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This study delves into the nuanced relationship between financial constraints, ownership structures (state-owned and foreign) and innovation engagement within China’s A-share market, aiming to uncover how these dynamics vary across different industries and regional contexts.

By retrieving data from various datasets in China (2010–2022), this study analyzed the effectiveness of each variable, employing various dimensions to reflect innovation engagement among Chinese listed companies. Meanwhile, for the measurement of financial constraints, this study tested all four typical ones and opted for the KZ Index, as it is the most suitable for China’s A-share market. Then, by fixing the industry and year effects, the study examined the main and moderating effects. At last, in order to address endogeneity issues and capture the dynamic nature of innovation activities, this study follow the suggestion of Khatib (2024) and employed the two-step system Generalized Method of Moments (GMM) estimation.

The results demonstrate that while the government has introduced many policies to promote innovation, state-owned ownership does not consistently enhance innovation engagement as expected, especially when firms are in financial dilemma. Particularly, in Hi-tech industries, foreign ownership demonstrates greater interest and confidence in the innovation capabilities of China’s A-share market. Findings also reveal significant regional heterogeneity in the moderating role of ownership structures. While state-owned and foreign ownerships have a buffering effect against financial constraints in the eastern and western regions, but this effect is notably different in the middle part, even though it is China’s political heartland.

The findings offer a different insight for policymakers and corporate strategists, suggesting that targeted financial and regulatory policies that leverage specific ownership structures can foster innovation in different ways, particularly in financially constrained environments. However, how to stimulate innovation vitality in the middle part of China still requires further research.

Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.

To address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).

The proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.

The proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling Framework (TC-Framework) that enables the planner to adapt to different end-effector constraints.

This work presents a standard constraint methodology for representing end-effector constraints as a collection of constraint primitives. The constraint primitives are merged sequentially into the planner, and a unified constraint input interface and constraint module are added to the standard sampling-based planner framework. This approach enables the realization of a generic planner framework that avoids the need to build separate planners for different end-effector constraints.

Simulation tests have demonstrated that the planner based on TC-framework can adapt to various end-effector constraints. Physical experiments have also confirmed that the framework can be used in real robotic systems to perform autonomous operational tasks. The framework’s strong compatibility with constraints allows for generalization to other tasks without modifying the scheduler, significantly reducing the difficulty of robot deployment in task-diverse scenarios.

This paper proposes a unified constraint method based on constraint primitives to enhance the sampling-based planner. The planner can now adapt to different end effector constraints by opening up the input interface for constraints. A series of simulation tests were conducted to evaluate the TC-Framework-based planner, which demonstrated its ability to adapt to various end-effector constraints. Tests on a physical experimental system show that the framework allows the robot to perform various operational tasks without requiring modifications to the planner. This enhances the value of robots for applications in fields with diverse tasks.

The main aims of this study were to develop analytical scales for yachting tourism push-pull motivations and constraints, and analyze how these factors may influence the revisit intention of yachting tourists in China.

The analysis was conducted using the PLS-SEM, including the evaluation of measurement models and the structural models. SPSS18.0 and SmartPLS 3.3.5 software were used for statistical analysis.

We conducted a survey of 451 respondents who participate in yachting activities in Dalian, China and identified six push motivational factors (novelty and stimulation, sightseeing and leisure, sports and learning, social relationships, self-esteem and prestige and self-realization), three pull motivational factors (featured activities and services, destination environment, destination facilities) and two constraints (internal and external). Partial least squares structural equation modelling showed that all hypothesized interactions between identified factors were statistically significant and meaningful.

The push-pull-constraint model offers a new interpretation to the traditional push-pull model in theory, and the results contribute to local yacht industry sectors.

A scheduling method for determining the critical path in linear projects is presented, that takes into account maximum time and distance constraints in addition to the commonly used minimum time and distance constraints. The maximum constraints, though often present in the specifications of a project, are not considered during the planning procedure, since no method existed to enable scheduling with them. The proposed method builds on the concept of the maximum constraints and expands on the necessary background for their implementation into the schedule. The introduced critical path algorithm allows for grouping linear activities into four categories regarding their critical status and their ability to influence project duration. The method is applied to a low‐pressure pipeline construction project and the results are presented.

Product configuration is considered as one of the most successful applications of knowledge‐based approaches in the past decade. Knowledge‐based configurations can be classified into three different approaches, namely, rule‐based, model‐based and case‐based approaches. Past research has mainly focused on the development of reasoning techniques for mapping requirements to configurations. Despite the success of certain conventional approaches, the acquisition of configuration knowledge is usually done manually. This paper aims to explore fundamental issues in product configuration system, and propose a novel approach based on data mining techniques to automatically discover configuration knowledge in constraint‐based configurations.

Given a set of product data comprising product requirements specification and configuration information, the paper adopted an association rule mining algorithm to discover useful patterns between requirement specification and product components, as well as the correlation among product components. A configuration was developed which takes XML‐based requirement specification as input and bases on a constraint knowledge base to produce product configuration as output consisting of a list of selected components and the structure and topology of the product. Three modules are developed, namely product data modelling, configuration knowledge generation and product configuration generation module. The proposed approach is implemented in the configuration knowledge generation module. The configuration generation module realizes a resolution of constraint satisfaction problem to generate the output configuration.

The significance and effectiveness of the proposed approach is demonstrated by its incorporation in our configuration system prototype. A case study was conducted and experimental results show that the approach is promising in finding constraints with given sufficient data.

Novel knowledge generation approach is proposed to assist constraint generation for Constraint‐based product configuration system.

This paper's objective is to explain the concept of proper kinematic constraint to guide requirements‐driven design of mechanical assemblies and to connects proper constraint to the datum flow chain (DFC) and key characteristics (KCs).

The paper presents proper constraint as a way to support the goal of placing key parts in particular geometric relationships with respect to one another so that a DFC can deliver KCs unambiguously. Such a DFC is said to be competent. Additionally, a competent DFC is robust in the sense that the constraint relationships between parts retain their definition and effect under all allowed variations in parts.

Failure to provide proper constraint can lead to undesired consequences including locked‐in stresses and difficult or inconsistent assembly. Some designs need to be over‐constrained, and this requires very careful control and tight tolerances on the over‐constrained degrees of freedom in order to avoid or at least understand the consequences listed above.

Mathematical methods exist to test designs for proper constraint. The simplest, and occasionally unsuccessful, is the Kutzbach criterion. Screw theory is the most reliable method but its application requires extra knowledge and mathematical tools.

Most CAD software and tolerance analysis software do not test designs for their state of constraint. The engineer needs to take account of this independently and be aware of the limitations of software as a guide. Tolerance analysis software that does not take account of constraint may yield incorrect answers.

The paper reinvigorates a once‐well‐known principle and makes engineers aware of it. It also links this concept to the concepts of DFC and KCs and supports a mathematically‐based method for designing assemblies.

To incentivize innovation, support competitiveness, lower skill scarcities, and alleviate the housing affordability difficulty, proponents underscore the pertinence of embracing contemporary construction methodologies, with particular emphasis on volumetric modular construction (VMC) as a sustainable paradigm for production and consumption. However, construction industry stakeholders in Australia have encountered profound challenges in adopting VMC, as its adoption remains significantly low. Therefore, this study investigated the constraints that hinder VMC in the Australian construction industry.

The study used qualitative methodology using semi-structured interviews as a core approach to glean professional experts' perspectives and insights, along with Pareto and mean index score analyses.

The study identified 77 reported and validated VMC constraints by professionals, categorizing them into eight categories: cultural, economic, knowledge, market, regulatory, stakeholder, supply chain, and technological. The mean index score analysis reveals stakeholder (µ = 9.67) constraints are the most significant, followed by cultural (µ = 9.62) and regulatory (µ = 9.11) constraints. Pareto analysis revealed 25 of the 77 constraints as ‘vital few” among different categories. This study presented causal relationships and mitigation strategies for VMC constraints, followed by an argument on whether VMC adoption in Australia requires a nudge or mandate.

This study offers guidance for efficient resource allocation, aiding management and government policy formulation. It's also valuable for global audiences, especially countries transitioning to modular construction.

This is one of the first studies to identify VMC constraints and delineate them into different categories in Australia, identify their causal interrelationships, and deliver countermeasures to overcome them.

The design of effective policies that increase access to agricultural credit should consider understanding credit constraint farmers’ groups and their response to changes in the credit conditions. To contribute to this understanding, this study surveyed farmers from Chile and classified them into five credit constraint categories discussed in credit literature. In addition, these farmers indicated how they would react to a series of hypothetical conditions related to changing interest rates, loan maturity and grace periods. Their responses were employed to measure credit demand scores (i.e. relative elasticities). Regression tests evaluated how different types of farmers reacted to changing credit conditions.

Farmers from Chile were surveyed using a mix of random and convenience sampling. Surveyed farmers were classified into five credit constraint categories proposed by previous research. Farmers rated their demand for credit on a five-point Likert-type scale for hypothetical changes in interest rates, loan maturities and grace periods. Their responses were employed to measure credit demand scores or relative credit elasticities. The study evaluated credit elasticity as a function of farmers’ credit constraint and some control variables using several regressions, including OLS, ordered probit and hierarchical regression.

The study identified 44% unconstrained nonborrowing farmers, 23% unconstrained borrowers, 14% quantity-constrained, 16% risk-constrained and 3% transaction cost-constrained farmers. Unconstrained borrowers and quantity-constrained farmers responded most to changing interest rates and loan maturity conditions. In addition, unconstrained nonborrowers and risk-constrained farmers were statistically less sensitive to changes in credit conditions than unconstrained borrowers. This finding is significant because, as discussed, unconstrained nonborrowers represent 44% of our sample. Furthermore, risk-constrained farmers were the least sensitive to changes in interest rates and loan maturity across all other credit categories.

This study gives insights that can guide agribusiness policies to enhance access to credit in developing countries such as Chile. Agricultural credit capital institutions can better target their clientele by identifying farmers’ possible reactions before implementing policy changes to increase access to credit. This study’s credit constraint categorization and the results discussed can guide that identification. For instance, policies directed toward unconstrained borrowing farmers may find positive responses. However, implementing policies targeting the other three groups (unconstrained nonborrowing, risk-constrained and transaction cost-constrained farmers) is more challenging because these farmers are less responsive to changing credit conditions.

This article correlates farmers’ propensity to borrow and credit constraints across five categories of farmers. Prior research using this categorization framework has not identified farmers into the five groups. Furthermore, in addition to interest rate and loan maturity credit demand relative elasticity, this study adds the grace period elasticity, which has not been included in previous studies on agricultural credit.

Collaborative robots (cobots) are widely used in various manipulation tasks within complex industrial environments. However, the manipulation capabilities of cobot manipulation planning are reduced by task, environment and joint physical constraints, especially in terms of force performance. Existing motion planning methods need to be more effective in addressing these issues. To overcome these challenges, the authors propose a novel method named force manipulability-oriented manipulation planning (FMMP) for cobots.

This method integrates force manipulability into a bidirectional sampling algorithm, thus planning a series of paths with high force manipulability while satisfying constraints. In this paper, the authors use the geometric properties of the force manipulability ellipsoid (FME) to determine appropriate manipulation configurations. First, the authors match the principal axes of FME with the task constraints at the robot’s end effector to determine manipulation poses, ensuring enhanced force generation in the desired direction. Next, the authors use the volume of FME as the cost function for the sampling algorithm, increasing force manipulability and avoiding kinematic singularities.

Through experimental comparisons with existing algorithms, the authors validate the effectiveness and superiority of the proposed method. The results demonstrate that the FMMP significantly improves the force performance of cobots under task, environmental and joint physical constraints.

To improve the force performance of manipulation planning, the FMMP introduces the FME into sampling-based path planning and comprehensively considers task, environment and joint physical constraints. The proposed method performs satisfactorily in experiments, including assembly and in situ measurement.