With the tourism industry continuing to boom, the importance of tourism mascots in promoting and publicizing tourism destinations is becoming increasingly prominent. Three core dimensions, market trend, appearance design, and audience feedback, are numerically investigated for deeply iterating tourism mascot design. Further, a subjective and objective evaluation weighting model based on the hierarchical analysis method (AHP) and entropy weighting method is proposed, aiming to utilize the advantages of these methods and ensure the entireness and correctness of results. Taking the mascots of six famous tourist attractions in Xi’an as an example, the feasibility and effectiveness of the evaluation model are verified. Data analysis and modeling results confirm that the three core evaluation indexes of scalability, innovation, and recommendation should be focused on in the design of tourism mascots in the three dimensions of market trends, appearance design, and audience feedback. The evaluation index scores are 0.1235, 0.1170, and 0.1123, respectively, which further illustrates the priority of mascot design. The evaluation model constructed by the research provides decision-makers with a comprehensive evaluation tool from the perspective of tourist experience, and also effectively assists the optimization process of mascot design. In addition, the model has good versatility and adaptability in structural design and evaluation logic and can be widely used in the optimization and evaluation research of brand mascots.

It is extremely important to optimize the C2 structure to suit to the information warfare. The proposed networked C2 structure, which is composed of a C2 capacity array and a task array, based on the long chain system in industry, aims to fill the gap. This article lucubrates two typical systems, the long chain system (LCS) and 3-chain system (3CS), to explore how they perform when tasks array with value weight array. By the research we find that the typical networked C2 systems, long chain and 3-chian system, have high flexibility to meet the situation when the combat tasks array has different value weights and have high flexibility to meet the changeable tasks in practical battlefield.

Generative AI (GenAI) is accelerating design space exploration and multimodal prototyping in industrial design (ID), bringing new efficiencies and possibilities to early-stage ideation and cross-media expression. Yet many studies do not clearly define stage-wise human–GenAI roles, preserve constraints as traceable cross-stage artifacts, or provide verifiable stage-wise evaluation, undermining traceability in both concept convergence and concept-to-engineering handover. To address these issues, this paper proposes GID-HGCC, a GenAI-driven human–GenAI co-creation ID framework that links four core stages: requirements confirmation, concept generation, concept evaluation, and 3D modeling. First, it specifies stage-wise responsibilities and defines the corresponding inputs and outputs. Second, it establishes a traceable cross-stage artifact flow—“structured prompts–candidate concepts–evaluation outputs–3D engineering issue list”—to support continuous constraint transmission and explicit documentation. Third, it integrates a multi-dimensional evaluation criteria system with IVIFNs–CRITIC–TOPSIS for concept ranking, and further strengthens convergence reliability via preference–consistency diagnostics. The framework is validated through a case study on a portable passive cervical spine rehabilitation training device. Expert preferences over stage-wise co-creation artifacts exhibit an overall medium-to-high level of consistency, and the Top-5 overlap between each expert and the group ranking ranges from 0.80 to 1.00. These results demonstrate that GID-HGCC offers an operational reference for constraint-guided human–GenAI co-creation in ID, improving traceability and handover reliability from requirements confirmation to engineering refinement.

With interdisciplinarity being an important characteristic of contemporary product design, the evaluation of design alternatives also involves multiple disciplines, and the evaluator group usually consists of evaluators from different fields and with obvious heterogeneous characteristics. To effectively satisfy the heterogeneous needs of evaluators and improve the credibility of evaluation results, the paper introduces a consensus‐reaching approach that incorporates multiple preferences to the evaluation of product design alternatives. First, in order to obtain individual preference information, each evaluator is asked to evaluate all the design alternatives using a preference structure that he/she is familiar with. Second, we use a transfer function to uniform the evaluation information obtained from various preference structures into a complementary judgment matrix. Then, we use the Hybrid Weighted Averaging (HWA) operator weight determination model to aggregate the preference information and obtain the group preference information. Then, we measure the consensus degree between individual evaluators and the group using a consensus measurement method. After that, we use the feedback mechanism to instruct individual evaluators to modify their preferences until a consensus is achieved. We explain the application steps and the feasibility of this approach through the evaluation of the design alternatives of multichannel fluorescence immunochromatography analyzers (MFIAs).

Apple picking is an inherently labor-intensive, time-consuming, and costly task, and robotic harvesting represents a potential alternative to address this challenge. This study presents the development and field evaluation of an integrated robotic system for apple harvesting, which combines machine vision, a dual four-degree-of-freedom (DoF) manipulator, and a mobile platform. The harvesting mechanism employed a streamlined 4-DoF manipulator driven by closed-loop stepper motors, incorporating a differential gear mechanism to execute yaw and pitch motions. Trajectory planning utilized linear interpolation with a harmonic acceleration/deceleration profile to ensure smooth end-effector movement. Fruit detection and localization within the canopy were performed by a stereo vision system running a lightweight deep neural network, achieving a mean hand-eye calibration accuracy of 4.7 ± 2.7 mm. Three negative-pressure driven soft end-effector designs—a suction soft end-effector (SSE), a grasping soft end-effector (GSE), and a suction-grasping soft end-effector (SGSE)—were assessed for their harvesting performance. Field trials conducted in a commercial spindle orchard demonstrated that the GSE achieved the highest performance, with a harvesting success rate of 80.80% among reachable fruits, a full-process success rate (from detection to collection) of 61.59%, an overall fruit damage rate of 10.89%, and an average single-fruit cycle time of 5.27 s. In contrast, the SSE and SGSE showed lower success rates (49.21% and 64.71%, respectively). This work provides a practical robotic harvesting solution. It validates the feasibility of a zoned, multi-manipulator harvesting strategy and delivers comparative data to guide the development of more efficient and robust harvesting robots.