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Ultralong phosphorescent materials have numerous applications across biological imaging, light-emitting devices, X-ray detection and anti-counterfeiting. Triplet-state molecular phosphorescence typically accompanies the singlet-state fluorescence during photoluminescence, and it is still difficult to achieve direct triplet photoemission as ultralong room temperature phosphorescence (RTP). Here, we have designed Zn-IMDC (IMDC, 4,5-imidazoledicarboxylic acid) and Cd-IMDC, two-dimensional (2D) hydrogen-bond organized metal-organic crystalline microsheets that exhibit rarely direct ultralong RTP upon UV excitation, benefiting from the appropriate heavy-atom effect and multiple triplet energy levels. The excitation-dependent and thermally stimulated ultralong phosphorescence endow the metal-organic systems great opportunities for information safety application and temperature-gated afterglow emission. The well-defined 2D microsheets present color-tunable and anisotropic optical waveguides under different excitation and temperature conditions, providing an effective way to obtain intelligent RTP-based photonic systems at the micro- and nano-scales.

Background: The global incidence and mortality rate of early-onset CRC has gradually increased. The clinicopathological features and pathogenesis of early-onset CRC were still not fully elucidated, and relative data were lacking in China. This research aims to examin the demographic, clinicopathological characteristics of early-onset CRC in China. Materials and methods: This retrospective study included 509 patients who were diagnosed with colorectal cancer from 2019 to 2021 in the General Surgery Department of the Second Xiangya Hospital, Central South University. Results: Early-onset CRC patients had significantly longer median symptom durations (90 vs 60 days, P < 0.001). There was no direct relationship between the symptoms duration and the stage of the disease at presentation in early-onset CRC patients (p = 0.750). Early-onset CRC patients showed a more advanced disease stage than late-onset CRC patients (65.7% VS 52.9%, P = 0.005). Additionally, early-onset CRC patients were more likely to have poorly differentiated (30.2% vs 16.7%, p = 0.001), mucinous or signet-ring tumors than late-onset CRC patients (22.4% vs 15.0%, P = 0.048). The d-MMR tumors were more common in early-onset CRC patients (15.9% vs 5.8%, P < 0.001). There was no significant difference in age, gender, BMI, or pathology between patients with right- and left-sided CRC in early-onset CRC patients. Conclusions: Early-onset CRC has different epidemiology, pathology, and molecular features compared to late-onset CRC in China. More research is required to understand better the pathophysiology of early-onset CRC and why there are different characteristics between the two types of CRC.

Lithium-sulfur (Li-S) batteries exhibit unparalleled theoretical capacity and energy density than conventional lithium ion batteries, but they are hindered by the dissatisfactory "shuttle effect" and the sluggish conversion kinetics owing to the low lithium ion transport kinetics, resulting in rapid capacity fading. Herein, a catalytic two-dimensional heterostructure composite is prepared by evenly grafting mesoporous carbon on the MXene nanosheet (denoted as OMC

For interconnection network losing processors, usually, when the surviving network has a large connected component, it can be used as a functional subsystem without leading to severe performance degradation. Consequently, it is crucial to characterize the interprocessor communication ability and efficiency of the surviving structure. In this article, we prove that when a subset $D$ of at most $6n-17$ processors is deleted from an $n$ -dimensional alternating group graph $\text{AG}_n$ , there exists a largest component with cardinality greater or equal to $|V(\text{AG}_n)|-|D|-3$ for $n\geq 6$ in the remaining network, and the union of small components is, first, an empty graph; or, second, a 3-cycle, or an edge, or a 2-path, or a singleton; or, third, an edge and a singleton, or two singletons. Then, we prove that when a subset $D$ of at most $8n-25$ processors is deleted from $\text{AG}_n$ , there exists a largest component with cardinality greater or equal to $|V(\text{AG}_n)|-|D|-5$ for $n\geq 6$ in the remaining network, and the union of small components is, first, an empty graph; or, second, a 5-cycle, or a 4-path, or a 4-claw, or a 4-cycle, or a 3-path, or a 3-claw, or a 3-cycle, or a 2-path, or an edge, or a singleton; or, third, a 4-cycle and a singleton, or a 3-path and a singleton, or a 3-claw and a singleton, or a 2-path and a singleton, two edges, an edge and a singleton, or two singletons; or, fourth, two edges and a singleton, or a 2-path and two singletons, or an edge and two singletons, or three singletons.

A t / s -diagnosable system refers to such a system that all the faulty nodes of the system can be isolated within a set of size at most s in the presence of at most t faulty nodes. Moreover, it increases the allowed faulty nodes, hence enhancing the diagnosability of the system. We can find that the t / s -diagnosability of n-dimensional folded hypercube F Q n has not been studied under the PMC model and MM* model. In this paper, we determine the t / s -diagnosability of F Q n under the PMC model and MM* model. First, we propose some new fault tolerant properties of F Q n . Then we prove that the t / s -diagnosability of n-dimensional folded hypercube F Q n is ( n + 1 ) g − 1 2 ( g − 1 ) ( g + 2 ) for 2 ≤ g ≤ 1 2 ( n − 1 ) where s = ( n + 1 ) g − 1 2 ( g − 1 ) ( g + 2 ) + g − 2 under both the PMC model and MM* model. In addition, we establish two t / s -diagnosis algorithms of complexity O ( N l o g 2 N ) and complexity O ( N ( l o g 2 N ) 2 ) to isolate the faulty nodes in a node subset of the system under the PMC model and MM* model, respectively. The comparison analysis results showed that the t / s -diagnosability of F Q n is the largest, and it increases faster than the other types of diagnosability as n increases.

Drug refractory epilepsy (RE) is believed to be associated with structural lesions, but some RE patients show no significant structural abnormalities (RE-no-SA) on conventional magnetic resonance imaging scans. Since most of the medically controlled epilepsy (MCE) patients also do not exhibit structural abnormalities, a reliable assessment needs to be developed to differentiate RE-no-SA patients and MCE patients to avoid misdiagnosis and inappropriate treatment. Using resting-state scalp electroencephalogram (EEG) datasets, we extracted the spatial pattern of network (SPN) features from the functional and effective EEG networks of both RE-no-SA patients and MCE patients. Compared to the performance of traditional resting-state EEG network properties, the SPN features exhibited remarkable superiority in classifying these two groups of epilepsy patients, and accuracy values of 90.00% and 80.00% were obtained for the SPN features of the functional and effective EEG networks, respectively. By further fusing the SPN features of functional and effective networks, we demonstrated that the highest accuracy value of 96.67% could be reached, with a sensitivity of 100% and specificity of 92.86%. Overall, these findings not only indicate that the fused functional and effective SPN features are promising as reliable measurements for distinguishing RE-no-SA patients and MCE patients but also may provide a new perspective to explore the complex neurophysiology of refractory epilepsy.

Background The presentation of epilepsy with depression/anxiety disorder (E-AD) is a complex comorbidity, and a systematic psychiatric screen poses a heavy economic burden for the patients. Traditionally, distinguishing between E-AD patients and epilepsy without depression/anxiety disorder (E-no-AD) needs multiple evaluations, which is incredibly difficult owing to the high costs. Therefore, there is an urgent need for a reliable assessment protocol to initially distinguish between the two types of patients. Results Based on the resting-state electroencephalography signals collected from E-AD and E-no-AD patients, the classification performances were estimated for three different network features, using the support vector machine. The results revealed that the SPN feature of functional network connectivity was remarkably superior to the traditional network feature for distinguishing between the two groups of patients, with appreciable classification accuracy values of 89.90% and 87.37% sensitivity and 91.67% specificity. Conclusions These findings demonstrate the superiority of functional SPN features as a reliable way in characterizing the differences of E-AD and E-no-AD patients and potentially provide insights for the complex mechanisms causing E-AD in patients.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder which is caused by the interplay of genetic and environmental risk factors such as second-hand smoke (SHS). The association between postnatal exposure to SHS and ADHD risk in children was still inconclusive. We performed a systematic review and meta-analysis to explore the definite association. We searched for relevant studies from PubMed, Embase, Ovid, and Web of Science databases up to January 2020. We used random effect models to calculate pooled odds ratio (OR) with 95% confidence interval (CI). Subgroup analyses and sensitive analyses were also performed to solve the heterogeneity. According to our inclusion criteria, 9 studies including 6 cross-sectional studies, 2 cohort studies, and 1 case-control study were included in the final analysis. Postnatal exposure to SHS increased the risk of ADHD in children (OR: 1.60; 95% CI: 1.37-1.87). Children who exposed to SHS were found a slight risk for conduct problems (OR: 1.33, 95% CI: 1.00-1.77). Among the studies which used cotinine as a biomarker for SHS exposure, a lower pooled OR (OR = 1.16, 95%CI = 1.01, 1.33) was observed between cotinine and ADHD in children. Our meta-analysis results suggested that SHS exposure may be a risk factor for ADHD. We also found that SHS exposure may be associated with some adverse behavioral outcomes. More prospective studies should be conducted to confirm the relationship between SHS exposure and ADHD in children.

The last few years have witnessed an increasing interest in the use of humanoid robots for diverse technological applications. Since the center of mass (CoM) of a typical humanoid lies at a relatively high elevation, such a robot often experiences instability during its operation and has a high likelihood of fall. Thus, it is necessary to endow these robots with a robust method to reduce damage that may result from the impact of a fall. Prior research on humans undergoing a forward fall has revealed that the deployment of a rollover strategy along the longitudinal axis can lower the impact force experienced on the hand to effectively reduce wrist injuries. Yet, analogous research for humanoids has received scant attention. To address this research gap, in this work, we consider the optimal design, implementation, and examination of a rollover strategy-similar to the one for humans-for a humanoid robot by employing a differential dynamic programming (DDP) approach. In addition to providing an overview of the theoretical formulation of our methodology, using results from repeated forward falling experiments with a humanoid, we demonstrate that a reliable application of the rollover strategy considerably reduces the impact force vis-a-vis the bimanual fall approach, to protect the wrist of our robot. Moreover, the experiments showcase that the proposed method is also effective in reducing the impact on the torso of the humanoid. With the use of the rollover methodology, a typical humanoid can overcome a key obstacle to its broad adoption and deployment in real-world applications.

Superhydrophobic surface with micro/nano structures has attracted extensive attention because of its excellent performance in passive anti-icing strategy. In this study, a 2D Computational Fluid Dynamics model was proposed to simulate the icing process of droplet on surface at low temperature by coupling the Volume of Fluid model with solidification/melting model. Then, the icing process of droplet on surfaces with different micro structures were simulated and compared. Furthermore, the samples with different micro-nano structures were obtained by laser ablation and their anti-icing performance were measured. Results showed that the icing time of bouncing droplet on the surface with micro/nano structures is significantly delayed by seven times, where the nucleation rate is related to the contact area of solid-liquid phases. It is also found that the larger size of the air cavity brought by the structure is more conducive to obtain enhanced anti-icing performance. Furthermore, the anti-icing mechanism of superhydrophobic surface was revealed by investigating variations in solidification/melting contour and solid volume fraction.

Privacy protection of individual users in social networks is becoming more and more important, thus it requires effective anonymization techniques. In this paper, we use Kruskal and Prim algorithms to model the linear programming of the minimum spanning tree. Finally, we execute the experiments on the number of anonymity solutions and time with different edge weights to analyze the Kruskal algorithm and Prim algorithm to verify their anonymity feasibility.

Containers have been widely adopted in production computing environments for its efficiency and low overhead of isolation. However, recent studies have shown that containerized applications are prone to various security attacks. Moreover, containerized applications are often highly dynamic and short-lived, which further exacerbates the problem. In this paper, we present CDL, a classified distributed learning framework to achieve efficient security attack detection for containerized applications. CDL integrates online application classification and anomaly detection to overcome the challenge of lacking sufficient training data for dynamic short-lived containers while considering diversified normal behaviors in different applications. We have implemented a prototype of CDL and evaluated it over 33 real world vulnerability attacks in 24 commonly used server applications. Our experimental results show that CDL can reduce the false positive rate from over 12% to 0.24% compared to traditional anomaly detection schemes without aggregating training data. By introducing application classification into container behavior learning, CDL can improve the detection rate from catching 20 attacks to 31 attacks before those attacks succeed. CDL is light-weight, which can complete application classification and anomaly detection for each data sample within a few milliseconds.

The sluggish redox kinetics and severe lithium polysulfides (LiPSs) shutting effect in lithium sulfur batteries (LSBs) greatly hinder their practical applications. In this work, phosphorus doped carbon nanosheet array (MPC) separator coating layer was synthesized by MgO template, and used as metal-free electrocatalyst to concurrently fulfill efficient polysulfide interception and conversion. By regulating the addition of phytic acid, copolymerization rate of formaldehyde, resorcinol and phytic acid can be conveniently adjusted to form a hierarchical porous MPC microstructure. Phosphorus doping introduces surface defects and enhances the polarity of MPC, which exhibits a metal-like LiPSs anchoring effect by forming Li-P and S-P bonds, avoiding the undesirable oxidation of LiPSs during adsorption. Symmetrical cell and Li2S precipitation experiments reveal that phosphorus doping reduces energy barrier for LiPSs reactions and facilitates Li2S precipitation behavior, exhibiting a strong electrocatalytic effect on Li-S chemistry. Stemming from the above advantages of MPC coated separator, cells exhibit a low interfacial reaction resistance and good anode stability. A capacity of 1161 mAh g-1 at 0.2 C and a low attenuation of 0.056% per cycle at 1 C over 800 cycles are achieved. For a high sulfur loading up to 4 mg cm-2, the areal capacity can still be stabilized at 2.86 mAh cm-2.