Search Results (89)

Microfluidics is a powerful tool with extensive applications, including chemical synthesis and biological detection. However, the limited channel size and high viscosity of samples/reagents make it difficult to fully mix liquids and improve the reaction efficiency inside microfluidic chips. Active mixing by rotors has been proven to be an effective method to promote mixing efficiency via a magnetic field. Here, we numerically investigated the mixing performance of rotors with different shapes (bar-shaped, Y-shaped, and cross-shaped). We systematically studied the influence of the arrangement of multiple cross-rotors and the rotation rate on mixing performance. The results are promising for instructing the design and manipulation of rotors for in-channel mixing. Full article

If artificial intelligence (AI) is to be applied in safety-critical domains, its performance needs to be evaluated reliably. The present study investigated how humans evaluate AI systems for person detection in automatic train operation. In three experiments, participants viewed image sequences of people moving in the vicinity of railway tracks. A simulated AI system highlighted all detected people—sometimes correctly and sometimes not. Participants had to provide a numerical rating of the AI’s performance and then verbally explain their rating. The experiments manipulated several factors that might influence human ratings: the types and plausibility of AI mistakes, the number of affected images, the number of people present in an image, the position of people relevant to the tracks, and the methods used to elicit human evaluations. While all these factors influenced human ratings, some effects were unexpected or deviated from normative standards. For instance, the factor with the strongest impact was people’s position relative to the tracks, although participants had explicitly been instructed that the AI could not process such information. Taken together, the results suggest that humans may sometimes evaluate more than the AI’s performance on the assigned task. Such mismatches between AI capabilities and human expectations should be taken into consideration when conducting safety audits of AI systems. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

Background/Objectives: Three distinct language comprehension phenotypes have previously been identified in individuals with language deficits: (1) individuals with the Command Phenotype are limited to understanding simple commands; (2) individuals with the Modifier Phenotype demonstrate additional comprehension of combinations of nouns and adjectives; and (3) individuals with the Syntactic Phenotype possess full syntactic comprehension. We hypothesized that typically developing children progress through these same three language comprehension phenotypes and aimed to determine the typical age at which each phenotype emerges. Methods: To assess comprehension in young children, we developed the 15-item Language Phenotype Assessment (LPA). This tool uses toy-animal manipulatives to avoid reliance on picture interpretation and employs brief instructions to reduce auditory memory load. LPA items incorporate elements such as colors, sizes, numbers, spatial prepositions, and other syntactic components, posing novel combinations of words that children had not previously encountered. The LPA was administered to 116 typically-developing children aged 1.5–7 years, recruited by approaching parents in local parks and inviting them to participate. Results: Findings revealed a developmental trajectory consistent with the three previously described phenotypes: 50% of children attained the Command Phenotype by 1.6 years of age, the Modifier Phenotype by 3.0 years of age, and the Syntactic Phenotype by 3.7 years of age. All children acquired the Command Phenotype by 3, the Modifier Phenotype by 4, and the Syntactic Phenotype by 5 years of age. Conclusions: The LPA is an effective tool for assessing comprehension in children aged 1.5–5 years. It allows for the early identification of comprehension difficulties, supporting the timely initiation of appropriate language interventions. Full article

Background: Cough reflex sensitivity during cough challenge testing has been found to be modifiable with distraction in groups of healthy individuals. The purpose of this study was to examine this phenomenon in healthy controls and patients with refractory chronic cough (RCC) to advance our understanding of the role attention plays in cough modulation and shed light on avenues for therapeutic advances for RCC. Methods: Thirteen adults with RCC (mean age = 60, 12 women) and twelve healthy controls (mean age = 60, 11 women) participated in this study. The participants completed cough challenge testing with nebulized capsaicin doses tailored to their individual cough reflex sensitivity under distraction and no-distraction conditions. The distraction condition consisted of cough challenge testing while completing a cognitive (visual memory) task on a tablet. Capsaicin doses included the dose that elicited two coughs (C2), and up to three doubling doses above C2. Capsaicin doses were administered in serial order with a placebo dose randomized into the order to control for an anticipation effect during each condition. For each dose administered, the participants were instructed to cough if they needed to. Cough frequency within 15 s and maximal urge-to-cough with each dose were recorded. The order of conditions (distraction or no distraction) was alternated, and all testing was completed within one session. Results: There were no meaningful differences in the dose–response rate parameters for cough frequency or urge-to-cough, respectively, when comparing the results from the RCC group in the condition without distraction to the condition with distraction (p = 0.647, 95% CI = −2.25, 1.15; p = 0.783, 95% CI = −1.94, 0.84), and to the healthy control group without distraction (p = 0.921, 95% CI = −2.11, 2.73, p = 0.887, 95% CI = −1.40, 0.80), and with distraction (p = 0.970, 95% CI = −2.16, 3.36), p = 0.808, 95% CI = −1.49, 0.89). Conclusions: Distraction with the cognitive task chosen in this study did not influence cough reflex sensitivity in either group, which is contrary to studies on healthy volunteers and anecdotal evidence reported by RCC patients. Attentional resources may not have been sufficiently taxed, or too few capsaicin doses were administered to capture an effect as there was high individual variability in cough frequency and urge-to-cough. Additional research is needed to tailor the difficulty of the cognitive task to each participant and incorporate a real-world distraction scenario that may better reveal how attentional manipulation could be harnessed to optimize the effectiveness of behavioral cough suppression therapy for patients with refractory chronic cough. Full article

The topological phase of matter brings extra inspiration for efficient light manipulation. Here, we propose two-parameter tunable topological transitions based on distorted Kagome photonic crystals. By selecting specific splicing boundaries, we successfully visualize several diverse types of robust edge states and corner states. Through introducing optical vortices with tunable orbital angular momentum, we demonstrate the directional excitation of multi-dimensional topological states as needed. Furthermore, we have studied the coupling effects of multi-dimensional photonic states and the modulation of source in three typical areas. This work provides an instructive avenue for manipulating light in integrated topological photonic devices. Full article

To elucidate the role of metacognitive reflection in the development of children’s executive function (EF) skills, the current study examined relations among implicit and explicit forms of metacognition in 7- to 9-year-olds during performance based on the Dimensional Change Card Sort (DCCS), while experimentally manipulating the propensity to reflect on the task. Results showed that instructions to reflect led to improved task accuracy and better metacognitive control, but only in younger children, likely because older children were already engaging in reflection. Individual differences in trait mindfulness were related to a similarly reflective mode of responding, characterized by improved task accuracy and metacognitive control. In contrast, articulatory suppression impaired children’s task accuracy and metacognitive monitoring. Additionally, simply asking children to make metacognitive judgments without extra instructions decreased the amplitude of event-related potential (ERP) indices of error detection (the error-related negativity; ERN) and conflict detection (the N2). Finally, individual differences in trait anxiety were related to larger Pe amplitudes. Taken together, the current findings reinforce theoretical frameworks integrating metacognition and EF and highlight the shared influence of metacognitive reflection across multiple levels of analysis. Full article

Large language models (LLMs) have made significant strides in generating coherent and contextually relevant responses across diverse domains. However, these advancements have also led to an increase in adversarial attacks, such as prompt injection, where attackers embed malicious instructions within prompts to bypass security filters and manipulate LLM outputs. Various injection techniques, including masking and encoding sensitive words, have been employed to circumvent security measures. While LLMs continuously enhance their security protocols, they remain vulnerable, particularly in multimodal contexts. This study introduces a novel method for bypassing LLM security policies by embedding malicious instructions within a mind map image. The attack leverages the intentional incompleteness of the mind map structure, specifically the absence of explanatory details. When the LLM processes the image and fills in the missing sections, it inadvertently generates unauthorized outputs, violating its intended security constraints. This approach applies to any LLM capable of extracting and interpreting text from images. Compared to the best-performing baseline method, which achieved an ASR of 30.5%, our method reaches an ASR of 90%, yielding an approximately threefold-higher attack success. Understanding this vulnerability is crucial for strengthening security policies in state-of-the-art LLMs. Full article

Research has advocated for the use of incorrect worked examples targeting specific conceptual barriers to enhance learning. From the perspective of cognitive load theory, we examined the relationship between instructional efficiency (correct and incorrect worked examples [CICWEs] vs. worked examples [WEs] vs. problem-solving [PS]), levels of expertise (low vs. high), and belief in achievement best (realistic vs. optimal) in learning linear equations across two experiments (N = 43 vs. N = 68). In the CICWE group, students compared an incorrect step in the incorrect worked example with the parallel correct step in the correct worked example and justified why the step was wrong. The WE group completed multiple worked example–equation pairs, while the PS group solved equivalent linear equations independently. As hypothesized, the WE group outperformed the PS group for low prior knowledge students, while the reverse occurred for high prior knowledge students, demonstrating the expertise reversal effect. In contrast, the CICWE group did not outperform either the PS or WE group. A student’s indication of optimal best, reflecting what is known as the ‘realistic–optimal achievement bests dichotomy’, aligns with his or her belief in their ability to perform tasks of varying complexity (simple task vs. complex task). Regarding the belief in achieving optimal best as an outcome of instructional manipulation, for low prior knowledge students, there were no significant differences across groups on either the realistic best or optimal best subscales. However, for high prior knowledge students, the groups differed significantly on the optimal best subscale, but not on the realistic best subscale. Importantly, the mental effort invested during learning was unrelated to students’ belief in achieving their optimal best. Full article

Previous research and classroom practices have focused on dispelling shame, assuming that it negatively impacts self-efficacy and performance, and overlook the potential for shame to facilitate learning. To investigate this gap, we designed an intervention with 132 tertiary education students (45.46% male, 64.4% European ethnicity) spanning diverse undergraduate majors to show how and why designing for experiences of shame and appropriately regulating them can differentially impact learning. Shame was induced through autobiographical recall, imagination, and failure-driven problem-solving before randomly assigning students to three conditions: two with explicit tips for either decreasing shame or maintaining shame (experimental groups) and one with no-regulation tips (control). Students worked on an introductory data science problem deliberately designed to lead to failure before receiving canonical instruction. Manipulation checks triangulating self-reported and facial expression analysis data suggested that shame was successfully regulated in the intended direction, depending on the condition. Our results, drawing on mixed-methods analyses, further suggested that relative to students decreasing shame, those who maintained shame during initial problem-solving had (i) similar post-test performance on a non-isomorphic question and improved performance on the transfer question, evidenced by accuracy in solving applied data science and inference tasks; (ii) complete reasoning across all post-test questions, as evidenced by elaborations justifying the usage of graphical and numerical representations across those tasks; and (iii) use of superior emotion regulation strategies focused on deploying attention to the problem and reappraising its inherently challenging nature with an approach orientation, as evidenced by a higher frequency of such codes derived from self-reported qualitative data during the intervention. Decreasing shame was as effective as not engaging in explicit regulation. Our results suggest that teaching efforts should be channeled to facilitate experiencing emotions that are conducive to goals, whether they feel pleasurable or not, which may inevitably involve emoting both positive and negative (e.g., shame) in moderation. However, it is paramount that emotional experiences are not merely seen by educators as tools for improved content learning but as an essential part of holistic student development. We advocate for the deliberate design of learning experiences that support, rather than overshadow, students’ emotional growth. Full article

Knitting, a cornerstone of textile manufacturing, is uniquely challenging to automate, particularly in terms of converting fabric designs into precise, machine-readable instructions. This research bridges the gap between textile production and robotic automation by proposing a novel deep learning-based pipeline for reverse knitting to integrate vision-based robotic systems into textile manufacturing. The pipeline employs a two-stage architecture, enabling robots to first identify front labels before inferring complete labels, ensuring accurate, scalable pattern generation. By incorporating diverse yarn structures, including single-yarn (sj) and multi-yarn (mj) patterns, this study demonstrates how our system can adapt to varying material complexities. Critical challenges in robotic textile manipulation, such as label imbalance, underrepresented stitch types, and the need for fine-grained control, are addressed by leveraging specialized deep-learning architectures. This work establishes a foundation for fully automated robotic knitting systems, enabling customizable, flexible production processes that integrate perception, planning, and actuation, thereby advancing textile manufacturing through intelligent robotic automation. Full article

Does the quality that renders multi-stable images fascinating, the sudden perceptual reorganization, the switching from one interpretation into another, also make these images appear beautiful? Or is the aesthetic quality of multi-stable figures unrelated to the ease with which they switch? Across two experiments, we presented multi-stable images and manipulated their perceptual stability. We also presented their unambiguous components in isolation. In the first experiment, this manipulation targeted the inherent stimulus stability through properties like figural size and composition. The second experiment added an instruction for observers to actively control the stability, by attempting to either enhance or prevent perceptual switches as best they could. We found that higher stability was associated with higher liking, positive valence, and lower arousal. This increase in appreciation was mainly driven by inherent stimulus properties. The stability instruction only increased the liking of figures that had been comparatively stable to begin with. We conclude that the fascinating feature of multi-stable images does not contribute to their aesthetic liking. In fact, perceptual switching is detrimental to it. Processing fluency can explain this counterintuitive finding. We also discuss the role of ambiguity in the aesthetic quality of multi-stable images. Full article

The integration of vision–language models (VLMs) with robotic systems represents a transformative advancement in autonomous task planning and execution. However, traditional robotic arms relying on pre-programmed instructions exhibit limited adaptability in dynamic environments and face semantic gaps between perception and execution, hindering their ability to handle complex task demands. This paper introduces GPTArm, an environment-aware robotic arm system driven by GPT-4V, designed to overcome these challenges through hierarchical task decomposition, closed-loop error recovery, and multimodal interaction. The proposed robotic task processing framework (RTPF) integrates real-time visual perception, contextual reasoning, and autonomous strategy planning, enabling robotic arms to interpret natural language commands, decompose user-defined tasks into executable subtasks, and dynamically recover from errors. Experimental evaluations across ten manipulation tasks demonstrate GPTArm’s superior performance, achieving a success rate of up to 91.4% in standardized benchmarks and robust generalization to unseen objects. Leveraging GPT-4V’s reasoning and YOLOv10’s precise small-object localization, the system surpasses existing methods in accuracy and adaptability. Furthermore, GPTArm supports flexible natural language interaction via voice and text, significantly enhancing user experience in human–robot collaboration. Full article

Background: This study evaluated the outcomes of sutureless small incision Descemet’s Stripping Automated Endothelial Keratoplasty (DSAEK-SI) for treating corneal endothelial decompensation. Methods and Analysis: This retrospective study reviewed patients with corneal endothelial decompensation who underwent DSAEK-SI between January 2018 and June 2021 at the Vietnam National Eye Hospital. All patients were followed for at least one year postoperatively. The endothelial graft was inserted into the anterior chamber through a 2.8 mm main corneal incision using a Busin glide. The normal pressure air tamponade of the anterior chamber was applied to attach the graft to the recipient bed. The small incision required no sutures, and no need to remove part of the air from the anterior chamber. This ensured that the surgery ended immediately after the air tamponade, without having to wait for 15 min like with regular DSAEK. The patients were instructed to lie supine for at least 6 h postoperatively. Patients with cataracts underwent combined phacoemulsification and intraocular lens implantation with DSAEK-SI. Results: Sixty eyes from sixty patients were enrolled. The success rate of the surgery was 93.3%. Postoperatively, the best spectacle-corrected visual acuity (BSCVA) improved from 20/3600 to 20/400 at discharge and reached 20/100 at 12 months. Mild astigmatism (0.5D to 2D) was observed in 91.8% of patients, with a mean cylinder of 0.9 ± 0.4D at 12 months. The endothelial cell loss rate after 12 months was 34.6 ± 16%. No graft dislocations or detachments were recorded. Conclusions: The sutureless DSAEK-SI technique with a 2.8 mm incision is a modified technique that achieves high success rates and potentially reduces surgical manipulation and complications. Full article

The rapid integration of connected technologies in modern vehicles has introduced significant cybersecurity challenges, particularly in securing critical systems against advanced threats such as IP spoofing and rule manipulation. This study investigates the application of CHERI (Capability Hardware Enhanced RISC Instructions) to enhance the security of Intrusion Detection Systems (IDSs) in automotive networks. By leveraging CHERI’s fine-grained memory protection and capability-based access control, the IDS ensures the robust protection of rule configurations against unauthorized access and manipulation. Experimental results demonstrate a 100% detection rate for spoofed IP packets and unauthorized rule modification attempts. The CHERI-enabled IDS framework achieves latency well within the acceptable limits defined by automotive standards for real-time applications, ensuring it remains suitable for safety-critical operations. The implementation on the ARM Morello board highlights CHERI’s practical applicability and low-latency performance in real-world automotive scenarios. This research underscores the potential of hardware-enforced memory safety in mitigating complex cyber threats and provides a scalable solution for securing increasingly connected and autonomous vehicles. Future work will focus on optimizing CHERI for resource-constrained environments and expanding its applications to broader automotive security use cases. Full article