Purpose The persistence of remote and hybrid work has resulted in socially isolated supply chain workers working in functional groups to perform sometimes repetitive and unengaging tasks, under the auspices of performance dashboards. This study examines perseverance, a personality trait that may be a potential factor in stimulating helping behavior and improving workgroup performance. In addition, we also explore common logistics and supply chain management task structures, including goal-setting and task complexity as moderating influences. Design/methodology/approach This paper utilizes an online scenario-based vignette experiment designed in collaboration with a senior supply chain management executive to explore personality and situational factors that influence helping behavior among remote workers performing an unengaging supply chain task. Findings We find that, while a worker's perseverance is positively related to their likelihood of helping coworkers, this direct relationship is under competitive mediation through normative ability, in which we also find that perseverant workers tend to focus more on their own performance, even though assessing their normative ability is positively tied to helping behavior. Further, both of these dynamics are respectively amplified by participative goal-setting and, unexpectedly, task complexity. Practical implications As firms continue to struggle with return-to-office mandates, our results offer insights into how supply chain managers can improve workgroup performance among remote workers, particularly those tasked with unengaging and unrewarding tasks that remain commonplace in the logistics and supply chain management domain. Originality/value To our best knowledge, this is the first study to examine remote supply chain worker performance. Even as technology continues to advance, there are certain repetitive and unengaging tasks that cannot be simply automated. Our study offers a potential path for supply chain managers to improve remote worker engagement and workgroup performance on these tasks.

Secondary education in Nigeria faces increasing demands to equip students with critical thinking and problem-solving skills necessary for higher education and the workforce. Despite policy efforts to introduce multidisciplinary curricula, challenges persist in effectively implementing these approaches in schools, limiting their potential impact on students’ cognitive development. The study employed a quantitative descriptive survey design with inferential analysis to examine multidisciplinary curriculum implementation in secondary schools in Anambra and Enugu States. A multi-stage sampling technique was used to select 500 students and educators from public and private schools. Data were collected using a validated and reliable structured questionnaire measuring demographic variables, problem-solving skills, educators’ perceived challenges, and students’ perceptions of relevance to critical thinking. Questionnaires were administered directly, with ethical standards observed. Data were analyzed using SPSS, applying descriptive statistics, independent samples t-tests, and simple linear regression at the 0.05 level of significance. The results indicate that multidisciplinary education enhances students’ problem-solving skills in both states, with Anambra students recording higher mean scores in applying knowledge (3.15 vs. 3.03), creativity (3.47 vs. 3.37), decision-making (3.20 vs. 3.12), and real-world problem solving (3.10 vs. 2.99), while Enugu students reported greater confidence in handling complex tasks (2.82 vs. 1.84). Educators in Enugu perceived more serious implementation challenges, especially inadequate training (2.82 vs. 1.84) and curriculum overload (2.66 vs. 1.41), though both states shared similar concerns about assessment methods (3.31). Students generally viewed multidisciplinary education as relevant to critical thinking, with Anambra showing slightly higher engagement (3.47 vs. 3.41) and deeper thinking (3.42 vs. 3.33). Regression analysis revealed a moderate, significant impact on problem-solving skills (R = 0.469; R² = 0.220; p < .001), educators’ challenges significantly influenced implementation (t = –8.238; p < .001), while students’ perceptions were not significantly related to critical thinking (p = .289). The study concludes that multidisciplinary curricula hold substantial potential to enhance students’ cognitive skills, but effective implementation requires targeted teacher training, curriculum adjustments, and improved resource provision. These findings offer guidance for policymakers, school administrators, and curriculum planners aiming to strengthen secondary education outcomes.

Autism spectrum disorder (ASD) is one of the major neurological symptoms affecting young children. Most neurological diseases are captured through speech, voice and changes in sbrain activity. Research leading to ASD diagnosis is done in different ways; still, the early ASD diagnosis is a complex task. Various co-occurring situations may hinder Automated ASD detection, and deep learners effectively tackle such issues and create a better design. Here, a novel automated autism detection approach is proposed employing a deep learning technique with the help of brain image. Initially, the brain images are garnered from the standard dataset links. These gathered images are employed for the pre-processing stage, which is accomplished by using contrast enhancement. Subsequently, the most noteworthy deep features are extracted from the image pre-processed using a multi-atlas-based residual network (MResNet). Finally, the detection process is carried out by influencing the adaptive cascaded attention long short term memory with bayesian learning (ACAL-BL), in which some of the hyperparameters are tuned optimally by the random fixed marine predators algorithm (RFMPA). The performance is examined under Python using various factors and contrasted with other classical models and the results show that our ACAL-BL achieved an FPR of 4.5%, representing relative improvements of 52%, 54%, 56%, 58%, and 60% compared to LSTM, CNN, ANN, auto encoder, and LSTM-Bayesian learning, respectively. Thus, the suggested technique has the tendency to exploit the outstanding results that aid clinical practitioners to diagnose the disease earlier.

Abstract Robotics and machine learning algorithms can potentially enhance upper limb rehabilitation, addressing the limitations of traditional therapy methods. This study presents a novel Human-Robot Interaction (HRI) platform with human brain activities assessment capability aimed at enhancing upper limb rehabilitation by addressing the limitations of conventional therapy. Utilizing a 7-DOF Franka Emika robotic arm, the system supports patients in performing lifting, grasping and reaching tasks structured based on Wolf Motor Function Test (WMFT). Functional near-infrared spectroscopy (fNIRS) concurrently monitors cortical activation and functional connectivity to evaluate neural engagement and recovery. Visual feedback guides participants, while forearm EMG and brain activity from the moving limb are recorded to train deep learning models that classify physiology movement and cognitive load in real time. Quantitative performance metrics, including average trajectory deviation and non-dimensional squared jerk, assess movement accuracy and smoothness, correlating with task complexity. The platform also incorporates a robot impedance control scheme and an interactive interface to adapt assistance dynamically based on predicted movement. By integrating biomechanical performance data with neural indicators, this approach enables a personalized, data-driven rehabilitation framework.

With the advancement of task-oriented reinforcement learning (RL), the capability of quadruped robots for motion generation and complex task completion has significantly improved. However, current control strategies require extensive domain expertise and time-consuming design processes to acquire operational skills and achieve multi-task motion control, often failing to effectively manage complex behaviors composed of multiple coordinated actions. To address these limitations, this paper proposes a motion policy generation method for quadruped robots based on multimodal motion primitives and imitation learning. A multimodal motion library was constructed using 3D engine motion design, motion capture data retargeting, and trajectory planning. A temporal domain-based behavior planner was designed to combine these primitives and generate complex behaviors. We developed a RL-based imitation learning training framework to achieve precise trajectory tracking and rapid policy deployment, ensuring the effective application of actions/behaviors on the quadruped platform. Simulation and physical experiments conducted on the Lite3 quadruped robot validated the efficacy of the proposed approach, offering a new paradigm for the deployment and development of motion strategies for quadruped robots.

Functional near-infrared spectroscopy (fNIRS) is a portable, non-invasive brain imaging method with growing applications in neurorehabilitation. However, signal variability, driven in part by differences in data processing pipelines, remains a major barrier to its clinical adoption. This study compares the robustness of two common processing approaches, General Linear Model (GLM) and Block Averaging (BA), in detecting cortical activation across task complexities. Eighteen neurotypical, healthy adults completed a simple hand grasp task and a more complex gross manual dexterity task while fNIRS data were recorded and analyzed using the BA and GLM pipelines. Results revealed significant effects of both pipeline and task complexity on oxygenated and deoxygenated hemoglobin amplitudes. BA produced significantly larger responses than GLM, and complex tasks elicited significantly greater activation than simple tasks. Notably, only the BA-Complex subgroup showed significant differences from all other conditions, suggesting BA more effectively detects task-related hemodynamic changes. These findings emphasize the need for careful analysis pipeline selection to reduce variability and enhance fNIRS reliability in neurorehabilitation research.

This study examines how the design and interaction features of AI-powered fintech chatbots shape the customer experience of Generation Z users by integrating the Stimulus-Organism-Response framework with dual-process perspectives. Two cross-sectional surveys were conducted in Türkiye. Study 1 (n = 166) examines the effect of social presence, interactivity, visual appeal, design originality, and usability on perceived competence and perceived warmth, which, in turn, shape the customer experience. Social presence and design originality significantly increased perceived competence (β = 0.47, p < 0.001), while visual appeal enhanced perceived warmth (β = 0.32, p < 0.001). Together, competence and warmth explained a substantial proportion of customer experience (R2 ≈ 0.60). Usability and interactivity showed no significant effects. Study 2 (n = 195) replicated these findings with trained users and introduced task complexity as a moderator. Under high task complexity, usability and interactivity became significant predictors of competence, which emerged as the primary driver of customer experience, whereas the influence of warmth diminished. Non-normal data distributions justified the use of Partial Least Squares Structural Equation Modeling. Overall, the findings suggest a shift from heuristic to systematic processing as fintech tasks become more complex, highlighting the growing importance of competence-based evaluations in fintech chatbot interactions.

ABSTRACT Digital monitoring represents a new dimension of external control. We focus on variation in the experiences and perceptions of digital monitoring among employees with differing access to resources due to their embeddedness in differentially resource‐rich organizations and jobs. Based on German linked employer–employee survey data, our results suggest that employees in resource‐rich organizations that are able and willing to pay relatively high wages to secure work performance are less likely to experience the use of automatically stored data on work steps for performance evaluation and to perceive digital monitoring as constant surveillance. The same is true of employees in resource‐rich jobs with high task complexity. These patterns did not emerge for the mere automatic storage of data about work steps, which suggests that—in contrast to more invasive levels of digital monitoring—employees' experiences of this basic level are less likely structured by their embeddedness in organizational inequality regimes.

Task complexity amplifies spatial asymmetry of muscle synergy plasticity in chronic stroke survivors.

Benchmarking foundation models and parameter-efficient fine-tuning for prognosis prediction in medical imaging.

The interplay between corrective feedback timing, task complexity and working memory in L2 development

La-LoRA: Parameter-efficient fine-tuning with layer-wise adaptive low-rank adaptation.

Large language models (LLMs) have shown remarkable results when tasked with the analysis and production of texts or images and for captioning images. Aerial images differ from other images since they exhibit many natural objects that have a highly variable color range and no clear contours. This paper reports to what extent an LLM, i.e., Llama-4, can be tasked with the identification and captioning in aerial images of natural objects, such as tree categories, uncultivated land, and some man-made objects, such as roads. This valuable automation is needed to scan large areas and detect the parts for which a sudden maintenance or an emergency intervention is due. Tests on the chosen LLM were performed against a custom image dataset built to overcome the limited availability of such a domain-specific aerial image set. To evaluate the identification and captioning results, the accuracy, precision and recall metrics were computed. The results given by a cutting-edge variant of Llama-4, namely Maverick, reveal its strengths and weaknesses in this context. Although it is remarkable that an out-of-the-box tool can give assistance in such a complex observation and detection task, substantial progress is needed for such a model to improve accuracy and constitute a reliable support, as accuracy is at most 58.6% and recall is at most 56.1%.

INTRODUCTION: Malnutrition is detected in 10–50 % of children with cancer. Providing adequate nutritional therapy in pediatric oncology is a complex task that requires systematic screening, timely nutritional interventions and assessment of their effectiveness at different stages of antitumor treatment. OBJECTIVE: To determine the prevalence of severe protein-energy malnutrition (PEM) in children receiving treatment for malignant neoplasms and to identify factors associated with the development of severe PEM. MATERIALS AND METHODS: A prospective, single-center, continuous cohort study was conducted from January 2023 to January 2024 at the Research Institute of Pediatric Oncology and Hematology, N.N. Blokhin National Medical Research Center of Oncology, among children aged 0 to 18 years with malignant neoplasms. RESULTS: Data from 3,455 children were analyzed, including 1,754 (50.8 %) boys and 1,701 (49.2 %) girls. Moderate or mild PEM was diagnosed in 635 (18 %) patients at stage 1 nutritional screening. At stage 2 screening, after the start of antitumor treatment, severe PEM was identified in 102 (3 %) children. There was a strong positive correlation between moderate and mild PEM at stage 1 and the subsequent development of severe PEM after treatment initiation (Kendall’s Τ = 0.69, Tcrit = 0.23). The odds of severe PEM were higher in children under 1 year of age than in those aged 1–18 years (odds ratio [OR] 5.868; 95 % confidence interval [CI] 2.985–11.500; standard error [SE] 0.345). Severe PEM was more likely in patients with solid malignant tumors than in those with hematologic malignancies (OR 3.14; 95% CI 1.60–6.08; SE 0.337). Among patients after hematopoietic stem cell transplantation, the probability of developing severe PEM was also higher (OR 5.3; 95% CI 2.80–9.76; SE 0.3). CONCLUSIONS: Severe PEM after the start of treatment was observed in 3 % of pediatric patients with cancer. Factors contributing to its development were: first moderate and mild PEM, age under 1-year, solid tumors, hematopoietic stem cell transplantation.

The increasing reliance in school regarding the performance task intensive learning environments has raised concerns about their possible impacts on students well being. This quantitative research aimed to investigate how such environments in school affect the social,psychological, and physical well- being of the senior high school students at Lourdes College. To conduct this quantitative study, the researchers utilized a descriptive- correlational design that explores the relationship between the complexity, frequency, and time demands of performance task and different dimensions of student well- being. A total of 600 participants willingly participated in the focus group discussion and the researchers employed online survey questionnaires as data collection tools. The findings revealed that there is a significant relationship between the performance task intensive learning environments and students well- being, highlighting the necessity to innovate educational strategies that balance academic rigor with the wellness of students.This research also contributes to filling the gap in understanding how the assessments of performance task influence the students well being within the educational context in the Philippines.

In this paper, we propose a pattern classification method based on the modified multi-spike Tempotron-like ReSuMe algorithm in a VCSEL-SA-based photonic spiking neuron network. Based on the multi-spike triggering mechanism, the proposed method can capture the global information to overcome the limitation of the traditional single-spike triggering algorithm, which can be used to effectively process more complex temporal information tasks, accompanied by good robustness to noise. The pattern classification task for the digits “1” to “4” demonstrates the superior performance of the proposed method in the information processing task. By adopting the bias current management strategy for the post-synaptic neuron, we can further improve the network’s noise robustness. Moreover, this proposed method is validated in a pattern classification task in the Wisconsin Breast Cancer (WBC) dataset, and a classification accuracy of 95.6% can be achieved.

Birds' extraordinary aerial agility and environmental interaction enable complex tasks such as mid-air hunting, perching, and nest-building, inspiring the development of advanced aerial robots with similar manipulation capabilities. However, existing platforms often face challenges such as large size, heavy payloads, end-effector torque interference, and limited functionality, severely restricting their practical deployment. Drawing inspiration from the biological, structural, and actuation characteristics of human hands, we propose a hand-like robot that integrates flight and grasping, demonstrating the synergistic advantages of compact structure, agile flight, and versatile manipulation. We propose an autonomous framework including efficient mission planning and multi-level adaptive control, enabling the robot to precisely and smoothly perform human-like grasping, opening doors, forest perching, object transport, and interactive tasks. Additionally, the framework supports human-robot collaboration, empowering individuals with mobility impairment to conduct remote transportation and airborne operations. Outdoor tests, which include perching in various scenarios, navigating confined spaces, and transporting payloads across challenging terrain, validate the proposed vehicle's potential in aerial delivery and manipulation tasks. These results demonstrate emerging possibilities for aerial operation, assistance, and delivery with integrated flight and manipulation abilities.

People with Parkinson's disease (PwPD) often experience difficulty performing dual-tasks (DT), negatively impacting mobility, fall risk and quality of life. The influence of task complexity on DT performance remains unclear. This study examined the effects of gait complexity and cognitive/speaking task on DT effects (DTE) in PwPD and healthy older adults. Forty-one PwPD and eleven healthy older adults completed two gait tasks-straight walking (simple) and figure-8 walking with obstacle crossing (complex)-on a pressure-sensitive walkway, paired with four cognitive/speaking tasks: oral trail making, counting, diadochokinetic task, and spontaneous speech. Each combination was performed three times for 20 seconds. Single-task (ST) and DT performance were assessed for gait parameters (step length, step length coefficient of variation) and cognitive/speaking task response rate. Motor DTE (mDTE), cognitive DTE (cogDTE), combined DTE (cDTE), and modified attention allocation index (mAAI) were calculated. A 2×5 repeated measures ANOVA tested effects of gait complexity and cognitive/speaking task on raw gait metrics, and a 2×4 ANOVA tested effects on DTE metrics. In PwPD, complex walking resulted in shorter step length and greater step length coefficient of variation compared to simple walking (ps < .001), whereas cognitive/speaking task did not affect raw gait metrics. For DTEs, cognitive/speaking task influenced step length-based mDTE, cDTE, and mAAI (ps < .05) and step length coefficient of variation-based mAAI (p < .05). In healthy older adults, gait complexity led to greater step length coefficient of variation (p < .001) while step length was unchanged. Cognitive/speaking task significantly influenced step length-based cogDTE, cDTE, and mAAI, as well as step length coefficient of variation-based cogDTE and mAAI (ps < .05). No significant interactions were observed. In PwPD, gait complexity influenced raw gait performance, whereas DTEs were more sensitive to cognitive/speaking task. In healthy older adults, gait complexity primarily affected step length variability, while DTEs were also influenced by cognitive/speaking task. These findings emphasize the value of diverse DT paradigms and the importance of assessing both raw performance and DTEs. Tailoring assessments to include a variety of motor and cognitive challenges may improve sensitivity in evaluating gait and fall risk, supporting more personalized interventions.

Achieving humanlike dexterity with anthropomorphic multifingered robotic hands requires precise finger coordination. However, dexterous manipulation remains highly challenging because of high-dimensional action-observation spaces, complex hand-object contact dynamics, and frequent occlusions. To address this, we drew inspiration from the human learning paradigm of observation and practice and propose a two-stage learning framework by learning visual-tactile integration representations via self-supervised learning from human demonstrations. We trained a unified multitask policy through reinforcement learning and online imitation learning. This decoupled learning enabled the robot to acquire generalizable manipulation skills using only monocular images and simple binary tactile signals. With the unified policy, we built a multifingered hand manipulation system that performs multiple complicated tasks with low-cost sensing. It achieved an 85% success rate across five complex tasks and 25 objects and further generalized to three unseen tasks that share similar hand-object coordination patterns with the training tasks.

Real-time monitoring of sustained attention fluctuations during continuous complex tasks is vital for enhancing task performance and preventing accidents. Attention modulates neurons in the visual cortex in various ways to improve the visual sensitivity at an attended location. EEG-based brain-computer interfaces (BCIs) offer one of the most effective approaches for monitoring the state of human individuals. Whether transient responses evoked by brief stimuli, steady-state responses elicited by prolonged stimuli, or spontaneous neural oscillations, researchers can extract recognized electrophysiological features that reflect attention levels. However, unimodal features face inherent limitations, such as the low signal-to-noise ratio of transient responses and susceptibility of spontaneous rhythms to electrophysiological interference. Nevertheless, few studies have explored multimodal feature fusion for attention state monitoring. Here, we developed an innovative continuous go/no-go task to concurrently evoke both event-related potential (ERP) and steady-state visual evoked potential (SSVEP), while modulating spontaneous oscillatory activities through attentional engagement. To maximize the attentional modulation effect, we integrated the contrast-response functions of the modulation effect of attention on SSVEP and implemented 12 stimulus contrast levels to identify optimal visual stimulation intensity. Results from 25 subjects demonstrated that the decline in sustained attention during a continuous task was predictable before behavioral mistakes. Classification performance peaked at 31.60% stimulus contrast condition using the fused features combining spontaneous beta-band oscillations and SSVEP responses (average: 74.48%; best: 90.83%). These findings advance the development of more robust real-time attention monitoring systems based on BCI technology.