Superstitions are unproven beliefs that shape decision-making. While many studies have examined their influence on corporate financial decisions, few have addressed their impact on corporate social responsibility (CSR). In this study, we focus on the superstition associated with the Chinese zodiac year – a belief linked to bad luck – and investigate its effect on firms’ charitable donations. Drawing on literature concerning stress appraisal, resource building, and corporate philanthropy, and using data from Chinese listed firms from 2008 to 2020, we find a positive association between a CEO’s zodiac year and corporate donations. Furthermore, this effect is weakened by CEO’s overconfidence and amplified by increased negative media coverage of CEOs during zodiac years. This study contributes to the literature on the outcomes of superstitions in management, the antecedents of corporate philanthropy, the boundary conditions of stress appraisal, and the agency motivations of corporate philanthropy. Managerial implications are also discussed.

The improvement of the accuracy and real-time performance of sector traffic flow prediction is of great significance to air traffic management decision-making. Sectors operate under complex spatial structures and time dimensions. Some neural network methods adopt sequence order to gradually transmit information, which makes it difficult to achieve complete parallel training. Not only does it take too long to train, resulting in low training efficiency, but it is also easy to lose the effective correlation information of long sequence data. To this end, a sector traffic flow prediction method based on attention-improved graph convolutional transformer (AGC-T) network is proposed to improve the current traffic prediction problem for sectors. First, the graph structure information and historical traffic data of the sector are input into the graph convolutional network improved based on the attention mechanism to fully capture the spatial relationship with sectors as nodes. Combined with the transformer’s multi-head self-attention mechanism, it can directly focus on the sequence data at any position without gradually transmitting information. Not only does it improve efficiency through parallel training, but the encoder-decoder structure can also mine the information features in the traffic data, focus on the traffic data features of key nodes and more accurately predict sector traffic. Finally, the operation traffic data of sectors in typical areas in central and southern China are taken as an example to analyse the model. The results show that compared with other prediction models, the AGC-T model $RSME$, $MAE$ and ${R^2}$ are 45.16%, 46.78% and 2.63% higher than the GCN model in the 15-min single-day traffic prediction task, and 41.74%, 35.27% and 1.20% higher than the GRU model. In the single-week traffic prediction task, $RSME$, $MAE$ and ${R^2}$ are 37.12%, 40.54% and 3.55% higher than the GCN model, and 35.15%, 35.17% and 0.65% higher than the GRU model, respectively, showing better prediction performance. This study will help air navigation service providers (ANSP) to make sector traffic predictions more accurately, thereby implementing more scientific and reasonable traffic management measures.

Recently, data-driven methods have shown great promise for discovering governing equations from simulation or experimental data. However, most existing approaches are limited to scalar equations, with few capable of identifying tensor relationships. In this work, we propose a general data-driven framework for identifying tensor equations, referred to as symbolic identification of tensor equations (SITE). The core idea of SITE – representing tensor equations using a host–plasmid structure – is inspired by the multidimensional gene expression programming approach. To improve the robustness of the evolutionary process, SITE adopts a genetic information retention strategy. Moreover, SITE introduces two key innovations beyond conventional evolutionary algorithms. First, it incorporates a dimensional homogeneity check to restrict the search space and eliminate physically invalid expressions. Second, it replaces traditional linear scaling with a tensor linear regression technique, greatly enhancing the efficiency of numerical coefficient optimization. We validate SITE using two benchmark scenarios, where it accurately recovers target equations from synthetic data, showing robustness to noise and flexible expressive capability. Furthermore, SITE is applied to identify constitutive relations directly from molecular simulation data, which are generated without reliance on macroscopic constitutive models. It adapts to both compressible and incompressible flow conditions and successfully identifies the corresponding macroscopic forms, highlighting its potential for data-driven discovery of tensor equation.

Research has demonstrated that emotion modulates specificity in recollection of personally experienced events and the words individuals use during recollection reflect their psychological states. Here, we investigated the linguistic features of autobiographical memory (AM) of different specificity for different emotional events to address how emotion would modulate the psychological mechanisms underlying AM of different specificity. We analyzed 122 participants’ narratives of AM categorized as specific and general under happy, sad, angry, fearful and neutral cues. The use of three groups (emotional process, cognitive process and thinking style) of words was, respectively, compared between specific and general AM in each emotion condition. In retrieval of sad, angry and fearful events, general relative to specific AM contained more affective process words, notably negative words. General AM featured more cognitive process words than specific AM, regardless of emotion type (except neutral). When recalling happy events, general AM featured more analytic thinking words than specific AM, while in recollection of fearful events, general AM featured fewer such words than specific AM. General relative to specific AM about happy experiences contained more narrative thinking words. These findings suggest that the psychological mechanisms underlying top-down and bottom-up retrieval differ between particular types of emotion engaged in AM.

Radio frequency (RF) coaxial connectors are crucial in high-speed digital systems for data transmission. Connectors applied in harsh environments will be degraded over time, affecting the signal integrity. At present, a suitable equalization technology is of substantial value for enhancing the quality of high-speed signal transmission. However, there is limited research focusing on specific equalization designs for degraded connectors. In this current work, based on the theoretical analysis, simulations validation and experimental testing, an equivalent circuit model of the degraded connector was developed. Furthermore, an equalization structure consisting of series resistance and capacitance was proposed. The transmission loss caused by the connector degradation was compensated effectively using this method. Meanwhile, eye diagram characteristics were improved significantly at the receiver where zero timing jitter and an almost full eye opening were achieved. Equalization enhances the eye opening factor by over 50% compared to the uncompensated system. This paper integrates electrical contact theory with high-speed equalization design. Tools and guidance were provided through the proposed modeling techniques for studying equalization effects of this degraded connector system in designs and applications.

Magnetite-enriched mining tailings are a cost-effective and abundant catalytic material with inherent magnetic recyclability. Yet their practical application in catalysis is often constrained by their limited surface area and sluggish reaction kinetics. To address these issues, we developed a facile one-step co-precipitation method to synthesize a magnetic nano-Fe3O4 (MNP) catalyst that exhibits enhanced surface reactivity for efficient activation of H2O2 towards tetracycline (TC) degradation. The system achieved complete (100%) removal of TC at an initial concentration of 20 mg L–1 within 90 min and demonstrated robust catalytic performance across weakly acidic to neutral pH conditions. Mechanistic investigations confirmed that ⋅OH is the primary reactive oxygen species involved, with ⋅O2⁻ and 1O2 providing supplementary contributions to the degradation. Remarkably, the intrinsic magnetic properties ensured efficient MNP catalyst recovery. This work provides a sustainable and scalable wastewater treatment strategy, leveraging mining tailings as a cost-effective resource to treat wastewater while also providing economic and environmental benefits.

A backward swept shape is one of the common features of the wings and fins in animals, which is argued to contribute to leading-edge vortex (LEV) attachment. Early research on delta wings proved that swept edges could enhance the axial flow inside the vortex. However, adopting this explanation to bio-inspired flapping wings and fins yields controversial conclusions, in that whether and how enhanced spanwise flow intensifies the vorticity convection and vortex stretching is still unclear. Here, the flapping wings and fins are simplified into revolving plates with their outboard 50 $\%$ span swept backward in either linear or nonlinear profiles. The local spanwise flow is found to be enhanced by these swept designs and further leads to stronger vorticity convection and vortex stretching, thus contributing to local LEV attachment and postponing bursting. These results further prove that a spanwise gradient of incident velocity is sufficient to trigger a regulation of LEV intensity, and a concomitant gradient of incident angle is not necessary. Moreover, an attached trailing-edge vortex is generated on a swept wing and induces an additional low-pressure region on the dorsal surface. The lift generation of swept wings is inferior to that of the rectangular wing because the extended stable LEV along the span and the additional suction force near the trailing edge are not comparable to the lift loss due to the reduced LEV intensity. Our findings evidence that a swept wing can enhance the spanwise flow and vorticity transport, as well as limit excessive LEV growth.

Grasp detection is a significant research direction in the field of robotics. Traditional analysis methods typically require prior knowledge of the object parameters, limiting grasp detection to structured environments and resulting in suboptimal performance. In recent years, the generative convolutional neural network (GCNN) has gained increasing attention, but they suffer from issues such as insufficient feature extraction capabilities and redundant noise. Therefore, we proposed an improved method for the GCNN, aimed at enabling fast and accurate grasp detection. First, a two-dimensional (2D) Gaussian kernel was introduced to re-encode grasp quality to address the issue of false positives in grasp rectangular metrics, emphasizing high-quality grasp poses near the central point. Additionally, to address the insufficient feature extraction capabilities of the shallow network, a receptive field module was added at the neck to enhance the network’s ability to extract distinctive features. Furthermore, the rich feature information in the decoding phase often contains redundant noise. To address this, we introduced a global-local feature fusion module to suppress noise and enhance features, enabling the model to focus more on target information. Finally, relevant evaluation experiments were conducted on public grasping datasets, including Cornell, Jacquard, and GraspNet-1 Billion, as well as in real-world robotic grasping scenarios. All results showed that the proposed method performs excellently in both prediction accuracy and inference speed and is practically feasible for robotic grasping.

In this paper, we study the existence of solutions to the following Hartree equation

\begin{align*}\begin{cases}-\Delta u+\lambda V(x) u+\mu u=\left(\int_{\mathbb{R}^N}\frac{|u|^p}{|x-y|^{N-\alpha}}\right)|u|^{p-2}u,\ \text{in}\ \mathbb{R}^N,\\\int_{\mathbb{R}^N}|u|^2=\omega,\end{cases}\end{align*}

Where $N\geq 3$, $\omega,\lambda \gt 0$, $p\in \left(\frac{N+\alpha}{N}, \frac{N+\alpha}{N-2}\right)\setminus\left\{\frac{N+\alpha+2}{N}\right\}$ and µ will appear as a Lagrange multiplier. We assume that $0\leq V\in L^{\infty}_{loc}(\mathbb{R}^N)$ has a bottom $int V^{-1}(0)$ composed of $\ell_0$ $(\ell_{0}\geq1)$ connected components $\{\Omega_i\}_{i=1}^{\ell_0}$, where $int V^{-1}(0)$ is the interior of the zero set $V^{-1}(0)=\{x\in\mathbb{R}^N| V(x)=0\}$ of V. It is worth pointing out that the penalization technique is no longer applicable to the local sublinear case $p\in \left(\frac{N+\alpha}{N},2\right)$. Therefore, we develop a new variational method in which the two deformation flows are established that reflect the properties of the potential. Moreover, we find a critical point without introducing a penalization term and give the existence result for $p\in \left(\frac{N+\alpha}{N}, \frac{N+\alpha}{N-2}\right)\setminus\left\{\frac{N+\alpha+2}{N}\right\}$. When ω is fixed and satisfies $\omega^{\frac{-(p-1)}{-Np+N+\alpha+2}}$ sufficiently small, we construct a $\ell$-bump $(1\leq\ell\leq \ell_{0})$ positive normalization solution, which concentrates at $\ell$ prescribed components $\{\Omega_i\}^{\ell}_{i=1}$ for large λ. We also consider the asymptotic profile of the solutions as $\lambda\rightarrow\infty$ and $\omega^{\frac{-(p-1)}{-Np+N+\alpha+2}}\rightarrow 0$.

The emergence, on the Loess Plateau of Central China, of settlements enclosed by circular ditches has engendered lively debate about the function of these (often extensive) ditch systems. Here, the authors report on a suite of new dates and sedimentological analyses from the late Yangshao (5300–4800 BP) triple-ditch system at the Shuanghuaishu site, Henan Province. Exploitation of natural topographic variations, and evidence for ditch maintenance and varied water flows, suggests a key function in hydrological management, while temporal overlap in the use of these three ditches reveals the large scale of this endeavour to adapt to the pressures of the natural environment.

The study presents a novel cable-driven serial robot based on flexible joints and tensegrity structures, which features a rapid response capability in complex dynamic environments. This makes it particularly suitable for human–robot interaction scenarios. Compared to traditional rigid serial robots, the design’s compliance demonstrates significant advantages in addressing complex demands. The study delves into kinematic and dynamic modeling methods and verifies their effectiveness through simulations. The kinematic model transforms the local coordinate system to the global one using general kinematic equations. First, the static and dynamic model of the robot is derived based on the torque balance equation, and then the dynamic model of the robot is constructed. By simplifying the robot model, the relationship between tension values from driving cables and the robot’s workspace is analyzed under the constraints of tensegrity structures and flexible joints. Additionally, trajectory simulations validate the kinematic and dynamic models. The kinetic energy variation curves based on the trajectories confirm the accuracy of the theoretical analysis. This method demonstrates broad applicability and can be applied to other serial robots with flexible structures, offering effective solutions for use in complex dynamic environments.