Traditional Model Research Articles

RCAT: Retentive CLIP Adapter Tuning for Improved Video Recognition

RCAT: Retentive CLIP Adapter Tuning for Improved Video Recognition

Design and Analysis of a New Fault-Tolerant QCA-based Nano-Scale Circuit for Morphological Operations in Image Processing

Quantum-dot cellular automata (QCA) is based on traditional cellular automata models in which each cell has one of the finite distinct states at any given time. Cellular automata refer to the use of Coulombic contact rather than wires to connect cells. The discrete character of cellular automata and quantum mechanics have been exploited to develop nano-scale devices that can conduct computations with high switching frequency and low power dissipation. QCA has been utilized in many categories for fault-tolerant and reversible logic computing. On the other hand, the morphology algorithm is an area of image processing that provides a unified methodology and strong tools for image/video processing applications. This work proposes a unique fault-tolerant approach based on QCA to execute the morphological operations in image processing. Two fault-tolerant 5-input majority gates are employed in the implemented QCA architecture, and a control line is used to accomplish dilation and erosion. To assess the capabilities of the proposed fault-tolerant circuit for morphological procedures, its performance is analyzed and confirmed. The proposed design is compared to earlier results, which significantly improve fault-tolerant ability.

Are Chinese live hog futures useful hedging tools?

ABSTRACT China’s newly listed live hog futures provide a standardized risk management tool for the hog market. Our paper provides the first systematic and quantitative evaluation of live hog futures’ hedging ability based on various traditional static and dynamic hedging models and an innovative quantile hedging model. Consistent results confirm that live hog futures are useful hedging tools. Specifically, the in-sample results of multiple traditional models show that the hedging ratios are around 0.4 and hedging effectiveness is 0.13. Out-of-sample hedging effectiveness increases significantly to 0.22. Quantile hedging results show more significant risk-management ability in bearish and average futures markets. We further compare live hog futures’ hedging effectiveness and market performance to other commodities. The comparison shows that the live hog futures market has modest hedging effectiveness and underperforms in liquidity, volatility, and basis. Following the empirical results, it is recommended to encourage hedging transactions, simplify trading processes, and enable night trading to enhance liquidity, lower volatility, and eventually improve hedging ability.

Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions.

This paper tackles the issue of moisture variation in wood-based materials, explicitly focusing on melamine-coated particleboard (hereafter referred to as melamine) and medium-density fiberboard (MDF) used in the third phase of wood industry transformation. The approach involves a comprehensive strategy for predicting moisture content variation, incorporating numerical simulation, experimental testing, and the application of artificial neural network (ANN) technology to enhance accuracy in furniture manufacturing. The developed ANN models are tailored to predict moisture content changes under specific thermal comfort conditions. Remarkably, these models demonstrate high precision, with an average error margin of only 1.40% for 8% moisture content (MC) and 2.85% for 12% MC in melamine, as well as 1.42% for 8% MC and 2.25% for 12% MC in MDF. These levels of precision surpass traditional models, emphasizing this study's novelty and practical relevance to the industrial context. The findings indicate that ANN models adapt to diverse environmental conditions, presenting a robust tool for optimizing moisture management in wood-based materials. This research contributes valuable insights for improving the reliability and efficiency of moisture content predictions in the wood industry.

Comparative Study of Monte Carlo Simulation and the Deterministic Model to Analyze Thermal Insulation Costs

The main purpose of this paper is to implement a simulation model in @RISKTM and study the impact of incorporating random variables, such as the degree days in a traditional deterministic model, for calculating the optimum thickness of thermal insulation in walls. Currently, green buildings have become important because of the increasing worldwide interest in the reduction of environmental pollution. One method of saving energy is to use thermal insulation. The optimum thickness of these insulators has traditionally been calculated using deterministic models. With the information generated from real data using the degree days required in a certain zone in Palestine during winter, random samples of the degree days required annually in this town were generated for periods of 10, 20, 50, and 70 years. The results showed that the probability of exceeding the net present value of the cost calculated using deterministic analysis ranges from 0% to 100%, without regard to the inflation rate. The results also show that, for design lifetimes greater than 40 years, the risk of overspending is lower if the building lasts longer than the period for which it was designed. Moreover, this risk is transferred to whomever will pay the operating costs of heating the building. The contribution of this research is twofold: (a) a stochastic approach is incorporated into the traditional models that determine the optimum thickness of thermal insulation used in buildings, by introducing the variability of the degree days required in a given region; (b) a measure of the economic risk incurred by building heating is established as a function of the years of use for which the building is designed and the number of years it is actually used.

Study the thermal response of a solar flat-plate collector under transient solar radiation experimentally and numerically

The target of the current study is to investigate and comparing the thermal response of solar flat plate collector (SFPC) for traditional and new model numerically and experimentally. The new model consists of elliptical-shaped riser pipes and a flat plate with longitudinal channels and wavy fins. The idea behind the new model is to increase the surface area exposed to solar radiation. All numerical models were created and simulated using ANSYS Fluid 19. R3. While, all of the experimental tests were conducted during the winter season in Baghdad, Iraq. The study showed that there are agree well between experimental and numerical results, and both models have the same behavior. The results showed that the new model outperformed the traditional one in terms of thermal response. The new one performed 23.6% better to compare the conventional model in terms of liquid temperature in the container. Additionally, the mass flow rate of liquid flowing within the closed loop of the new model is 7.9% higher than the traditional model. Moreover, in terms of overall thermal efficiency, the new model outperformed the traditional model by 22.4%.

A novel predictive formula for highly accurate discrimination between truly Helicobacter pylori-uninfected and currently infected/spontaneously eradicated individuals for gastric cancer screening.

The ABC classification, which categorizes gastric cancer risk based on serum Helicobacter pylori (H pylori) antibody and pepsinogen levels, has a limitation of potentially misclassifying high-risk individuals as low risk. To overcome the problem, we previously developed a 4-parameter predictive formula (age, serum H pylori antibody, PGI, and PGII) using logistic regression analysis to accurately identify low-risk truly H pylori-uninfected status. Our predictive formula demonstrated superior sensitivity and specificity in distinguishing between low-risk truly uninfected individuals and high-risk currently/spontaneously eradicated status individuals, compared to the modified ABC classification based on latex immunoassay kits (traditional 3-parameter model). This study aimed to revalidate the diagnostic accuracy of the predictive formula in a new and different study population. We applied the predictive formula to the target population and compared the sensitivity and specificity with those of the traditional 3-parameter model. A total of 788 enrollees were analyzed: 703 were classified as truly uninfected, 45 as currently infected, and 40 as spontaneously eradicated according to the results of stool antigen testing and endoscopic findings. The sensitivities and specificities of the predictive formula and the traditional 3-parameter model were 89.5% and 87.1% versus 89.8% and 80.0%, respectively. The specificity of the predictive formula was superior in the 70 to 89 age range and H pylori antibody < 3 U/mL groups. The predictive formula had higher specificity than the traditional 3-parameter model. The results should contribute to efficient gastric cancer screening by predicting H pylori infection status.

Intelligent system for solid waste classification using combination of image processing and machine learning models

ABSTRACT Solid waste is a major issue in all cities around the world. Classification and segregation of solid waste prior to reuse, recycle or recover is an important step towards sustainable waste management. Traditional manual sorting of solid waste is a labour-intensive process that may pose health risks to the workers. Currently, automated classification of solid waste using machine learning techniques is widely applied. This study aims to develop an automated waste classification model by testing traditional and deep machine learning models. To achieve that, both open and generated datasets were used in the model training and testing. The study results showed relatively low prediction capability of the traditional machine learning models like Random Forest (RF) and Support Vector Machine (SVM) as compared to the deep machine learning Convolutional Neural Network (CNN). The testing of the three models on a combined data set of Trashnet with local garbage data set resulted in accuracy of 62.5% for SVM, 72.0% for RF and 92.7% for CNN. JONET deep learning model has been developed using a combination of pre-trained base model (DenseNet 201) with a new architicture that contains a fully connected layer in the classification stage with 1024 neurons. The model is capable to identify six classes of solid waste items with various accuracies. When tested on the Trashnet dataset, the accuracy was 96.06%, while testing on the local garbage dataset gave an accuracy of 94.40%. JONET has been tested also on multi object images which gave an acceptable prediction accuracy.

Ten-Year Outcomes: Community Health Center/Academic Medicine Partnership for Rural Family Medicine Training.

The widening gap between urban and rural health outcomes is exacerbated by physician shortages that disproportionately affect rural communities. Rural residencies are an effective mechanism to increase physician placement in rural and medically underserved areas yet are limited in number due to funding. Community health center/academic medicine partnerships (CHAMPs) can serve as a collaborative framework for expansion of academic primary care residencies outside of traditional funding models. This report describes 10-year outcomes of a rural training pathway developed as part of a CHAMP collaboration. Using data from internal registries and public sources, our retrospective study examined demographic and postgraduation practice characteristics for rural pathway graduates. We identified the rates of postgraduation placement in rural (Federal Office of Rural Health Policy grant-eligible) and federally designated Medically Underserved Areas/Populations (MUA/Ps). We assessed current placement for graduates &gt;3 years from program completion. Over a 10-year period, 25 trainees graduated from the two residency expansion sites. Immediately postgraduation, 84% (21) were in primary care Health Professional Shortage Areas (HPSAs), 80% (20) in MUA/Ps, and 60% (15) in rural locations. Sixteen graduates were &gt;3 years from program completion, including 69% (11) in primary care HPSAs, 69% (11) in MUA/Ps, and 50% (5) in rural locations. This CHAMP collaboration supported development of a rural pathway that embedded family medicine residents in community health centers and effectively increased placement in rural and MUA/Ps. This report adds to national research on rural workforce development, highlighting the role of academic-community partnerships in expanding rural residency training outside of traditional funding models.

Language-based game theory in the age of artificial intelligence.

Understanding human behaviour in decision problems and strategic interactions has wide-ranging applications in economics, psychology and artificial intelligence. Game theory offers a robust foundation for this understanding, based on the idea that individuals aim to maximize a utility function. However, the exact factors influencing strategy choices remain elusive. While traditional models try to explain human behaviour as a function of the outcomes of available actions, recent experimental research reveals that linguistic content significantly impacts decision-making, thus prompting a paradigm shift from outcome-based to language-based utility functions. This shift is more urgent than ever, given the advancement of generative AI, which has the potential to support humans in making critical decisions through language-based interactions. We propose sentiment analysis as a fundamental tool for this shift and take an initial step by analysing 61 experimental instructions from the dictator game, an economic game capturing the balance between self-interest and the interest of others, which is at the core of many social interactions. Our meta-analysis shows that sentiment analysis can explain human behaviour beyond economic outcomes. We discuss future research directions. We hope this work sets the stage for a novel game-theoretical approach that emphasizes the importance of language in human decisions.

Advanced collision risk estimation in terminal manoeuvring areas using a disentangled variational autoencoder for uncertainty quantification

Air Traffic Management aims at ensuring safety during aircraft operations, particularly within Terminal Manoeuvring Areas where traffic density is high. The challenge lies in balancing safety and efficiency by closely managing the likelihood of mid-air collisions regarding the airport movements. Traditional models like the Reich and Anderson-Hsu models have been influential, but they fall short in representing the complex reality of Terminal Manoeuvring Areas. Data-driven approaches are emerging, with Monte Carlo simulations offering a more flexible methodology for collision risk estimation. This paper introduces a framework for assessing Mid-Air Collision likelihood resulting from Terminal Manoeuvring Area procedures by combining the field of Deep Generative Modelling using a Variational Autoencoder with the domain of rare event statistics through Subset Simulation. By incorporating disentanglement into the Variational Autoencoders model, we create a latent space that aligns dimensions with distinctive trajectory traits. Then, Subset Simulation is employed to gauge Mid-Air Collision probability, utilizing latent representations as input. Finally, sensitivity analysis reveals pivotal factors influencing collision risk, correlated with trajectory attributes via disentanglement. The methodology is applied to traffic around Zurich Airport: it evaluates the risk arising from go-around and take-off procedures using Automatic Dependent Surveillance-Broadcast data.

Assessment of the effectiveness of the BOPPPS model combined with case-based learning on nursing residency education for newly recruited nurses in China: a mixed methods study

BackgroundExpanding new nurse training and education is a priority for nursing educators as well as a critical initiative to stabilize the nursing workforce. Given that there is currently no standardized program for the training of new nurses in China, we investigated the effectiveness of the bridge-in, objective, pre-assessment, participatory learning, post-assessment, and summary model combined with case-based learning ((BOPPPS-CBL) for the standardized training of new nurses.MethodsThe mixed method approach with explanatory sequential (quantitative-qualitative) method was used. A questionnaire was used to compare the impact of the BOPPPS-CBL model and the Traditional Learning Model (TLM) on the core competencies of 185 new nurses for two years of standardized training. Quantitative data were analyzed using SPSS 22.0. Focus group interviews were used with four groups of new nurses and perceptions of BOPPPS-CBL training were recorded. Qualitative data were analyzed thematically.ResultsAccording to the quantitative data, more new nurses agreed that the BOPPPS-CBL model stimulated their learning and improved their core nursing competencies than the TLM. The BOPPPS-CBL group outperformed the TLM group on theoretical knowledge tests. Qualitative data revealed that 87.5% of new nurses agreed on the value of BOPPPS-CBL training, and three themes were extracted: (1) role promotion; (2) formation of new thinking to solve clinical problems; and (3) suggestions for improvement.ConclusionBOPPPS-CBL training had a significant impact on improving new nurses’ core competencies and promoting the transition of new nurses to clinical practice nurses in China. The study recommends BOPPPS-CBL training as an effective teaching model for the standardized training and education of new nurses.

Hybrid uncertainty propagation based on multi-fidelity surrogate model

There always exist multiple uncertainties including random uncertainty, interval uncertainty, and fuzzy uncertainty in engineering structures. In the presence of hybrid uncertainties, the hybrid uncertainty propagation analysis can be a challenging problem, which suffers from the computational burden of double-loop procedure when numerical simulation techniques are employed. In this work, a novel method for efficient hybrid uncertainty propagation analysis with the three types of uncertainties is proposed. Generally, multi-fidelity surrogate models, such as Co-Kriging, can greatly improve the computational efficiency by leveraging information from a low-fidelity model to build a high-fidelity approximate model. However, the traditional multi-fidelity surrogate model methods always calculate the hybrid uncertainty propagation result by combining with several numerical simulation techniques. This process can introduce post-processing errors unless unlimited number of samples are used, which is impossible in engineering application. In order to address this issue, the analytical solutions of the output mean and output variance are derived based on the Co-Kriging, and the resulting mean and variance are both random variables. Moreover, a new adaptive framework is established to strengthen the estimation accuracy of the hybrid uncertainty propagation result, by combining the augmented expected improvement function and the derived mean random variable. Several applications are introduced to demonstrate the effectiveness of the proposed method for solving hybrid uncertainty propagation problems.

Pre-class mode "flipped" again: Making videos instead of just watching them

"Flipped classroom" has subverted the traditional classroom teaching model and is believed to have a positive impact on knowledge acquisition and skill training for higher education students. The pre-class learning phase is considered a key factor in the success of flipped classrooms. However, currently, the pre-class learning phase of flipped classrooms mainly relies on video watching, which makes students passive receivers. Teachers need to invest a lot of time and resources in developing online videos, which greatly increases students' learning time and tasks. This also hinders the promotion of this teaching model. This study designs a pre-active learning strategy based on student participation in video production and a flipped classroom teaching model, and uses questionnaire surveys and interviews to observe students' reactions, explore its impact on students' satisfaction, and empirically analyze the path it affects students' satisfaction. We found that in the pre-class phase, the greater the ease of use and usefulness perceived by students in video production, the higher the students' satisfaction. Perceived enjoyment and perceived value are important intermediary paths. In addition, based on the research results, this study proposes suggestions for a flipped classroom teaching model based on video production. The results of this study will provide important references for the pre-class learning phase of flipped classrooms.

Mode Innovation and Discussion of Enterprise Financial Management in the New Economic Background

The development of the globalized economy has also made enterprise financial management work into a new stage of reform, the; transformation of the market economic system has also made the enterprise financial management work content and mode of change the traditional financial management model has failed to meet the needs of the current enterprise, and even hinder the development of enterprises. In the new economic background promoting financial management reform and innovation is not only the way of enterprise development. It is also in line with the trend of the times, where the article through the trend of economic development and in-depth analysis of the current financial management situation.By analyzing published literature and using the literature analysis method, we summarize the current problems in corporate financial management and propose corresponding solutions to the problems based on the key points of the problems, thereby providing reference and help for corporate development.

Digital economy and green total factor productivity in China.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.

Landslide spatial prediction using cluster analysis

Temporal clustering is an intrinsic nature of landslide occurrences, therefore it should be considered in data-driven landslide spatial prediction (i.e., susceptibility assessment). However, it remains problematic regarding how to determine landslide temporal clusters and how to integrate susceptibility maps derived from different landslide temporal clusters. In this paper, a general framework of landslide spatial prediction model considering the temporal clustering of landslides is proposed. This novel framework first assesses landslide susceptibility separately based on each landslide temporal cluster identified by spatiotemporal clustering analysis and then integrates separate assessments by weighted averaging. In a case study, this general framework is implemented using the stacking network landslide susceptibility assessment method and used in the landslide spatial prediction of the Sanming City and Wenchuan seismic areas. The results show that the proposed framework outperformed traditional susceptibility models that do not consider landslide temporal clustering, and the integration of susceptibility models based on all landslide temporal clusters will promote the performance of landslide spatial prediction because levels of knowledge in long-term spatiotemporal landslide activities are considered. This novel general framework highlights the benefit of considering landslide temporal clustering in landslide spatial prediction and can provide better support for landslide risk management.

Comparison of two 3D reconstruction models for understanding of complicated female pelvic tumors.

Three-dimensional (3D) reconstructed models have been shown to improve visualization in complex female pelvic tumors. Cinematic rendering (CR) is a 3D imaging technique for computed tomography (CT) images, which creates more realistic images with the ability to enhance imaging of anatomical features for diagnosis. This study was set up to compare two types of 3D models and to validate the use of 3D anatomical techniques for the diagnosis of complex female pelvic tumors. The preclinical, randomized, two-sequence crossover investigation was performed from December 2022 to January 2023 at First Affiliated Hospital of Chongqing Medical University. Sixteen residents and 10 attending surgeons assessed the cases of 23 patients with two types of 3D model images. The surgeons were randomly assigned to two assessment sequences (CR-3D model group and CT-3D model group). For each case, participants selected one question that probed fundamental questions about the tumor's genesis throughout each assessment period. Following a 4-week washout period, case assessments were transferred to the other image modality. The main result assessment was the accuracy of the answers. The time to answer the questions and the case assessment questionnaire was added as a secondary outcome. The mean scores in the CR-3D models (19.35 ± 1.87) varied significantly from those in the CT-CR group (16.77 ± 1.8) (P < 0.001), and solving the questions in the CT-3D model sequence (41.96 ± 6.31 s) varied significantly from that in the CR-3D model sequence (52.88 ± 5.95 s) (P < 0.001). Subgroup analysis revealed that there were statistically significant variations in the scores of female reproductive tumors, pelvic tumors other than the reproductive system, and retroperitoneal tumors (P = 0.005). Analysis of the assessment questionnaire showed that more surgeons choose CR 3D reconstruction (8.31 ± 0.76 vs 7.15 ± 1.19, P < 0.001). The results suggest that each 3D reconstruction method has its own advantages. Surgeons feel that CR reconstruction models are a useful technique that can improve their comprehension of complex pelvic tumors, while traditional 3D models have an advantage in terms of speed to diagnosis.

Employing deep learning in non‐parametric inverse visualization of elastic–plastic mechanisms in dual‐phase steels

AbstractEnhancing the interpretability of machine learning methods for predicting material properties is a key, yet complex topic in materials science. This study proposes an interpretable convolutional neural network (CNN) to establish the relationship between the microstructural evolution and mechanical properties of non‐uniform and nonlinear multisystem dual‐phase steel materials and achieve an inverse analysis of the elastic‐plastic mechanism. This study demonstrates that the developed CNN model achieves an accuracy of 94% in predicting the stress‐strain curves of dual‐phase steel microstructures with different compositions and processes, with the mean absolute error not exceeding 50 MPa, representing merely 5.26% of the average tensile strength of dual‐phase steels in the dataset. The reverse visualization results of the CNN model indicate that, during tensile deformation, the grain boundaries maintain deformation coordination within the grains by impeding dislocation slip. This results in a significant stress concentration at the grain boundaries, with stresses at the boundaries being higher than those borne by the martensitic phase and minimal stresses in the ferrite phase. Moreover, compared with traditional crystal plasticity models, the CNN model exhibits a substantial improvement in computational efficiency. This method provides a generic plan for improving the interpretability of machine learning methods for predicting material properties and can be easily applied to other alloy systems.

Integration of natural selection across the life cycle stabilizes a marine mussel hybrid zone.

Hybridization among related species is now recognized as common but it remains unclear how hybrid zones persist for prolonged periods. Here, we test the hypothesis that selection in different components of the life cycle may stabilize a hybrid zone. A hybrid zone occurs in southwest England between the marine mussels Mytilus edulis and M. galloprovincialis. Previous studies have found strong directional selection against alleles from M. edulis occurs among hybrids in the adult stage. Traditional hybrid zone models argue that alleles that are selected within the hybrid zone are replaced by migration from neighboring parental population into the hybrid zone. In this system, however, migration occurs out of this hybrid zone into neighboring parental populations. This hybrid zone should therefore be unstable and dissipate, yet this zone has persisted for more than 30 years. We tested and rejected the hypothesis that differences in fecundity may select for M. edulis alleles within this hybrid zone and thus counter the selection observed against these alleles among adults. We also tested the hypothesis that selection during the larval stage may counter selection against M. edulis alleles in the adult stage. We found that selection favors M. edulis alleles during the veliger stage of larval development. The direction and strength of selection during the larval stage are sufficient to counter strong selection during the adult portion of the life cycle. This hybrid zone is stabilized by opposing forms of directional selection operating in different portions of the life cycle.