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Acoustic metasurfaces are widely used for noise attenuation due to their outstanding acoustic performance and subwavelength characteristics. The paper introduces a topology-optimized inverse design approach for broadband sound-absorbing metasurfaces, aiming to achieve efficient sound absorption performance across a wide frequency spectrum. By integrating genetic algorithms with the finite element method and driven by objectives such as the absorption frequency range and absorption coefficient, we can transcend the limitations of manual design and fully exploit the design potential of structures. Furthermore, the study investigates the sound absorption mechanism and thermal-viscous dissipation through finite element simulations and experimental validations. The research findings reveal that the proposed broadband sound-absorbing metasurfaces demonstrate excellent sound absorption capabilities across the designated frequency range, boasting an average sound absorption coefficient exceeding 85%. These findings offer fresh perspectives and methodologies for advancing the research and practical application of broadband sound-absorbing metasurfaces. [ABSTRACT FROM AUTHOR]

To understand biosafety's current situation in laboratory animal research and risk factors affecting occupational health. Compliance surveys were conducted by questionnaire via Questionnaire Star (an application app on the Internet) in Chinese. Thirty-nine anonymous questionnaires were collected. The surveyed institution has established 24 types of ABSL (Animal Biosafety Laboratory) and biosafety management organizations and systems equipped with safety equipment. Our study also suggests that the principal of the laboratory establishment fails to perform supervision and inspection responsibilities, the inappropriate design of the animal biosafety laboratory, non-standardized personnel training and health management, non-strict waste management, and insufficient emergency management. The administrative department and work units should address certain safety and occupational health risks in laboratory animal research. The author proposes control strategies based on organizational guarantee, personnel management, emergency management, etc., to help prevent risks and ensure occupational health. Due to regional limitations and small sample size, the results may not be generalisable to all parts of the world. However, some of the key common issues may also be present in other regions, so we believe that this research still has some relevance.

Information technology (IT) innovation development within non-IT firms has been a key interest, but it is fraught with challenges because these firms lack sufficient IT knowledge. This study takes a resource dependence perspective to examine how engaging interlocking directorates with IT firms, or IT interlocks, affects non-IT firms' innovation outcomes. Despite the acknowledged role of board interlocks in knowledge transfer, the role of IT interlocks in transferring IT knowledge has not been studied. Using a large-scale panel dataset of Chinese public firms between 2000 and 2020, our findings reveal that IT interlocks of non-IT firms positively impact their IT innovation by transferring IT knowledge, particularly when the interlocked IT firms are knowledge-intensive. Our research contributes to the information systems literature by affirming IT interlocks' positive impact on innovation outcomes and highlighting the value of specific board relational capital in transferring external knowledge in need. It also offers practical implications for non-IT firms overcoming innovation challenges by establishing directorate connections with IT firms. [ABSTRACT FROM AUTHOR]

Bayesian inference for structural equation models (SEMs) is increasingly popular in social and psychological sciences owing to its flexibility to adapt to more complex models and the ability to include prior information if available. However, there are two major hurdles in using the traditional Bayesian SEM in practice: (1) the information nested in the prior distributions is hard to control; and (2) the MCMC iterative procedures naturally lead to Markov chains with serial dependence and the diagnostics of their convergence are often difficult. In this study, we present an alternative procedure for Bayesian SEM aiming to address the two challenges. In the new Bayesian SEM procedure, we specify a prior distribution on the population covariance matrix parameter [sigma] and obtain its posterior distribution p([sigma]|data). We then construct a posterior distribution of model parameters [theta] in the hypothetical SEM model by transforming the posterior distribution of [sigma] to a distribution of model parameter [theta]. The new procedure eases the practice of Bayesian SEM significantly and has a better control over the information nested in the prior distribution. We evaluated its performance through a simulation study and demonstrate its application through an empirical example.

We synthesized and developed 1,3-dihydro-2H-imidazo[4,5-b]phenazine-2-thione as a ratiometric chemosensor for [Hg.sup.2+] recognition in a DMSO/[H.sub.2]O (v/v = 9:1) binary solution. We rationally introduced the phenazine imidazole group as the fluorophore and the thione moiety as the recognition site to bind [Hg.sup.2+]. Interestingly, the chemosensor showed an ultrasensitive response to [Hg.sup.2+], and the lowest limit of detection was 0.167 nM. In addition, it can also separate [Hg.sup.2+] from aqueous solutions with excellent ingestion capacity, with an adsorption ratio of up to 96%. Furthermore, ion test strips based on chemosensors were fabricated for convenient and efficient detection of [Hg.sup.2+]. Key words: fluorometric, chemosensor, imidazo[4,5-b]phenazine-2-thione, ultrasensitive detection, separation, [Hg.sup.2+]. Nous avons synthetise la 1,3-dihydro-2H-imidazo[4,5-b]phenazine-2-thione afin de mettre au point un capteur chimique ratiometrique pour la reconnaissance des ions [Hg.sup.2+] dans une solution binaire de DMSO/[H.sub.2]O (v/v = 9:1). Nous avons selectionne de maniere rationnelle le groupe imidazophenazine comme fluorophore et le fragment thione comme site de reconnaissance afin qu'il se lie a l'ion [Hg.sup.2+]. Il est interessant de noter que le capteur chimique presente une reponse ultrasensible aux ions [Hg.sup.2+] et une limite inferieure de detection de 0,167 nM. De plus, il permet de separer les ions [Hg.sup.2+] de la solution aqueuse avec une excellente capacite d'ingestion, le rapport d'adsorption atteignant 96 %. En outre, nous avons fabrique des bandelettes d'analyse ioniques permettant de detecter facilement et efficacement les ions [Hg.sup.2+] a l'aide du capteur chimique. [Traduit par la Redaction] Mots-cles : fluorimetrique, capteur chimique, imidazo[4,5-b]phenazine-2-thione, detection ultrasensible, separation, [Hg.sup.2+]., Introduction Recently, ultrasensitive detection has received significant attention owing to its crucial biological, chemical, and environmental applications. (1-5) Over the past few years, numerous methods have been reported for ultrasensitive [...]

Objective: Although non-suicidal self-injury (i.e., NSSI) has been suggested as a robust risk factor of suicide, NSSI related information that is most related to suicide risk remains unclear. Commonly studied NSSI characteristics are its frequency and the number of methods endorsed. However, it may not be merely how frequent or how many different methods that matters, but "why," which alludes to the importance of NSSI functions (or why individuals engage in NSSI). Thus, this study examined how the interactions between NSSI characteristics and functions are associated with suicide risk. Methods: Undergraduate students (n = 820) with a lifetime history of NSSI, filled out self-report measures on NSSI and suicide risk. A hierarchical regression analysis was conducted to examine the moderation effects between four 2-way interactions (i.e., method X intrapersonal; method X interpersonal; frequency X intrapersonal; frequency X interpersonal) on suicide risk. Results: Main effects of all four independent variables were statistically significant. In terms of interactions, the intrapersonal function moderated both the effects of NSSI frequency and methods on suicide risk, whereas the interpersonal function moderated the effects of NSSI frequency on suicide risk. Conclusion: Our results highlight that some NSSI related information than others are more indicative of suicide risk. In particular, the combination of NSSI functions, along with its frequency and number of methods, holds promise when assessing for current and lifetime suicidal thoughts and behaviors. [ABSTRACT FROM AUTHOR]

With the rapid advancement of robotics technology, an increasing number of researchers are exploring the use of natural language as a communication channel between humans and robots. In scenarios where language conditioned manipulation grounding, prevailing methods rely heavily on supervised multimodal deep learning. In this paradigm, robots assimilate knowledge from both language instructions and visual input. However, these approaches lack external knowledge for comprehending natural language instructions and are hindered by the substantial demand for a large amount of paired data, where vision and language are usually linked through manual annotation for the creation of realistic datasets. To address the above problems, we propose the knowledge enhanced bottom-up affordance grounding network (KBAG-Net), which enhances natural language understanding through external knowledge, improving accuracy in object grasping affordance segmentation. In addition, we introduce a semi-automatic data generation method aimed at facilitating the quick establishment of the language following manipulation grounding dataset. The experimental results on two standard dataset demonstrate that our method outperforms existing methods with the external knowledge. Specifically, our method outperforms the two-stage method by 12.98% and 1.22% of mIoU on the two dataset, respectively. For broader community engagement, we will make the semi-automatic data construction method publicly available at https://github.com/wmqu/Automated-Dataset-Construction4LGM. [ABSTRACT FROM AUTHOR]

The integration of language and vision for object affordance understanding is pivotal for the advancement of embodied agents. Current approaches are often limited by reliance on segregated pre-processing stages for language interpretation and object localization, leading to inefficiencies and error propagation in affordance segmentation. To overcome these limitations, this study introduces a unique task, part-level affordance grounding, in direct response to natural language instructions. We present the Instruction-based Affordance Grounding Network (IAG-Net), a novel architecture that unifies language–vision interactions through a varied-scale multimodal attention mechanism. Unlike existing models, IAG-Net employs two textual–visual feature fusion strategies, capturing both sentence-level and task-specific textual features alongside multiscale visual features for precise and efficient affordance prediction. Our evaluation on two newly constructed vision–language affordance datasets, ITT-AFF VL and UMD VL, demonstrates a significant leap in performance, with an improvement of 11.78% and 0.42% in mean Intersection over Union (mIoU) over cascaded models, bolstering both accuracy and processing speed. We contribute to the research community by releasing our source code and datasets, fostering further innovation and replication of our findings. [ABSTRACT FROM AUTHOR]

In recent decades, enhancing underwater images has become a crucial challenge when obtaining high-quality visual information in underwater environment detection, attracting increasing attention. Original underwater images are affected by a variety of underwater environmental factors and exhibit complex degradation phenomena such as low contrast, blurred details, and color distortion. However, most encoder-decoder-based methods fail to restore the details of underwater images due to information loss during downsampling. The noise in images also influences the recovery of underwater images with complex degradation. In order to address these challenges, this paper introduces a simple but effective denoising multiscale back-projection feature fusion network, which represents a novel approach to restoring underwater images with complex degradation. The proposed method incorporates a multiscale back-projection feature fusion mechanism and a denoising block to restore underwater images. Furthermore, we designed a multiple degradation knowledge distillation strategy to extend our method to enhance various types of degraded images, such as snowy images and hazy images. Extensive experiments on the standard datasets demonstrate the superior performance of the proposed method. Qualitative and quantitative analyses validate the effectiveness of the model compared to several state-of-the-art models. The proposed method outperforms previous deep learning models in recovering both the blur and color bias of underwater images. [ABSTRACT FROM AUTHOR]