Agricultural robotic systems frequently operate in highly dynamic and uncertain environmental conditions, where incomplete sensor readings, weather variability, terrain irregularities, and crop-state ambiguity significantly affect decision-making performance. To address these challenges, this study introduces the concept of Circular Complex Intuitionistic Fuzzy Sets (CrC-IFS) as an advanced mathematical tool for modeling uncertainty in agricultural robot decision-making processes. The Circular Complex Intuitionistic Fuzzy Set extends classical complex intuitionistic and circular intuitionistic fuzzy structures by incorporating enhanced higher-order membership flexibility for representing imprecise and multidimensional environmental information. To strengthen uncertainty modeling capability, refined algebraic operational laws for CrC-IFS are developed, including direct sum, direct product, and scalar multiplication based on generalized t-norms and t-conorms. Furthermore, circular complex intuitionistic fuzzy weighted and ordered weighted aggregation operators are proposed to integrate multiple environmental and operational criteria, such as terrain conditions, obstacle density, energy consumption, crop maturity levels, and weather fluctuations. Building upon these theoretical developments, a robust multi-criteria decision-making framework is constructed to optimize agricultural robot strategies, enabling systematic prioritization of navigation paths, task allocation, harvesting schedules, and adaptive control actions. The results demonstrate that the proposed framework enhances decision robustness, improves operational efficiency, and supports intelligent autonomous behavior under highly uncertain agricultural field conditions.

The increasing labor costs in agriculture have accelerated the adoption of multirobot systems for orchard harvesting. However, efficiently coordinating these systems is challenging due to the complex interplay between makespan and energy consumption, particularly under practical constraints like load-dependent speed variations and battery limitations. This article defines the multiobjective agricultural multielectrical-robot task allocation (AMERTA) problem, which systematically incorporates these often-overlooked real-world constraints. To address this problem, we propose a hybrid hierarchical route reconstruction algorithm (HRRA) that integrates several innovative mechanisms, including a hierarchical encoding structure, a dual-phase initialization method, task-sequence optimizers, and specialized route reconstruction operators. Extensive experiments on 45 test instances demonstrate HRRA's superior performance against seven state-of-the-art algorithms. Statistical analysis, including the Wilcoxon signed-rank and Friedman tests, empirically validates HRRA's competitiveness and its unique ability to explore previously inaccessible regions of the solution space. In general, this research contributes to the theoretical understanding of multirobot coordination by offering a novel problem formulation and an effective algorithm, thereby also providing practical insights for agricultural automation.

A Task Allocation Algorithm in Mobile Crowdsensing Under Time Constraints

HAC-FRL: A learning-driven distributed task allocation framework for large-scale warehouse automation

Multiple unmanned aerial vehicles task allocation algorithm for agricultural scenarios based on improved non-dominated sorting genetic algorithm II

Multi-type crowdsourcing task allocation based on a low-constraint two-stage alternating multi-objective evolutionary

The increasing demand for personalized, real-time healthcare necessitates efficient, secure patient data management. Digital Twins (DTs) enable AI-powered monitoring and decision support but also introduce challenges related to latency, computational cost, and security. This paper proposes a cost-optimized, AI-driven Medical Digital Twin (MDT) framework that manages task allocation across heterogeneous edge, fog, and cloud infrastructures. The system is formulated as a tri-objective optimization model that jointly minimizes latency and operational cost while maximizing security, subject to resource and clinical-priority constraints. To solve this problem, three complementary approaches are developed: (i) an exact Integer Linear Programming (ILP) model for optimal benchmarking, (ii) a Patient-Aware Task Intelligence Greedy (PATI-Greedy) heuristic algorithm for low-latency decision-making, and (iii) a Hybrid Q-Learning Enhanced Genetic Algorithm (HybridQeGA) for scalable, near-optimal performance in complex environments. Extensive simulations in a smart ICU scenario with 4, 8, and 12 patients demonstrate that ILP consistently achieves the best objective values but is computationally impractical for large instances. PATI-Greedy executes rapidly with polynomial complexity, achieving results within 5-[Formula: see text] of ILP for small- to medium-scale workloads. HybridQeGA offers the closest match to ILP in larger problem sizes, with less than [Formula: see text] deviation in overall objective value while maintaining scalability. Security-sensitive scenarios highlight HybridQeGA's adaptability, improving security scores by an average of [Formula: see text] compared to PATI-Greedy. These findings establish a balanced trade-off between accuracy and computational efficiency, positioning the proposed framework as a robust and deployable solution for intelligent and trustworthy digital health ecosystems.

From an early age, children perceive power imbalances between genders, but their attitudes toward gendered power remain largely unexplored. We studied this issue using a resource allocation task with 653 French children aged 3-8 (50.15% girls) recruited between 2022 and 2023. Participants were exposed to a dyadic power interaction and had to distribute more resources to either the dominant or the subordinate character. We tested three hypotheses: H1 predicted a male dominance bias; H2 predicted own-gender favoritism; and H3 predicted sensitivity to hierarchical status only. Contrary to H1, no pro-male bias was found. Results supported H3: younger children favored dominant characters, while older children favored subordinates. H2 was partially supported, showing own-gender bias, stronger in girls, without overriding sensitivity to status.

This article advances the thesis that algorithmic subordination should operate as a legally cognisable indicator of employment status within European labour law. Building upon the Court of Justice’s autonomous concept of the “worker” and the Union legislature’s recent intervention in Directive (EU) 2024/2831 of 23 October 2024 on improving working conditions in platform work (OJ L 2024/2831, hereinafter referred to as: “the Directive”), the argument proceeds in three steps. First, it reconstructs subordination as a functional criterion that can be revealed through digital control measures, in particular the allocation of tasks, monitoring, price setting, evaluation and deactivation effected by algorithms. Secondly, it examines how this control reconfigures both sides of the personal work relation, by sharpening tests for employee status and by pressing toward a re‑specification of the employer concept, including scenarios of joint or indirect employer responsibility. Thirdly, it draws on Member State practice, notably Spain and Italy, to show how rebuttable presumptions, transparency obligations and data‑protection enforcement can be linedup to evidence and to constrain algorithmic power in the workplace. The article concludes that algorithmic subordination, when bounded by appropriate procedural and substantive safeguards, warrants recognition as a criterion for classifying work performed in atypical arrangements as employment. It recommends legislative and judicial techniques to ensure the effective implementation of Directive (EU) 2024/2831, while preserving the distinction between genuine self‑employment and employment.

The exponential growth in demand for data storage and computing has led to a rapid expansion in the energy consumption and carbon emissions of data centers (DCs). Hybrid energy systems that integrate renewable energy sources are regarded as a sustainable and low-carbon solution for powering the DCs. This study proposes an optimal cooperation scheduling strategy for the hybrid energy system powering the DC and electric vehicles (EVs). The strategy is based on load transferring and operates within a carbon trading mechanism, explicitly addressing the coupling between computational loads and power consumption. An optimization model is constructed that considers economic objectives, including operational cost and a stepped carbon trading cost, to obtain optimal energy dispatch and computational task allocation strategies. This framework ensures the economic interests of EVs’ owners while satisfying the energy demands of both the DC and the EVs. The results of a case study based in Shanghai demonstrate that the proposed hybrid energy system with multiple sources has significant economic and environmental advantages in spite of operational complexity. Furthermore, the collaborative strategy further enhances the cost reduction and carbon emission reduction. Specifically, the cooperative strategy achieves a 5.21% reduction in total cost compared to Case 1 (without V2G) and a 22.80% reduction compared to Case 2 (without computing task transferring). By adopting the optimal scheduling solution, carbon emissions can be reduced by 16.74% relative to Case 1 while remaining at a level comparable to Case 2. Furthermore, the impact of the carbon trading mechanism on the system’s cost and carbon emissions is analyzed. The results indicate that while a stricter carbon trading mechanism leads to an increase in the total cost, it also results in a reduction in carbon emission from the DC’s hybrid energy system.

This study examines the role of Human Resource Management (HRM) in enhancing employee loyalty at Yayasan Baitul Insan Khoir. The research adopts a qualitative approach using semi-structured interviews and field observations involving foundation leaders, administrative staff, and program officers. Data were analyzed through thematic analysis to identify patterns related to HRM practices, employee loyalty, influencing factors, and organizational challenges. The findings reveal that HRM within the foundation is predominantly operational, focusing on administrative functions such as recruitment, attendance, and task allocation, while strategic HR components particularly career development, performance appraisal, and talent management remain underdeveloped. Employee loyalty is largely affective in nature, driven by emotional attachment to the organization’s social mission, value alignment, and supportive interpersonal relationships. However, long-term loyalty is weakened by limited career pathways, workload imbalance, and financial constraints. Organizational support and shared values emerge as the most influential factors sustaining short-term commitment, whereas the absence of structured career development systems and formal performance evaluation significantly reduces employees’ intention to remain. The study also identifies key organizational barriers, including limited resources, role overlap, lack of recognition mechanisms, and the positioning of HRM as an administrative rather than strategic function. The research concludes that intrinsic motivation alone is insufficient to sustain long-term employee loyalty. Strategic HRM practices integrating career development, continuous training, performance management, and organizational support are essential for strengthening employee commitment and organizational sustainability. This study contributes to HRM literature by providing empirical insights from a nonprofit context and offers practical recommendations for enhancing employee loyalty through strategic HRM implementation.

To address the task-grouping problem for air-ground integrated Unmanned Aerial Vehicle (UAV) swarm missions in three-dimensional (3D) environments, this study proposes a data-preprocessing and hybrid initialization clustering method based on 3D spatial features. A dual-modal prototype meta-heuristic optimization model, Dual-Prototype Metaheuristic K-Means (DPM-Kmeans), is constructed accordingly. First, to overcome spatial information loss in high-dimensional task allocation, a 3D spatial task data preprocessing technique and a hybrid initialization strategy based on the golden spiral distribution are designed. This ensures the diversity and environmental adaptability of the initial solutions. Second, a dual-modal prototype optimization framework incorporating row prototypes (local refinement) and column prototypes (global combination) was constructed using meta-heuristics and clustering algorithms. The prototype-driven replacement update mechanism simultaneously performs global and local search, balancing the algorithm's exploration and exploitation capabilities while expanding the solution space. This effectively addresses premature convergence issues in complex search spaces. Simultaneously, a collaborative multi-constraint, dynamically weighted optimization model was constructed, incorporating task requirements and flight distance constraints to ensure that the grouping scheme approximates the global optimum. Simulation results demonstrate that compared to traditional K-means and mainstream meta-heuristic optimization algorithms, DPM-Kmeans achieves an overall improvement of 2-10% in Sum of Squared Errors (SSE), Silhouette Coefficient (SC), and Davies-Bouldin Index (DB) metrics. It exhibits superior convergence speed and solution quality, proving the method's excellent scalability and robustness in multi-constraint, large-scale 3D scenarios.

Maritime dynamic observation missions, such as environmental monitoring, marine ranching inspection, and emergency response, typically require large-scale and high-efficiency operations in complex and variable maritime environments. Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) offer complementary advantages in such missions: USVs provide long endurance and stable platform support, while UAVs enable rapid, high-coverage aerial perception. However, limited UAV battery capacity and dynamic task environments pose significant challenges to autonomous collaborative operations. This study proposes a collaborative operation and energy replenishment strategy for USV–UAV systems in maritime dynamic observation missions. Under a unified framework, task allocation, collaborative path planning, and energy replenishment are jointly optimized, where the USV serves as a mobile replenishment platform to provide energy support for the UAV. The proposed method incorporates dynamic task updates, environmental disturbances, and energy constraints, achieving real-time adaptive collaboration between heterogeneous agents. Validation through both simulations and actual sea trials demonstrates that the proposed strategy significantly outperforms four baseline methods (greedy strategy, static planning, multi-objective genetic algorithm, and reinforcement learning scheduler) across five core metrics: task completion rate (91.74% in simulation/90.85% in sea trials), total energy consumption (1284.66 kJ/1298.42 kJ), mission completion time (40.28 min/41.12 min), average response time (10.21 s/10.35 s), and path redundancy (13.79%/14.03%). Furthermore, ablation experiments verify that the energy replenishment strategy enhances the task completion rate in both simulation and field tests. This method provides a feasible and scalable collaborative solution for autonomous multi-agent systems, offering significant guidance for the practical deployment of future maritime observation and monitoring missions.

Strategic potential assessment integrates both quantitative and qualitative methods. Innovative clusters require innovative analytical tools. The article introduces a new qualitative methodology for assessing the strategic innovative potential of cluster structures that rely on the strategic innovative potential of their parts for the sustainable development and competitiveness of the entire structure. A review of available studies on strategic potential and clustering made it possible to integrate quantitative and qualitative methods into a single assessment tool and test its performance on a specific cluster. Using the method of taxonomic analysis, the author ranked the innovation potential indicators, while the methods of structural analysis and comparison revealed the internal relationships between cluster elements. The experiment yielded a set of practical recommendations for increasing the innovative potential of clusters. The taxonomy method proved especially effective. First, it provided an integrated assessment of the innovative potential for each company within a cluster. Second, it revealed the possibility of tracking the progress of individual indicators and determining their contribution. Third, it established the cause-and-effect relationships between parameters that had a positive or negative impact on the strategic innovative sustainability of each cluster. The new methodology is a universal strategic analysis tool that stimulates innovations, compares cluster parts, and optimizes the task allocation while increasing the transparency and manageability of the entire cluster. Its application may strengthen the role of cluster structures in innovative regional and industrial strategic planning.

Optimizing task allocation in multi-robot order picking systems for warehouses

Effective and accurate management of dynamic workloads in wireless sensor networks (WSNs) is critical to ensure energy efficiency, network longevity, and reliable information delivery in heterogeneous environments. Traditional heterogeneous WSN solutions often suffer from unbalanced energy usage, poor node placement and load distribution optimization, leading to poor performance and high operating costs. This proposes a new energy-aware load balancing architecture for heterogeneous WSNs that incorporates a multi-level optimization approach. First, Energy Weighted Probabilistic Deployment (EWPD) takes energy efficiency into account, guaranteeing optimal node placement to maximize network coverage and extend lifetime. Second, network initialization and node classification based on Sliding Time Windows (STW) predict task execution patterns and allow adaptive allocation of nodes according to task performance. Third, Expected Time to Task Completion (ETTC) scheduling with cluster formation and path discovery can predict workload times to maximize routing selection. Fourth, workload analysis is a tool based on Spider Swarm Intelligence (SSI) that measures the computational needs of nodes and finds the most efficient allocation of workload resources. Finally, the Minimum-Maximum Priority Energy-Aware Self-Scheduling Mechanism (DMMPS-EASSM) is a dynamic operation and migration mechanism designed to balance energy consumption and provide efficient data transmission. Experimental results demonstrate that the proposed framework can significantly improve network performance, reduce energy wastage, and improve resource utilization compared with traditional approaches based on heterogeneous WSNs.

Remotely operated vehicles (ROVs) typically rely on over-actuated propulsion systems to achieve precise dynamic positioning and maneuvering in complex underwater environments. In practice, however, conventional propulsion management based on thrust allocation is often challenged by non-ideal actuator behaviors, such as cavitation-induced thrust degradation, low-speed dead-zone effects, inter-thruster coupling, and partial actuator failures. Most existing approaches treat propulsion management as a static force distribution problem and implicitly assume ideal or fast thrust execution, which limits performance when actuator dynamics and execution uncertainty become significant. To address these limitations, this paper proposes a control-oriented thruster management framework that reformulates propulsion management as a feedback regulation problem rather than a static allocation task. In the proposed framework, actuator dynamics and thrust execution uncertainty are explicitly incorporated into the control loop. At the actuator level, thrust degradation and low-speed operation are compensated through disturbance-aware feedback control, while at the system level an LQI-based controller with thrust response compensation is employed to coordinate multi-degree-of-freedom (DOF) force and moment regulation and suppress cross-axis coupling. Fault tolerance is achieved inherently through feedback regulation without relying on explicit fault detection or reallocation. Experimental results obtained from an ROV propulsion platform, including single-thruster tests, coupled multi-DOF control, and a thruster shutdown scenario, demonstrate improved thrust executability, reduced coupling-induced disturbances, and enhanced fault-tolerant performance compared with conventional direct thrust allocation strategies.

ABSTRACT Efficient budget allocation is essential for sustaining mobile crowdsensing systems (MCS), where resource distribution must balance cost-effectiveness and participant motivation to ensure optimal task execution. This paper introduces two novel winner selection mechanisms designed to maximize system rewards while adhering to financial constraints: a) Targeted Regional Incentive (TRI), and b) TRI with Critical Value (TRI-CV). The proposed incentive mechanisms are structured through regional divisions within the area of interest for effective task allocation. TRI employs a computationally efficient, low-overlap prioritization strategy that supports real-time task allocation across spatial regions. TRI-CV builds on this foundation by introducing region-specific critical value thresholds and a lightweight audit reassignment step, which reallocates budget from overrepresented to underserved regions to enhance spatial coverage and fairness. Simulation results show that TRI improves platform utility by 45–50% and reduces auction costs by 35–36%, while TRI-CV achieves 50–58% higher utility and 40–42% lower costs compared to established mechanisms such as FIRE, GRUS, and BFM. Additionally, TRI-CV demonstrates superior participant retention and scalability, positioning it as a robust and fairness-aware solution for dynamic Mobile Crowd Sensing environments.

In Ethiopia, the enrollment of females in school is still lower than that of males. Women also struggle greatly in Dabia Primary School to participate in the educational process. this study aims to identify the factors that influence the academic achievement of grade eight female students at Dabia Primary School. A cros sectional study was conducted from to, may 30 to July 01, 2024 on 53 respondents in all, including 36 female students in grade 8 and 17 teachers, were sampled for this study. The students were selected employing simple random selection techniques, while teachers were chosen via purposive sampling. The primary instruments for acquiring data were interviews and questionnaires. Frequency counts, percentages, and mean values were used to examine the data. The study's findings showed that psychological elements (96%) like shyness and concern of what others will think of them were highly rated by both teachers and female pupils. The most detrimental influences were rated as psychological factors (92.5%), sociocultural factors (92.5%), personal factors (53%) such as poor academic backgrounds of students and poor communication skills, and sociocultural factors (92.5%) such as livelihood dependence of the respondents and negative attitudes toward female education and the allocation of household tasks to women.

Abstract Achieving stable and coordinated locomotion in high-dimensional humanoid robots remains challenging, particularly considering variations in support phases, model uncertainties, and redundancy arising from multi-degrees-of-freedom joints. To address these issues, a predictive control framework that integrates gravity-compensated model predictive control (GC-MPC) with hierarchical whole-body control (WBC) was proposed, targeting force prediction issues under varying contact conditions. The introduction of distributed reference plantar forces derived from rigid-body dynamics into the MPC formulation enabled more stable and physically consistent force tracking (by a controller) across support transitions. The developed hierarchical WBC was efficiently integrated with GC-MPC, featuring flat-foot constraints for enhanced contact stability and task allocation to resolve kinematic redundancy. This enables a real-time joint-level control under physical and task constraints, effectively mitigating the gap between the predicted and the actual system behavior, improving robustness in high-dimensional humanoid systems. The proposed approach was validated on AzureLoong, a full-size humanoid robot (1.85 m, 75.5 kg) via simulation and physical experiments. The obtained results demonstrated a stable walking performance, balance control, accurate torso tracking, and smooth transitions under diverse motion commands, achieving a 25.57% reduction in the average decay rate of the plantar force prediction. This work reliably deploys predictive control on large-scale humanoid systems and establishes a solid foundation for subsequent integration with, e.g., navigation, manipulation, reinforcement learning-based whole body, or predictive control, particularly for the responsive locomotion control under diverse and concurrent task commands.