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The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood Model serves as the theoretical framework for understanding the cognitive processing involved in consumers' responses to these advertising appeals and product combinations.

This paper aims to investigate the impact of advertising appeals on consumers' intentions to purchase organic food. We explored the interaction between advertising appeals (egoistic vs altruistic) and product types (virtue vs vice) and purchase intention. The goal is to provide insights that can enhance the advertising effectiveness of organic food manufacturers and retailers.

The analysis reveals significant effects on consumers' purchase intentions based on the matching of advertising appeals with product types. Specifically, when egoistic appeals align with virtuous products, there is an improvement in consumers' purchase intentions. When altruistic appeals match vice products, a positive impact on purchase intention is observed. The results suggest that the matching of advertising appeals with product types enhances processing fluency, contributing to increased purchase intention.

This research contributes to the field by providing nuanced insights into the interplay between advertising appeals and product types within the context of organic food. The findings highlight the importance of considering the synergy between egoistic appeals and virtuous products, as well as altruistic appeals and vice products. This understanding can be strategically employed by organic food manufacturers and retailers to optimize their advertising strategies, thereby improving their overall effectiveness in influencing consumers' purchase intentions.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

Pune is a prominent information technology (IT) hub in India, where snacking has become a customary practice among IT professionals. This study aims to determine the pattern and factors associated with snacking among IT professionals from various multinational corporations (MNCs) in Pune, Maharashtra, India.

This cross-sectional study considered 404 IT professionals aged 21 to 50 years. A convenient sampling method was adopted to administer a validated questionnaire. Information on snacking patterns and factors associated with snacking were recorded. Descriptive and inferential statistics were used to analyze the data with p = 0.05. The participation was voluntary, and confidentiality was ensured.

The mean age of the participants was 31 ± 7.9 years. Almost half (51.5%) of the participants engaged in daily snacking. The sociodemographic factors such as younger age (0.000), marital status (p = 0.001), salary package (p = 0.006), living situation (p = 0.05), designation (p = 0.042) and work experience (p = 0.001) significantly related with the unhealthy snacking pattern scores. Daily snacking was significantly associated with hunger (p = 0.001), stress (p = 0.001), weight (p = 0.000), peer influence (p = 0.041) and taste (p = 0.001). Hunger, stress, taste, peer influence, boredom and weight were significantly (p = 0.05) associated with unhealthy snacking patterns.

The mean age of the participants was 31 ± 7.9 years. Almost half (51.5%) of the participants engaged in daily snacking. The sociodemographic factors such as younger age (0.000), marital status (p = 0.001), salary package (p = 0.006), living situation (p = 0.05), designation (p = 0.042) and work experience (p = 0.001) significantly related with the unhealthy snacking pattern scores. Daily snacking was significantly associated with hunger (p = 0.001), stress (p = 0.001), weight (p = 0.000), peer influence (p = 0.041) and taste (p = 0.001). Overall, hunger, stress, taste, peer influence, boredom and weight were significantly (p = 0.05) associated with unhealthy snacking patterns such as snacking in between, prioritizing taste over nutrition, exclusion of fruits and vegetables in snacks, lack of control over snacking and snacking habit.

This study uniquely identifies the snacking pattern of IT professionals from Pune, India, which primarily includes unhealthy snacking. Various socio-demographic and other factors such as hunger, taste, stress, boredom, convenience, weight and peer influence, were associated with unhealthy snacking. Understanding the snacking pattern and its determinants can help create nutrition interventions to promote healthy snacking and decrease the risk of noncommunicable diseases in IT professionals.