Hydrogel-based flexible artificial tactility is equipped to intelligent robots to mimic human mechanosensory perception. However, it remains a great challenge for hydrogel sensors to maintain flexibility and sensory performances during cyclic loadings at high or low temperatures due to water loss or freezing. Here, a flexible robot tactility is developed with high robustness based on organohydrogel sensor arrays with negligent hysteresis and temperature tolerance. Conductive polyaniline chains are interpenetrated through a poly(acrylamide-co-acrylic acid) network with glycerin/water mixture with interchain electrostatic interactions and hydrogen bonds, yielding a high dissipated energy of 1.58 MJ m-3, and ultralow hysteresis during 1000 cyclic loadings. Moreover, the binary solvent provides the gels with outstanding tolerance from -100 to 60 °C and the organohydrogel sensors remain flexible, fatigue resistant, conductive (0.27 S m-1), highly strain sensitive (GF of 3.88) and pressure sensitive (35.8 MPa-1). The organohydrogel sensor arrays are equipped on manipulator finger dorsa and pads to simultaneously monitor the finger motions and detect the pressure distribution exerted by grasped objects. A machine learning model is used to train the system to recognize the shape of grasped objects with 100% accuracy. The flexible robot tactility based on organohydrogels is promising for novel intelligent robots.

This paper introduces an efficient methodology for conducting rat anesthesia experiments, aimed at enhancing the quality of raw brain signals obtained. The proposed approach enables the acquisition of animal brain signals during experiments without the confounding influence of muscle noise. Initially, the use of alpha-chloralose (a-c) in conjunction with Isoflurane is introduced to induce anesthesia in rats. Subsequently, Dexdomitor is administered to prevent muscular movements during the collection of brain signals, further refining the signal quality. Experimental outcomes conclusively demonstrate that our anesthesia method produces cleaner raw signals and exhibits improved robustness during data acquisition, outperforming existing methods that rely solely on Isoflurane or the Ketamine-Xylazine combination. Notably, this improved performance is achieved with minimal alterations to vital physiological parameters, including body temperature, respiration, and heart rates. Moreover, the efficacy of a-c in maintaining anesthesia for up to 7 h stands in contrast to the shorter durations achievable with continuous Isoflurane administration or the 30-min window offered by Ketamine-Xylazine, highlighting the practical advantages of our proposed method. Finally, post-experiment observations confirmed that the animals gradually returned to normal behavior without any signs of distress or adverse effects, indicating that our method was both effective and safe.

Prior research on cascading failures within interdependent networks has predominantly emphasized the coupling of nodes. Nevertheless, in practical networks, interactions often exist not just through the nodes themselves but also via the connections (edges) linking them, a configuration referred to as edge-coupled interdependent networks. Past research has shown that introducing a certain percentage of reinforced nodes or connecting edges can prevent catastrophic network collapses. However, the effect of reinforced inter-layer links in edge-coupled interdependent networks has yet to be addressed. Here, we develop a theoretical framework for studying percolation models in edge-coupled interdependent networks by introducing a proportion of reinforced inter-layer links and deriving detailed expressions for the giant and finite components and the percolation phase transition threshold. We find that there exists a required minimum proportion of the reinforced inter-layer links to prevent abrupt network collapse, which serves as a boundary to distinguish different phase transition types of a network. We provide both analytical and numerical solutions for random and scale-free networks, demonstrating that the proposed method exhibits superior reinforcement efficiency compared to intra-layer link reinforcement strategies. Theoretical analysis, simulation results, and real network systems validate our model and indicate that introducing a specific proportion of reinforced inter-layer links can prevent abrupt system failure and enhance network robustness in edge-coupled interdependent networks.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Superhydrophobic surfaces hold immense potential in underwater drag reduction. However, as the Reynolds number increases, the drag reduction rate decreases, and it may even lead to a drag increase. The reason lies in the collapse of the air mattress. To address this issue, this paper develops a pyramid-shaped robust superhydrophobic surface with wedged microgrooves, which exhibits a high gas fraction when immersed underwater and good ability to achieve complete spreading and recovery of the air mattress through air replenishment in the case of collapse of the air mattress. Pressure drop tests in a water tunnel confirm that with continuous air injection, the drag reduction reaches 64.8% in laminar flow conditions, substantially greater than 38.4% in the case without air injection, and can achieve 50.8% drag reduction in turbulent flow. This result highlights the potential applications of superhydrophobic surfaces with air mattress recovery for drag reduction.

UNASSIGNED: The locked vision plan can make the left breast cancer heart and lung organs dose.
UNASSIGNED: The aim of the present study was to compare the dosimetric differences between field-locked and field-split plans in intensity-modulated radiotherapy for left-sided breast cancer, to explore the effect of field-locking on the low-dose region, and to evaluate its robustness to the radiotherapy target, in order to provide a reference for the selection of clinical radiotherapy protocols.
UNASSIGNED: A total of 30 patients were selected after radical left breast cancer surgery, and 7-field locked-field and split-field plans were developed to compare the dose difference (∆D) between the target area and each organ at risk, and to introduce offsets of 3, 5 and 7 mm in six directions and recalculate the perturbed dose distributions, and to compare the ∆D between the original and the perturbed plans according to the robustness of the plans.
UNASSIGNED: The results revealed that the D98%, D95% and Dmean values of the planning target volume (PTV) of the two plans differed little and were not statistically different. The locked field plan provided better protection for the left lung, right lung, heart, right breast and left anterior descending coronary artery. For PTV∆D98%, PTV∆D95%, PTV∆Dmean, the ∆D was higher for the Locked Fields plan, and for LungL∆5, LungL∆20 and Heart∆mean, the ∆D was higher for the original plan.
UNASSIGNED: It was concluded that the field-locking plan could reduce the low-dose area of the affected lung and provide improved protection to the remaining critical organs, and the field-locking plan was more robust in protecting critical organs. Meanwhile, the field-locking plan showed higher sensitivity to positional deviation for target PTV.

Highly robust soft strain gauges are rapidly emerging as a promising candidate in the fields of vital signs and machine conditions monitoring. However, it is still a key challenge to achieve high-performance strain sensing in these sensors with mechanical/electrical robustness for long-term usage. The multilayer structural design of sensors enhances sensing performance while the interfacial connection of heterogeneous materials between different layers is weak. Herein, inspired by the efficient perception mechanism of scorpion slit sensilla with tough interface interconnections, the synergy of ultra-high electrical performance and mechanical robustness is successfully achieved via interface design engineering. The developed multilayer soft strain gauge (MSSG) exhibits a strain sensitivity beyond 105, a lower detection limit of 8.3 µm, a frequency resolution within 0.1 Hz, and cyclic stability over 63 000 strain cycles. Also, the tough interface improves the level of heterogeneous integration in the MSSG which allows to endure different stresses. Furthermore, an MSSG-based wireless strain monitoring system is developed that enables applications on different complex dynamic surfaces, including accurate identification of human throat activity and monitoring of rolling bearing conditions.

Passive radiative cooling material of cellulose by coupling inorganic nanoparticles, have demonstrated competitive advantages in sustainably cooling buildings and constructions due to their voluminous availability, biodegradability, renewability, and natural origin. However, the weak stability of cellulose-inorganic nanoparticle materials when exposed to water or external forces remains a significant challenge that impedes their practical application. In this study, we proposed an easy-to-prepare, scalable, and robust cooling cellulose composite by coupling nano-SiO2 and cellulose acetate (CA) within cellulose fibers, using the mature pulping and paper process (filling of inorganic particles of nano-SiO2 and subsequent sizing of polymer of CA). More importantly, the CA molecules form the strong bonding with the cellulose molecules due to the high similarity of their molecular structure, which makes CA function as a \"glue\" to effectively fasten nano-SiO2 on the cellulose fibers. Correspondingly, our cellulose composite features desirable robustness and structural stability even undergoing mechanical beating and water-soaking treatments, demonstrating its excellent robustness and desirable adaptability to natural environments, such as wind and rain. As a result, despite undergoing water-soaking (for 40 days) or environmental exposure (for 90 days), the cooling cellulose composite still exhibits excellent solar reflectance (>95 %) and infrared thermal emissivity (>0.95 at 8-13 μm), enabling sub-ambient temperature (∼6.5 °C during daytime and ∼8 °C at nighttime) throughout the day. Our cooling cellulose composite demonstrates promising potential as an environmentally friendly, low-cost, and stable cooling material in our low-carbon society.

This paper introduces a novel bounded loss framework for SVM and SVR. Specifically, using the Pinball loss as an illustration, we devise a novel bounded exponential quantile loss (Leq-loss) for both support vector machine classification and regression tasks. For Leq-loss, it not only enhances the robustness of SVM and SVR against outliers but also improves the robustness of SVM to resampling from a different perspective. Furthermore, EQSVM and EQSVR were constructed based on Leq-loss, and the influence functions and breakdown point lower bounds of their estimators are derived. It is proved that the influence functions are bounded, and the breakdown point lower bounds can reach the highest asymptotic breakdown point of 1/2. Additionally, we demonstrated the robustness of EQSVM to resampling and derived its generalization error bound based on Rademacher complexity. Due to the Leq-loss being non-convex, we can use the concave-convex procedure (CCCP) technique to transform the problem into a series of convex optimization problems and use the ClipDCD algorithm to solve these convex optimization problems. Numerous experiments have been conducted to confirm the effectiveness of the proposed EQSVM and EQSVR.

Climate change is altering species distribution and modifying interactions in microbial communities. Understanding microbial community structure and their interactions is crucial to interpreting ecosystem responses to climate change. Here, we examined the assemblages of stream bacteria and fungi, and the associations between the two groups along elevational gradients in two regions with contrasting precipitation and temperature, that is the Galong and Qilian mountains of the Tibetan Plateau. In the wetter and warmer region, the species richness significantly increased and decreased with elevation for bacteria and fungi, respectively, while were nonsignificant in the drier and colder region. Their bipartite network structure was also different by showing significant increases in connectance and nestedness towards higher elevations only in the wetter and warmer region. In addition, these correlation network structure generally exhibited similar positive association with species richness in the wetter and warmer region and the drier and colder region. In the wetter and warmer region, climatic change along elevation was more important in determining connectance and nestedness, whereas microbial species richness exerted a stronger influence on network structure and robustness in the drier and colder region. These findings indicate substantial forthcoming changes in microbial diversity and network structure in warming climates, especially in wetter and warmer regions on Earth, advancing the understanding of microbial bipartite interactions\' response to climate change.