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The work engagement of foreign language teachers enhances both their performance and the quality of instruction. Recent studies have highlighted the pivotal roles of emotional intelligence, job burnout, and self-efficacy in fostering teachers' work engagement. Despite this, the complex interactions between these factors in the context of foreign language teaching remain underexplored. This study conducts a meta-analytic review to examine the connections among work engagement, emotional intelligence, burnout, and self-efficacy among foreign language educators. Utilizing a chain mediation model, it probes the mechanism through which emotional intelligence impacts work engagement, based on an analysis of 49 scholarly articles. Results from the meta-analysis robustly validate the hypothesized model. Specifically, emotional intelligence contributes to work engagement via four distinct routes: direct mediation through self-efficacy, direct mediation through job burnout, and chain mediation involving self-efficacy and job burnout. Of the evaluated factors, emotional intelligence had the most significant impact on enhancing work engagement. Implications of these findings may facilitate the training and ongoing professional development of foreign language teachers.

Super-resolution (SR) of remote sensing images is essential to compensate for missing information in the original high-resolution (HR) images. Single-image super-resolution (SISR) technique aims to recover high-resolution images from low-resolution (LR) images. However, traditional SISR methods often result in blurred and unclear images due to the loss of high-frequency details in LR images at high magnifications. In this paper, a super-segmental reconstruction model STGAN for remote sensing images is proposed, which fuses the Generative Adversarial Networks (GANs) and self-attention mechanism based on the Reference Super Resolution method (RefSR). The core module of the model consists of multiple CNN-Swin Transformer blocks (MCST), each of which consists of a CNN layer and a specific modified Swin Transformer, constituting the feature extraction channel. In image hypersegmentation reconstruction, the optimized and improved correlation attention block (RAM-V) uses feature maps and gradient maps to improve the robustness of the model under different scenarios (such as land cover change). The experimental results show that the STGAN model proposed in this paper exhibits the best image data perception quality results with the best performance of LPIPS and PI metrics in the test set under RRSSRD public datasets. In the experimental test set, the PSNR reaches 31.4151, the SSIM is 0.8408, and the performance on the RMSE and SAM metrics is excellent, which demonstrate the model’s superior image reconstruction details in super-resolution reconstruction and highlighting the great potential of RefSR’s application to the task of super-scalar processing of remotely sensed images.

Abstract Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness. Mutation-based greybox fuzzing generates input cases by mutating the input seed, i.e., applying a sequence of mutation operators to randomly selected mutation positions of the seed. However, existing fruitful research work focuses on scheduling mutation operators, leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency. This paper proposes a novel greybox fuzzing method, PosFuzz, that statistically schedules mutation positions based on their historical performance. PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations. PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator. It then leverages two sampling methods in different mutating stages to select the positions from the distribution. We have implemented PosFuzz on top of AFL, AFLFast and MOPT, called Pos-AFL, -AFLFast and -MOPT respectively, and evaluated them on the UNIFUZZ benchmark (20 widely used open source programs) and LAVA-M dataset. The result shows that, under the same testing time budget, the Pos-AFL, -AFLFast and -MOPT outperform their counterparts in code coverage and vulnerability discovery ability. Compared with AFL, AFLFast, and MOPT, PosFuzz gets 21% more edge coverage and finds 133% more paths on average. It also triggers 275% more unique bugs on average.

Abstract Introduction Stretch marks are common atrophic dermal scars with significant physical and psychological effects. Therefore, there is a need for effective cosmetics and procedures for stretch mark treatment. This study aimed to evaluate the efficacy and safety of a novel treatment for stretch marks that is made up of topical formulations containing beta-glucan combined with nanofractional radiofrequency. Methods This randomized, blinded control trial enrolled 64 Chinese women aged 20–45 years at > 6 months after delivery with obvious white or silver abdominal stretch marks. Participants were randomly allocated to group A (blank group), group B (topical product group), group C (product combined with nanofractional radiofrequency), and group D (vehicle combined with nanofractional radiofrequency). The stretch mark width, skin elasticity, skin color, skin thickness, and collagen density were noninvasively measured. Two trained assessors evaluated the severity, color, outline, and relaxation of the striae. Results Group C showed the best treatment efficacy, with no adverse effects observed during the study period. Conclusion Our findings indicate that stretch mark treatment using topical formulations containing beta-glucan, combined with nanofractional radiofrequency plus magnetic nanofractional radiofrequency, is tolerable and effective. Trial registration ChiCTR2200056725.

BackgroundThe developments in cosmetic sciences and technologies have generated a gap between the cosmetics and their users. Users including regular customers, clinicians, industry personnel, researchers, testing agencies, beauty salon workers, and mass media hardly possess the ability to distinguish truth from falsehood. The gap remained as one major reason for inappropriate cosmetics usage, insufficient efficacy, and even cosmetics adverse reactions (CARs).MethodsAiming at enhancing the relevant practitioners’ cosmetic and dermatologic sciences, we launched a cosmetic and dermatologic sciences continuing medical education (CME) since 2008. The objective of the current study was to evaluate the effectiveness of the CME. We summarized and analyzed the project for the last 15 years. Meanwhile, an online survey consisted of three parts was performed to evaluate the CME and to collect the trainees’ comments.ResultsA total of 3,923 trainees have participated in the CME project from 2008 to 2022. The trainees included clinicians, industry staffs, biomedical researchers, third-party cosmetics testing staffs, beauty salon staffs, students, and media staffs. The trainees had theory courses on cosmetic and dermatologic sciences, cosmetics DIY practice & video watching, and an optional guided tour during the 4.5-day CME. Eight hundred and twenty-three trainees and 586 control subjects responded to the online survey. The comprehensive test in the second part of the survey demonstrated that compared with the control group, the CME project significantly enhanced the trainees’ perception and knowledge regarding the cosmetics formula sciences, basic dermatologic sciences, cosmetics usage, noninvasive measurements, new advances, CARs, and laws (p = 0.000). Trainees of all occupations ranked “basic dermatologic sciences and skin diseases” as the most significant sections. Trainees of all occupations believed the CME has contributed most in “understand the function & efficacy of cosmetics.” We noticed the occupational variances. Over 97% of trainees were willing to recommend the CME to the others.ConclusionThe CME project significantly enhanced the trainees’ cosmetic and dermatologic sciences, which bridged the gap between cosmetics and public skin health. This multidisciplinary CME also contributed to establishing an interdisciplinary interaction and cooperation platform for the multiple occupations involved in the public skin health maintenance and promotion.

Abstract Network function virtualization provides programmable in-network middlewares by leveraging virtualization technologies and commodity hardware and has gained popularity among all mainstream network device manufacturers. Yet it is challenging to apply coverage-guided fuzzing, one of the state-of-the-art vulnerability discovery approaches, to those virtualized network devices, due to inevitable integrity protection adopted by those devices. In this paper, we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protection bypassing method, which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference technique. We implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols, SNMP , DHCP and NTP , on nine popular virtualized network devices. NDFuzz obtains an average 36% coverage improvement in comparison with its black-box counterparts. NDFuzz discovers 2 0-Day vulnerabilities and 1 1-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of them. All discovered vulnerabilities are confirmed by corresponding vendors.

Abstract Grey-box fuzzing techniques have been widely used in software bug finding. In general, there are many decisions to make in the fuzzing process, including which code block in the target program should be explored first, which bytes of an input seed should be mutated to reach the target code block, and how to mutate the chosen input bytes. However, existing solutions usually rely on random exploration or certain heuristics to choose where and how to fuzz, which limits the efficiency of fuzzing. In this paper, we propose a novel solution CAMFuzz to guide the fuzzing process with explainable decisions in explainable artificial intelligence (XAI). First, we propose a dynamic weight adjustment algorithm, which considers both the difficulty of reaching a block and the number of unvisited blocks nearby, to find code blocks worthy to explore first. Second, we utilize a widely used local interpretation technique, i.e., class activation mapping (CAM), to recognize which part of an input seed should be mutated to reach a given target code block. Therefore, CAMFuzz can distinguish which part of code in the program is more important and which positions in the input file should be mutated first, in order to achieve a better code coverage and bug finding efficiency. Third, to further help the fuzzer increase fuzzing efficiency, we leverage a lightweight static program analysis to help the fuzzer identify magic values. We implement a prototype of CAMFuzz and evaluate it on 13 real-world programs (including 11 open source targets, 2 closed-source commercial products including a Microsoft component and Hancom Office) Results show that CAMFuzz outperforms state-of-the-art fuzzers in both code coverage and bug finding. To detail, CAMFuzz on average achieves 2.07 $$\times$$ × more bugs and 1.17 $$\times$$ × coverage improvements. In total, it found 19 previously unknown vulnerabilities, of which 6 have been assigned by CVE so far.

Background: Hemorrhagic transformation (HT) is a common complication of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS) and may lead to neurological deterioration. This article discusses whether monocyte count to high-density lipoprotein ratio (MHR) level is associated with HT in AIS patients. Materials and methods: The clinical data of AIS patients who underwent rt-PA IVT treatment were continuously collected. According to whether HT occurred, patients were divided into HT group and non-HT group. Potential association between MHR and HT in different subtypes AIS was explored by using logistic regression. Results: A total of 444 AIS patients were retrospective analyzed. The MHR level was lower in HT group compared with the non-HT group in all AIS patients (0.28 vs 0.36, P = .031) and in large-artery atherosclerosis (LAA) type AIS patients (0.31 vs 0.37, P = .032). Low MHR was independently related to HT (OR:0.035, 95%CI:0.003−0.390, P = .006). Among all TOAST subtypes, low MHR was only independently associated with HT in patients of LAA-type AIS after adjusting for confounding factors (OR:0.01, 95%CI:0.00−0.62, P = .031), with an optimal cut-off value of 0.41, sensitivity of 85.7%, and specificity of 43.1%. MHR was not correlated with SVO, VE, and CE subtype AIS. Conclusion: Low MHR may be an independent predictor of HT in patients with AIS and this conclusion only existed in LAA-type AIS.

Aiming to improve the comprehensive aerodynamic performance of a high-speed train, a multi-objective shape optimization method for a streamlined train head is proposed in this work. The shape of the streamlined train head is parameterized with some spline curves. The optimization design variables are uniformly sampled using the optimal Latin hypercube design method. The aerodynamic resistance and dipole noise sources are chosen as the optimization objectives, which can be obtained through the computational fluid dynamics (CFD) method. An approximate calculation model is established by the radial basis function neural network so as to effectively predict the values of optimization objectives. The error between the predicted values and actual values of the aerodynamic resistance is less than 1%, and that of the dipole noise source is less than 3 dB, which demonstrate the validity of the approximate calculation model. In the optimization process, the algorithm NSGA-II is adopted to update the values of the optimization design variables, and the approximate calculation model is used to calculate the optimization objectives, which greatly reduces the optimization computation time of the streamlined head shape. Through iterative computation of the optimization algorithm in the design space, each optimized design variable shows a trend of convergence, and the aerodynamic resistance and dipole noise source generally show a decreasing trend. The Pareto front is corrected by the CFD method after optimization. The aerodynamic resistance can be reduced by up to 4.5%, and the dipole noise source can be reduced by up to 3.9 dB.

Introduction: There is limited understanding of the association of grandparenting with the wellbeing of older adults with chronic diseases. This study aimed to examine grandparenting practices and their association with physical and mental well-being among older adults. Methods: The cross-sectional study was conducted at an outpatient clinic in a tertiary hospital in the Klang Valley, Malaysia. A total of 421 older patients with grandparenting experience were interviewed. The SF-12 Version 2.0 Health Survey was utilised to assess mental and physical wellbeing. Sociodemographic information, chronic disease care and grandparenting practices were evaluated to investigate any potential relationship with physical and mental well-being. Results: Of the participants, 80% were aged 60-74 years; 62.5% were women; and 81.4% completed secondary education. More than half of the participants voluntarily took on the role of grandparenting. The participants showed an impaired physical function but a preserved mental wellbeing. Never missing regular medication due to grandparenting and taking up grandparenting based on the circumstances were related to mental health. However, no factor was significantly associated with physical well-being. Conclusion: Two factors are linked to improved mental well-being. In contrast, there is no significant relationship found between a decline in physical health and grandparenting practices. Further research is needed to determine the causal relationship between physical health challenges and grandparenting practices. [ABSTRACT FROM AUTHOR]

The purpose was to explore the spatial centrality of the whole brain functional network related to migraine and to investigate the potential functional hubs associated with migraine. 32 migraine patients and 55 healthy controls were recruited and they received resting-state functional magnetic resonance imaging voluntarily. Voxel-wise Degree Centrality (DC) was measured across the whole brain, and group differences in DC were compared. False Discovery Rate and permutation test (5000 times) were used for multiple comparisons. Finally, significant differences in functional connectivity (FC) between seeds and other brain regions were further researched by the seed-based approach. The correlation analyses between the changes in the brain function and clinical features were also performed. The results showed that, compared to healthy controls, migraine patients exhibited significantly increased DC in the left anterior cingulate cortex (ACC), slightly increased DC in the right ACC and the right medial superior frontal gyrus (SFG). No significant correlation was found between DC and clinical variables. The seed-based analyses showed that migraine patients showed increased FC between the right SFG and left ACC, decreased FC between the left ACC and left superior temporal gyrus (STG). FC value of the right SFG was positively correlated with the score of migraine-specific quality-of-life questionnaire about role in functionpreventive in migraine patients. According to relatively changed DC, we found that migraine patients exhibited specific abnormal intrinsic functional hubs. These findings expand our understanding of functional characteristics of migraine, and may provide new insights into understanding the dysfunction and pathophysiology of migraine patients. [ABSTRACT FROM AUTHOR]