Complex Task Research Articles

Transforming task-related features into project performance through job crafting in construction project teams: a person-environment fit perspective

PurposeDue to the flexibility and proactivity emphasized by job crafting in today’s uncertain and complex era, scholars have preliminarily proved its positive consequences in the project context. Nevertheless, little is known about the task-related antecedents of job crafting in the construction project environment and whether these task factors can be further converted into project performance through job crafting of project teams, thereby motivating this study.Design/methodology/approachDrawing on the person-environment fit theory, this study hypothesized job crafting of construction project teams as a mediator that links the project task-related job resources (job autonomy and informal coordination) and job demands (time urgency and task complexity) with project performance. The proposed conceptual model was empirically validated with survey data from 244 members of construction project teams.FindingsThe results show that job autonomy and informal coordination motivate project teams to engage in job crafting and thus improve project performance. Further, the direct effect of task complexity on team job crafting and the indirect effect on project performance are negative. Moreover, informal coordination is found to buffer the negative impact of task complexity on team job crafting and project performance.Originality/valueThis research explores how construction task characteristics can be leveraged to boost project performance from a team proactivity perspective. The findings of this study offer novel insights into effectively incentivizing job crafting of project teams and transforming task characteristics into project performance within the construction management field.

Semantic SLAM system for mobile robots based on large visual model in complex environments

Simultaneous localization and mapping (SLAM) plays an important role in many fields, one of which is to help unmanned devices such as drones, self-driving cars and intelligent robots to achieve precise positioning and mapping. However, when facing complex or changing surroundings, especially when healthcare robots face a large number of mobile healthcare workers and patients in wards, the hospital environment is relatively complex, and the traditional positioning and mapping methods based on geometric features, such as points and lines, are not able to achieve accurate positioning and mapping results for healthcare robots. This paper mainly focuses on the characteristics of complex dynamic environment, and proposes a method to obtain semantic information of surrounding ring and dynamic point culling strategy for robot localisation and mapping. Experiments show that compared with the current popular SLAM technology, the semantic-based SLAM technology proposed in this paper can help the robot to obtain more accurate localisation and mapping, in addition, using this semantic information, the robot can also better identify the surrounding objects, which lays the foundation for performing more complex tasks.

Exploring Teachers’ Priorities in Teaching English Poetry: A Focus Area Analysis

Background: Teaching English poetry is a complex task that demands imaginative, creative, experienced, dedicated, and resourceful teachers. Effective poetry instruction helps students develop a sound evaluation, veritable appreciation, and balanced interpretation of poetic works. Understanding teachers’ priorities in teaching poetry can enhance pedagogical strategies and improve student engagement with literary texts. Objective: This study aims to explore the key focus areas prioritized by teachers while teaching English poetry in grades XI and XII. The research specifically examines instructional emphases in 20 academic institutions within Hetauda sub-metropolitan city, Nepal. Methods: A cross-sectional research survey design was employed. Primary data were collected through a structured questionnaire administered to 50 English teachers in 2024. The sample was drawn from a population of 57 using a simple random sampling technique with a 5% margin of error. Descriptive analysis was used to interpret the data. Additionally, secondary data from books and journal articles were reviewed to provide context and support the primary findings. Findings: The study identified key areas emphasized in teaching English poetry, including title, theme, speaker, tone, setting, diction, figurative language, sound devices, historical and cultural context, and form and structure. Among these, title, theme, and speaker were the most frequently prioritized aspects. Conversely, elements such as epigraph, motif, layout, punctuation, and epitaph were given minimal attention in poetry instruction. Conclusion: The findings suggest that teachers focus primarily on thematic and structural aspects of poetry, with less emphasis on certain literary and stylistic devices. This highlights potential gaps in poetry instruction and suggests the need for a more comprehensive teaching approach that incorporates overlooked poetic elements. Novelty: This study contributes to the pedagogical literature on English poetry education by systematically identifying and analyzing teachers' instructional priorities. It provides empirical insights into poetry teaching practices, offering valuable guidance for curriculum design and teacher training to enhance poetry appreciation and literary analysis skills among students.

A verifiably secure and robust authentication protocol for synergistically-assisted IoD deployment drones.

Drones have limited computing and storage resources, which makes them unable to perform complex tactical tasks; drones working in clusters can be employed for such complex tactical task completion. But, the synergy between these drones working in clusters is mandatory. Otherwise, the adversary can easily target them, disturb their routine work, and even disturb the whole system. Only a robust and lightweight authentication protocol can handle both information fusion and collaboration and coordination of drones working in clusters efficiently and effectively and is one of the essential components of the Internet of Drones (IoD) environment as it is deployed for quick and intelligent decisions. In this regard, the already available security mechanisms for IoD deployment drones have either design flaws or failed to provide security against session key disclosure, spoofing, man in the middle (MITM), and denial of service (DoS) attacks. Therefore, this article presents a protocol based on elliptic curve cryptography (ECC), one-way hash, concatenation, and exclusive-OR (XOR) operations to establish a secure communication session among drones which makes them a powerful system to perform complex tactical tasks and information fusion intelligently. The security analysis of the proposed protocol has formally been scrutinized via BAN logic and ProVerif and informally via realistic discussion and illustrations. The results obtained from the analysis sections demonstrate that the proposed protocol is robust in security and is 63.87% more efficient in terms of communication cost compared to its competitors, and 66.46% better in terms of computational cost.

Trade-Offs in Navigation Problems Using Value-Based Methods

Deep Q-Networks (DQNs) have shown remarkable results over the last decade in scenarios ranging from simple 2D fully observable short episodes to partially observable, graphically intensive, and complex tasks. However, the base architecture of a vanilla DQN presents several shortcomings, some of which were mitigated by new variants focusing on increased stability, faster convergence, and time dependencies. These additions, on the other hand, bring increased costs in terms of the required memory and lengthier training times. In this paper, we analyze the performance of state-of-the-art DQN families in a simple partially observable mission created in Minecraft and try to determine the optimal architecture for such problem classes in terms of the cost and accuracy. To the best of our knowledge, the analyzed methods have not been tested on the same scenario before, and hence a more in-depth comparison is required to understand the real performance improvement they provide better. This manuscript also offers a detailed overview of state-of-the-art DQN methods, together with the training heuristics and performance metrics registered during the proposed mission, allowing researchers to select better-suited models to solving future problems. Our experiments show that Double DQN networks are capable of handling partially observable scenarios gracefully while maintaining a low hardware footprint, Recurrent Double DQNs can be a good candidate even when the resources must be restricted, and double-dueling DQNs are a well-performing middle ground in terms of their cost and performance.

Human Attitudes in Robotic Path Programming: A Pilot Study of User Experience in Manual and XR-Controlled Robotic Arm Manipulation

Extended reality (XR) and collaborative robots are reshaping human–robot interaction (HRI) by introducing novel control methods that enhance user experience (UX). However, human factors such as cognitive workload, usability, trust, and task performance are often underexplored. This study evaluated UX during robotic manipulation tasks under three interaction modalities: manual control, XR-based control at real-time speed (RS), and XR-based control at reduced safety speed (SS). Twenty-one participants performed a series of tasks across three scenarios, where we measured usability, workload, flow state, trust, and agency using a subjective questionnaire adapted from SUS, NASA-TLX, FSS, SoAS, and Trust in Industrial Human–Robot Collaboration Questionnaire, and objective task metrics (completion time, errors, and attempts). Our results reveal that RS-based control modes significantly reduced physical workload and improved usability compared to manual control. RS control at real-time speed enhanced task efficiency but increased error rates during complex tasks, while SS mode mitigated errors at the cost of prolonged completion times. Trust and agency remained stable across all modalities, indicating extended reality technologies do not undermine user confidence. These findings contribute to the field of human–robot collaboration by offering insights regarding efficiency, accuracy, and UX. The results are particularly relevant for industries seeking to optimize safety, productivity, and human-centric robotic systems.

A Prediction of the Shooting Trajectory for a Tuna Purse Seine Using the Double Deep Q-Network (DDQN) Algorithm

The purse seine is a fishing method in which a net is used to encircle a fish school, capturing isolated fish by tightening a purse line at the bottom of the net. Tuna purse seine operations are technically complex, requiring the evaluation of fish movements, vessel dynamics, and their interactions, with success largely dependent on the expertise of the crew. In particular, efficiency in terms of highly complex tasks, such as calculating the shooting trajectory during fishing operations, varies significantly based on the fisher’s skill level. To address this challenge, developing techniques to support less experienced fishers is necessary, particularly for operations targeting free-swimming fish schools, which are more difficult to capture compared to those utilizing Fish Aggregating Devices (FADs). This study proposes a method for predicting shooting trajectories using the Double Deep Q-Network (DDQN) algorithm. Observation states, actions, and reward functions were designed to identify optimal scenarios for shooting, and the catchability of the predicted trajectories was evaluated through gear behavior analysis. The findings of this study are expected to aid in the development of a trajectory prediction system for inexperienced fishers and serve as foundational data for automating purse seine fishing systems.

Learning reshapes the hippocampal representation hierarchy

A key feature of biological and artificial neural networks is the progressive refinement of their neural representations with experience. In neuroscience, this fact has inspired several recent studies in sensory and motor systems. However, less is known about how higher associational cortical areas, such as the hippocampus, modify representations throughout the learning of complex tasks. Here, we focus on associative learning, a process that requires forming a connection between the representations of different variables for appropriate behavioral response. We trained rats in a space-context associative task and monitored hippocampal neural activity throughout the entire learning period, over several days. This allowed us to assess changes in the representations of context, movement direction, and position, as well as their relationship to behavior. We identified a hierarchical representational structure in the encoding of these three task variables that was preserved throughout learning. Nevertheless, we also observed changes at the lower levels of the hierarchy where context was encoded. These changes were local in neural activity space and restricted to physical positions where context identification was necessary for correct decision-making, supporting better context decoding and contextual code compression. Our results demonstrate that the hippocampal code not only accommodates hierarchical relationships between different variables but also enables efficient learning through minimal changes in neural activity space. Beyond the hippocampus, our work reveals a representation learning mechanism that might be implemented in other biological and artificial networks performing similar tasks.

Usability Evaluation of Integrated and Separated Interfaces in an Immersive Authoring Tool based on Panoramic Videos.

Immersive authoring tools have emerged as key enablers for trainers-designers to create Virtual Reality Learning Systems (VRLS) without requiring extensive programming skills. However, the design of such tools presents significant challenges in terms of interaction, usability, and interface complexity. These challenges underscore the need to validate an appropriate design interface to ensure these tools can be effectively utilized by non-technical users. This study evaluates the usability of an immersive authoring tool for VRLS using interactive panoramic videos. Two types of interfaces were compared: one that integrates storyboarding and rendering visualization, and one that separates these functionalities. Quantitative and qualitative data were collected from 24 participants divided into two groups. The results indicated that the separated interface was more effective, efficient, and satisfactory, particularly for more complex scenarios. Moreover, the integrated interface led to more pronounced symptoms of cybersickness while motivation levels did not differ significantly between the two groups. These findings highlight that integrating all functionalities into a single interface may not always be the best approach, especially for complex tasks, as it can lead to decreased usability and increased physical discomfort.

What Task Feature Determines the Dominant Task in Dual-Task Conditions?

When attempting to concurrently perform two distinct cognitive tasks, the performance of either task is frequently compromised. This phenomenon is known as dual-task interference. Although multiple task features have been postulated to influence on dual-task interference, the primary determinant remains unclear. The determinant factor causing dual-task interference is an important issue to understand its mechanism and associated functions including switching tasks and planning task order. The present study investigated this issue using monkeys and three behavioral tasks requiring distinct cognitive processes (spatial working memory, SWM; working memory and long-term memory of objects, PA; object working memory, DMS) and manipulating task pair (SWM and PA or SWM and DMS), task order (fixed or randomized) and task difficulty (different delay lengths). The task introduced first showed better performance as compared to the task introduced second, suggesting the task order as an important factor. However, the performance of the SWM task decreased when preceded by the PA and DMS tasks, while the latter tasks were unaffected when the SWM task was introduced first. This tendency was more obvious in random-order conditions than fixed-order conditions. Further, interference effect increased as task difficulty increased. Although the task order is one determinant, our results show the difference in cognitive process needed for tasks, its complexity, and the demand of working memory resources as more significant determinants for deciding the dominant task in dual-task conditions, indicating importance of neural mechanisms including managing working memory resources and coordinating multiple cognitive processes to understand the cause of dual-task interference.Significance Statement Although multiple task features have been postulated to influence on dual-task interference, the primary cause of dual-task interference remains unclear. Using monkeys and three behavioral tasks, each requiring distinct cognitive processes, and manipulating task pairs, task order, and task difficulty, we found the difference in necessary cognitive processes among tasks, complexity and difficulty of tasks, and fixed- or random-order conditions as critical task factors causing dual-task interference. These task factors are closely related to the mechanisms how different cognitive processes are coordinated and how finite working memory resources are managed and are associated with executive control. Dual-task paradigms are the best method to examine neural mechanism of executive control.

Self-Rectifying Dynamic Memristor Circuits for Periodic LIF Refractory Period Emulation and TTFS/Rate Signal Encoding.

Spiking Neural Networks (SNNs) have gained attention due to their potential to improve computational efficiency compared to traditional artificial neural networks. This study investigates the use of dynamic memristors Ta/IGZO/TaOx/Pt combined with peripheral circuits to emulate the leaky integrate-and-fire behavior of neurons, focusing on incorporating a refractory period. The refractory period is crucial as it prevents neurons from becoming overactive and ensures precise timing in signal processing. This improvement allows the memristor to mimic biological neuron behavior more accurately. The memristor's transient resistance exhibits nonlinear I-V hysteresis and changes in response to pulses, enabling functions of integration, leakage, and firing. Additionally, the memristor is configured as an encoder, converting external signals into voltage pulse sequences. Using coding methods, including rate coding and time-to-first-spike (TTFS) coding, the encoder demonstrates improved signal processing, with TTFS occurring within 21 to 62ms and encoder frequencies from 2500 to 9500Hz. Experimental results show that this approach enhances SNN performance, making it more suitable for real-time applications and complex temporal signal processing tasks. This research highlights the potential of dynamic memristors to bridge the gap between neurons and artificial neurons, paving the way for more efficient neuromorphic computing systems.

Laser powder bed fusion dataset for relative density prediction of commercial metallic alloys

Laser-based powder bed fusion (L-PBF) technology stands out for its ability to create complex, high-performance parts, optimizing design freedom and material efficiency. Despite technical and financial challenges, it is attractive to industries where performance, weight reduction, and customization are critical. In L-PBF, relative density (RD) is a key factor that directly impacts the mechanical properties and overall quality of printed parts. However, predicting RD is a complex and costly task due to the numerous factors involved. This study addresses this need by creating a large-scale dataset for RD prediction in L-PBF, consisting of 1579 samples of commercial alloys from the literature. It includes printing conditions and other crucial inputs like protective atmosphere, powder size distribution, and part geometry. This dataset offers a valuable resource for researchers to benchmark their results, better understand key factors influencing RD, and validate models or explore new machine-learning approaches tailored to L-PBF.

A Multimodal Data Fusion and Embedding Attention Mechanism-Based Method for Eggplant Disease Detection

A novel eggplant disease detection method based on multimodal data fusion and attention mechanisms is proposed in this study, aimed at improving both the accuracy and robustness of disease detection. The method integrates image and sensor data, optimizing the fusion of multimodal features through an embedded attention mechanism, which enhances the model’s ability to focus on disease-related features. Experimental results demonstrate that the proposed method excels across various evaluation metrics, achieving a precision of 0.94, recall of 0.90, accuracy of 0.92, and mAP@75 of 0.91, indicating excellent classification accuracy and object localization capability. Further experiments, through ablation studies, evaluated the impact of different attention mechanisms and loss functions on model performance, all of which showed superior performance for the proposed approach. The multimodal data fusion combined with the embedded attention mechanism effectively enhances the accuracy and robustness of the eggplant disease detection model, making it highly suitable for complex disease identification tasks and demonstrating significant potential for widespread application.

Accurate extraction of electrical parameters in three-diode photovoltaic systems through the enhanced mother tree methodology: A novel approach for parameter estimation.

Accurately simulating photovoltaic (PV) modules requires precise parameter extraction, a complex task due to the nonlinear nature of these systems. This study introduces the Mother Tree Optimization with Climate Change (MTO-CL) algorithm to address this challenge by enhancing parameter estimation for a solar PV three-diode model. MTO-CL improves optimization performance by incorporating climate change-inspired adaptations, which affect two key phases: elimination (refreshing 20% of suboptimal solutions) and distortion (slight adjustments to 80% of remaining solutions). This balance between exploration and exploitation allows the algorithm to dynamically and effectively identify optimal parameters. Compared to seven alternative methods, MTO-CL shows superior performance in parameter estimation for various solar modules, including ST40 and SM55, across different irradiances and temperatures. It achieves exceptionally low Root Mean Square Error (RMSE) values from 0.0025A to 0.0165A and Mean Squared Error (MSE) values between 6.2 × 10^-6 and 2.7 × 10^-4, while also significantly minimizing power errors, ranging from 22.86 mW to 239.40 mW. These results demonstrate MTO-CL's effectiveness in improving the accuracy and reliability of PV system modeling, offering a robust tool for enhanced solar energy applications.

Exploring a digital music teaching model integrated with recurrent neural networks under artificial intelligence

This study proposes an intelligent digital music teaching model based on Artificial Intelligence (AI) and Long Short-Term Memory (LSTM) networks to enhance personalized assessment and feedback in music education. Within this teaching model, the music evaluation module employs a three-layer Bidirectional LSTM (Bi-LSTM) combined with an attention mechanism, effectively capturing the long-term sequential features of Musical Instrument Digital Interface (MIDI) music to support the assessment of students’ musical performances. In comparative experiments with multiple models, the three-layer Bi-LSTM achieved a final accuracy of 91.9%, significantly outperforming other models and validating the advantages of deep network structures in complex tasks. Further comparisons of precision, recall, and F1-score demonstrated that the average values for the three-layer Bi-LSTM model reached 0.87, 0.854, and 0.86, respectively, showcasing superior classification accuracy and stability. A usability survey indicated that both piano teachers and students rated the overall satisfaction, teaching effectiveness feedback, user experience, teaching engagement, and ease of use of the model above 4.0, highlighting its excellent applicability and potential for broader adoption in teaching practice. This study provides a novel approach and practical reference for AI-driven personalized music education.

Support vector machine (SVM) algorithm optimization and innovative talent matching model for employment needs

Support vector machine (SVM) algorithm optimization and innovative talent matching model for employment needs

Electrical load forecasting in power systems based on quantum computing using time series-based quantum artificial intelligence

A proper, reliable, and economic operation of a power system relies on a precise energy management strategy. For a reliable energy management strategy, information about the power system including power production and power consumption is required. However, consumer behaviour can be unpredictable, which can result to a high level of uncertainties for the load profile. So, this type of issue (existence of the uncertainty in power system) makes the energy management a complex task. The knowledge about the future state of the power system (e.g., the values of loads) can reduce the difficulty of this task, and it can lead to a more efficient energy management. This paper implements quantum computing-based artificial neural network to predict the future values of loads. For this purpose, this paper uses hybrid quantum/classical artificial neural network for a short-term forecasting of loads. The implemented quantum computing-based strategy is deployed using time series-based technique without using extra information (e.g., the weather condition, and behaviour of the consumer), and it only uses the current and historical values of the load to predict the future value of that. To examine the effectiveness of the hybrid quantum/classical artificial neural network, two different types of loads are selected from an experimental lab and the quantum-based approach is tested on those loads. The obtained results can proof the potential of quantum artificial intelligence to be used for forecasting-based challenges in smart grids.

The Role of Cognitive Abilities in Project-Based Teaching: A Mixed Methods Study

Cognitive abilities are foundational to complex tasks, which may be also important in complex project-based teaching. However, the role of teachers’ cognitive abilities in project-based teaching is still unknown. Therefore, this study aimed to explore the relationship between teachers’ cognitive abilities and project-based teaching using a mixed methods design. In Study 1, a quantitative regression analysis was conducted with 62 primary school teachers. They completed the project-based teaching questionnaire and performed four cognitive tasks: remote association (creativity), object detail memory (object detail processing ability), paper folding (spatial ability), and sentence comprehension (verbal ability). Regression analysis revealed that spatial ability significantly predicted a teacher’s project-based teaching ability, even after controlling for age, gender, teaching experience, and project-based teaching experience. In Study 2, a qualitative exploratory case study was employed to examine how spatial ability manifests in two teachers’ project-based teaching plans. The teacher with higher spatial ability used schemata, abstract concepts, a better overall plan, and a deeper understanding of mathematics than teachers with lower spatial ability. This study indicated that a teacher’s spatial ability is closely associated with their project-based teaching, and it provides a new perspective for teachers to cultivate project-based teaching.

Optimal Trajectory Generation of Various English Alphabets Using Deep Learning Model for 3‐R Manipulator

ABSTRACTThe modern era of medicine and industry extensively utilizes the manipulator's hand for a variety of vital automated activities. Handling a manipulator hand is a complex task. Due to the nonlinear characteristics of inverse kinematics (IK) mathematical model, inverse kinematics is a time‐consuming and laborious procedure, making it difficult to provide a mathematical solution. This research employs a 3‐R (revolute) robotic manipulator to achieve joint trajectories for drawing different alphabets and shapes. The IK problem has been solved using a hybrid model. The model is a hybrid of an artificial neural network (ANN) based model, the forward and backward reaching inverse kinematics (FABRIK) technique provides stability and the control barrier function (CBF) with the Lyapunov function. Using the proposed model, coordinates for different alphabets and shapes within the confined workspace were calculated. The ANN automatically obtains specific end‐effector coordinates. This model combines the CBF with the Lyapunov function to ensure that a safe region is selected. The accuracy of the model exceeds 99.5%. We have calculated the mean square error (MSE) as 1.66, the root mean square error (RMSE) as 1.25, and the mean absolute error (MAE) as 0.96 for our model. The error between the model's predicted and actual coordinates also demonstrates letter coordinates and shapes drawn using a physical 3R manipulator model. As a result, this method can be applied to precisely estimate the angles in intricate 3DoF inverse kinematics models.

The Reciprocal Relationship Between Short- and Long-Term Motor Learning and Neurometabolites.

Skill acquisition requires practice to stimulate neuroplasticity. Changes in inhibitory and excitatory neurotransmitters, such as gamma-aminobutyric acid (GABA) and glutamate, are believed to play a crucial role in promoting neuroplasticity. Magnetic resonance spectroscopy (MRS) at 3 T, using the MEGA-PRESS sequence, and behavioral data were collected from 62 volunteers. Participants completed a 4-week protocol, practicing either complex (n = 32) or simple (n = 30) bimanual tracking tasks (BTT). Neurotransmitter levels and skill levels at baseline, after 2 and 4 weeks of motor training were compared for the left and right primary sensorimotor cortex (SM1) and the left dorsal premotor cortex (PMd). Furthermore, task-related modulations of neurotransmitter levels in the left PMd were assessed. The study yielded that baseline neurotransmitter levels in motor-related brain regions predicted training success. Furthermore, lower GABA+ (p = 0.0347) and higher Glx (glutamate + glutamine compound) levels (p = 0.0234) in left PMd correlated with better long-term learning of simple and complex tasks, respectively, whereas higher GABA+ in right SM1 correlated with complex task learning (p = 0.0064). Resting neurometabolite levels changed during the intervention: Left SM1 Glx decreased with complex training toward Week 4 (p = 0.0135), whereas right SM1 Glx was increased at Week 2 (p = 0.0043), regardless of training type. Group-level analysis showed no task-related neurometabolite modulation in the left PMd. However, individual baseline GABA+ and Glx modulation influenced short-term motor learning (interaction: p = 0.0213). These findings underscore the importance of an interplay between inhibitory and excitatory neurotransmitters during motor learning and suggest potential for future personalized approaches to optimize motor learning.