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The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

This paper investigates the relationship between the sustainable finance disclosure regulation (SFDR) and the performance of unlisted real estate funds.

While existing literature has primarily focused on the impact of voluntary sustainability disclosure, such as certifications or reporting standards, this study addresses a significant research gap by constructing and analyzing the financial J-Curve of 40 funds under the SFDR. The authors employ a panel regression analysis to examine the effects of different SFDR categories on fund performance.

The findings reveal that funds categorized under Article 8 of the SFDR do not exhibit significantly poorer performance compared to funds categorized under Article 6 during the initial phase after launch. On average, Article 8 funds even demonstrate positive returns earlier than their peers. However, the panel regression analysis suggests that Article 8 funds slightly underperform when compared to Article 6 funds over time.

While investors may not anticipate lower initial returns when opting for higher SFDR categories, they should nevertheless be aware of the limitations inherent in the existing SFDR labeling system within the unlisted real estate sector.

To the best of our knowledge, this study represents the first quantitative examination of unlisted real estate fund performance under the SFDR. By providing unique insights into the J-Curves of funds, our research contributes to the existing body of knowledge on the impact of sustainability regulations in the financial sector.

The purpose of this paper is to contribute to science communication literature by further highlighting the underexplored role of organizational and corporate perspectives in science communication.

The paper takes the form of a conceptual article that uses two illustrative vignettes to highlight the power of corporate science communication.

The key argument is that corporate science communication is a compound ideology that results from merging the hegemonic corporate voice with the ultimate/god-term science (see the work of Kenneth Burke) to form a mega-ideological construct and discourse. Such communication can be so powerful that vulnerable publics and powerful advocates speaking on their behalf have little to no recourse to effectively challenge such discourse. While critiques of corporate science communication in practice are not new, what the authors offer is a possible explanation as to why such discourse is so powerful and hard to combat.

The value of this paper is in the degree to which it both sets an important applied research agenda for the field and fills a critical void in the science communication literature. This conceptual article, in the form of a critical analysis, fills the void by advocating for the inclusion of organizational perspectives in science communication research because of the great potential that organizations have, via science communication, to shape societal behavior and outcomes both positively and negatively. It also coins the terms “compound ideology” and “mega-ideology” to denote that while all ideologies are powerful, ideologies can operate in concert (compound) to change their meaning and effectiveness. By exposing the hegemonic power of corporate science communication, future researchers and practitioners can use these findings as a foundation to combat misinformation and disinformation campaigns wielded by big corporate science entities and the public relations firms often hired to carry out these campaigns.

This study aims to identify the readiness of institutional repositories in Indonesia to implement digital humanities (DH) data curation. Data curation is a method of managing research data that maintains the data’s accuracy and makes it available for reuse. It requires controlled data management.

The study uses a qualitative approach. Data collection was carried out through a focus group discussion in September–October 2022, interviews and document analysis. The informants came from four institutions in Indonesia.

The findings reveal that the national research repository has implemented data curation, albeit not optimally. Within the case study, one of the university repositories diligently curates its humanities data and has established networks extending to various ASEAN countries. Both the national archive repository and the other university repository have implemented rudimentary data curation practices but have not prioritized them. In conclusion, the readiness of the national research repository and the university repository stand at the high-capacity stage, while the national archive repository and the other university repository are at the established and early stages of data curation, respectively.

This study examined only four repositories due to time constraints. Nonetheless, the four institutions were able to provide a comprehensive picture of their readiness for DH data curation management.

This study provides insight into strategies for developing DH data curation activities in institutional repositories. It also highlights the need for professional development for curators so they can devise and implement stronger ownership policies and data privacy to support a data-driven research agenda.

This study describes the preparations that must be considered by institutional repositories in the development of DH data curation activities.

This study tries to investigate and examine the extent of power dynamics of diverse actors in supply chains (SCs) in affecting the (un)stable connections within the logistics service providers (LSPs) triad in manufacturing firms (MFs) in Australia as a developed country.

This study adopted a qualitative research approach by conducting 28 face-to-face semi-structured interviews, which was performed over a five-month period, between May and October 2022 with the targeted respondents via several Case MFs studies focusing on the triadic SC relationships through the power dynamics of LSPs (e.g. Case 1/MF-A, Case 2/MF-B, Case 3/MF-C, Case 4/MF-D and Case 5/MF-E) in order to gather primary data from the perspectives of logistics service clients, providers and suppliers that are dealing with MFs in Australia country. Interviewees were selected based on their knowledge, professional, expertise and tasks that are related to the study context if they are from MFs and/or LSPs. The researchers performed NVivo 12 Software as a tool for analyzing the collected primary data from the targeted interviewees, thus using a purposive sampling approach. This study constitutes a cornerstone for a conceptual exploration of the field of B2B and as it expands existing fields of broadcasting and investigation that focus on B2C within the manufacturing firms' context.

The findings clearly show the importance of stable purchase volume and resource ability for a focal company in the logistics triad. This leads it to have a strong long-term relationship through monitoring its partners and curbing evolutions in different dyads.

Identifying influential factors offers complementary insights to practitioners for depicting the SC relationship dynamics using the dominant power used by logistics service providers. Such findings encourage investigating an issues in emerging countries.

The study provides an extending novel piece of work on how to establish the relational embeddedness of two dyads through the power dynamics of the dominant player represented by LSPs that can significantly affect the triadic SC relationships in manufacturing firms (MFs). Such a perspective seems not to be similar to the classical standpoint in the SCM literature, in which the LSPs as a dominant and dynamic power derived from bridging the customer-supplier sustainable dyads relationship in the MFs context.

To do this, the authors carried out a systematic literature review to answer three questions: (RQ1) Which external pressures affect an FC and its suppliers in an MSC? (RQ2) What influences power relationships between an FC and its suppliers for MSC compliance? and (RQ3) Which governance mechanisms support an FC to achieve compliance for managing its MSC?

This research aims to identify how external pressures affect chain agents to achieve compliance and implement governance mechanisms and analyzes the influence of the power relationship between FC and their suppliers.

The results identify how external pressures from different stakeholders act on FC and FT and ST suppliers. A combination of contractual governance mechanisms (auditing, certification, assessment, code of conduct and monitoring) with relational ones (third-party, cooperation) is identified, facilitating compliance between agents. Furthermore, different power relationships (power position, level of resources and institutional distance) that influence the implementation of governance mechanisms are explored.

This article comprised only a systematic literature review and content analysis. Carrying out empirical research, covering the theme of this article, is the next step, which is being completed and will be discussed in due course in another publication.

The results can help professionals of the FC to understand their role in multi-tier supply chain (MSC), the external pressures exerted and the governance mechanisms that can be implemented to achieve compliance.

This article develops three relevant issues constantly addressed in MSC, which have not yet been combined to understand the management of multi-tier suppliers.

This research investigates how the value creation interests and activities of different stakeholder groups within one open source software (OSS) project influence the project's development over time.

The authors conducted a case study of OpenSimulator using textual and thematic analyses of the initial four years of OpenSimulator developer mailing list to identify each stakeholder group and guide our analysis of their interests and value creation activities over time.

The analysis revealed that while each stakeholder group was active within the OSS project's development, the different groups possessed complementary interests that enabled the project to evolve. In the formative period, entrepreneurs were interested in the software's strategic direction in the market, academics and SMEs in software functionality and large firms and hobbyists in software testing. Each group retained its primary interest in the maturing period with academics and SMEs separating into server- and client-side usability. The analysis shed light on how the different stakeholder groups overcame tensions amongst themselves and took specific actions to sustain the project.

The authors extend stakeholder theory by reconceptualizing the focal organization and its stakeholders for OSS projects. To date, OSS research has primarily focused on examining one project relative to its marketplace. Using stakeholder theory, we identified stakeholder groups within a single OSS project to demonstrate their distinct interests and how these interests influence their value creation activities over time. Collectively, these interests enable the project's long-term development.

The aim of the study is threefold: understanding the interconnections amongst visual and verbal multimodal communication strategies used in food discourse; identifying the themes of celebrity chef's food discourse with respect to pro-environmental behaviour; and providing a methodological framework to visually analyse food-themed videos.

This study uses mise-en-scène and critical discourse and multimodal analyses to gain insights on food discourse from 20 videos shared by a Michelin starred chef on social media platforms.

Results show that a pro-environmental cooking philosophy challenges the normative discourse on food and educates general audiences and foodies alike. Mise-en-scène and discourse analyses of Instagram visual content reveal that leftovers are central to the ethical message and are intertwined – through the aesthetic of the videos-with concepts of inclusivity, diversity and nourishment.

Chefs, and restaurants, are encouraged to recognise their responsibility as role models, thus able to influence the societal production of food discourse.

The findings provide new insights into the role of a celebrity chef in promoting sustainable food preparation and consumption.

This study aims to provide the best estimate of a stock's next day's closing price for a given day with the help of the hidden Markov model–Gaussian mixture model (HMM-GMM). The results were compared with Hassan and Nath’s (2005) study using HMM and artificial neural network (ANN).

The study adopted an initialization approach wherein the hidden states of the HMM are modelled as GMM using two different approaches. Training of the HMM-GMM model is carried out using two methods. The prediction was performed by taking the closest closing price (having a log-likelihood within the tolerance range) to that of the present one as the closing price for the next day. Mean absolute percentage error (MAPE) has been used to compare the proposed GMM-HMM model against the models of the research study (Hassan and Nath, 2005).

Comparing this study with Hassan and Nath (2005) reveals that the proposed model outperformed in 66 out of the 72 different test cases. The results affirm that the model can be used for more accurate time series prediction. Further, compared with the results of the ANN model from Hassan's study, the proposed HMM model outperformed 24 of the 36 test cases.

The study introduced a novel initialization and two training/prediction approaches for the HMM-GMM model. It is to be noted that the study has introduced a GMM-HMM-based closing price estimator for stock price prediction. The proposed method of forecasting the stock prices using GMM-HMM is explainable and has a solid statistical foundation.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.