•Second-screen viewing leads to lower news recall and comprehension.•Effects of second-screen viewing on news learning are mediated by cognitive load.•Second-screen viewing leads to higher cognitive ...load than single screen-viewing.•Higher levels of cognitive load lead to lower news recall and comprehension.•No differences exist between effects of related and unrelated second screen-viewing.
Second-screen viewing—the use of smartphones, tablets, and laptops while watching television—has increased dramatically in the last few years. Using multiple resource theory and threaded cognition theory, this study investigated the effects of second-screen viewing on cognitive load, factual recall and comprehension of news. Second, we examined the effects of relevant (i.e., looking up information related to the news story) and irrelevant (i.e., looking up information unrelated to the story) second-screen viewing on learning from news. Results from an experiment (N=85) showed that second-screen viewing led to lower factual recall and comprehension of news content than single-screen viewing. These effects were mediated by cognitive load: second-screen viewing led to a higher cognitive load than single-screen viewing, with higher cognitive load, in turn, leading towards lower factual recall and comprehension of news content. Contrary to our expectations, we found no statistically significant differences between effects of relevant and irrelevant second-screen viewing.
The present study investigated the relation among texting in the classroom, problematic mobile phone use, and executive function. Undergraduate students aged between 18 and 25 years old (68 males and ...95 females) completed questionnaires to assess the frequency of texting in the classroom as well as levels of problematic mobile phone use and executive functioning. Hierarchical regressions were performed to evaluate the relative contributions of problematic mobile phone use and executive function in predicting the frequency of texting in class. The results show that problematic mobile phone use was a significant predictor of texting in the classroom after controlling for age, gender, and years of education. The results also show that a subscale of executive function, impulse control, was a significant predictor of texting in the classroom over and above problematic mobile phone use after controlling for the demographic variables. These results support a general conclusion that problematic mobile phone use and impulse control are important factors in one form of media multitasking—texting in the classroom.
•Multi-level structure to obtain a global mapping of the occluded region of the hand.•Palmar segmentation features help with overall hand alignment.•Fusion module combines multilevel features to ...adjust hand shape.
We propose a multi-task progressive Transformer framework to reconstruct hand poses from a single RGB image to address challenges such as hand occlusion hand distraction, and hand shape bias. Our proposed framework comprises three key components: the feature extraction branch, palm segmentation branch, and parameter prediction branch. The feature extraction branch initially employs the progressive Transformer to extract multi-scale features from the input image. Subsequently, these multi-scale features are fed into a multi-layer perceptron layer (MLP) for acquiring palm alignment features. We employ an efficient fusion module to enhance the parameter prediction further features to integrate the palm alignment features with the backbone features. A dense hand model is generated using a pre-computed articulated mesh deformed hand model. We evaluate the performance of our proposed method on STEREO, FreiHAND, and HO3D datasets separately. The experimental results demonstrate that our approach achieves 3D mean error metrics of 10.92 mm, 12.33 mm and 9.6 mm for the respective datasets.
Background
Interruptions can adversely impact human performance, particularly in fast‐paced and high‐risk environments such as the emergency department (ED). Understanding physician behaviors before, ...during, and after interruptions is important to the design and promotion of safe and effective workflow solutions. However, traditional human factors–based interruption models do not accurately reflect the complexities of real‐world environments like the ED and may not capture multiple interruptions and multitasking.
Methods
We present a more comprehensive framework for understanding interruptions that is composed of three phases, each with multiple levels: interruption start transition, interruption engagement, and interruption end transition. This three‐phase framework is not constrained to discrete task transitions, providing a robust method to categorize multitasking behaviors around interruptions. We apply this framework in categorizing 457 interruption episodes.
Results
A total of 457 interruption episodes were captured during 36 hours of observation. The interrupted task was immediately suspended 348 (76.1%) times. Participants engaged in new self‐initiated tasks during the interrupting task 164 (35.9%) times and did not directly resume the interrupted task in 284 (62.1%) interruption episodes.
Conclusion
Using this framework provides a more detailed description of physician behaviors in complex environments. Understanding the different types of interruption and resumption patterns, which may have a different impact on performance, can support the design of interruption mitigation strategies.
The future integrated sensing and communication (ISAC) networks is expected to equip with sufficient computation resources. However, current research focuses on single-domain resource allocation in ...ISAC and computing force networks, leaving the joint optimization of sensing, communication, and computation resource allocation unexplored. In this paper, we propose a novel approach to this problem by deep incorporating computation resources, combined with a federated learning framework, while considering sensing precision and power consumption. Firstly, a multi-objective optimization is designed, involving Cramer-Rao Bound, sum rate of ISAC networks, and power consumption of computing force networks. Subsequently, the multi-objective optimization is transformed into a multi-task learning model. We aim to obtain joint optimization of sensing, communication, and computation resource allocation via deep learning techniques. Towards the multi-task learning model, the multiple-gradient descent algorithm is utilized to obtain the multi-objective optimization. Furthermore, a practical low-complexity the multiple-gradient descent algorithm is developed to reduce the computational cost. Finally, the effectiveness of the proposed deep learning algorithms is verified by simulations results.
By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic theory about semantic ...information quantification and performance evaluation makes it challenging to perform resource allocation for semantic communications, especially when multiple tasks coexist in the network. To cope with this challenge, we propose a quality-of-experience (QoE) based semantic-aware resource allocation method for multi-task networks in this paper. First, semantic entropy is defined to quantify the semantic information for different tasks, and the relationship between semantic entropy and Shannon entropy is analyzed. Then, we develop a novel QoE model to formulate the semantic-aware resource allocation in terms of semantic compression, channel assignment, and transmit power. The compatibility of the formulated problem with conventional communications is further demonstrated. To solve this problem, we decouple it into two subproblems and solved them by a developed deep Q-network (DQN) based method and a proposed low-complexity matching algorithm, respectively. Finally, simulation results validate the effectiveness and superiority of the proposed method, as well as its compatibility with conventional communications.
The aim of this study was to examine the effects of walking at different speeds while using a mobile phone on spatiotemporal stride parameters among young adults. Ten participants (7 male, 3 female; ...age = 24.7 ± 4.4 years, mean ± 1SD) completed 12 walking trials. Trials consisted of tasks performed at both normal and fast walking speeds-walking only, walking while texting, and walking while talking on a mobile phone. Gait velocity, stride length, cadence, and double support time were computed using data from accelerometers on either shoe.
The effects of distracted walking were not significantly larger when performed at a self-selected fast walking speed compared with a normal walking speed. However, walking while texting produced significant decreases in gait velocity, stride length, and cadence, with a significant increase in double support time at both walking speeds. Moreover texting increased the size of the relative variability of walking, observed through a significant increase in the coefficient of variation of cadence, stride length, and double support time. The observed changes may be suggestive of compromised balance when walking while texting regardless of walking speed. This may place the individual at a greater risk of, slips, trips and falls.
Biofilms have been a long-standing challenge for healthcare, water transport, and many other industries. They lead to bacterial growth and infections in animals, food products, and humans, cause ...premature removal of the implanted materials or devices from patients, and facilitate fouling and corrosion of metals. Despite some published and patented methods on minimizing the effects of biofilms for a short period (less than two weeks), there exists no successful means to mitigate or prevent the long-term formation of biofilms. It is even more challenging to integrate critical anti-fouling properties with other needed physical and chemical properties for a range of applications. In this study, we developed a novel approach for combining incompatible, highly polar anti-fouling groups with less polar, mechanically modifying groups into one material. A multifunctional carboxybetaine precursor was designed and introduced into polyurethane. The carboxybetaine precursors undergo rapid, self-catalyzed hydrolysis at the water/material interface and provide critical anti-fouling properties that lead to undetectable bacterial attachment and zero biofilm formation after six months of constant exposure to
Pseudomonas aeruginosa
and
Staphylococcus epidermidis
under the static condition in a nutrient-rich medium. This zwitterionic polyurethane is the first material to demonstrate both critical anti-biofilm properties and tunable mechanical properties and directly validates the unproven anti-fouling strategy and hypothesis for biofilm formation prevention. This approach of designing 'multitasking materials' will be useful for the development of next generation anti-fouling materials for a variety of applications.
To prevent biofilms and biofoulings, a versatile zwitterionic polyurethane material platform was invented with an unmatched anti-fouling potency, as shown by a 6-month study where no bacterial attachment or biofilm formation was observed.
Multiobjective multitasking optimization (MO-MTO) can solve multiple optimization tasks simultaneously through knowledge transfer across tasks. However, how to design an efficient knowledge transfer ...method is the main challenge. Keeping this in mind, this paper proposes an evolutionary multitasking algorithm based on Kalman filter prediction strategy. Specifically, the incremental support vector machine classifier is used to find valuable solutions. Moreover, the Kalman filter prediction strategy is designed to utilize valuable solutions and historical evolutionary information to estimate the predictive solutions. Finally, the scoring scheme is constructed to adaptively select valuable solutions and predictive solutions as transfer knowledge. Experimental results on three MO-MTO test suites demonstrate that the proposed algorithm can achieve competitive performance.
•The scoring scheme can adaptively control the selection proportion.•The Kalman filter prediction strategy is designed to estimate predictive solutions.•The scoring scheme can adaptively control the selection proportion.
While the public is concerned that emphasizing research performance among university faculty results in inadequate attention to undergraduate teaching, research on the relationship between research ...and teaching in higher education has failed to confirm or deny the validity of this concern. To empirically test this popular concern, we examined how the change in performance-based incentive systems to improve faculty publications influenced student evaluations of their teaching in a Korean university. The analysis of a panel dataset of individual faculty members shows that financial incentives on research rather than teaching could have redirected attention of some professors from teaching to research, thus reducing teaching quality, as proposed by advocates of multitasking theory. Therefore, these findings suggest that, when multiple tasks are significant to organizational values, the incentive structure must assure that each task or activity offers professors the same marginal return on their efforts.
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