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This paper presents a novel optical fiber axial strain sensor based on a Fabry–Perot interferometer (FPI) cavity incorporating Fiber Bragg Gratings (FBGs) and a tapered fiber, which has been experimentally validated. The sensor structure primarily consists of two identical FBGs with a bi-conical tapered fiber segment between them, achieving a strain sensitivity of 13.19 pm/με. This represents a 12-fold enhancement compared to conventional FBG-FPI, along with a resolution limit of 3.7 × 10−4 με. The proposed sensor offers notable advantages including low fabrication cost, compact structure, and excellent linearity, demonstrating significant potential for high-precision axial strain measurement applications.

This paper proposes a novel fixed-time super-twisting sliding mode control (ST-SMC) strategy for uncertain robotic arm systems, aiming to address the issues of control chattering and the uncontrollable upper bound of convergence time in traditional sliding mode control algorithms. The proposed approach enhances system robustness, suppresses chattering, and ensures that the convergence time of the robotic arm can be explicitly bounded. First, a sliding surface with fixed-time convergence characteristics is constructed to guarantee that the tracking errors on this surface converge to the origin within a prescribed time. Then, an immersion and invariance (I&I) disturbance observer with exponential convergence properties is designed to estimate large, time-varying disturbances in real time, thereby compensating for system uncertainties. Based on this observer, a new super-twisting sliding mode controller is developed to drive the trajectory tracking errors toward the sliding surface within fixed time, achieving global fixed-time convergence of the tracking errors. Simulation results demonstrate that, regardless of the initial conditions, the proposed controller ensures fixed-time convergence of the tracking errors, effectively eliminates control torque chattering, and achieves a tracking error accuracy as low as 2 × 10−9. These results validate the proposed method’s applicability and robustness for high-precision robotic systems.

To solve the problem of high-energy consumption in activated sludge wastewater treatment, a reinforcement learning-based particle swarm optimization (RLPSO) was proposed to optimize the control setting in the sewage process. This algorithm tries to take advantage of the valid history information to guide the behavior of particles through a reinforcement learning strategy. First, an elite network is constructed by selecting elite particles and recording their successful search behavior. Then the network is trained and evaluated to effectively predict the particle velocity. In the periodic wastewater treatment process, the RLPSO runs repeatedly according to the optimized cycle. Finally, RLPSO was tested based on Benchmark Simulation Model 1 (BSM1) of sewage treatment, and the simulation results showed that it could effectively reduce the energy consumption on the premise of ensuring qualified water quality. Furthermore, the performance of RLPSO was analyzed using the benchmarks with higher dimension, which verifies the effectiveness of the algorithm and provides the possibility for RLPSO to be applied to a wider range of problems.

We demonstrate an all-fiber, high-sensitivity, dual-parameter sensor for humidity and temperature. The sensor consists of a symmetrical, non-adiabatic, tapered, single-mode optical fiber, operating at the wavelength near the dispersion turning point, and a cascaded fiber Bragg grating (FBG) for temperature compensation. At one end of the fiber’s tapered region, part of the fundamental mode is coupled to a higher-order mode, and vice versa at the other end. Under the circumstances that the two modes have the same group index, the transmission spectrum would show an interference fringe with uneven dips. In the tapered region of the sensor, some of the light transmits to the air, so it is sensitive to changes in the refractive index caused by the ambient humidity. In the absence of moisture-sensitive materials, the humidity sensitivity of our sensor sample can reach −286 pm/%RH. In order to address the temperature and humidity crosstalk and achieve a dual-parameter measurement, we cascaded a humidity-insensitive FBG. In addition, the sensor has a good humidity stability and a response time of 0.26 s, which shows its potential in fields such as medical respiratory dynamic monitoring.

With the intensification of global climate change and increasing anthropogenic pressures, effective water resource management has become a critical challenge for sustainable development. Small watershed cascade reservoirs must balance multiple competing objectives, including flood control, hydropower generation, and water supply for ecological, agricultural, and industrial uses. This study develops a many-objective optimization scheduling model for cascade reservoirs in the Lushui River Basin and proposes a constrained many-objective evolutionary multitasking optimization algorithm (EMCMOA) to solve it. The algorithm incorporates a dual-task structure with dynamic knowledge transfer to improve search efficiency and solution quality. Experimental results show that EMCMOA outperforms several state-of-the-art algorithms on benchmarks and real-world scenarios, achieving up to 15.7% improvement in IGD and 12.6% increase in HV. Furthermore, EMCMOA demonstrates strong adaptability to varying hydrological conditions, providing reliable and adaptive scheduling strategies. These results highlight its capability to support flexible, many-objective trade-offs in real-world water resource management.

EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments’ accuracy of 99.42% and subject-independent experiments’ accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.

•FFA engages in tactile face processing even after a decade of early-onset blindness.•Early visual experience primes cross-modal face specialization in the FFA.•Absence or extensive visual experience declines cross-modal face specialization. The fusiform face area (FFA) is a core cortical region for face information processing. Evidence suggests that its sensitivity to faces is largely innate and tuned by visual experience. However, how experience in different time windows shape the plasticity of the FFA remains unclear. In this study, we investigated the role of visual experience at different time points of an individual's early development in the cross-modal face specialization of the FFA. Participants (n = 74) were classified into five groups: congenital blind, early blind, late blind, low vision, and sighted control. Functional magnetic resonance imaging data were acquired when the participants haptically processed carved faces and other objects. Our results showed a robust and highly consistent face-selective activation in the FFA region in the early blind participants, invariant to size and level of abstraction of the face stimuli. The cross-modal face activation in the FFA was much less consistent in other groups. These results suggest that early visual experience primes cross-modal specialization of the FFA, and even after the absence of visual experience for more than 14 years in early blind participants, their FFA can engage in cross-modal processing of face information.

The concentrations of nitrogen (N), phosphorus (P), potassium (K), organic matter (OM), and pH in soil are critical markers of fertility that influence crop growth and yield. Traditional wet-chemical analyses are labor-intensive, time-consuming, and costly, thereby constraining timely soil information acquisition for precision agriculture. This study evaluates whether multi-output Gaussian process regression (MOGPR) can enhance the prediction accuracy of multiple soil nutrients by exploiting their intrinsic correlations, in comparison with single-output Gaussian process regression (SOGPR). Near-infrared (NIR) spectroscopy was applied to 622 typical black soil samples collected from the Farm 855 (45°43′ N, 131°35′ E), Heilongjiang Province, China. Corresponding MOGPR and SOGPR models were developed for systematic performance comparison. Results indicated that MOGPR significantly outperformed SOGPR for nutrients exhibiting moderate-to-strong intercorrelations (N, P, K, and OM), yielding R2 improvements of 0.070.28 and RPD increases of 16–40%, whereas only limited gains were observed for pH due to its weak correlations with other nutrients. These findings indicate that combining NIR spectroscopy with MOGPR offers significant potential for rapid, nondestructive assessment of multiple soil nutrients. This study further establishes a correlation-aware multi-output modeling framework that links shared spectral responses with an inter-nutrient dependency structure, providing methodological guidance for multi-nutrient soil prediction.

PurposeThe aim of our meta-analysis was to explore whether pre-pregnancy obesity is regarded as an important risk factor for predicting macrosomia or not.MethodsThree databases were systematically reviewed and reference lists of relevant articles were checked. Meta-analysis of published cohort studies comparing whether pre-pregnancy obesity was associated with macrosomia and adjusting for potential confounding factors. Calculations of pooled estimates were conducted in random-effect model. Heterogeneity was tested by using Chi-square test and I2 statistics. Publication bias was estimated from Egger’s test (linear regression method) and Begg’s test (rank correlation method).ResultsSixteen cohort studies met the inclusion criteria. The meta-analysis showed that pre-pregnancy obesity was associated with macrosomia as an important risk factor. The adjusted odds ratio was 1.93, 95% CI (1.65, 2.27) in random-effect model, stratified analyses showed no differences regarding different quality grade, definition of macrosomia, location of study and number of confounding factors adjusted for. There was no indication of a publication bias either from the result of Egger’s test or Begg’s test.ConclusionOur findings indicated that pre-pregnancy obesity should be considered as an important risk factor for macrosomia. The effect of pre-pregnancy obesity on macrosomia need to be carefully assessed and monitored.

Low-rate denial of service (LDoS) attacks send attacking bursts intermittently to the network which can severely degrade the victim system’s Quality of Service (QoS). The low-rate nature of such attacks complicates attack detection. LDoS attacks repeatedly trigger the congestion control mechanism, which can make TCP traffic extremely unstable. This paper investigates the network traffic’ characteristics, in which variance and entropy are used to evaluate the TCP traffic’s characteristics, and the ratio of UDP traffic to TCP traffic (UTR) is also analyzed. Thus, a detection method combining two-step cluster analysis and UTR analysis is proposed. Through two-step cluster analysis which is one of the machine learning algorithms, network traffic is divided into multiple clusters and then clusters subjected to LDoS attacks are determined using UTR analysis. NS2 simulation platform and test-bed network environment aim to evaluate the detection approach’s performance. To better assess the effectiveness of the method, public dataset WIDE is also utilized. Experimental results with a good performance prove that the proposed detection approach can accurately detect LDoS attacks.