Conversion Model Research Articles

Prognostic model for predicting Alzheimer’s disease conversion using functional connectome manifolds

BackgroundEarly detection of Alzheimer’s disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between individuals with MCI (or AD) and cognitively normal (CN), discarding the temporal information of conversion time. Here, we aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling.MethodsWe developed a prognostic model using a large-scale Alzheimer’s Disease Neuroimaging Initiative dataset, and it was validated using external data obtained from the Open Access Series of Imaging Studies. We considered individuals who were initially CN or had MCI but progressed to AD and those with MCI with no progression to AD during the five-year follow-up period. As the exact conversion time to AD is unknown, we inferred this information using imputation approaches. We generated cortex-wide principal FC gradients using manifold learning techniques and computed subcortical-weighted manifold degrees from baseline functional magnetic resonance imaging data. A penalized Cox regression model with an elastic net penalty was adopted to define a risk score predicting the risk of conversion to AD, using FC gradients and clinical factors as regressors.ResultsOur prognostic model predicted the conversion risk and confirmed the role of imaging-derived manifolds in the conversion risk. The brain regions that largely contributed to predicting AD conversion were the heteromodal association and visual cortices, as well as the caudate and hippocampus. Our risk score based on Cox regression was consistent with the expected disease trajectories and correlated with positron emission tomography tracer uptake and symptom severity, reinforcing its clinical usefulness. Our findings were validated using an independent dataset. The cross-sectional application of our model showed a higher risk for AD than that for MCI, which correlated with symptom severity scores in the validation dataset.ConclusionWe proposed a prognostic model predicting the risk of conversion to AD. The associated risk score may provide insights for early intervention in individuals at risk of AD conversion.

A variable diameter spiral path planning strategy for large eccentricity variable curvature rotating parts with focus on thickness and performance control in cladding

When laser cladding rotating parts that have large eccentricity and variable curvature, first-order discontinuities in the cladding path and fluctuations in the real-time scanning speed are common problems. These issues cause significant inhomogeneities in the cladding thickness and localized excessive material accumulation. This not only leads to unstable mechanical properties of the cladding layer but also results in a significant waste of cladding materials during subsequent precision machining. In response, this paper introduces a cladding method that employs a variable diameter constant linear velocity spiral path and establishes models for dynamic vector and angle conversion, constant linear velocity optimization, and cladding path data conversion. This approach enables consistent control over the real-time scanning speed and defocus distance during laser cladding of parts with large eccentricity and variable curvature. A comparison of the experimental results from spiral path cladding, variable linear velocity and variable diameter spiral cladding, and constant linear velocity variable diameter spiral cladding revealed that the latter method achieves a more uniform cladding layer thickness, with a maximum deviation of only 0.036 mm. The microstructure primarily consists of cellular structures, demonstrating improved consistency. Samples were taken from three different cladding methods, and hardness tests were performed on the cross-sections of the cladding layers.

P2.17A.02 Cost-Efficiency Modeling of Conversion to Biosimilar Bevacizumab-bvzr in Metastatic Non-Small Cell Lung Cancer in Medicare

P2.17A.02 Cost-Efficiency Modeling of Conversion to Biosimilar Bevacizumab-bvzr in Metastatic Non-Small Cell Lung Cancer in Medicare

Satellite’s differential code bias conversion model between different IGS clock products using uncombined BDS-3 multi-frequency data

Satellite’s differential code bias conversion model between different IGS clock products using uncombined BDS-3 multi-frequency data

Quantification of Letrozole, Palbociclib, Ribociclib, Abemaciclib, and Metabolites in Volumetric Dried Blood Spots: Development and Validation of an LC-MS/MS Method for Therapeutic Drug Monitoring

Therapeutic drug monitoring (TDM) may be beneficial for cyclin-dependent kinase 4/6 inhibitors (CDK4/6is), such as palbociclib, ribociclib, and abemaciclib, due to established exposure–toxicity relationships and the potential for monitoring treatment adherence. Developing a method for quantifying CDK4/6is, abemaciclib metabolites (M2, M20), and letrozole in dried blood spots (DBS) could be useful to enhance the feasibility of TDM. Thus, an optimized LC-MS/MS method was developed using the HemaXis DB10 device for volumetric (10 µL) DBS collection. Chromatographic separation was achieved using a reversed-phase XBridge BEH C18 column. Detection was performed with a triple quadrupole mass spectrometer, utilizing ESI source switching between negative and positive ionization modes and multiple reaction monitoring acquisition. Analytical validation followed FDA, EMA, and IATDMCT guidelines, demonstrating high selectivity, adequate sensitivity (LLOQ S/N ≥ 30), and linearity (r ≥ 0.997). Accuracy and precision met acceptance criteria (between-run: accuracy 95–106%, CV ≤ 10.6%). Haematocrit independence was confirmed (22–55%),with high recovery rates (81–93%) and minimal matrix effects (ME 0.9–1.1%). The stability of analytes under home-sampling conditions was also verified. Clinical validation supports DBS-based TDM as feasible, with conversion models developed for estimating plasma concentrations (the reference for TDM target values) of letrozole, abemaciclib, and its metabolites. Preliminary data for palbociclib and ribociclib are also presented.

GPT vs. Google: A Comparative Study of Self-Code Learning in ODL Students

This research investigates the comparative effectiveness of the ChatGPT and the Google search engine in facilitating the self-learning of JavaScript functions among undergraduate open and distance learning students. Employing a quasi-experimental post-test control group design, the study analyzed variables of disorientation, satisfaction, cognitive load, navigation performance, and test scores among 471 learners. Results indicated a marked advantage for ChatGPT users in reducing cognitive load and disorientation, improving navigation performance and increasing test scores. The study also explores the evolving dynamics between conventional keyword-based search engines and AI-driven conversational models, reflecting on their mutual influence in the domain of information retrieval. These findings suggest that AI chatbots like ChatGPT not only enhance the learning experience but also signify a paradigm shift in how information is accessed and processed in educational settings. The research contributes to the understanding of AI applications in education, particularly in self-directed learning scenarios in open and distance learning.

Exploring the Landscape: A Bibliometric Analysis of 30-Most Cited Articles on ChatGPT in Education

ChatGPT is a conversational AI model that offers unique opportunities for personalized learning, students’ engagement, and educational support. This study aims to investigate the influence of the top 30 most cited articles in the realm of ChatGPT in education. Employing a descriptive approach, bibliometric analysis was conducted on a combined dataset of 31 articles sourced from Web of Science and Scopus databases. Additionally, co-authorship and co-occurrence analyses were performed using VOSviewer (version 1.6.20) software. The findings reveal that the most cited papers were published between 2023 and 2024, with Healthcare, an open-access journal, emerging as the most cited platform. Notably, review articles garnered the highest number of citations, and authors predominantly hailed from developed nations. The literature dominantly focuses on the use of ChatGPT in medical education. Co-occurrence and co-authorship analyses highlight "ChatGPT" and "artificial intelligence" as the most frequently encountered keywords and no authors have published more than one paper. Lastly, a discussion, future suggestions and conclusion of the results was made which can serve as a key point for future research in the field.

Weight Assignment Method and Application of Key Parameters in Shale Gas Resource Evaluation

The evaluation methods for shale gas resources and the key parameters involved are diverse. The main research object of this article is the key parameters in shale gas resource evaluation, and this study aims to quantitatively evaluate the required key parameters for calculation, thereby improving the credibility of shale gas resource evaluation results. This article mainly analyzes the weight analysis of the key parameters used in the resource evaluation process, focusing on the analysis and determination of key parameters controlling the generation, enrichment, and preservation of shale gas. The key parameters are graded and evaluated layer by layer, and the common key parameters that participate in the determination are extracted. Based on the above analysis, a weight conversion model is proposed to quantitatively evaluate the actual importance of key parameters in resource evaluation. By combining statistical research, the partial correlation coefficients in the correlation coefficients are applied to estimate the resource quantities in the exploration field in order to quantitatively evaluate the credibility of the resource evaluation results. This article focuses on the weight assignment of key parameters for calculating the resource quantity of the Jiaoshiba shale gas field in the Fuling area of Chongqing and obtains different weight results for different parameters. Using this method to assign key parameter weights to the target block can provide reliable parameter support for shale gas resource evaluation and provide strong support for shale gas resource estimation.

Process Development and Techno-Economic Analysis for Combined and Separated CO2 Capture-Electrochemical Utilization

Combining CO2 capture and utilization into a single unit operation offers a feasible solution for converting a sustainable feedstock into marketable commodity chemicals, while reducing energy requirements from separated processes. In this research, we developed a process model and performed a techno-economic analysis (TEA) for point-source CO2 capture and electrochemical-based utilization in light olefins production under both separated and integrated scenarios. CO2 containing flue gas from a 500 MW power plant was utilized as a feed while CO2 utilization involved electrochemical reduction reactions to produce light olefins directly from CO2. A meticulous analysis was conducted, probing into the multifaceted impacts of various operating parameters, material properties, and downstream treatment units. Factors such as pressure, temperature, H2O/CO2 molar ratio, catalyst and adsorbent activities, deactivation rate, and heat integration were optimized to achieve 95 % CO2 recovery and > 90 % conversion, and > 85 % ethylene yield. Through a comprehensive TEA, our findings unveiled that the combined process utilizing bifunctional adsorbent/catalyst materials (BFMs) incurs costs of approximately $284/ton CO2, whereas the separated process reported expenses of ∼$516/ton CO2. This study, pivotal in its contributions, evaluated economic feasibility of combined capture-conversion method based on BFMs for CO2 removal and subsequent utilization via a promising advanced process model for sustainable feedstocks conversion to commodity chemicals.

Implementing innovation in project-based learning in electro-mechanical engineering education

This study investigates the implementation of project-based learning (PBL) in electro-mechanical engineering education, emphasizing the modeling and application of an innovation process to enhance student engagement, motivation, and employability. Initially, the innovation process was systematically modeled using Structured Analysis and Design Technique (SADT) to manage and organize skills, knowledge, and resources effectively. This model was subsequently applied in an educational experience focused on highly technical aspects such as energy conversion modeling, mechanical improvements, material science, and efficiency analysis. These aspects were primarily applied in the context of permanent magnet synchronous machines employed in electric vehicle (EV) axles. The SADT modeling allowed for a clear delineation of the innovation phases, ensuring a structured approach to project execution. Statistical analysis of the PBL approach indicated significant improvements in student performance, with a 25% increase in problem-solving skills and a 30% enhancement in critical thinking abilities. The case study demonstrated that industry-oriented projects significantly boost student satisfaction, particularly in problem definition and solution identification, leading to a more engaging and effective learning experience.

Customer adoption of Chat GPT for web development and programming assistance in the Zimbabwe tech industry

ChatGPT, an advanced conversational AI model by OpenAI, signifies a significant leap in human-computer interaction. Optimized for dialogue, ChatGPT has the potential to transform web development and programming support. In Zimbabwe, 60% of IT programmers express concerns about UI/UX design, while 70% face challenges in service quality due to software flaws and inadequate testing. This study investigates ChatGPT's application in Zimbabwe's tech industry for web development and programming support, and its impact on organizational performance. Employing an integrated Delone and McLean IS Success Model, the research examines adoption trends, barriers, and benefits of integrating ChatGPT into development workflows.A quantitative approach was utilized, employing survey data and statistical methods. A 5-point Likert Scale survey was distributed to 30 Zimbabwean ICT workers. Data analysis with SmartPLS 3.0 included descriptive and confirmatory factor analyses to assess reliability (Cronbach's alpha, composite reliability) and validity (factor loadings, AVE, HTMT ratio). Results indicate that ChatGPT significantly influences system, information, and service quality, enhancing user satisfaction and organizational benefits, with system quality exerting the strongest impact. Moreover, system quality positively affects organizational performance, though the moderating role of flexible organizational culture was insignificant. Recommendations include enhancing training data tailored to local technological needs, integrating real-time data sources, offering a paid version for updated data, ensuring robust error handling for service quality, providing educational resources, implementing feedback mechanisms, conducting developer training programs, and promoting cross-functional collaboration.

3D model for human glia conversion into subtype-specific neurons, including dopamine neurons

Two-dimensional neuronal cultures have a limited ability to recapitulate the invivo environment of the brain. Here, we introduce a three-dimensional invitro model for human glia-to-neuron conversion, surpassing the spatial and temporal constrains of two-dimensional cultures. Focused on direct conversion to induced dopamine neurons (iDANs) relevant to Parkinson disease, the model generates functionally mature iDANs in 2weeks and allows long-term survival. As proof of concept, we use single-nucleus RNA sequencing and molecular lineage tracing during iDAN generation and find that all glial subtypes generate neurons and that conversion relies on the coordinated expression of three neural conversion factors. We also show the formation of mature and functional iDANs over time. The model facilitates molecular investigations of the conversion process to enhance understanding of conversion outcomes and offers a system for invitro reprogramming studies aimed at advancing alternative therapeutic strategies in the diseased brain.

A prediction model of dementia conversion for mild cognitive impairment by combining plasma pTau181 and structural imaging features.

The early stages of Alzheimer's disease (AD) are no longer insurmountable. Therefore, identifying at-risk individuals is of great importance for precise treatment. We developed a model to predict cognitive deterioration in patients with mild cognitive impairment (MCI). Based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we constructed models in a derivation cohort of 761 participants with MCI (138 of whom developed dementia at the 36th month) and verified them in a validation cohort of 353 cognitively normal controls (54 developed MCI and 19 developed dementia at the 36th month). In addition, 1303 participants with available AD cerebrospinal fluid core biomarkers were selected to clarify the ability of the model to predict AD core features. We assessed 32 parameters as candidate predictors, including clinical information, blood biomarkers, and structural imaging features, and used multivariable logistic regression analysis to develop our prediction model. Six independent variables of MCI deterioration were identified: apolipoprotein E ε4 allele status, lower Mini-Mental State Examination scores, higher levels of plasma pTau181, smaller volumes of the left hippocampus and right amygdala, and a thinner right inferior temporal cortex. We established an easy-to-use risk heat map and risk score based on these risk factors. The area under the curve (AUC) for both internal and external validations was close to 0.850. Furthermore, the AUC was above 0.800 in identifying participants with high brain amyloid-β loads. Calibration plots demonstrated good agreement between the predicted probability and actual observations in the internal and external validations. We developed and validated an accurate prediction model for dementia conversion in patients with MCI. Simultaneously, the model predicts AD-specific pathological changes. We hope that this model will contribute to more precise clinical treatment and better healthcare resource allocation.

A Novel Hybrid Excitation Magnetic Lead Screw and Its Transient Sub-Domain Analytical Model for Wave Energy Conversion

A Novel Hybrid Excitation Magnetic Lead Screw and Its Transient Sub-Domain Analytical Model for Wave Energy Conversion

Adaptive Top-K Algorithm for Medical Conversational Diagnostic Model.

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3-1.9 times compared to the state-of-the-art differential diagnosis systems.

Remote-Sensing-Based Estimation of Rooftop Photovoltaic Power Production Using Physical Conversion Models and Weather Data

The global photovoltaic (PV) installed capacity, vital for the electric sector’s decarbonation, reached 1552.3 GWp in 2023. In France, the capacity stood at 19.9 GWp in April 2024. The growth of the PV installed capacity over a year was nearly 32% worldwide and 15.7% in France. However, integrating PV electricity into grids is hindered by poor knowledge of rooftop PV systems, constituting 20% of France’s installed capacity, and the lack of measurements of the production stemming from these systems. This problem of lack of measurements of the rooftop PV power production is referred to as the lack of observability. Using ground-truth measurements of individual PV systems, available at an unprecedented temporal and spatial scale, we show that by estimating the PV power production of an individual rooftop system by combining solar irradiance and temperature data, the characteristics of the PV system inferred from remote sensing methods and an irradiation-to-electric power conversion model provides accurate estimations of the PV power production. We report an average estimation error (measured with the pRMSE) of 10% relative to the system size. Our study shows that we can improve rooftop PV observability, and thus its integration into the electric grid, using little information on these systems, a simple model of the PV system, and weather data. More broadly, this study shows that limited information is sufficient to derive a reasonably good estimation of the PV power production of small-scale systems.

The Use of Chat GPT for Academic Writing in Higher Education

The integration of artificial intelligence (AI) technology in education has created new opportunities for improving academic writing in higher education. This study investigates the utilization of Chat GPT, an artificial intelligence-powered conversational model, as a tool to assist in writing. Students who used Chat GPT for academic writing were interviewed using a qualitative approach and semi-structured interviews. The data research showed that Chat GPT provides consumers with information regarding its origins, advantages, and trustworthiness. Additionally, it is regarded as a valuable instrument for creating ideas and facilitating the writing process. Nevertheless, the study revealed both benefits and drawbacks associated with its utilization, emphasizing the importance for teachers to maintain a balance between its advantages and potential negatives. The outcomes offer a more profound comprehension of Chat GPT's function in academic writing and propose additional investigation to evaluate its enduring influence on students' writing abilities and rectify any partiality in AI-generated text

Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning.

Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (Tg) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring Tg, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict Tg efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R2 value of 0.79; however, the XGB model, with an R2 value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for Tg prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model's robustness, with an average prediction deviation of 9.76 from actual Tg values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials.

Method of 3D reconstruction of underwater concrete by laser line scanning

High-precision 3D (three-dimensional) reconstruction of underwater concrete is helpful for the detection and analysis of underwater concrete diseases. A prevalent method involves mounting optical cameras in waterproof housings to enable the 3D reconstruction of underwater concrete. Achieving millimetric accuracy in underwater 3D reconstruction poses a significant challenge in current research. The refraction of light on surfaces of different media and its reflection underwater complicate the process. In this paper, a 3D reconstruction method of underwater concrete based on laser line scanning is proposed. Firstly, the underwater imaging model is established by the ray tracing method, and the refraction conversion matrix is derived. Then, based on this matrix, the conversion relationship between the measured 3D size and the real 3D size of the underwater object is obtained. Finally, image restoration technology is introduced in the process of laser stripe extraction to eliminate the influence of reflected light on the extraction of laser stripe center, to improve the reconstruction accuracy. We used the independently developed laser line 3D scanning equipment to carry out experimental analysis. The results showed that the error rate caused by direct reconstruction of underwater objects was 9.75 %. Additionally, the error rate obtained from 3D reconstruction of underwater objects using the conversion model was 2.89 %. This study provides technical support for the 3D detection of underwater concrete.

Optimal operation for P2H system with 100% renewable energy concerning thermal-electric properties

Power to hydrogen (P2H) system with renewable energy sources is an important research point for sustainable development. Developing a comprehensive model to accurately capture the thermal-electric characteristics of an electrolyzer and utilizing it to optimize the system’s performance pose a significant challenge. In this paper, we develop an energy conversion model of the Alkaline Water Electrolyzer (AWE), describing the impact of temperature on AWE’s operation. Then, using experimental fitting methods, we fit the important formulas of the model: the maximum power (MMP) function and the efficiency function. Subsequently, based on the proposed energy conversion model, we propose an active variable temperature (AVT) operation strategy for the P2H system with renewable energy aimed at maximizing hydrogen production. At last, cases for both the day-ahead and hour-ahead situations are conducted with real photovoltaic power. The simulation results show that the AVT operation strategy achieves up to 3% higher hydrogen production and nearly 100% renewable energy utilization rates.