Task Failure Research Articles

Case study of a world hour record simulation in an elite cyclist: Insight into task failure.

The 'cycling hour-record' is one of the most prestigious events in cycling. However, little detailed analysis of such attempts is available. In preparation for a successful cycling hour-record attempt, an elite cyclist performed a full-hour simulation to provide insights into performance, physiological, aerodynamic and biomechanical limitations that could be identified in the preparation for a subsequent official attempt. Performance (speed, lap time, power and cadence), physiological (heart rate and estimated body temperature), aerodynamic (CDA, helmet angle, rotation and rock) and biomechanical (helmet, thigh and foot position changes) measurements were made throughout the attempt, in which an even-pacing strategy was employed where the point of task failure was defined as the lap which the rider could no longer perform at the targeted lap split (16.6s) or quicker. The cyclist did not achieve the target distance (54,000m) during the simulation. The final distance achieved for the hour was 53,250m. Task failure occurred at 38min and 33s (lap 139/34,750m) into the simulation. Notably, there was a decrease in power output, accompanied with an increase in the estimated body temperature, changes in pedalling kinematics and an increase in aerodynamic drag. The reduction in performance (leading to task failure) during a cycling hour record simulation is underpinned by a decrease in power output as well as an increase in aerodynamic drag due to biomechanical changes in the cycling technique.

Time Spent Near Maximal Oxygen Uptake During Exercise at Different Regions of the Severe-Intensity Domain.

Faricier, R, Fleitas-Paniagua, PR, Iannetta, D, Millet, GY, Keir, DA, and Murias, JM. Time spent near maximal oxygen uptake during exercise at different regions of the severe-intensity domain. J Strength Cond Res XX(X): 000-000, 2024-This study applied the critical power (CP) model and several bouts of constant-power exercise within different regions of the severe-intensity domain to determine whether there exists an optimal intensity to maximize time spent near V̇o2peak. Subjects visited the laboratory 9 times. After a ramp-incremental test to determine V̇o2peak and peak power output (POpeak), 9 active individuals (5 females) performed 4 constant-power bouts to task failure between 65 and 100%POpeak to estimate CP and total finite work above CP (W'). Subjects then completed 4 additional exhaustive trials predicted to result in task failure in ∼3, 6, 9, and 12 minutes. Time spent at V̇o2peak was calculated as the duration at which V̇o2 ≥ 95% of the trial-specific V̇o2peak. The level of significance set for the study was p < 0.05. Mean CP and W' were 201 ± 48 W and 17.6 ± 8.4 kJ, respectively. For each bout, time to task failure was 2.7 ± 0.5, 6.3 ± 0.6, 9.5 ± 1.2, and 13.1 ± 3.1 minutes for the 3-, 6-, 9-, and 12-minute conditions. Time spent at V̇o2peak during the 3-minute trial (45 ± 22 seconds) was shorter than during the 9-minute (204 ± 104 seconds; p = 0.002) and 12-minute trials (260 ± 155 seconds; p < 0.001). The 6-minute trial (117 ± 46 seconds) had shorter (p = 0.005) time spent at V̇o2peak compared with the 12-minute trial. At least when performing single bouts of exercise, intensities closer to CP (i.e., those sustainable for ∼9 minutes or longer) seem preferable compared with POs in the upper regions of the severe-intensity domain to maximize time at V̇o2peak.

Effect of prior neuromuscular electrical stimulation of vastus lateralis on the fatigue induced by a sustained voluntary knee extension in men

PurposeThe purpose of this study was to investigate the effect of vastus lateralis (VL) selective fatigue induced by neuromuscular electrical stimulation (NMES) on knee extensor electromyographic (EMG) activity during a sustained submaximal isometric contraction. MethodsThirteen healthy men (28 ± 5 years) completed two experimental sessions in which either the VL was pre-fatigued for 17 min (NMES session) or no intervention was performed (control session, CTRL). Subsequently, participants were asked to sustain an isometric knee extension at 20 % of maximal voluntary contraction (MVC) torque until task failure. ResultsVL M−wave amplitude was reduced (−34 ± 26 %, P = 0.008) following the NMES intervention, while MVC torque was reduced by 26 ± 10 %. The time to task failure was 23 ± 10 % shorter (P = 0.002) in NMES (186 ± 75 s) than in CTRL (251 ± 128 s). EMG activity measured during the sustained contraction was higher for vastus medialis and rectus femoris muscles in NMES compared to CTRL (P < 0.001), but was comparable for VL (P > 0.05). The extent and origin of neuromuscular fatigue at task failure measured through MVCs combined with electrically-evoked contractions did not differ between NMES and CTRL. ConclusionCompensatory activity from synergist muscles occurred in response to a pre-fatigue intervention, which reduced the time to task failure of a sustained submaximal contraction but did not affect the extent and origin of neuromuscular fatigue.

Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning.

With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network instability and dynamic resource variations, which can lead to task interruptions or failures. To address these issues, developing a fault-tolerant scheduling mechanism is crucial to ensure that a system continues to operate efficiently even when some nodes experience failures. In this paper, we propose an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning. This model incorporates a deep reinforcement learning framework built upon a graph neural network, allowing it to accurately capture the complex communication relationships between computing nodes. The model generates fault-tolerant scheduling actions as output, ensuring robust performance in dynamic environments. Additionally, we introduce an asynchronous model update strategy, which enhances the model's capability of real-time dynamic scheduling through multi-threaded parallel interactions with the environment and frequent model updates via running threads. The experimental results demonstrate that the proposed method outperformed the baseline algorithms in terms of quality of service (QoS) assurance and fault-tolerant scheduling capabilities.

The Effect of Transcranial Direct Current Stimulation on Lower-Limb Endurance Performance: A Systematic Review

This study systematically reviews the literature on transcranial direct current stimulation (tDCS) interventions for lower-limb endurance performance in healthy adults and provides a summary of the effects and underlying mechanisms of tDCS on lower-limb endurance performance. Systematic searches were performed in PubMed, Web of Science, EBSCO, and ScienceDirect. The risk of bias was assessed using the Cochrane risk of bias assessment tool. The electronic search totaled 341 studies. Twenty-one studies were included in the review after screening. The results show that tDCS effectively improved time to task failure (TTF), increased blood lactate accumulation, and reduced the rating of perceived exertion during cycling. However, the tDCS failed to significantly improve the TTF, relieve muscle pain, and reduce fatigue indices during single-joint fatigue tasks in the knee. Moreover, tDCS intervention caused the effective improvement of the overall lower-limb endurance performance but exerted no uniformly conclusive effect on knee endurance performance. This finding can be partly attributed to varying stimulation protocols across studies. Future studies may focus on the effects of the application of stimulation protocols, such as multitarget stimulation and personalized dosage, to develop targeted stimulation protocols.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Impact of Quadriceps Muscle Fatigue on Ankle Joint Compensation Strategies During Single-Leg Vertical Jump Landing.

This study investigates the impact of quadriceps fatigue on lower limb biomechanics during the landing phase of a single-leg vertical jump (SLJ) in 25 amateur male basketball players from Ningbo University. Fatigue was induced through single-leg knee flexion and extension exercises until task failure. Kinematic and dynamic data were collected pre-fatigue (PRF) and post-fatigue (POF) using the Vicon motion capture system and the AMTI force platform and analyzed using an OpenSim musculoskeletal model. Paired sample t-tests revealed significant changes in knee and hip biomechanics under different fatigue conditions, with knee joint angle (p < 0.001), velocity (p = 0.006), moment (p = 0.006), and power (p = 0.036) showing significant alterations. Hip joint angle (p = 0.002), moment (p = 0.033), and power (p < 0.001) also exhibited significant changes. Muscle activation and joint power were significantly higher in the POF condition, while joint stiffness was lower. These findings suggest that quadriceps fatigue leads to biomechanical adjustments in the knee and hip joints, which may increase the risk of injury despite aiding in landing stability.

Intentional or Designed? The Impact of Stance Attribution on Cognitive Processing of Generative AI Service Failures.

With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human-computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human-computer interactions. This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users' unconscious cognitive processing mechanisms during service failures. The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. This study found that stance attribution explanations profoundly affect users' mental models of AI, which determine their responses to service failure.

Fault tolerant & priority basis task offloading and scheduling model for IoT logistics

The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.

Intelligent Swarm: Concept, Design and Validation of Self-Organized UAVs Based on Leader–Followers Paradigm for Autonomous Mission Planning

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors. Equipped with sophisticated sensors and communication devices, drones can potentially form a multi-UAV system, also called an autonomous swarm, in which UAVs work together with little or no operator control. According to the complexity of the mission and coverage area, swarm operations require important considerations regarding the intelligence and self-organization of the UAVs. Factors including the types of drones, the communication protocol and architecture, task planning, consensus control, and many other swarm mobility considerations must be investigated. While several papers highlight the use cases for UAV swarms, there is a lack of research that addresses in depth the challenges posed by deploying an intelligent UAV swarm. Against this backdrop, we propose a computation framework of a self-organized swarm for autonomous and collaborative missions. The proposed approach is based on the Leader–Followers paradigm, which involves the distribution of ROS nodes among follower UAVs, while leaders perform supervision. Additionally, we have integrated background services that autonomously manage the complexities relating to task coordination, control policy, and failure management. In comparison with several research efforts, the proposed multi-UAV system is more autonomous and resilient since it can recover swiftly from system failure. It is also reliable and has been deployed on real UAVs for outdoor survey missions. This validates the applicability of the theoretical underpinnings of the proposed swarming concept. Experimental tests carried out as part of an area coverage mission with 6 quadcopters (2 leaders and 4 followers) reveal that the proposed swarming concept is very promising and inspiring for aerial vehicle technology. Compared with the conventional planning approach, the results are highly satisfactory, highlighting a significant gain in terms of flight time, and enabling missions to be achieved rapidly while optimizing energy consumption. This gives the advantage of exploring large areas without having to make frequent downtime to recharge and/or charge the batteries. This manuscript has the potential to be extremely useful for future research into the application of unmanned swarms for autonomous missions.

Professional Awareness and its Relation to Achievement Drive Among the War Correspondents

Life is a series of inputs, procedures, and outputs that shape an individual’s personality. The role of work is not limited to providing a material and moral resource only. An individual with the drive to achieve can overcome the challenges he will face, perform the tasks required of him, and struggle for success (Bani Younis, 2009, p. 3). Once the reporter joins the work, he sets goals to achieve. Some try to increase their knowledge and develop their skills, there are those who seek to achieve distinction and mastery, there are those who only want to work, and there are those who have no motivation or achievements (Harackiewicz, &amp; et al, 1998, p. 638). Some correspondents make mistakes as a result of weak professional awareness and the absence of a language for communicating with others, for reasons including habituation to following the familiar and submitting to the experienced, which leads to poor task accomplishment, weak ambition, and failure to achieve oneself until the correspondent becomes unable to confront situations and overcome obstacles (Sweller, 2014, p: 19). Man is naturally a social being who tends to share ideas, achievements, and experiences with others. This is the way successful people in life are distinguished by professional awareness and openness to others (Hammoud, 2014, p. 45). The achievement motive is qualitative knowledge, not quantitative. The correspondent who reaches the appreciation of his superiors must achieve himself and feel satisfied (Bani Mufrej, 2009, p. 4).

Electromyographically controlled prosthetic wrist improves dexterity and reduces compensatory movements without added cognitive load

Wrist function is a top priority for transradial amputees. However, the combined functional, biomechanical, and cognitive impact of using a powered prosthetic wrist is unclear. Here, we quantify task performance, compensatory movements, and cognitive load while three transradial amputees performed a modified Clothespin Relocation Task using two myoelectric prostheses with and without the wrists. The two myoelectric prostheses include a commercial prosthesis with a built-in powered wrist, and a newly developed inexpensive prosthetic wrist for research purposes, called the “Utah wrist”, that can be adapted to work with various sockets and prostheses. For these three participants, task failure rate decreased significantly from 66% ± 12% without the wrist to 39% ± 9% with the Utah wrist. Compensatory forward leaning movements also decreased significantly, from 24.2° ± 2.5 without the wrist to 12.6° ± 1.0 with the Utah wrist, and from 23.6° ± 7.6 to 15.3° ± 7.2 with the commercial prosthesis with an integrated wrist. Compensatory leftward bending movements also significantly decreased, from 20.8° ± 8.6 to 12.3° ± 5.3, for the commercial with an integrated wrist. Importantly, simultaneous myoelectric control of either prosthetic wrist had no significant impact on cognitive load, as assessed by the NASA Task Load Index survey and a secondary detection response task. This work suggests that functional prosthetic wrists can improve dexterity and reduce compensation without significantly increasing cognitive effort. These results, and the introduction of a new inexpensive prosthetic wrist for research purposes, can aid future research and development and guide the prescription of upper-limb prostheses.

319P MRI gluteal/thigh muscle fat fraction detects NSAD motor task failures in men with Becker muscular dystrophy

319P MRI gluteal/thigh muscle fat fraction detects NSAD motor task failures in men with Becker muscular dystrophy

Are Ratings of Perceived Exertion during Endurance Tasks of Predictive Value? Findings in Trunk Muscles Require Special Attention.

Background: Subjective rating scales of perceived exertion are often used to quantify effort levels during various endurance exercises, particularly submaximal tasks. The aim of the current study was to determine whether predictive conclusions can be drawn from perceived exertion levels surveyed at the start of defined submaximal endurance tasks. Methods: In this study, healthy participants performed a 10-min endurance task at 50% of their upper body weight, targeting either the back muscles (n = 47, 24 women) or abdominal muscles (n = 32, 17 women). At the end of each minute, participants were asked to rate their perceived exertion (RPE) using the 14-points Borg Scale. Based on their initial and final RPE levels, and for each muscle group separately, participants were divided into subgroups reflecting low (good start/good end) and high (bad start/bad end) strain levels. These values were then compared over the duration of the exercise. Comparisons of RPE levels between subgroups were made using the Mann-Whitney U-test for independent samples, with Bonferroni-Holm correction to account for multiple comparisons. Results: Overall, strain levels increased throughout the duration of the exercise. For the abdominal muscles, the difference between the two RPE groups remained constant over time: participants with good start/end ratings consistently showed different strain levels from those with bad start/end ratings, regardless of whether the grouping was based on initial or final exertion levels. In contrast, for the back muscles, the initial grouping showed a crossover in strain values: by the end of the task, participants in the good start group tended to report higher strain than those in the bad start group. No differences were found in initial RPE values when the grouping was based on final exertion levels. Conclusions: For endurance tasks involving the abdominal muscles, initial strain levels have strong predictive value, whereas this is not the case for the back muscles. Because back muscles are frequently loaded, continuous monitoring of RPE levels is necessary to prevent unexpected task failure, as initial RPE values are not predictive. In contrast, RPE values of 11 or higher on the 14-points Borg scale predict complete exhaustion or even premature task failure with high certainty for abdominal muscle exercises, while lower RPE levels indicate that exercise intensity can be increased.

The effects of sex and contraction intensity on fatigability and muscle oxygenation in trained individuals.

Fatigability varies depending on sex and contraction intensity during sustained exercise. This study examined the responses of time to task failure (TTF), performance fatigability (PF), and muscle oxygenation (SmO2) in males and females during isometric handgrip holds to failure (HTF) at 30% and 60% maximum voluntary isometric contraction (MVIC). Males (n=12) and females (n=12) performed a pre-MVIC, handgrip HTF at randomly ordered percentages of MVIC (either 30% or 60%), followed by a post-MVIC on the dominant arm. During the HTF testing, the TTF and SmO2 responses were recorded, and PF was determined from the pre- to post-MVICs. TTF for 30% MVIC HTF was greater than 60% MVIC HTF (p<0.001), but was not different between males and females (p=0.117). PF exhibited an inverse relationship with intensity for each sex, while males demonstrated greater PF than females for both 30% and 60% MVIC HTF. For the 60% MVIC HTF, males demonstrated greater desaturation than females (CI95% = [-28.1, -2.6%], p=0.021, d=0.621), but not for the 30% MVIC HTF (CI95% = [-12.2, 7.9%], p=0.315, d=0.621). Sex differences in PF and SmO2 may be attributed to the differences in muscle mass, absolute strength, contractile properties, and muscle metabolism between males and females. However, these proposed differences between males and females may not fully inform exercise performance (e.g., TTF). Sex-specific fatigue responses may be affected by complex physio-psychological mechanisms, and therefore, additional investigations under diverse exercise conditions are required to better prescribe exercise for both males and females.

Sex-based differences in the blood pressure responses to muscle metaboreflex activation are consistent between limb and respiratory muscle.

The purpose of this study was to compare sex-based differences in the mean arterial blood pressure (MAP) response to limb and inspiratory metaboreflex activation, during relative and absolute workloads. Healthy males (M; n=9) and females (F; n=8) completed pulmonary function testing, forearm volume and circumference measurements, and bouts of limb and inspiratory muscle exercise. The exercises performed included bouts of rhythmic handgrip exercise (RHG) and inspiratory pressure threshold loading (PTL) to task failure, performed in a randomized order, and separated by 30 minutes of rest. Participants performed both RHG and PTL at predetermined relative (R) and absolute (A) workloads while cardiopulmonary measurements were recorded continuously. A time-dependent rise in MAP was observed in all participants, regardless of sex, muscle, or workload (p<0.001). MAP was greater in males than females during all exercise bouts regardless of muscle group or workload (p<0.001). The change in MAP from baseline was also greater in males (R-RHG: ∆31±12 mmHg; R-PTL: ∆31±9; A-RHG: ∆35±6; A-PTL: ∆30±7) than females (R-RHG: ∆21±7 mmHg; R-PTL: ∆13±7; A-RHG: ∆21±7; A-PTL: ∆14±3) (p<0.001). Results from this study show that when the forearm and diaphragm perform the same relative or absolute work, the blood pressure response is statistically similar, and both responses are greater in males than females. The findings from the present study suggest that the sex-based difference in the response to metaboreflex activation is similar between the limb and respiratory musculature.

The effects of low vs. high rating of perceived exertion clamp exercise on performance, neuromuscular, and muscle oxygen saturation responses in females.

This study examined the time course of changes in force, relative to critical force (CF), electromyographic amplitude (EMG AMP), neuromuscular efficiency (NE), and muscle oxygen saturation (SmO2), as well as time to task failure (TTF) and performance fatigability (PF) during isometric handgrip holds to failure (HTF) anchored to the rating of perceived exertion (RPE) at 3 and 7. Ten females completed pre-test maximal voluntary isometric contractions (MVICs), submaximal HTF at four percentages of MVIC, an HTF at RPE = 3 and 7, and post-test MVICs. Analyses included paired samples t-tests, repeated measures ANOVAs and planned comparisons. TTF was not different between RPE 3 (540.4 ± 262.1s) and 7 (592.2 ± 299.6s), but PF for RPE 7 (42.1 ± 19.1%) was greater than RPE 3 (33.5 ± 15.4%) (p < 0.05). There were RPE-dependent decreases in force, EMG AMP, and NE across three discernable phases during the HTF (p < 0.01), but there were no significant changes in SmO2 across time. Although there were overall similar patterns across time for force, neuromuscular, and muscle metabolic responses between the RPE holds, the greater PF at RPE 7 than RPE 3 may be explained by the longer sustained time above CF at RPE 7, resulting in greater accumulation of intramuscular metabolites and afferent feedback. Throughout each trial, it is possible that force was adjusted to avoid the sensory tolerance limit, and the task was ended when force could no longer be reduced to maintain the assigned RPE, resulting in a similar TTF for RPE 7 and RPE 3.

Adaptive intelligent agent for cloud edge collaborative industrial inspection driven by multimodal data fusion and deep transformation networks

Currently, the rapid development of the industrial Internet has led to the creation of a massive number of intelligent agents that are widely and distributively applied in various edge scenarios. The work conditions in these edge scenarios are complex, uncertain, and random. Traditional manual updates or human judgments are used for task decision-making in large-scale distributive intelligent agent edge work scenarios, which lack dynamic perception and autonomous recognition capabilities for edge work conditions. This inevitably leads to low decision-making accuracy, poor reliability, and ultimately, task failure. To address this issue, this study proposes an adaptive task identification strategy based on cloud-edge collaboration. This method utilizes a cloud-edge collaborative industrial intelligent application architecture to achieve cloud-based training and encapsulation of the task model, with online calling at the edge-end. Then, edge-end intelligent agents identify edge work conditions through multi-source data fusion, enabling accurate task decision-making. Finally, the edge-end requests the cloud for task model matching. The effectiveness of the proposed method is validated in an industrial safety situation virtual detection system.

Establishing a user demand hierarchy model driven by a mental model for complex operating systems

In complex human-computer interactions, issues such as task failures, system malfunctions, and frequent accidents caused by user errors are common. Therefore, it is necessary to study complex system interactions to enhance overall efficiency. This study focuses on the task interface of a digital twin system for computer numerical control (CNC) machine tools and examines the relationship between the user mental model and interface design elements. Key mental information is obtained through questionnaires and interviews, forming the basis for establishing a user mental model. High-frequency information words are extracted, experimental samples are designed, and importance rating surveys are conducted. Quantitative analysis methods, including factor analysis and weight calculation, are utilized to analyze the needs of target users. Consequently, a user demand hierarchy model is constructed. This approach aims to effectively reduce user errors in the human-computer interaction process within complex systems and enhance cognitive efficiency.

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and result in the decline or even failure of the main task's performance. Therefore, extracting richer lesion features and alleviating information-sharing conflicts has become a significant challenge for breast cancer classification. This study proposes a novel Multi-Feature Fusion Multi-Task (MFFMT) model to effectively address this issue. Firstly, in order to better capture the local and global feature relationships of lesion areas, a Contextual Lesion Enhancement Perception (CLEP) module is designed, which integrates channel attention mechanisms with detailed spatial positional information to extract more comprehensive lesion feature information. Secondly, a novel Multi-Feature Fusion (MFF) module is presented. The MFF module effectively extracts differential features that distinguish between lesion-specific characteristics and the semantic features used for tumor classification, and enhances the common feature information of them as well. Experimental results on two public breast ultrasound imaging datasets validate the effectiveness of our proposed method. Additionally, a comprehensive study on the impact of various factors on the model's performance is conducted to gain a deeper understanding of the working mechanism of the proposed framework.