Inadequacy Of Model Research Articles

Users’ voice-switching behaviors, embodiments and perceptions of voice assistants

Purpose This paper aims to investigate user voice-switching behavior in voice assistants (VAs), embodiments and perceived trust in information accuracy, usefulness and intelligence. The authors addressed four research questions: RQ1. What is the nature of users’ voice-switching behavior in VAs? RQ2: What are user preferences for embodied voice interfaces (EVIs), and do their preferred EVIs influence their decision to switch the voice on their VAs? RQ3: What are the users’ perceptions of their VAs concerning: a. information accuracy, b. usefulness, c. intelligence and d. the most important characteristics they must possess? RQ4: Do users prefer their voice interface to match their characteristics (age, gender, accent and race/ethnicity)? Design/methodology/approach The authors used a 52-question survey questionnaire to collect quantitative and qualitative data. The population was undergraduate students (freshmen and sophomores) at a research university in the USA. The students were enrolled in two required courses with a research participation assignment offered for credits. Students must register for research participation credits in the SONA Research Participation System www.sona-systems.com/platform/research-management/ Registered students cannot be invited or sampled to participate in a research study. There were 1,700 students enrolled in both courses. After the survey’s URL was posted in SONA, the authors received (n = 632) responses. Of these, (n = 150) completed the survey and provided valid responses. Findings Participants (43%) switched the voice interface in their VAs. They preferred American and British accents but trusted the latter. The British accent with a male voice was more trusted than the American accent with a female voice. Voice-switching decisions varied in the case of most and least preferred EVIs. Participants preferred EVIs that matched their characteristics. Most trusted their VAs’ information accuracy because they used the internet to find information, reflecting inadequate mental models. Lack of trust is attributed to misunderstanding requests and inability to respond accurately. A significant correlation was found between the participants’ perceived intelligence of their VAs and trust in information accuracy. Research limitations/implications Due to the wide variability in the data (e.g. 84% White, 6% Asian and 6% Black), the authors did not perform a statistical test to identify the significance between the selected EVIs and participants’ races or ethnicities. The self-reported survey questionnaire may be prone to inaccuracy. The participants’ interest in earning research credit for participation in this study and using SONA is a potential bias. The EVIs the authors used as embodiments are limited in their representation of people from diverse backgrounds, races, ethnicities, ages and genders. However, they could be examples for building prototypes to test in VAs. Practical implications Educators and information professionals should lead the way in offering artificial intelligence (AI) literacy programs to enable young adults to form more adequate mental models of VAs and support their learning and interactions. VA designers should address the failures and other issues the participants experienced in VAs to minimize frustrations. They should also train machine learning models on large data sets of complex queries to augment success. Furthermore, they should consider augmenting VAs’ personification with EVIs to enrich voice interactions and enhance personalization. Researchers should use a mixed research method with data triangulation instead of only a survey. Social implications There is a dire need to teach young adults AI literacy skills to enable them to build adequate mental models of VAs. Failures in VAs could affect users’ willingness to use them in the future. VAs can be effective teaching and learning tools, supporting students’ autonomous and personalized learning. Integrating EVIs with diverse characteristics could advance inclusivity in designing VAs and support personalization beyond language, accent and gender. Originality/value This study advances research on user voice-switching behavior in VAs, which has hardly been investigated in VA research. It brings attention to users’ experiential learning and the need for exposure to AI literacy to enable them to form adequate mental models of VAs. This study contributes to research on personifying VAs through EVIs with diverse characteristics to visualize voice interactions. Reasons for not switching the voice interface due to satisfaction with the current voice or a lack of knowledge of this feature did not support the status quo theory. Incorporating satisfaction and lack of knowledge as new factors could advance this theory. Switching the voice interface to avoid visualizing the least preferred EVIs in VAs is a new theme emerging from this study. Users’ trust in VAs’ information accuracy is intertwined with perceived intelligence and usefulness, but perceived intelligence is the strongest factor influencing trust.

Characterization of uncertainties in electron-argon collision cross sections

Abstract The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the corresponding reaction rates and electron transport properties. The values of cross sections can be known only approximately either through experiments or simulations and are thus subject to uncertainties. Quantifying the uncertainties in plasma simulations allows us to assess the reliability of simulations and to provide a basis for interpreting discrepancies between simulations and experiments. For such uncertainty quantification of plasma simulations, it is essential to quantify the uncertainties of the underlying cross sections. Although much effort has been committed to calibrate the cross section values, their uncertainties are not well investigated. We characterize uncertainties in electron-argon atom collision cross sections using a Bayesian framework. Six collision processes—elastic momentum transfer, ionization, and four excitations—are characterized with semi-empirical models, which effectively capture the features important to the macroscopic properties of the plasma. A probability model for the uncertain parameters of these semi-empirical models is developed. Specifically, a Gaussian-process likelihood model is proposed to capture discrepancies among data sets, as well as the model-form inadequacies of the semiempirical models. Two other likelihood models are compared with the proposed Gaussian-process model, to illustrate the importance of the choice of the likelihood model. The cross section models are calibrated using the electron-beam experiments and ab-inito quantum simulations. The resulting calibrated uncertainties capture well the scattering among the data sets. The calibrated cross section models are further validated against swarm-parameter experiments and zero-dimensional Boltzmann equation simulations of widely used cross section datasets.

Prediction Models for Postoperative Pneumonia in Elderly Hip Fracture Patients: A Systematic Review and Critical Appraisal.

Although several models have been developed to predict postoperative pneumonia in elderly hip fracture patients, no systematic review of the model quality and clinical applicability has been reported. To systematically review and critically appraise existing models for postoperative pneumonia in elderly hip fracture patients. Systematic review and meta-analysis. 10 databases were systematically searched from inception to April 15, 2024, updated on August 26. Two reviewers independently performed literature selection, information extraction and quality assessment. A narrative synthesis was employed to summarise the characteristics of the models. Meta-analysis was performed using Stata 17.0. 13 studies containing 25 models were included. The prevalence of pneumonia was 9.62% (95% CI: 7.62%-11.62%). Age (53.8%), hypoproteinemia (46.2%), chronic obstructive pulmonary disease (COPD, 30.8%), gender (30.8%), activity of daily living score (ADL, 30.8%) and American Society of Anesthesiologists (ASA, 30.8%) score were the top six predictors. All models reported area under curve (AUC: 0.617-0.996). 9 studies (69.2%) used the Hosmer-Lemeshow (H-L) test, calibration curves, or Brier scores to evaluate the calibration. 5 studies (38.5%) performed internal validation, 4 studies (30.8%) performed external validation. All studies had a high risk of bias due to single sample source, inappropriate data processing, inadequate model evaluation, and negligence of calibration and validation. 10 studies (76.9%) had good applicability. Prediction models for postoperative pneumonia in elderly hip fracture patients are still in the developing stage. The validation and evaluation of existing models are poor. Future studies should focus on robust external validation and updating. Additionally, the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + artificial intelligence (TRIPOD+AI) statement should be followed. Prediction models are effective in discriminating postoperative pneumonia in elderly hip fracture patients, but further external validation and adjustment are still warranted.

Evaluating UCE Data Adequacy and Integrating Uncertainty in a Comprehensive Phylogeny of Ants.

While some relationships in phylogenomic studies have remained stable since the Sanger sequencing era, many challenging nodes remain, even with genome-scale data. Incongruence or lack of resolution in the phylogenomic era is frequently attributed to inadequate data modeling and analytical issues that lead to systematic biases. However, few studies investigate the potential for random error or establish expectations for the level of resolution achievable with a given empirical dataset and integrate uncertainties across methods when faced with conflicting results. Ants are the most species-rich lineage of social insects and one of the most ecologically important terrestrial animals. Consequently, ants have garnered significant research attention, including their systematics. Despite this, there has been no comprehensive genus-level phylogeny of the ants inferred using genomic data that thoroughly evaluates both signal strength and incongruence. In this study, we provide insight into and quantify uncertainty across the ant tree of life by utilizing the most taxonomically comprehensive Ultraconserved Elements dataset of ants to date, including 277 (81%) of recognized ant genera from all 16 extant subfamilies, and representing over 98% of described species. We use simulations to establish expectations for resolution, identify branches with less-than-expected concordance, and dissect the effects of data and model selection on recalcitrant nodes. Simulations show that hundreds of loci are needed to resolve recalcitrant nodes on our genus-level ant phylogeny. This demonstrates the continued role of random error in phylogenomic studies. Our analyses provide a comprehensive picture of support and incongruence across the ant phylogeny, while offering a more nuanced depiction of uncertainty and significantly expanding generic sampling. We use a consensus approach to integrate uncertainty across different analyses and find that assumptions about root age exert substantial influence on divergence dating. Our results suggest that advancing the understanding of ant phylogeny will require not only more data but also more refined phylogenetic models. We also provide a workflow for identifying under-supported nodes in concatenation analyses, outline a pragmatic way to reconcile conflicting results in phylogenomics, and introduce a user-friendly locus selection tool for divergence dating.

Understanding mode of gene action governing green forage yield and its component traits in pearl millet

Understanding the genetic control of forage yield and related traits can decide breeding strategies for high-biomass cultivars. In this study, generation mean analysis was used to evaluate gene effects and non-allelic interactions for forage yield and its component traits in pearl millet using two experimental crosses developed by using two high-yielding inbreds IP 18168 and IP 22419 and their six generations (P1, P2, F1, BC1, BC2 and F2) were independently evaluated during Kharif season 2022. Significant variability and the inadequacy of an additive-dominance model highlighted the role of epistasis, with additive × dominance interactions prominent for forage yield and plant height. Dominance and epistatic effects were also crucial for traits like leaf and internode number, leaf length and leaf width. These findings indicated one or two generations of selfing followed by recurrent selection in advanced generations will be useful in enhancing the frequency of genes with increasing effects on green forage yield. Our findings could be helpful in designing the forage pearl millet breeding programs in India.

Dengue virus: Etiology, epidemiology, pathobiology, and developments in diagnosis and control - A comprehensive review.

Dengue flavivirus (DENV) is the virus that causes dengue, one of the most dangerous and common viral diseases in humans that are carried by mosquitoes and can lead to fatalities. Every year, there are over 400 million cases of dengue fever worldwide, and 22,000 fatalities. It has been documented in tropical and subtropical climates in over 100 nations. Unfortunately, there is no specific treatment approach, but prevention, adequate awareness, diagnosis in the early stages of viral infection and proper medical care can reduce the mortality rate. The first licensed vaccine for dengue virus (CYD Denvaxia) was quadrivalent, but it is not approved in all countries. The primary barriers to vaccine development include inadequate animal models, inadequate etiology mechanistic studies, and adverse drug events. This study provides current knowledge and a comprehensive view of the biology, production and reproduction, transmission, pathogenesis and diagnosis, epidemiology and control measures of dengue virus.

New concept on the transformation of the zone of oil product pollution of the geological environment near of the «Olexandria» Arboretum (Bila Tserkva city, Ukraine)

In Ukraine, numerous geological environment sites are contaminated with lost oil products. Among them, there are many that have a long history of pollution transformation and accumulation. Due to the lack of long-term monitoring, much of the pollution history remains unknown, resulting in inadequate conceptual models that have a negative impact on the design of remediation projects. This article examines a solution to this problem, focusing on studies of the territory of the former Bila Tserkva aircraft repair plant (BARP), bordering the State Arboretum «Olexandria». Since 1990, various organizations have assessed geological contamination and proposed remediation strategies for the factory site and surrounding areas. The article provides information on the main results of the performed research. Emphasis is placed on clarifying the reasons for the sharp difference in the data of hydrogeological investigations conducted in 1990 and 2007. Initially, in 1990, liquid petroleum products (aviation kerosene) were detected only at the site of natural groundwater discharge in the upper reaches of the Western Ravine within the arboretum. On the plant’s territory, where there were undoubtedly sources of pollution and migration pathways of petroleum products in the soil, the presence of pollution was recorded only by the specific smell and color of core samples. However, during prospecting and exploration work in 2007 in the same area, three clearly defined lenses of light non-aqueous phase liquids (LNAPL) were discovered in sub-moraine sands. This phenomenon can be explained by the fact that the surface of groundwater, measured in 1990, dropped from a layer of moraine loams, where the mobility of light oil products is considered negligible, to sub-moraine sands, which are characterized by a much higher permeability.It should be noted that in the western part of the plant’s territory, where aviation fuel storage tanks were located, traces of contamination were found not only in sub-moraine sands and moraine loams, where the groundwater level fluctuates, but also in soils lying on the moraine. This supports the assumption that in the past, there were emergency releases (losses) of a large volume of fuel into the unsaturated soil layer and the subsequent relatively rapid lateral spread of the oil product in the aeration zone.

Fundus Vascular Segmentation Based on Data Enhancement and Invariant Feature Extraction

Deep neural networks have emerged as the predominant method for medical image segmentation, owing to their robust feature learning capabilities, enabling accurate automatic segmentation of target structures within intricate medical images. Fundus images, being crucial in diagnosing ophthalmic diseases, underscore the importance of effective segmentation techniques. However, fundus vascular images pose challenges due to their high complexity and subtle individual differences, necessitating improvement in existing segmentation methodologies for enhanced disease classification accuracy. This paper introduces a fundus blood vessel segmentation model, employing data augmentation and invariant feature extraction, to systematically tackle the core challenges in medical image processing, particularly in fundus blood vessel segmentation. These challenges include limited source domain samples and inadequate model domain generalization. The model adopts a dual-dimensional strategy. Firstly, it delves into data augmentation technology to enhance the diversity and representativeness of samples within the finite source domain. This is achieved through an image enhancement module based on Fourier transform, mitigating the impact of data scarcity on model training effectiveness. Secondly, the research focuses on interdomain invariant feature extraction, aiming to extract feature representations that consistently characterize fundus blood vessel structure and pathology across different data distributions, thereby enhancing model generalization performance in unfamiliar domains. Specifically, the paper designs a fundus image enhancement module based on Fourier transform in the data augmentation dimension. In the feature extraction dimension, it proposes a normalization module based on uncertainty theory, departing from traditional normalization methods. Experimental results demonstrate the efficacy of the proposed method, showcasing superior generalization performance compared to existing techniques in retinal blood vessel and OD/OC segmentation tasks. Experience validates the model-agnostic nature of the learned strategy, indicating its potential for seamless transferability to other models, thereby offering robust support for advancing medical image segmentation research and applications.

Numerical Study of Near-Field Radionuclides Dispersion Around Barakah Power Plant During Postulated Accidental Release Scenarios

AbstractThis study explores the assessment of hazards arising from nuclear power plant incidents, informed by the Fukushima catastrophe. It evaluates the environmental impact of noble gases, such as iodine-131 releases, recognizing the limitations of current local computational tools, particularly in predicting near-field dispersion accurately. Utilizing computational fluid dynamics (CFD), this study validates this approach’s effectiveness in predicting pollutant dispersion around buildings. Among the five turbulence models tested, the Lag Elliptic Blending (EB) k-ε model emerges as the most suitable for simulating radioactive pollutant dispersion due to its superior performance in capturing flow dynamics. The findings underscore the inadequacy of traditional Gaussian plume models in accounting for the effects of buildings on dispersion patterns. Notably, simulations around the Barakah nuclear site located in the United Arab Emirates reveal the significant influence of buildings on the trajectory of radioactive pollutants from hypothetical cracks. Consequently, it advocates caution in relying solely on classical Gaussian plume models for evacuation plans, as they may overlook crucial flow patterns due to building presence, potentially leading to distorted assessments of gas distribution and deposition rates.

Elucidating Degradation Mechanisms through Modeling of Proton-Exchange Membrane Electrolytic Cells

Proton exchange membrane (PEM) electrolytic cells (ECs) have gained significant importance in the realm of hydrogen production. With the increasing demand for commercializing PEMECs, there is a pressing need for enhancing their performance and durability. In addition to experiments, macro-scale modeling plays a pivotal role in comprehending the multi-physics processes within PEMECs.This presentation highlights the development of physics-based and data-based models tailored for PEMECs. They serve as the foundation for elucidating degradation mechanisms of PEMECs, aiming to prolong their lifespan and improve their efficiency for a quick technology ramp up. These models explore the electrochemical processes and transport phenomena occurring within the porous transport layers (PTLs), catalyst layers (CLs), and the PEM. The high-aspect-ratio PEMECs are well-suited for one-dimensional (1D) analysis [1-3]. The 1D analysis is implemented in Python, with COMSOL also utilized for 2D and 3D simulations.Unfortunately, the complex nature of PEMECs as thermal, fluidic, and electrochemical systems poses challenges in accurate diagnostics. Modeling these cells involves addressing uncertainties in operational conditions, material properties, and design features. Additionally, uncertainties in experimental data, model selection, parameters, and model inadequacies must be carefully considered during PEMEC modeling. While uncertainty quantification (UQ) and sensitivity analysis (SA) can unveil how uncertainties propagate from input parameters to the output of PEMEC models, UQ has not been extensively integrated into PEMEC modeling. Finally, this presentation underscores the potential benefits of physics-based modeling combined with UQ and SA for PEM electrolysis [3]. Acknowledgment: Financial support was provided by the German Federal Ministry of Education and Research (BMBF) within the H2Giga project DERIEL (grant number 03HY122C). Literature: [1] P. Trinke, 2021.[2] P. A. García-Salaberri, J. Power Sources, 2022, 521.[3] V. Karyofylli, Y. Danner, K. Ashoke Raman, H. Kungl, A. Karl, E. Jodat, R.-A. Eichel, J. Power Sources, 2024, 600, 234209.

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.

This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models. PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks. Data extraction followed the CHARMS checklist. Quality appraisal followed the PROBAST tool. Data synthesis was guided by the socio-ecological conceptual framework. Thirteen studies were included, mainly focusing on respiratory infections and nursing homes. Most used regression models with structured electronic health record data. Since 2020, there has been a shift toward advanced ML algorithms and multimodal data, biosensors, and clinical notes being significant sources of unstructured data. Despite these advances, there is insufficient evidence to support performance improvements over traditional models. Individual-level risk predictors, like impaired cognition, declined function, and tachycardia, were commonly used, while contextual-level predictors were barely utilized, consequently limiting model fairness. Major sources of bias included lack of external validation, inadequate model calibration, and insufficient consideration of data complexity. Despite the growth of advanced modeling approaches in infection-related models in PAC settings, evidence supporting their superiority remains limited. Future research should leverage a socio-ecological lens for predictor selection and model construction, exploring optimal data modalities and ML model usage in PAC, while ensuring rigorous methodologies and fairness considerations.

A Brief Study on Indian Startups- Analytic Research on the Success and Failure Factors of Swiggy, Boat, Doodhwala, And Shopx

India’s startup ecosystem has witnessed unprecedented growth in recent years, emerging as one of the most dynamic and fast-paced environments globally. This research paper examines the primary factors leading to the high failure rate of Indian startups, focusing on familiar challenges such as inadequate business models, poor product-market fit, cash flow problems, regulatory hurdles, and inefficient management. Through case studies of successful ventures like Swiggy, and BoAT, this paper highlights critical success factors that have enabled these companies to thrive, including data-driven decision-making, customer-centric strategies, adaptability, and strong strategic partnerships. The study also explores the pivotal role of venture capitalists (VCs) in shaping startup trajectories, from early seed funding to advanced growth stages. Additionally, the paper delves into the influence of Indian government initiatives, such as Startup India and the Atal Innovation Mission, in fostering a supportive ecosystem that encourages entrepreneurship and innovation. Keywords: Entrepreneur, Start-up, Economy, Future market, Challenges

Parental obesity, health determinants, and cardiometabolic risk according to sleep duration in schoolchildren: analysis through structural equations

Backgroundthe independent association between parental obesity, sleep and lifestyle habits with cardiometabolic risk factors in children and adolescents has been widely explored in the literature. Our study represents a novel approach to comprehensively investigate a complex model encompassing various aspects associated with cardiometabolic risk in youth. Thus, the aim of the present study was to verify the relationship between parental obesity and cardiometabolic risk in children and adolescents, considering the mediator role of health indicators according to sleep time.MethodsThis cross-sectional study was conducted on 3,973 children and adolescents aged 6 to 17 years attending public and private schools in a city located in Southern Brazil. Sleep duration, lifestyle, physical activity, natural food consumption, and parental obesity were evaluated through self-reported questionnaires. Physical fitness was evaluated according to the protocols of Projeto Esporte Brasil. The body fat percentage was evaluated through the measures of tricipital and subscapular folds, and the body mass index was calculated. The clustered metabolic risk score (cMetS) considered the summing z-scores of waist circumference, systolic blood pressure, triglycerides, total cholesterol/high-density lipoprotein cholesterol ratio, and fasting glucose, divided by five. A structural equation model was applied for statistical analysis. Results: The relationship between parental obesity and cardiometabolic risk was observed in children and adolescents with adequate sleep, being significant (p < 0.05) mediated by physical activity, natural food, physical fitness, and lifestyle. This was also observed when parental education was included in the inadequate sleep cluster model. Conclusion: Present findings underscore the importance of sufficient sleep duration as a critical factor in understanding the complex interplay between parental obesity and cardiometabolic risk in children and adolescents.

NFSA-DTI: A Novel Drug-Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism.

Existing deep learning methods have shown outstanding performance in predicting drug-target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local crucial interaction sites in drug-target interaction pairs. In this study, we propose a novel drug-target interaction prediction model called the Neural Fingerprint and Self-Attention Mechanism (NFSA-DTI), which effectively integrates the local information of drug molecules and target sequences with their respective global features. The neural fingerprint method is used in this model to extract global features of drug molecules, while the self-attention mechanism is utilized to enhance CNN's capability in capturing the long-distance dependencies between the subsequences in the target amino acid sequence. In the feature fusion module, we improve the bilinear attention network by incorporating attention pooling, which enhances the model's ability to learn local crucial interaction sites in the drug-target pair. The experimental results on three benchmark datasets demonstrated that NFSA-DTI outperformed all baseline models in predictive performance. Furthermore, case studies illustrated that our model could provide valuable insights for drug discovery. Moreover, our model offers molecular-level interpretations.

Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods.

Regularization by double complementary priors for full waveform inversion

Full-waveform inversion (FWI) represents an advanced geophysical imaging technique focused on intricately depicting subsurface physical properties by iteratively minimizing the differences between the simulated and observed seismograms. Unfortunately, the conventional FWI utilizing a least-squares loss function suffers from various drawbacks, including the challenge of local minima and the necessity for human intervention in parameter fine-tuning. It is particularly problematic when handling noisy data and inadequate initial models. Recent works have exhibited promising performance in two-dimensional FWI by integrating structural sparse representation to procure adaptive dictionaries. Drawing inspiration from the competitiveness of structural sparse representation, we introduce a paradigm of group sparse residuals that integrates two types of complementary prior information by harnessing both the internal and external subsurface media models. The proposed algorithm is based on an alternate minimization algorithm to guarantee workflow flexibility and efficient optimization capabilities. We experimentally validate our method for two baseline geological models, and a comparison of the results demonstrates that the proposed algorithm faithfully recovers the velocity models and consistently outperforms other traditional or learning-based algorithms. A further benefit from the group sparse coding used in this method is that it reduces the sensitivity to data noise.

Evaluation of Drivers’ Mental Model, Trust, and Reliance Toward Level 2 Automated Vehicles

Level 2 automated vehicles (AVs) have been commercially available for years, yet the extent to which drivers are cognizant of their capabilities and limitations remains largely unknown. This study aimed to assess the public’s mental model of Level 2 AVs and investigate the relationships between mental model, trust, and reliance. Moreover, it sought to explore driver heterogeneity through cluster analysis to identify typical driver classes. Employing Tesla Autopilot as a representative Level 2 automation system, we designed a questionnaire measuring mental model from three dimensions: functional condition, sensorimotor preparation, and automation monitoring. This questionnaire was administered to both Tesla Autopilot owners (N = 357) and non-owners (N = 357). The results showed that, on average, drivers possessed an inadequate mental model regarding Level 2 AVs, and those with a relatively better mental model exhibited lower levels of trust and reliance. Owning a Level 2 AV did not result in a better understanding of the system, but it led to a higher trust level. Moreover, the results suggested that drivers’ trust and reliance in AVs tended to increase within the initial 6 months of usage and then kept stabilized after that. Through cluster analysis, we identified three distinct driver classes that exhibited significant differences in their mental model levels and attitudes towards AVs: Class 1 (blurred positive), Class 2 (sober neutral), and Class 3 (cautiously positive). This further validates the presence of heterogeneity among drivers. The insights gained from this study can serve as a valuable resource for supporting and guiding the development and implementation of training programs aimed at enhancing drivers’ comprehension of Level 2 AVs.

Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism.

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground-background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground-Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground-background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark.

Non-Parametric Fuzzy Regression Discontinuity Design in Modelling Viral Load Suppression Among People Living With HIV/AIDS (PLWHA)

Adherence to antiretroviral therapy (ART) is a significant determinant of viral load suppression in HIV patients. There are inadequate statistical models that bring out the direct effects of ART on the suppression of HIV/AIDS. Traditional regression models address the general determinants of viral load suppression. Regression discontinuity designs, on the other hand, bring out the causal effects of ART on viral load suppression based on various thresholds. This study used the non-parametric fuzzy regression discontinuity design (FRDD) to model viral load suppression in PLWHA. The study began with developing a non-parametric FRDD, simulating the model to assess its performance, and applying the model to the Quality-of-Care dataset from Kaggle. The study focused on viral load suppression as the outcome variable, CD4 count and age as the running variables, gender, and whether a patient received counseling as additional covariates. The optimal thresholds were 40.5 years and 320 cubic millimeters for the CD4. There was an increasing negative treatment effect of ART on viral load suppression as the cutoff points for CD4 count increase. At the same time, there was an increasing negative treatment effect of ART on viral load suppression with increasing age. The compliance ratios for respondents increased with the negative increase in the treatment effect. Other analyses, such as the McCrary density test, bunching test, and manipulation test, indicated that the non-parametric fuzzy regression discontinuity design is effective in modeling viral load suppression.

Development of a Microfluidic Vascularized Osteochondral Model as a Drug Testing Platform for Osteoarthritis.

Osteoarthritis (OA) is a degenerative joint disease characterized by changes in cartilage and subchondral bone. To date, there are no available drugs that can counteract the progression of OA, partly due to the inadequacy of current models to recapitulate the relevant cellular complexity. In this study, an osteochondral microfluidic model is developed using human primary cells to mimic an OA-like microenvironment and this study validates it as a drug testing platform. In the model, the cartilage compartment is created by embedding articular chondrocytes in fibrin hydrogel while the bone compartment is obtained by embedding osteoblasts, osteoclasts, endothelial cells, and mesenchymal stem cells in a fibrin hydrogel enriched with calcium phosphate nanoparticles. After developing and characterizing the model, Interleukin-1β is applied to induce OA-like conditions. Subsequently, the model potential is evaluated as a drug testing platform by assessing the effect of two anti-inflammatory drugs (Interleukin-1 Receptor antagonist and Celecoxib) on the regulation of inflammation- and matrix degradation-related markers. The model responded to inflammation and demonstrated differences in drug efficacy. Finally, it compares the behavior of the "Cartilage" and "Cartilage+Bone" models, emphasizing the necessity of incorporating both cartilage and bone compartments to capture the complex pathophysiology of OA.