Transformation Model Research Articles

Investigation of phase transformation model, densification mechanism, and mechanical properties of TiH2 powder metallurgy materials

TiH2 has gradually gained attention in powder metallurgy because of its unique properties. This study systematically investigated the porosity, density, mechanical tensile properties, and fracture toughness of sintered samples of TiH2 as a powder metallurgy starting material. The results showed that, compared with hydrogenation-dehydrogenation titanium (HDH Ti), the density, elongation, and fracture toughness of the sintered TiH2 samples significantly improved. In addition, the contributions of grain size, porosity, and oxygen content to the yield strength of the samples are discussed, and the results revealed that a lower oxygen content and porosity are the main reasons for the differences in the mechanical properties between HDH Ti and TiH2. Finally, the experimental results are combined with first-principles calculations and simulations to construct a TiH2 phase transition model. The contribution of H atoms to the TiH2 phase transition energy barrier and activation sintering densification was investigated and elucidated, revealing the TiH2 sintering densification mechanism from the perspective of phase transition. The derived results contribute to a deeper understanding of the intrinsic mechanism of TiH2 activation sintering densification, which is highly important for the development of advanced titanium materials.

Corporate Social Responsibility and Society 5.0: Assessing Consumer Awareness, Loyalty, and Trust toward Socially Responsible Organizations

Corporate social responsibility (CSR) and Society 5.0 (s5.0) are transformative models that both seek a human-centric approach by integrating certain capabilities for the betterment of stakeholders. This study investigates the nexus among CSR, brand awareness, customer trust (CrT), the s5.0 model, and customer loyalty (CrL) from the perspective of a developing nation, China. Structural equation modeling using partial least squares was applied to a sample size of eight hundred and ninety-three (N = 893). First, the study confirmed a positive relationship between CSR, brand awareness, and customer loyalty. Second, the study revealed a positive linkage through the mediation of customer trust. Finally, a moderating effect of the s5.0 model was found at the nexus of CSR, brand awareness, and brand loyalty. This study provides several insights into corporate social responsibility, brand awareness, and brand loyalty with additional facts on how customer trust and Society 5.0 may play important roles in engaging stakeholders such as customers in the present era. Therefore, managers should prioritize CSR along with Society 5.0 practices that can help to win over customers and elicit their loyalty and trust toward organizations.

Topology of Intermetallic Crystals: Classification, Uniformity, and Transitions.

We have analyzed the crystal structures of 7551 binary intermetallic compounds, which are deposited in the inorganic crystal structure database, using a combined geometrical-topological approach as implemented in our program package ToposPro. We represented each crystal structure with two models: (i) the topological model of periodic atomic net and (ii) the geometrical model of the Voronoi partition of crystal space. Within the former model, we have classified all intermetallics into 949 topological types, 20 of which cover 57% of the whole sample. Using our recently developed topological network model of reconstructive solid-state transformations, we have described possible mechanisms of displacive transitions between the most abundant topological types and revealed a key role of the body-centered lattice in these transitions. The Voronoi partition model enabled us to introduce the concept of uniformity of the overall structure and the environment of separate atoms; the uniformity is estimated by the second moment of inertia of atomic Voronoi polyhedra. We have shown that the structures with a low uniformity value either contain mistakes in the crystallographic data or have significantly directed interatomic bonds. The proposed uniformity criteria can serve for the estimation of the robustness of an intermetallic structure.

A Domain-Specific Lexicon for Improving Emergency Management in Gas Pipeline Networks through Knowledge Fusing

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks.

Exploring the Influence of Family Cultural Capital, School Environment, and Peer Relationships on the Scientific Literacy of Primary School Students: A Conceptual Analysis in Chengdu City, China

This paper explores the impact of blended education on the quality of college student learning in Southwest China during the post-epidemic era. The study highlights the potential of blended learning to enhance student engagement, adaptability, and learning outcomes through the integration of digital tools with traditional teaching methods. It identifies critical factors such as institutional support, technological infrastructure, and faculty training as essential for maximizing the benefits of blended education. Additionally, the paper examines the unique socio-cultural context of Southwest China, emphasizing the need to address regional disparities in digital access to ensure educational equity. The findings suggest that blended education can serve as a transformative model for higher education in the region, fostering essential 21st-century skills and reducing educational inequalities. The paper concludes with recommendations for policymakers and educators to support the sustainable implementation of blended education, contributing to a more resilient and inclusive higher education system.

Comprehensive model for predicting toxic equivalents (TEQ) reduction due to dechlorination of polychlorinated dibenzo-p-dioxin and dibenzofurans (PCDD/F congeners)

Remediation-focused predictive tools for polychlorinated dibenzo-p-dioxins (PCDD) and polychlorinated dibenzofurans (PCDF) rely on transformation models to evaluate the reduction in total contaminant load and toxic equivalency (TEQ). In this study, a comprehensive model predicting the profiles of PCDD/F congeners and the associated TEQs was developed. The model employs first-order kinetics to describe the transformation of 256 reactions for 75 PCDD congeners and 421 reactions for 135 PCDF congeners. It integrates the growth of anaerobic microbial guilds using Monod kinetics on hydrogen release compounds and stoichiometric growth for Dehalococcoides sp. The effects of temperature, salinity, pH, and availability of vitamin B12 (a cofactor) were also integrated. The PCDD/F congeners model was used to extract the first-order dechlorination rate constants from a number of pure culture and mixed microbial microcosm studies. Simulations for the transformation of PCDD/F congeners at concentrations representative of the Tittabawassee or Saginaw Rivers and watershed in MI, USA were carried out. For a starting TEQ of 5000 ng per kg dry sediment (ppt), the model predicted a decrease in the overall TEQ to below 2000 ppt after 2.6 years and below 250 ppt after ∼21 years. The developed model may be used for extracting rates from microcosm studies and to evaluate the effect of engineering interventions on TEQ reduction.

A model for the precipitate transformation of Mg–Si-rich clusters into Mg5Si6 β″ in Al–Mg–Si aluminum alloys

Abstract A model is developed that describes the kinetics of precipitate transformations in the course of natural and artificial aging of Al alloys containing Mg and Si additions. In our approach, the disordered Mg–Si-rich clusters, which form during natural aging in the highly supersaturated Al matrix, can directly transform into the monoclinic Mg5Si6 (β″), without prior dissolution of the clusters and independent nucleation of β″ in the Al matrix. The transformation rate is evaluated with classical nucleation theory (CNT), assuming that the clusters represent an infinitely large matrix phase in which the β″ precipitates can nucleate. The adapted CNT model is described, and the basic features of the precipitate transformation are discussed in a parameter study. The model can also account for the observation that, during natural aging, the parent clusters occur in a variety of Mg to Si ratios, all of which have a characteristic probability of either transforming into the β″ phase or dissolving.

Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities

In response to global climate challenges, urban low-carbon transformation has become a critical strategy for sustainable development. This study constructs a theoretical model for urban low-carbon transformation using the multi-level perspective framework. We focused on three batches of low-carbon pilot cities in China and employed fuzzy set qualitative comparative analysis to investigate the transformation pathways and impact mechanisms during the periods 2010–2012, 2012–2017, and 2017–2019. The results indicate that none of the six antecedent conditions is necessary for urban low-carbon transformation. Initially, the transformation is primarily driven by a pathway led by low-carbon industries. In the mid-stage, two pathways emerge: one dominated by the combination of low-carbon industries and research and development (R&D) human capital and another led by low-carbon consumption awareness and economic development levels. In the later stage, the influencing factors involve a combination across micro, meso, and macro levels, reflecting an increasingly diversified and intricate configuration. The regional industrial structure consistently plays a dominant role, while awareness of low-carbon consumption has grown over time. This study not only enhances our understanding of the underlying mechanisms but also provides practical policy recommendations for local governments to tailor their strategies for effective low-carbon transformation.

Multiscale mushy layer model for Arctic marginal ice zone dynamics

Perhaps the most dynamic component of the Arctic sea ice cover is the marginal ice zone (MIZ), the transitional region between dense pack ice to the north and open ocean to the south. It widens by a factor of four while seasonally migrating more than 1600 km poleward in the Bering–Chukchi Sea sector, impacting climate dynamics, ecological processes, and human accessibility to the Arctic. Here we showcase a transformative mathematical modeling approach to understanding changes in MIZ location and width, focusing on their seasonal cycles as observed by satellites. We view the MIZ as a liquid-solid phase transition region, or mushy zone, on the scale of the Arctic Ocean. Invoking the physics of phase changes, the MIZ is modeled as a dynamic, multiscale composite material layer; this model captures 96% of the annual cycle of MIZ location and 78% of the annual cycle of MIZ width. Temperature in the upper ocean is described by a nonlinear heat equation with effective parameters obtained using homogenization theory for a random medium of ice floes in a sea water host. Observations and simulations together indicate that MIZ location closely tracks the below-ice 273 K isotherm while the width of the MIZ follows vertical heat flux convergence, but with a three-week lag.

Ecological drivers of carrion beetle (Staphylinidae: Silphinae) diversity on small to large mammals.

Silphinae (Staphylinidae; carrion beetles) are important contributors to the efficient decomposition and recycling of carrion necromass. Their community composition is important for the provision of this ecosystem function and can be affected by abiotic and biotic factors. However, investigations are lacking on the effects of carrion characteristics on Silphinae diversity. Carrion body mass may affect Silphinae diversity following the more individuals hypothesis (MIH). The MIH predicts a higher number of species at larger carrion because higher numbers of individuals can be supported on the resource patch. Additionally, biotic factors like carrion species identity or decomposition stage, and the abiotic factors elevation, season and temperature could affect Silphinae diversity. To test the hypotheses, we collected Silphinae throughout the decomposition of 100 carcasses representing 10 mammal species ranging from 0.04 to 124 kg. Experimental carcasses were exposed in a mountain forest landscape in Germany during spring and summer of 2021. We analysed Silphinae diversity using recently developed transformation models that considered the difficult data distribution we obtained. We found no consistent effect of carrion body mass on Silphinae species richness and, therefore, rejected the MIH. Carrion decomposition stage, in contrast, strongly influenced Silphinae diversity. Abundance and species richness increased with the decomposition process. Silphinae abundance increased with temperature and decreased with elevation. Furthermore, Silphinae abundance was lower in summer compared to spring, likely due to increased co-occurrence and competition with dipteran larvae in summer. Neither carrion species identity nor any abiotic factor affected Silphinae species richness following a pattern consistent throughout the seasons. Our approach combining a broad study design with an improved method for data analysis, transformation models, revealed new insights into mechanisms driving carrion beetle diversity during carrion decomposition. Overall, our study illustrates the complexity and multifactorial nature of biotic and abiotic factors affecting diversity.

Harmony search for hyperparameters optimization of a low resource language transformer model trained with a novel parallel corpus Ocelotl Nahuatl – Spanish

Nahuatl, a low-resource language, does not have an online translator application. Instead, resources are limited to dictionaries, web pages, or digital books. Given this condition, it is vital to provide as much support to the language as possible. This research aims to enhance the BLEU score in machine translation by applying the harmony search heuristic method to state-of-the-art transformers models. This is conducted by finding the optimal hyperparameter settings for the models. Models are trained and tested using a fresh moderate-size parallel corpus of 1.5k phrases. By utilizing harmony search, the study shows an improvement in the BLEU score, enhancing it by 2.569%. In order to accomplish this, various factors related to the hyperparameters need to be considered. The application of harmony search with transformers can be extended to various parallel corpora or models, taking these considerations into account.

Research on the Influence of Firm Digital Intelligence Transformation and Management Innovation on Performance and Sustainable Development: Empirical Evidence from China

From the perspective of firm capability, this study explores the impact of digital intelligence transformation and management innovation on the performance and sustainable development of Chinese firms. On the basis of constructing an evaluation index system of firm digital intelligence transformation and management innovation, a comprehensive evaluation model of firm digital intelligence transformation and management innovation is constructed via the analytic network process (ANP) and fuzzy comprehensive evaluation method. Taking Chinese film and television firms as an example, this study selects 160 data samples from 85 firms and constructs a structural equation model to empirically analyze the impact of digital intelligence transformation and management innovation on the performance of firms. The study results show that, in the context of digitalization and intelligence, firm digital intelligence capability and dynamic capability have a significant positive effect on firm performance. Firm digital intelligence transformation and management innovation directly impact performance and can play a role through firm digital intelligence capability and dynamic capability as intermediaries, and the chain intermediary effect of digital intelligence capability and dynamic capability is significant. The research results show that firm digital intelligence and dynamic capabilities provide a functional path for firm digital intelligence transformation and management innovation to improve performance. Moreover, the innovative application of digital intelligence technology has an important impact on the sustainable development of firms. Digital transformation and management innovation can help firms enhance their ability to manage environmental uncertainties, improve firm performance and foster the achievement of sustainable development goals.

Circ_0025373 inhibits carbon black nanoparticles-induced malignant transformation of human bronchial epithelial cells by affecting DNA damage through binding to MSH2

Carbon black nanoparticles (CBNPs) have been demonstrated to induce DNA damage in epithelial cells. However, the potential of the damage to initiate carcinogenesis and the underlying mechanism remain poorly understood. Therefore, we constructed an in vitro model of malignant transformation of human bronchial epithelial cells (16HBE-T) by treating 40 μg/mL CBNPs for 120 passages. We observed tumor-like transformation and sustained DNA damage. Using transcriptome sequencing and RIP-seq, we identified the overexpression of the critical DNA mismatch repair genes MutS homolog 2 (MSH2) and its related circular RNA, circ_0025373, in the 16HBE-T cells. Mechanistically, circ_0025373 was found to inhibit DNA damage by binding to MSH2, thereby modifying its expression and influencing its nuclear and cytoplasmic distribution, which lead to inhibition of CBNP-induced malignant transformation of human bronchial epithelial cells. Our findings provide novel evidence on the carcinogenicity of CBNPs, and offer biological insights into the potential epigenetic regulation and potential therapeutic targets for lung carcinogenesis.

TRANSFORMATIVE BUSINESS GROWTH STAGE MODEL TO FACE UNCERTAINTY

The business landscape was characterized by rapid change, intense competition, and various substitute products. These factors shortened business life cycles and hindered business growth. Therefore, there was a need for a more appropriate model that could suit the current condition. The study aimed to develop a transformative Growth Stage Model (GSM) effectively adapted to modern challenges and uncertainty. This qualitative approach involved inductive theory building with seven case studies of SME owners in Yogjakarta and Central Java. Developed models, patterns, and interview themes were built, including resilience, business performance, and leadership for the early growth, growth, and decline stages. In addition, these patterns and themes were used to develop three stages of transformative GSM known as GSM 4.0.

Exploring RoBERTa model for cross-domain suggestion detection in online reviews

Detecting suggestions in online review requires contextual understanding of review text, which is an important real-world application of natural language processing. Given the disparate text domains found in product reviews, a common strategy involves fine-tuning bidirectional encoder representations from transformers (BERT) models using reviews from various domains. However, there hasn't been an empirical examination of how BERT models behave across different domains in tasks related to detecting suggestion sentences from online reviews. In this study, we explore BERT models for suggestion classification that have been fine-tuned using single-domain and cross-domain Amazon review datasets. Our results indicate that while single-domain models achieved slightly better performance within their respective domains compared to cross-domain models, the latter outperformed single-domain models when evaluated on cross-domain data. This was also observed for single-domain data not used for fine-tuning the single-domain model and on average across all tests. Although fine-tuning single-domain models can lead to minor accuracy improvements, employing multi-domain models that perform well across domains can help in cold start problems and reduce annotation costs.

Virality classification from Twitter data using pre-trained language model and multi-layer perceptron

Twitter is one of the well-known text-based social media that is often used to disseminate content. According to Katadata, Indonesia ranked fifth in the world in 2023. So many people or organizations want to make tweets go viral. Therefore, this research aims to develop a model that uses tweet data from the Indonesian language Twitter social media to categorize the level of virality. There are several tasks in classifying the level of virality, such as upsampling data, predicting sentiment and emotion, and text embedding. Upsampling data was carried out because the dataset used was an imbalanced dataset. Data upsampling, emotions, and text embedding is carried out using the bidirectional encoder representation from transformers (BERT) model. Meanwhile, sentiment prediction uses the Ro-bustly optimized BERT pretraining approach (RoBERTa). The results of text embedding, sentiment, emotion, will be combined with Twitter metadata then all features will be fed into the multi-layer perceptron (MLP) model to classifying the level of virality which is divided into 3 classes based on the number of retweets, namely low, medium and high. The proposed method produces an F1-score of 49% and an accuracy of 95% and performs better than the baseline model.

Hidden emotional trends on social media regarding the Thailand–China high-speed railway project: a deep learning approach with ChatGPT integration

Leveraging sentiment analysis on social media reveals hidden emotional trends by providing deep insights into the public’s collective opinions and feelings because social media can reflect most opinions based on true feelings. However, there is no time-series analysis of Thai opinions on social media regarding government projects in Thailand. Therefore, the focus of this research is to survey and dynamically analyze sentiment on social media platforms, particularly YouTube, regarding the Thailand–China High-Speed Rail Project (Thai–China HSR Project). This is a major project involving an investment of over 500 billion baht, a collaborative effort between the Thai and Chinese governments aimed at developing transportation infrastructure. It leads the public concerns of Thai people about the potential economic and social impacts. The deep learning (DL) technique was used for this analysis by the Bidirectional Encoder Representations from Transformers (BERT) model, together with WangchanBERTa, a Thai language model specifically designed to analyze opinions from Thai comments received from YouTube from July 2015 to January 2024. The analysis reveals that citizens’ sentiments are primarily neutral, with more negative than positive sentiments with the passing years. Results also indicate that the data volume has increased since 2019 and peaked in 2022. Negative comments peaked in January 2022, and based on our analysis, such negative comments followed news covering the opening of the China–Laos Railway on December 1, 2021. Video contents were related to the Thai–China HSR Project, which provides comparative reviews and analysis of government operations. Additionally, we explore the possibility of integrating AI technology based on the ChatGPT 4 model to support data interpretation. Although the AI model can interpret data quickly and efficiently serve as a convenient method for users, the results lack the in-depth analysis that should be done by experts in the field. The proposed framework could be useful to government agencies and can be used to quickly and efficiently survey and track public opinion. The results can be used for policy planning, strategic decision-making, and improving communications to better inform citizens about projects and operations, leading to the country’s sustainable economic and social development.

Virtual Assistant for Local Government Units Using BERT-Based Natural Language Processing

Local Government Units (LGUs) play a critical role in public service delivery, yet they often encounter challenges such as limited resources, bureaucratic inefficiencies, and high volumes of citizen inquiries. To address these issues, this paper proposes the development of a virtual assistant powered by Natural Language Processing (NLP), specifically utilizing the Bidirectional Encoder Representations from Transformers (BERT) model. This assistant is designed to automate the handling of common citizen inquiries, thereby enhancing the efficiency of service delivery. The virtual assistant was implemented as a web application using the Django framework, with an evaluation based on ISO 9126 standards, including aspects of functionality, reliability, usability, and efficiency. The findings demonstrate that the BERT-based NLP system significantly improves the responsiveness and accuracy of LGU services, leading to enhanced citizen satisfaction and more streamlined public service operations. The model achieved an Exact Match score of 48.0 and an F1 score of 68.01, indicating its effectiveness in accurately understanding and responding to citizen queries. Additionally, the system scored highly in usability, efficiency, maintainability, and portability, confirming its ease of use and operational effectiveness. These results give rather strong evidence that the use of the NLP-driven virtual assistant can be beneficial for increasing the citizen satisfaction and improving the effectiveness of the public service delivery.

Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT.

In acupuncture diagnosis and treatment, non-quantitative clinical descriptions have limited the development of standardized treatment methods. This study explores the effectiveness and the reasons for discrepancies in the entity recognition and classification of meridians in acupuncture indication using the Acupuncture Bidirectional Encoder Representations from Transformers (ACUBERT) model. During the research process, we selected 54 593 different entities from 82 acupuncture medical books as the pretraining corpus for medical literature, conducting classification research on Chinese medical literature using the BERT model. Additionally, we employed the support vector machine and Random Forest models as comparative benchmarks and optimized them through parameter tuning, ultimately leading to the development of the ACUBERT model. The results show that the ACUBERT model outperforms other baseline models in classification effectiveness, achieving the best performance at Epoch = 5. The model's "precision," "recall," and F1 scores reached above 0.8. Moreover, our study has a unique feature: it trains the meridian differentiation model based on the eight principles of differentiation and zang-fu differentiation as foundational labels. It establishes an acupuncture-indication knowledge base (ACU-IKD) and ACUBERT model with traditional Chinese medicine characteristics. In summary, the ACUBERT model significantly enhances the classification effectiveness of meridian attribution in the acupuncture indication database and also demonstrates the classification advantages of deep learning methods based on BERT in multi-category, large-scale training sets. Database URL: http://acuai.njucm.edu.cn:8081/#/user/login?tenantUrl=default.

Smart cities in Lima: A transformative model for a sustainable future

Smart cities in Lima: A transformative model for a sustainable future