomena. In this study, an AI algorithm incorporating a fuzzy system, informed by expert neurologist input, is proposed to enhance the assignment of unknown class labels related to IFN-$\beta$ response in MS patients. Additionally, a genetic algorithm (GA) is introduced to identify optimal solutions within the search space, facilitating hyperparameter optimization of a deep learning (DL) model trained with genetic biomarkers to identify patients likely to benefit from this therapy. Experimental results demonstrate that the fuzzy system achieved $80\%$ classification efficiency, in contrast to $64\%$ with conventional hierarchical clustering. Furthermore, an artificial neural network (ANN) model, with hyperparameters optimized by the GA, achieved an accuracy of 0.8–1.0, surpassing the multi-layer perceptron (MLP), which achieved 0.6–0.8 accuracy using conventional tuning methods. Full article

Suppose that we have a statistical model with q unknown parameters w, and an estimate $\widehat{w}$, based on a sample of size n. A basic question is: what is the covariance of the estimate? The covariance is needed for the Central Limit Theorem (CLT). This gives a first approximation for the distribution of $\widehat{w}$. But what if $q_n=n$ increases with n? How fast can it increase and the CLT still hold? An answer has so far only been given for the sample mean. The same is true for the Edgeworth expansions. These are expansions in powers of $n^{-1/2}$ for the density and distribution of $\widehat{w}$. For fixed q, these expansions are important, as they show how small n can be for the CLT to apply. When it does, they can greatly improve the accuracy of the CLT. I give conditions that allow for the Edgeworth expansions to remain valid when $q_n=q$ increases with n. Earlier Edgeworth expansions when $q_n=q$ increases, have only been done for a sample mean, and only for a 2nd order Edgeworth expansion. In contrast, I consider a very large class of estimates, the class of non-lattice standard estimates. An estimate is said to be a standard estimate if its mean converges to its true value as n increases, and for $r\ge 1$, its rth order cumulants have magnitude $n^{1-r}$ and can be expanded in powers of $n^{-1}$. For this class of estimates, I show that the Edgeworth expansions hold if $q_n$ grows as a power of n less than $1/6.$ That is, I give these expansions in powers of $n^{-1/2}{q}_n^3$. This large class of estimates has a huge range of potential applications, as estimates of high dimension are common in nearly all areas of applied statistics. The most important type of standard estimate is when $\widehat{w}$ is a smooth function of a sample mean, of dimension p say. When either or both $q_n=q$ and $p_n=p$ increase with n, I give conditions on their growth for the Edgeworth expansions for $\widehat{w}$ to remain valid: the eighth power of p times the sixth power of q cannot grow as fast as n. This holds for fixed $q=q_n$ if $p_n$ grows less than a power of n less than $1/8$. This appears to be the first time when Edgeworth expansions have been given when not one, but two dimensions, are allowed to increase to ∞ with n. This gives two different pathways for allowing an increase in dimensionality. When $q=1$, I give 5th order Edgeworth-Cornish-Fisher expansions for the standardized distribution and its quantiles of any smooth function of a sample mean of dimension $p_n$, when $p_n$ is a power of n less than $1/2$. However for the special case when this function is linear, there is no restriction whatever on how fast $p_n$ can increase! If also the components of the sample mean are independent, then these expansions are in powers of ${\left(np\right)}^{-1/2}$. I also give a method that greatly reduces the number of terms needed for the 2nd and 3rd order terms in the Edgeworth expansions, that is, for the 1st and 2nd order corrections to the CLTs. I also extend these results to the case where $\widehat{w}\in R^q$ is a function of several independent sample means, each of dimension increasing with n, with total dimension p. Full article

In this paper, we introduce an efficient computational framework for pricing geometric Asian options based on a collocation method. The approach employs a collocation scheme utilizing a specific class of diagonal polynomials to construct operational matrices. The sparse structure of these matrices, containing a significant number of zeros, enhances computational efficiency. We solve the governing partial differential equation (PDE) by representing the solution as a series of multivariate diagonal functions with unknown coefficients. Subsequently, we derive the operational matrices for the differential operators and their associated partial derivatives, demonstrating how this formalism transforms the original pricing problem into a tractable system of nonlinear algebraic equations. Furthermore, we provide a rigorous convergence analysis of the proposed collocation method. Finally, we present numerical examples that demonstrate the method’s applicability, robustness, and computational effectiveness. The obtained results, supported by a strong theoretical foundation, indicate the considerable potential of this approach for practical financial applications. Full article

This paper presents a unified analytical and computational framework for the study of typhoid fever transmission dynamics governed by a Caputo fractional-order compartmental model of order $\kappa \in (0,1]$. The population is stratified into five epidemiological classes, namely susceptible $\left(\mathcal{S}\right)$, asymptomatic $\left(\mathcal{A}\right)$, symptomatic $\left(\mathcal{I}\right)$, hospitalised $\left(\mathcal{H}\right)$, and recovered $\left(\mathcal{R}\right)$, and the governing system explicitly incorporates asymptomatic transmission, treatment dynamics, and temporary immunity with waning. The use of the Caputo fractional derivative is motivated by the well-documented existence of chronic asymptomatic Salmonella Typhi carriers, whose heavy-tailed sojourn times in the carrier state are naturally encoded by the Mittag–Leffler waiting-time distribution arising from the fractional operator. A complete qualitative analysis of the fractional system is carried out: the basic reproduction number ${\mathcal{R}}_0$ is derived via the next-generation matrix method; local and global asymptotic stability of both the disease-free equilibrium ${\mathcal{E}}_0$ (when ${\mathcal{R}}_0\le 1$) and the endemic equilibrium ${\mathcal{E}}^{*}$ (when ${\mathcal{R}}_0>1$) are established using fractional Lyapunov theory and the LaSalle invariance principle; and the normalised sensitivity indices of ${\mathcal{R}}_0$ are computed to identify transmission-amplifying and transmission-suppressing parameters. Existence, uniqueness, and Ulam–Hyers stability of solutions are established via Banach and Leray–Schauder fixed-point arguments. To complement the analytical results, a fractional physics-informed neural network ($\mathcal{PINN}$) framework is developed to simultaneously reconstruct compartmental trajectories and identify unknown biological parameters from sparse synthetic observations. $\mathcal{PINN}$ embeds the L1-Caputo discretisation directly into the training residuals and employs a four-stage Adam–L-BFGS optimisation strategy to recover five trainable parameters $\mathbf{\Theta }$ = $\{\varphi ,\mu ,\sigma ,\psi ,\beta \}$ across three fractional orders $\kappa \in \{1.0,0.95,0.9\}$. The estimated parameters show strong agreement with the true values at the classical limit $\kappa =1.0$ ($\mathrm{MAPE}=2.27\%$), with the natural mortality rate $\mu$ recovered with $\mathrm{APE}\le 0.51\%$ and the transmission rate $\beta$ with $\mathrm{APE}\le 3.63\%$ across all fractional orders, confirming the structural identifiability of the model. Pairwise correlation analysis of the learned parameters establishes the absence of equifinality, validating that $\beta$ can be reliably included in the trainable set. Noise robustness experiments under Gaussian perturbations of $1\%$, $3\%$, and $5\%$ demonstrate graceful degradation ($\mathrm{MAPE}$: $0.82\%\to 3.10\%\to 7.31\%$), confirming the reliability of the proposed framework under realistic observational conditions. Full article

We developed a real-time guinea pig breed classification system using You Only Look Once Version 9 (YOLOv9)-small, deployed on a Raspberry Pi 5 with Camera Module 3 and Hailo-8L acceleration module. The system targeted three breeds, Abyssinian, American, and Peruvian, using a dataset of 4500 images split into a 70:20:10 ratio for training, validation, and testing. After optimization for Hailo-8L, the model was tested on live samples, with hamsters included as an unknown class. A total of 600 frame blocks were extracted from the video input and analyzed using a multi-class confusion matrix. Results showed an 89% overall accuracy (94.67% for Abyssinian, 94.33% for American, 98.67% for Peruvian, and 90.33% for unknown classification accuracy). The results showed the feasibility of deploying YOLOv9-small on embedded devices for accurate and real-time animal classification. Full article

Chronic wounds are ulcers unable to heal due to vascular insufficiency, diabetes, or obesity. Adipose-derived stromal cells (ASCs) are promising candidates for regenerative therapies owing to their pro-healing and angiogenic properties. Compared with autologous approaches, allogeneic ASC therapies offer the opportunity for off-the-shelf use, enabling immediate availability, standardized qualification, and consistent potency. Gelatin sponges have been shown to reprogram ASCs toward a highly angiogenic phenotype. However, because this activation also modulates some immune-related genes, including MHC, its impact on immunogenicity is unknown and could be critical for allogeneic applications. This study evaluated whether ASCs embedded in a gelatin sponge could be used in an allogeneic setting for ischemic wound repair. To mimic clinical allogeneic conditions, a controlled MHC mismatch was introduced in a rat ischemic wound model: donor ASCs carrying RT1^n or RT1^l haplotypes were implanted into outbred RT1^a recipients. Embedding ASCs within the gelatin sponge upregulated MHC class I but not class II expression, without inducing systemic or local alloreactivity. Serum acute-phase proteins remained unchanged, and no CD3⁺ T-cell infiltration was detected. Histology confirmed efficacy on ischemic wounds, with increased granulation tissue thickness, red blood cell infiltration, and enhanced vessel density versus controls. Allogeneic ASCs activated by a gelatin scaffold promote wound revascularization without eliciting immune rejection, supporting their development as standardized, off-the-shelf therapies for chronic ischemic wounds. Full article

Specific Emitter Identification (SEI) distinguishes individual emitters by extracting subtle features from intercepted radio frequency signals. This process relies on the design and extraction of specific features. Current methods for selecting and characterizing radio frequency fingerprints vary by individual, and the extraction process is closely coupled with environmental conditions. As a result, the generality of such identification algorithms is often limited, particularly when the application environment does not match the premise of feature design, leading to rapid degradation or even failure of individual identification performance. This paper proposes a deep clustering model based on polarization feature learning for identifying individual communication emitters. The approach involves constructing a guided network to extract datasets of polarization features from communication signals and utilizing a contrastive representation learning network to extract dual-polarization features from I/Q data samples. Subsequently, a Bayesian nonparametric (BNP) class mixture model algorithm, capable of inferring an unknown number of clusters, is employed to build a multi-level clustering network for clustering analysis of the extracted features. Under 5 dB conditions, the method described in this paper achieves an average recognition accuracy of 87.5%. Full article

Autonomous Underwater Vehicles (AUVs) play a crucial role in deep-sea exploration missions. In the complex and highly dynamic marine environment, it is essential for AUVs to possess robust fault diagnosis capabilities to enhance their operational safety. In the context of AUV fault diagnosis, closed-set recognition methods tend to misclassify unknown faults as known ones, which may lead to severe operational consequences. In order to enable AUVs to adapt to new and unknown deep-sea environments and effectively detect new unknown faults, this paper proposes an open-set AUV fault recognition method based on a Convolutional Neural Network (CNN). Firstly, the CNN is employed to extract high-level discriminative features from raw sensor data. Then, a committee consisting of multiple one-class SVMs (OC-SVMs) is constructed to determine whether the input sample belongs to a known category. Finally, the identified known samples are accurately classified via the designed classifier module. This method can effectively distinguish between known faults and unknown faults. To improve the recognition accuracy of the model, an attention mechanism is introduced. By learning to automatically assign weights to different feature channels, the model can focus on more important or relevant feature channels. Experiments based on the “Haizhe” dataset demonstrate that the proposed CNN-OC-SVM model exhibits superior performance in AUV fault diagnosis tasks compared with the state-of-the-art and traditional methods. Full article

Few-shot open-set object detection (FS-OSOD) remains challenging in real-world scenarios, where detectors must accurately recognize known objects from few examples while reliably rejecting vast unknown categories. Under this setting, decision boundaries between known and unknown classes are easily distorted by data scarcity and background clutter, leading to severe overfitting on base classes and overconfident misclassification of unknowns. Recent research attempts to alleviate these issues by regularizing detection heads to suppress base-class bias, or by leveraging vision–language priors through open-vocabulary alignment and prompt tuning to enhance semantic transferability. However, these solutions often overlook explicit modeling of truly out-of-set unknowns and the instability of prompt adaptation in low-data regimes, which can cause boundary drifts and make unknown proposals be absorbed by similar seen classes or even suppressed as background. To alleviate these issues, a guided prompt–monument network (GPMN) that is proposed, which jointly enhances prompt learning and feature representation learning for FS-OSOD. First, the contrastive distilled prompts (CDP) module employs a teacher–student prompt framework to decouple optimization across base, novel, and unknown classes. This strategy preserves transferability between zero-shot and few-shot settings while enhancing discrimination on base categories. Second, a synthesized monument module (SMM) maintains class-centered memory with momentum-updated prototypes and a non-parametric classifier, which compresses the overlap between seen and unseen distributions and provides a stable rejection margin for unknowns with strong co-occurrence and background noise. Compared with existing head-regularization and open-vocabulary prompt-tuning pipelines, GPMN explicitly targets both base-class bias and seen–unseen overlap at the region level. Extensive experiments on VOC10-5-5 and VOC-COCO benchmarks demonstrate that GPMN consistently improves unknown recall and few-shot mAP over representative FS-OSOD baselines. These results suggest that prompt-level decoupling mitigates base-class bias, whereas memory-anchored regularization enlarges the seen–unseen margin, jointly supporting reliable unknown rejection in scarce-supervision regimes. Full article

The Domains of Unknown Functions 1664 (DUF1664) genes are a class of genes with unknown functions, and their roles in abiotic stresses responses have not yet been reported. Using the hidden Markov model (HMM) profile of DUF1664 (PF07889) obtained from the Pfam database, along with Arabidopsis thaliana DUF1664 family protein sequences as reference, and verifying complete DUF1664 domains with the NCBI CD-Search online tool, seven DUF1664 family members were identified in the peanut (Arachis hypogaea) genome, designated as AhDUF1664-1A through AhDUF1664-4. Promoter analysis revealed that cis-acting elements in AhDUF1664 genes are associated with growth and development, stress responses, and plant hormone signaling, and these genes exhibit relatively conserved motifs. Functional validation showed that ectopic expression of AhDUF1664-1A enhanced tolerance to salt and drought stresses in Arabidopsis thaliana by modulating the expression of ABA signaling-related genes. Our findings identify the AhDUF1664 gene family in peanut and provide a basis for further investigation into the biological functions of these genes. Full article

Background: Despite the general interest in WPV against healthcare workers, there is evidence that this topic has comparatively fewer studies conducted in the context of community settings than in hospital settings. Given the current general transition of care from hospital to community, this study aims to analyze whether community settings present different characteristics in comparison with hospital settings on this topic in Italy. Methods: A retrospective observational study was conducted from 2020 to 2024 on aggressions reported by HCWs in hospitals and community settings belonging to a Local Health Authority of Turin in Piedmont. For physical and non-physical aggressions, a monthly time trend series was constructed. A Mantel–Haenszel fixed-effect meta-analysis was performed to obtain the odds ratio (OR) in two settings. Variables relative to aggressions included the gender of victims, their professional category (medical doctors, nurses, other HCWs), the type and gender of perpetrators (relative, patient, or unknown person), age groups of perpetrators (under 30, 30–49, ≥50), the nature of aggression (physical, non-physical), recidivism, involvement of law enforcement, and time of occurrence (morning, afternoon, or evening/night). Events within hospitals were further classified into emergency department, psychiatric ward, and other wards, while events within community settings were classified as drug addiction service units (serDs), long-term care (including specialist outpatient services, home services, and nursing homes) (LTC), mental health centres, and penitentiary assistance. Results: The results highlighted that fewer WPV incidents were reported in community settings than in hospital settings, even though reported incidents showed a more pronounced increase over time. Differences were observed in a few characteristics of WPV (age classes of aggressors, recidivism, time of aggression, profession of the assaulted worker, and specific location). Only the gender of the assaulted (female workers) (OR = 3.11, 95% CI: 1.27–7.61; p = 0.013; OR = 0.32, 95% CI: 0.13–0.79; p = 0.013 for non-physical and physical violence, respectively, compared to male workers) was identified as a specific risk factor for community settings. Conclusions: Modern health systems are experiencing a transition from hospital-centred to community-centred care settings. This study suggested that WPV is a significant concern, even outside the hospital. Community-based services often involve direct interaction with frail and chronically ill patients and their caregivers, as well as care delivery in diverse and sometimes less controlled environments, which may influence exposure to aggressive behaviours. The identification of setting-specific risk patterns in both hospital and community contexts provides valuable insights into workplace violence and may support the planning and implementation of targeted interventions aimed at mitigating the frequency and burden of WPV. Full article