Conversion Model Research Articles

Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end-to-end fingerprint conversion based on FT-NIR spectroscopy.

Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near-infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)-NIR to HPLC conversion of fingerprints. A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT-NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT-NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT-NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF.

Lagrangian solver for coupling hydrodynamics with biokinetic conversion modelling in anaerobic digesters

Conventional anaerobic digestion models used in wastewater treatment plants suffer from inaccuracies due to the limited consideration given to hydrodynamics within the digester tank. A solution to this is to combine computational fluid dynamics simulations with anaerobic models. This paper introduces a novel methodology in the form of a software toolbox that implements the standard anaerobic digestion model no.1 in C++ and can interface with particle-based Lagrangian simulations. This method provides significantly more insights into the biochemical conversion process by accounting for the impact of the hydrodynamics on the biochemical reactions. The paper presents the background of the method along with a conceptual and numerical verification. It also presents a case study of a 3D lab scale digester comparing the results from the solver with the standard anaerobic digestion model. This integrated approach can be used by operators and designers for optimisations and also for predictive modelling.

Navigating careers at sea: Career proactivity in extreme work contexts

AbstractExtreme work contexts are characterized by highly demanding labor under intense, stressful, and risky conditions. The literature has to date been predominantly focused on operational, organizational, and institutional responses to these challenges. Consequently, scant attention has been paid to how individuals understand and respond to extreme work contexts when managing their careers. We conducted semi‐structured interviews with 33 Iranian merchant ship officers and developed a grounded theory model of career proactivity in extreme work contexts. Our model delineates two stages leading to proactive career behaviors: The first stage is sensemaking, where individuals wake up to the challenges of their extreme work context, engage in comparative elaboration, and arrive at a settled understanding of the extremity of their work context. The second stage is agency, where individuals engage in experimentation of when they can modify situations or adapt to them, which ultimately helps them choose one of the following proactive career behaviors: exit planning, job crafting, career drifting, and job embracing. To support the generalizability of our model, we interviewed nine firefighters, which confirmed the model's applicability to another extreme context. We discuss the theoretical and critical implications of our model for recent conversations in extreme context research and career research.

Role of H2O in Catalytic Conversion of C1 Molecules.

Due to their role in controlling global climate change, the selective conversion of C1 molecules such as CH4, CO, and CO2 has attracted widespread attention. Typically, H2O competes with the reactant molecules to adsorb on the active sites and therefore inhibits the reaction or causes catalyst deactivation. However, H2O can also participate in the catalytic conversion of C1 molecules as a reactant or a promoter. Herein, we provide a perspective on recent progress in the mechanistic studies of H2O-mediated conversion of C1 molecules. We aim to provide an in-depth and systematic understanding of H2O as a promoter, a proton-transfer agent, an oxidant, a direct source of hydrogen or oxygen, and its influence on the catalytic activity, selectivity, and stability. We also summarize strategies for modifying catalysts or catalytic microenvironments by chemical or physical means to optimize the positive effects and minimize the negative effects of H2O on the reactions of C1 molecules. Finally, we discuss challenges and opportunities in catalyst design, characterization techniques, and theoretical modeling of the H2O-mediated catalytic conversion of C1 molecules.

ChatGPT-A Generative Pre-Trained Transformer

ChatGPT is an advanced conversational AI model developed by OpenAI. It is designed to engage in natural and coherent conversations with users, providing human-like responses to a wide range of topics and questions. It leverages deep learning technique. ChatGPT uses a combination of machine learning techniques, including deep learning and natural language processing, to understand and generate human-like text. The model has been trained on a vast amount of internet text data to ensure its ability to generate relevant and contextually accurate responses. ChatGPT has applications in customer service, virtual assistants, and other conversational interfaces, offering a powerful tool for natural language understanding and generation. It is most likely used to generate human like responses and makes the communication interactive.

Effect of humid environment on electromechanical performance of piezoelectric micromachined ultrasonic transducers (PMUTs)

In recent years, piezoelectric micromechanical ultrasonic transducers (PMUTs) have received widespread attention. The deployment of PMUTs in humid environment exposes them to changes in relative humidity, which effects their electromechanical performance. This paper investigates the effect of humid environments (30 %−80 %) on the electromechanical performance of uncoated PMUTs from the perspective of electromechanical conversion model and equivalent circuit. Two types of transducers with different top layer materials were experimented to compare the effects of moisture absorption on the electromechanical performances of the transducers. The top layer material of Device A is SiO2 film, and the top layer material of Device B is Si3N4 film. The experimental results show that with the rise of relative humidity, the frequency of Device A decreases by −0.47 %, while the frequency of Device B increases by 0.13 %, and the phase resonance peaks of both increases by 0.441° and 0.285°, respectively. Equivalent circuit fitting was conducted on the impedance characteristics of Device A and Device B, and the fitting results were discussed based on acoustic impedance and moisture absorption theory. The electromechanical performances of the two devices show a different variation trend, with the potential reason being that Device A is mainly affected by the combined effects of moisture absorption leading to a decrease in Young’s modulus of the membrane and a decrease in the acoustic impedance of the humid air, while Device B is mainly affected by the acoustic impedance.

Question about Mary Magdalene by Benedict Hesse of Cracow (Ms. Kraków, Biblioteka Jagiellońska, 1386, f. IIIv): Study and Critical Edition

The article deals with the question about Mary Magdalene composed by Benedict Hesse of Cracow (ca. 1389–1456), Professor of Theology at the University of Cracow: it presents a study of the question and a critical edition of the text. The author addresses the question of attribution of the analyzed text and examines the dating of the text, establishing the terminus post quem et ante quem as 1431–1449. In the question, Hesse asks if Mary Magdalene committed a carnal sin. The medieval theologian analyzes the arguments for and against, and according to the long exegetic tradition concludes that Mary Magdalene committed a sin and is a conversion model for sinners. Hesse specifies that this question should be debated only at the university and not in the churches to avoid scandalizing the faithful.

Noise-robust voice conversion using adversarial training with multi-feature decoupling

Most existing voice conversion methods focus primarily on separating speech content from speaker information while overlooking the decoupling of pitch information. Additionally, the quality of converted speech significantly degrades when the speech of the target speaker is contaminated by noises. To address these issues, this paper proposes a noise-robust voice conversion model with multi-feature decoupling based on adversarial training. The proposed framework utilizes three distinct encoders to encode speech content, speaker identity, and pitch information independently, which aims to enhance the performance of decoupling by minimizing their mutual information and reduce the correlations between feature vectors. Moreover, a gradient reversal layer and a noise decoupling discriminator are incorporated into the framework, which extracts noise-resistant speaker representations and content representations through adversarial training to facilitate the synthesis of high-quality speech. In order to optimize the learning process, a training strategy is developed which involves alternating between clean and noisy data during the training of the encoder. This strategy effectively guides and expedites the convergence of the model. Experimental results demonstrate that compared to the state-of-the-art baselines of noise-robust voice conversion, the proposed model achieves improvements around 0.31 and 0.39 in terms of speech naturalness and speaker similarity evaluation metrics, respectively.

ChatGPT: a reliable fertility decision-making tool?

The internet is the primary source of infertility-related information for most people who are experiencing fertility issues. Although no longer shrouded in stigma, the privacy of interacting only with a computer provides a sense of safety when engaging with sensitive content and allows for diverse and geographically dispersed communities to connect and share their experiences. It also provides businesses with a virtual marketplace for their products. The introduction of ChatGPT, a conversational language model developed by OpenAI to understand and generate human-like text in response to user input, in November 2022, and other emerging generative artificial intelligence (AI) language models, has changed and will continue to change the way we interact with large volumes of digital information. When it comes to its application in health information seeking, specifically in relation to fertility in this case, is ChatGPT a friend or foe in helping people make well-informed decisions? Furthermore, if deemed useful, how can we ensure this technology supports fertility-related decision-making? After conducting a study into the quality of the information provided by ChatGPT to people seeking information on fertility, we explore the potential benefits and pitfalls of using generative AI as a tool to support decision-making.

Human-AI Collaboration: Understanding User Trust in ChatGPT Conversations

This research paper delves into the critical dimension of Human-AI Collaboration, with a specific focus on unraveling the intricacies of user trust in ChatGPT conversations. In an era marked by increasing AI integration into various aspects of human life, understanding and fostering user trust in conversational AI systems like ChatGPT is essential for effective collaboration. The study employs a comprehensive approach, investigating metrics for trust measurement, analyzing user experiences, and exploring the factors that influence trust. By examining the evolving impact of trust on collaboration and conducting comparative analyses with other conversational AI models, the research aims to provide valuable insights. Ultimately, the paper not only contributes to a nuanced understanding of user trust in ChatGPT conversations but also offers practical recommendations for developers and stakeholders to enhance the collaborative potential of AI systems in real-world applications. Keywords: Human-AI Collaboration, ChatGPT Conversations, Conversational AI, Trust Metrics, User Trust.

Protein-lipid interactions and protein anchoring modulate the modes of association of the globular domain of the Prion protein and Doppel protein to model membrane patches.

The Prion protein is the molecular hallmark of the incurable prion diseases affecting mammals, including humans. The protein-only hypothesis states that the misfolding, accumulation, and deposition of the Prion protein play a critical role in toxicity. The cellular Prion protein (PrPC) anchors to the extracellular leaflet of the plasma membrane and prefers cholesterol- and sphingomyelin-rich membrane domains. Conformational Prion protein conversion into the pathological isoform happens on the cell surface. In vitro and in vivo experiments indicate that Prion protein misfolding, aggregation, and toxicity are sensitive to the lipid composition of plasma membranes and vesicles. A picture of the underlying biophysical driving forces that explain the effect of Prion protein - lipid interactions in physiological conditions is needed to develop a structural model of Prion protein conformational conversion. To this end, we use molecular dynamics simulations that mimic the interactions between the globular domain of PrPC anchored to model membrane patches. In addition, we also simulate the Doppel protein anchored to such membrane patches. The Doppel protein is the closest in the phylogenetic tree to PrPC, localizes in an extracellular milieu similar to that of PrPC, and exhibits a similar topology to PrPC even if the amino acid sequence is only 25% identical. Our simulations show that specific protein-lipid interactions and conformational constraints imposed by GPI anchoring together favor specific binding sites in globular PrPC but not in Doppel. Interestingly, the binding sites we found in PrPC correspond to prion protein loops, which are critical in aggregation and prion disease transmission barrier (β2-α2 loop) and in initial spontaneous misfolding (α2-α3 loop). We also found that the membrane re-arranges locally to accommodate protein residues inserted in the membrane surface as a response to protein binding.

Process evaluation of mild hydrothermal carbonization to convert wet biomass residue streams into intermediate bioenergy carriers

Hydrothermal carbonization (HTC) is a promising process for the upgrading of wet biomass residues. Models of HTC processes, in particular at continuous pilot-scale, are needed to move HTC from lab-scale to industrial scale. This study presents a process model for mild HTC, dewatering and conversion to intermediate energy carriers (bio-pellets and biogas for power and/or heat production) of three wet biomass residue streams: paper sludge, olive pomace and orange peels, based on lab- and pilot-scale experiments. In addition, the process energy efficiency and feedstock utilization of the HTC process is calculated and compared with conventional treatment options for the chosen residues, i.e., direct anaerobic digestion (olive pomace, orange peels) or combustion after conventional dewatering (paper sludge). The process model indicates that the HTC pilot-scale process is much more efficient in terms of feedstock utilization to produce heat and/or power than the reference scenarios. The process energy efficiency of the HTC process (pilot-scale) was calculated to be 26 %, 63 % and 40 % for paper sludge, olive pomace and orange peel feedstocks, respectively. For all feedstocks, both the solid and liquid-generated products are equally important for improving the overall process energy efficiency. This study demonstrates the potential benefits of HTC processes for upgrading wet biomass waste streams based on continuous pilot-scale data.

Ethical Considerations of Artificial Intelligence in Health Care: Examining the Role of Generative Pretrained Transformer-4.

The integration of artificial intelligence technologies, such as large language models (LLMs), in health care holds potential for improved efficiency and decision support. However, ethical concerns must be addressed before widespread adoption. This article focuses on the ethical principles surrounding the use of Generative Pretrained Transformer-4 and its conversational model, ChatGPT, in healthcare settings. One concern is potential inaccuracies in generated content. LLMs can produce believable yet incorrect information, risking errors in medical records. Opacity of training data exacerbates this, hindering accuracy assessment. To mitigate, LLMs should train on precise, validated medical data sets. Model bias is another critical concern because LLMs may perpetuate biases from their training, leading to medically inaccurate and discriminatory responses. Sampling, programming, and compliance biases contribute necessitating careful consideration to avoid perpetuating harmful stereotypes. Privacy is paramount in health care, using public LLMs raises risks. Strict data-sharing agreements and Health Insurance Portability and Accountability Act (HIPAA)-compliant training protocols are necessary to protect patient privacy. Although artificial intelligence technologies offer promising opportunities in health care, careful consideration of ethical principles is crucial. Addressing concerns of inaccuracy, bias, and privacy will ensure responsible and patient-centered implementation, benefiting both healthcare professionals and patients.

Rediscovering the Spiritual in Religious Conversion Studies

This article is a review of conversion research and how it views the spiritual within conversion. Central to missions is the understanding of the process of conversion. Missiologists, psychologists, sociologists, and theologians have all studied the process of conversion. An evaluation of conversion studies shows that researchers tend to ignore or downplay the role of spiritual agency or the supernatural in the process of conversion. Recent studies in a majority world context have identified spiritual agency or the supernatural as central to conversion. We need to reexamine our models of conversion and further study the universality of spiritual agency in consideration of these results.

Exploring Students’ and Faculty’s Knowledge, Attitudes, and Perceptions Towards ChatGPT: A Cross-Sectional Empirical Study

Aim/Purpose: This study explores the Knowledge, Attitude, and Perception (KAP) towards ChatGPT among university students and faculty. It also examines the faculty’s readiness to cope with the challenges and leverage the opportunities presented by AI-powered conversational models. Background: Launched on November 30, 2022, ChatGPT took the world by storm with its capability to generate high-quality written expressions in a conversational manner. The reactions to this innovation varied, from enthusiasm regarding its potential to enrich students’ learning to concerns about its threat to students’ cognitive development and academic integrity. A systematic exploration of students’ and faculty’s KAP towards ChatGPT can play an important role in addressing the multifaceted dimensions of AI-driven conversational models. Methodology: This study employs a cross-sectional survey research design based on questionnaires distributed to 145 faculty members, as well as 855 undergraduate and graduate students at the ESPRIT School of Engineering and School of Business. The student sample was based on stratified and convenience sampling, while the faculty sample was based on a consensus sampling approach. Contribution: To the best of our knowledge, this is the first reported study that contributes to understanding the KAP of students and faculty towards ChatGPT, as well as the readiness of faculty to effectively adopt AI-driven conversational models. Furthermore, our research contributes to the body of knowledge by taking Vygotsky’s (1978) principle of social interaction and its role in promoting cognitive development to a new level by hypothesizing that if students were to acquire the competencies to actively engage with AI-driven chatbots in meaningful discussions and collaborative conversations, they might be able to develop some higher-order thinking skills further. Findings: Our results indicated that faculty demonstrated a higher level of ChatGPT knowledge than students and that more than 40% of surveyed students and faculty expressed some trust in the reliability of ChatGPT’s responses, a perception that does not align with reality. Faculty attitude towards ChatGPT was comparatively more reserved compared to that of students and showcased varying opinions. Furthermore, the surveyed faculty showcased a more negative perception of ChatGPT than students, and they expressed a greater degree of skepticism. Our research revealed that 63.4% of surveyed faculty reported that they lack the requisite training and resources to integrate ChatGPT into their pedagogical practices. Recommendations for Practitioners: HEIs should take appropriate measures to enhance students’ and faculty’s knowledge, attitude, and perception regarding ChatGPT to stimulate ethical, meaningful, innovative, and engaging interactions and learning experiences. Recommendation for Researchers: Our study has shed light on some moderating factors that shape the acceptance of AI-driven conversational models and some adoption barriers. It delves into the perceptions, biases, and misconceptions held by both students and faculty, thereby providing a basis for future investigations on the effective integration of AI-driven conversational models in higher education. Impact on Society: This research provides new insights that can harness the potential merits of ChatGPT in enhancing students’ learning while mitigating potential pitfalls. It suggests facilitating open forums and dialogues among students, faculty, employers, and other key stakeholders to debate the impact of AI-driven conversational models on students’ learning and faculty’s teaching and assessment. Future Research: We invite researchers to conduct cross-cultural studies on this topic while also taking into consideration a qualitative research design approach. Future research can also test the hypothesis that AI-driven conversational models inhibit critical thinking by facilitating the passive consumption of information.

Construction and Evaluation of a Machine Learning-Based Conversation Model for English Spoken Tourism Conversation

Abstract The development of language macromodeling has led to the widespread adoption of spoken English conversation systems in various industries. Similarly, it has had an impact on the tourism industry, where the involvement of machine learning has taken a big step forward. The purpose of this paper is to explore the use of machine learning methods in spoken dialog systems to construct a dialog model that is suitable for the tourism industry. Speech, map image, and tourism information text data are collected first, which form the database of the dialog model through processing, storage, and application. A bag-of-words model and word vectors represent the text, while speech activity and an English conversation system construct the speech and intention recognition model. On the test set, the best speech recognition rate using double-threshold endpoint detection is up to 98.73%, and the corresponding sentence recognition rate is also up to 93.67%. This paper’s model achieves the highest intention recognition accuracy, scoring 0.943 and 0.955 on the CCKS2018 and SMP2018 datasets, respectively. This paper proposes a machine-learning conversation model that provides technical support for conversational AI development in the tourism industry.

Towards a model of photon-axion conversion in the host galaxy of GRB 221009A

GRB 221009A was the brightest gamma-ray burst ever detected on Earth. In its early afterglow phase, photons with exceptional energies above 10 TeV were observed by LHAASO, and a photon-like air shower above 200 TeV was detected by Carpet-2. Gamma rays of very high energies can hardly reach us from the distant GRB because of pair production on cosmic background radiation. Though final results on the highest-energy photons from this GRB have not been published yet, a number of particle-physics solutions to this problem were discussed in recent months. One of the most popular ones invokes the mixing of photons with axion-like particles (ALPs). Whether this is a viable scenario, depends crucially on the magnetic fields along the line of sight, which are poorly known. Here, we use the results of recent Hubble Space Telescope observations of the host galaxy of GRB 221009A, combined with magnetic-field measurements and simulations for other galaxies, to construct a toy model of the host-galaxy magnetic field and to estimate the rate of the photon-axion conversion there. Thanks, in particular, to the exceptional edge-on orientation of the host galaxy, strong mixing appears to be natural, both for LHAASO and Carpet-2 energy bands, for a wide range of ALP masses m ≲ 10-5 eV and photon couplings g ≳ 10-11 GeV-1.

Study on the Equivalent Conversion and Utilisation Model of Solar Energy and its Benefits

Relying on the energy local area network (LAN), we study the equivalent conversion relationship between different “energy qualities” in the process of solar energy conversion and utilisation and its benefit model. Considering the impact of load demand characteristics and current energy consumption price factors on solar energy utilization, a multi-objective optimization model of solar energy conversion and utilization is constructed with the goals of maximizing solar energy utilization and maximizing the comprehensive benefits of system operation; then, the particle swarm optimization algorithm with quantum behaviour is used to optimize and solve the model, and a comparative analysis is carried out with the traditional single-energy supply mode. The results show that the proposed model can effectively improve the comprehensive utilisation and consumption of solar energy by the energy consumption system, and achieve higher energy sales benefits. Therefore, with the help of energy storage technology, the fluctuation and intermittency of the solar PV system output can be suppressed, and flexible grid connection can be realised, so as to enhance the grid-connected capacity of solar energy; at the same time, higher economic benefits can be obtained with the help of optimisation, coordination and control system operation. Optimised control of solar PV power generation systems under different scenarios ensures the safety and stability of the system’s power output and improves the grid-connected capability of solar PV power generation systems. The validity and feasibility of the proposed model and its operation mode are verified, providing a new idea for the development and utilisation of large-scale solar energy.

Large language model, AI and scientific research: why ChatGPT is only the beginning.

ChatGPT, a conversational artificial intelligence model based on the generative pre-trained transformer GPT architecture, has garnered widespread attention due to its user-friendly nature and diverse capabilities. This technology enables users of all backgrounds to effortlessly engage in human-like conversations and receive coherent and intelligible responses. Beyond casual interactions, ChatGPT offers compelling prospects for scientific research, facilitating tasks like literature review and content summarization, ultimately expediting and enhancing the academic writing process. Still, in the field of medicine and surgery, it has already shown its endless potential in many tasks (enhancing decision-making processes, aiding in surgical planning and simulation, providing real-time assistance during surgery, improving postoperative care and rehabilitation, contributing to training, education, research, and development). However, it is crucial to acknowledge the model's limitations, encompassing knowledge constraints and the potential for erroneous responses, as well as ethical and legal considerations. This paper explores the potential benefits and pitfalls of these innovative technologies in scientific research, shedding light on their transformative impact while addressing concerns surrounding their use.

GPT Modeling for English Learning Assistance Functions in Non-Native Language Environments

Abstract In this paper, we design an English multi-round dialogue model based on the pre-trained model GPT-2 and use Top-p and Top-k sampling instead of greedy search and cluster search sampling to optimize the decoding strategy. After that, a dialog history keyword replication mechanism is introduced to improve context consistency. Based on the GPT-2 English Multi-Round Dialogue Model, a conversational teaching model for teaching English in a non-native language environment has been constructed. The impact of this teaching model on students’ theoretical learning and teaching skills was examined. The results show that the BLEU1 index value of the GPT-2DH model after pre-training in the chatterbot corpus is 0.356, which is almost twice as much as that of seq2seq. As a result, the model in this paper is superior. Compared with traditional teaching methods, the GPT dialogic learning model significantly improved students’ theoretical learning performance, English listening and speaking skills, learning attitude (P=0.029<0.05), and extrinsic learning motivation (P=0.046<0.05) but had no significant effect on their instructional design skills and intrinsic learning motivation.