Mendeley Data Showcase

Gambang Semarang

Semi-structured interview with Dengue Expert in Mexico

A Python package and Grasshopper example files for running a pre-trained Mixture of Experts (MoE) surrogate model to predict building heating and cooling EUI in real time. The generalized and modular MoE model is developed to support complex, multi-zone office building designs under various climatic conditions. 1. Python Package: The dataset includes a snapshot of the initial release of the Python package and MoE model (v0.1.0), which is still under development. The package contains: - A pre-trained surrogate model file - A scaler object for input feature normalization - Source code for parametric model translation, features extraction, prediction, and integration with Grasshopper. Refer to the README.md file for full installation and usage instructions. 2. Grasshopper example files: These example files demonstrate a unified modeling and prediction platform. The parametric models follow the Honeybee modeling workflow and can be evaluated using either EnergyPlus or the surrogate model. Ladybug Tools and Hops plugins for Grasshopper are required. The final model is serialized into a '.hbpkl' file and passed to the 'hops' component for processing. Note: The parametric model is designed to predict a single building (comprising multiple zones) at a time. To enable batch prediction for multiple buildings (e.g., for evolutionary optimization), the Grasshopper definition and the server.py script can be modified accordingly. Refer to the README.md file for usage instructions.

This dataset accompanies the research study titled "The Future of U.S. Retail Banking – A Comparative Analysis of AI versus Human Interaction Driving Service Excellence and Customer Satisfaction." It contains survey responses from 50 undergraduate and graduate students at the Raj Soin College of Business, Wright State University. The data captures participant insights on the role of AI and human interaction in customer service within the U.S. retail banking sector. Key metrics include customer preferences, satisfaction levels, perceived efficiency, trust factors, and expectations from AI-driven and human-assisted banking experiences. The dataset aims to support analysis on the effectiveness of generative AI (GenAI) in transactional efficiency versus the necessity of human interaction in fostering trust, empathy, and personalized financial guidance. Findings from this dataset will contribute to recommendations for an optimized, hybrid approach in retail banking customer service. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5133933

This study investigates the two-step wedge cut blasting technique, crucial for deep hard rock excavation under high stress. Our research assumes that this method significantly influences rock damage and fragmentation under varying stress conditions. Data from numerical simulations and experiments reveal the following: First-stage cutting initiates cracks and local damage in the cut area, while second-stage cutting propagates these cracks, forming a wider damage zone around the cavity. Under hydrostatic stress exceeding 20 MPa, radial crack growth is restricted, with damage concentrated near blast holes, exhibiting uniform compressive cracks. In non-hydrostatic fields, cracks grow in diverse directions with intersecting patterns. High in-situ stress enhances the efficiency of explosive energy usage, resulting in more complete rock breakage and a larger cavity volume. The non-hydrostatic field shows higher energy-release efficiency than the hydrostatic one, creating a wider failure-extension area. These findings indicate that stress conditions considerably affect rock fragmentation and cavity formation in two-step wedge cut blasting. The data was obtained through calibrated numerical models in LS-DYNA and experimental tests, ensuring reliability. This research offers valuable insights for optimizing blasting design in deep hard rock excavation under high stress, providing both theoretical understanding and practical guidance for improving efficiency in such challenging environments.

This study explores the determinants of impulse buying within Live Stream Shopping applications, examining the impact of Physical Characteristic Similarity, Value Similarity, Product Attributes, Parasocial Relationships, Product Fit, Trust, and Perceived Product Quality. This research adopts a quantitative approach with primary data sources. Data collection was conducted using a questionnaire. The sample consists of 421 consumers who have experienced live streaming shopping. Purposive sampling was employed to select the participants. Data analysis was performed using Structural Equation Modeling (SEM) with the assistance of Smart PLS statistical software. The results reveal that Physical Characteristic Similarity, Value Similarity, Product Attributes, Parasocial Relationships, and Trust significantly enhance Product Fit and Perceived Product Quality, which in turn drive Impulse Buying. This research underscores the importance of optimizing live stream shopping experiences to improve consumer satisfaction and highlights the growing role of live stream shopping in Indonesia's vibrant e-commerce landscape

The global tourism industry faces increasing environmental, economic, and social challenges, making sustainability a critical priority. As young people represent a significant segment of global travelers, understanding their awareness and attitudes toward sustainable tourism is essential for fostering responsible tourism behaviors (Ketter, 2020; UNWTO, 2019). However, despite the growing emphasis on sustainability, there remains a gap in research exploring how youth perceptions translate into concrete actions. This study addresses this gap by examining the key factors that shape young people's awareness of sustainable tourism and their implications for education and policy development. Using a Structural Equation Modeling (PLS-SEM) approach, this study analyzes survey data to assess constructs such as environmental awareness, economic and social impacts, and behavioral intentions toward sustainability. PLS-SEM was chosen for its ability to model complex interdependencies among latent variables, offering robust insights into the drivers of sustainable tourism awareness (Sarstedt et al., 2022). The findings reveal significant relationships between young people's knowledge of sustainable practices and their attitudes toward responsible tourism, highlighting the role of education in shaping pro-environmental behaviors (Choi & Sirakaya, 2005; UNESCO, 2016) This research contributes to the broader discourse on sustainability by providing empirical evidence on the importance of multidimensional awareness in tourism. By identifying the key drivers of sustainable tourism awareness, this study offers actionable recommendations for integrating sustainability concepts into tourism curricula, guiding policymakers, educators, and industry stakeholders in promoting long-term responsible tourism practices (Goodwin, 2017).

The organizational structure in SAP plays a crucial role in managing business processes efficiently. In SAP HCM (Human Capital Management), this structure defines hierarchies, organizational units, and relationships between employees. This article explores how to read and interpret the organizational structure using ABAP.

The study assessed the effects of a social media break intervention grounded in Self-Determination Theory on depression, anxiety, stress, well-being, body image, and sleep. A total of six 2 (pre, post) X 2 (iWeek, control) mixed ANOVAs assessed changes in pre- and post-intervention scores on each outcome variable for iWeek compared to the control condition. Results indicated improvements to depression, anxiety, stress, body image, and sleep among the intervention group. No significant effects were observed for well-being.

Research Data