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This paper aims to identify the effects of two types of teaching approaches, namely, project-based learning and place-based learning, on the development of pro-environmental behaviors among university students.

This study used a pre-test–post-test experimental design to evaluate the efficacy of project- and placed-based learning in promoting pro-environmental behaviors among university students. Participants were randomly allocated to either the project-based learning cohort (consisting of 50 participants) or the place-based learning cohort (also consisting of 50 participants). The pre-test assessments evaluated the initial pro-environmental behaviors of the participants. Then, interventions were conducted with the help of instructors. The project-based learning intervention necessitated participants to collectively take part in real-life problem-solving endeavors about environmental matters. On the other hand, the place-based learning intervention prompted participants to record and contemplate their interactions with the surrounding environment. Both interventions sought to augment participants’ understanding, beliefs and actions related to the environment. After the interventions, post-test assessments were carried out to assess any alterations in participants’ pro-environmental behaviors. Mean analysis and paired sample t-test were performed to examine the differences among the pre-test and post-test for both project- and place-based learning.

The findings of this study indicate that the participants have exhibited improved pro-environmental behaviors, including environmental activism, non-activist behaviors and private sphere green behaviors.

This research is original in its analysis of project- and place-based learning approaches for fostering pro-environmental behaviors. By using a pre-test–post-test experimental design, this study provides empirical evidence on the effectiveness of these active learning strategies in shaping environmental attitudes and actions.

Based on affective events theory, this research attempted to investigate how negative gossip about organizational change drives employees to experience negative emotions and direct their aggression toward customers.

We conducted a scenario-based experiment (Study 1) and a multiwave field survey (Study 2) to test our hypotheses.

The results show that (1) negative emotions mediate the relationship between change-related negative gossip and displaced aggression toward customers; (2) perceived organizational constraints strengthen the relationship between change-related negative gossip and negative emotions; (3) future work self-salience weakens the relationship between change-related negative gossip and negative emotions; and (4) change-related negative gossip has a strengthened (weakened) indirect effect on displaced aggression via negative emotions when employees have high perceived organizational constraints (future work self-salience).

The study expands research on organizational change and displaced aggression and provides practical implications for managing organizational change.

The success of online health communities (OHCs) depends on maintaining long-term relationships with physicians and preventing churn. Even so, the reasons for physician churn are poorly understood. In this study, an empirical model was proposed from a social influence perspective to explore the effects of online social influence and offline social influence on physician churn, as well as the moderating effect of their online returns.

The empirical data of 4,145 physicians from a Chinese OHC, and probit regression models were employed to verify the proposed theoretical model.

The results suggest that physicians' churn intention is influenced by online and offline social influences, and the offline social influence is more powerful. Physicians' reputational and economic returns could weaken the effect of online social influence on churn intention. However, physicians' economic returns could strengthen the effect of offline social influence on churn intention.

This research study is the first attempt to explore physician churn and divides the social influence into online and offline social influences according to the source of social relationship. The findings contribute to the literature on e-Health, user churn and social influence and provide management implications for OHC managers.

Despite widespread use of virtual streamers, academic research on this subject remains limited. This study aims to explore the mechanisms by which consumer perceptions of virtual streamers influence consumer purchase intentions.

We used partial least squares structural equation modeling to analyze validated online survey data from 414 consumers watching virtual streamers.

Consumer perceptions of virtual streamers (perceived competence, perceived interaction quality and perceived warmth) promote the establishment of transactional psychological contract and relational psychological contract between consumers and virtual streamers, which further affects consumers’ purchase intention.

This study enriches the research on virtual streamers, facilitates their adoption and introduces the psychological contract into a new research context by revealing the formation of the psychological contract from the perspective of virtual streamers. Moreover, this study provides a new understanding of the relationship between disembodied artificial intelligence and consumers.

This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context.

This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography.

This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency.

In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

Iron deficiency anemia (IDA) is the most common form of anemia in the world, affecting children, women and the elderly, while also being a common comorbidity in several medical conditions. Several studies have suggested a possible association between IDA and neurological dysfunction. Epilepsy, one of the common neurological disorders, has an unknown association with IDA. This pa per aims to investigate whether there is a causal relationship between IDA and epilepsy using a two-sample Mendelian randomization (MR) design.

This paper obtained summary data on IDA and epilepsy from the FinnGen consortium. Genetic variants significantly associated with IDA were used as instrumental variables (IVs). Epilepsy, focal epilepsy and generalized epilepsy were the outcomes. This paper used inverse variance weighted (IVW) as the primary estimate, and other MR methods were used as supplementary measures. Sensitivity analysis was also performed to assess heterogeneity and pleiotropy.

IVW estimates genetically predicted a causal relationship between IDA and the risk of epilepsy [odds ratio (OR), 1.15; 95% confidence interval (95% CI), 1.05–1.26; p = 0.002] and focal epilepsy (OR, 1.978, 95% CI, 1.58–2.48, p ≤ 0.0001), while no significant causal relationship was found with generalized epilepsy (OR, 1.1, 95% CI, 0.94–1.3, p = 0.24). There was no evidence of horizontal pleiotropy and heterogeneity in the sensitivity analysis.

This two-sample MR study found that IDA has a negative effect on the development of epilepsy. Clinical control of IDA may be helpful in the prevention of epilepsy. There is a need for further studies to explain the underlying mechanisms of this association.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

This paper explores whether fintech paves the way for the transition to carbon neutrality in the context of China’s climate policy uncertainty (CCPU) and the influence of the ocean carbon sink market.

We apply a novel wavelet analysis technique to investigate the time-frequency dependence between the CCPU index, the CSI (China Securities Index) Fintech Theme Index (CFTI) and the Carbon Neutral Concept Index (CNCI).

The empirical results show that CCPU and CFTI have a detrimental effect on CNCI in high-frequency bands. Furthermore, in low-frequency domains, the development of CFTI can effectively promote the realization of carbon neutrality.

Our findings show that information from the CCPU and CFTI can be utilized to forecast the movement of CNCI. Therefore, the government should strike a balance between fintech development and environmental regulation and, hence, promote the use of renewable energy to reduce carbon emissions, facilitating the orderly and regular development of the ocean carbon sink market.

The development of high-quality fintech and positive climate policy reforms are crucial for achieving carbon neutrality targets and promoting the growth of the marine carbon sink market.

Despite the acknowledgment of the significant role leaders play in knowledge management, there has been limited focus on the dynamic relationship between one particular leader’s behavior and knowledge sharing of employees. This study aims to investigate the impact of leader generosity on employee knowledge sharing.

Drawing upon the generalized reciprocity theory, we develop a process model that examines the impact of leader generosity on knowledge sharing, and empirically tests the resulting mechanism. Based on event-contingent ESM that collected 1147 episodic data points, the data are analyzed by Mplus 8.30 and R software.

The findings indicate that employee gratitude and prosocial motivation respectively play a mediating role in the relationship between leader generosity and knowledge sharing. Additionally, leader generosity has an indirect impact on knowledge sharing via employee gratitude and prosocial motivation. Furthermore, relational self positively moderates the relationship between leader generosity and employee gratitude, while also moderates the chain mediation effect between leader generosity and knowledge sharing.

By exploring the important influence of leaders in implementing knowledge management practices, the authors demonstrate that a specific leader behavior (i.e. leader generosity) shapes the positive emotion and reciprocal motivation of employees and its subsequent impact on knowledge sharing. This paper complements the focus on knowledge sharing in the daily behavior literature of leaders, suggesting that leader generosity matters for employee knowledge sharing.