Search Results (39)

This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry. Full article

Persistent atrial fibrillation (AF), a prevalent cardiac arrhythmia, is primarily sustained by rotor-type reentries, with their localization crucial for successful ablation treatment. Fractionated atrial electrogram (EGM) signals have been associated with the tips of the rotors and are thus considered as ablation targets. However, the typical noise problems of physiological signals affect the results of EGM processing tools, and consequently the ablation outcome. This study proposes a data fusion framework based on the Joint Directors of Laboratories model with six levels and information quality (IQ) assessment for locating rotor tips from EGMs simulated in a two-dimensional model of human atrial tissue under AF conditions. Validation tests were conducted using a set of 13 IQ criteria and their corresponding metrics. First, EGMs were contaminated with different types of noise and artifacts (power-line interference, spikes, loss of samples, and loss of resolution) to assess tolerance. The signals were then preprocessed, and five statistical features (sample entropy, approximate entropy, Shannon entropy, mean amplitude, and standard deviation) were extracted to generate rotor location maps using a wavelet fusion technique. Fuzzy inference was applied for situation and risk assessment, followed by IQ mapping using a support vector machine by level. Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. The proposed framework outperformed existing EGM-based rotor detection methods, demonstrating superior functionality and performance compared to existing EGM-based rotor detection methods. It achieved an accuracy of approximately 90%, with improvements of up to 10% through tuning and adjustments based on IQ variables, aligned with higher-level system requirements. The novelty of this approach lies in evaluating the IQ across signal-processing stages and optimizing it through data fusion to enhance rotor tip position estimation. This advancement could help specialists make more informed decisions in EGM acquisition and treatment application. Full article

This study investigates the effects of financial globalization, trade globalization, and information and communication technology on the ecological footprint in G9 industrial economies (China, the United States, Japan, Germany, India, South Korea, Italy, France, and the United Kingdom) from 2000Q1 to 2018Q4. A distinctive Method of Moments Quantile Regression (MMQR) model was employed to analyze these relationships, and the Bootstrap Quantile Regression (BSQR) model was used to validate the results. The findings reveal that financial globalization (FG), environmental tax (ETAX), and institutional quality (IQ) contribute to environmentally sustainable development by reducing the ecological footprint (ECOFP). In contrast, trade globalization, information and communication technology (ICT), and gross domestic product (GDP) have a significant positive impact on the ecological footprint, leading to increased environmental degradation. The BSQR results corroborate these findings, confirming the roles of financial globalization, institutional quality, environmental tax, trade globalization, information and communication technology, and gross domestic product in shaping the ecological footprint. Based on these results, policymakers in G9 industrial nations should promote financial globalization as a tool to reduce the ecological footprint by encouraging green financing and environmentally sustainable investments. For trade globalization, stricter environmental regulations and sustainable trade practices are essential to mitigate its adverse environmental effects. Also, efforts to minimize the ecological impact of information and communication technology should focus on integrating renewable energy into ICT infrastructure and advancing green technology innovations. Full article

Background/Objectives: Our understanding of the transdiagnostic factors that influence health-related quality of life (HRQOL) in individuals with neurodivergent conditions is very sparse and highly siloed by diagnosis labels. Research on transdiagnostic predictors of HRQOL across neurodevelopmental conditions is needed to enable care models that address shared needs of neurodivergent individuals beyond diagnostic boundaries. Our objective was to identify transdiagnostic factors associated with HRQOL in children with autism, epilepsy, or comorbid autism/epilepsy. Methods: This cross-sectional study included 37 autistic and/or epileptic children (mean age = 9.2; SD = 3.9; boys = 28). Parents provided sociodemographic information and completed the following measures: Social Communication Questionnaire (measure of severity of autistic symptoms); Parenting Stress Index, Fourth Edition; Pediatric Quality of Life Inventory; and the Behavioral Assessment System for Children, Third Edition. Child intellectual functioning was measured using age-appropriate scales: the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition: Canadian or the Wechsler Intelligence Scale for Children-Fifth Edition: Canadian. Results: Higher autistic symptom severity (OR = 0.851 95% CI: 0.732–0.988, p = 0.034) and parenting stress (OR = 0.687 95% CI: 0.493–0.959, p = 0.027) were associated with poorer HRQOL. Full Scale IQ and adaptive skills showed trend level associations with HRQOL. Sociodemographic factors including maternal education, child sex, and child age as well as child diagnosis were not associated with HRQOL. Conclusions: In this transdiagnostic sample of children, autism symptom severity and parenting stress were shared predictors of HRQOL. Interventions targeting child autistic symptoms and parents’ levels of stress could result in improved HRQOL in neurodivergent populations. Full article

The COVID-19 pandemic disrupted economic processes and various facets of daily life, including education, necessitating adjustments to help society adapt to the temporary status quo, with Romanian educational institutions being profoundly affected, and a full transition to online learning was mandated by central authorities in March 2020. The paper’s scope is to assess the sustainability of e-learning in Romanian higher education in the aftermath of the COVID-19 outbreak. The study was conducted on bachelor students from three Romanian universities through an online questionnaire with a sample size of 505 valid responses. This study aims to investigate the relationships between information quality (IQ), system quality (SQ), service quality (SEQ), and quality of life (QL) within an integrated model, based on the variables of the technology acceptance model (TAM) and performance models of information systems (IS). Specifically, the research explores how these factors, along with the mediating roles of perceived usefulness (PU) and perceived ease of use (PEOU), influence students’ behavioral intention to adopt e-learning systems (BISE) and actual use of them (EUOES) as a sustainable solution for post-pandemic COVID-19 education. Partial least squares structural equation modeling (PLS–SEM) was the selected method for data analysis performed with SmartPLS 4.0 software. The research results demonstrated that PU and PEOU showed a positive correlation relationship and were significantly influenced by IQ, SQ, and QL in the educational setting. The study also revealed that PEOU and PU exerted a positive influence on students’ behavioral intention to adopt e-learning systems (BISE) sustainably and on their actual use (EUOES). This study benefits universities and higher education institutions by providing insights into enhancing e-learning platforms and integrating technology effectively, as well as by supporting the formulation of sustainable online learning strategies beyond the COVID-19 pandemic. Full article

Background/Objectives: The implementation of information quality value stream maps (IQ-VSMs) in food animal production systems can increase our understanding of the opportunities and challenges when using laboratory testing for antimicrobial resistance (AMR) to support antimicrobial stewardship (AMS). Our objectives were to (1) explore the implementation of information quality value stream mapping as a continuous improvement tool to inform decisions for bovine respiratory disease (BRD) management and AMS and (2) apply the information quality dimensions to identified Kaizen opportunities for the integration of laboratory data into BRD management systems to assess the appropriateness of BRD treatment plans in western Canadian feedlot production. Methods: A ‘Current State’ IQ-VSM outlined the processes, available information, information processing steps, and control decisions contributing to BRD management and treatment in commercial western Canadian feedlots, recognizing that laboratory BRD pathogens and AMR data are typically not part of BRD management. Results: The ‘Future State’ IQ-VSM incorporated Kaizen opportunities for improvement, including (i) the strategic collection of respiratory samples from representative samples of calves for laboratory analysis, regardless of clinical BRD status, (ii) compilation of laboratory data at the pen and feedlot levels, and (iii) analysis of pen- and feedlot-level laboratory data to inform the veterinarian’s assessment of the appropriateness of current BRD treatment plans. Conclusions: The IQ-VSMs provided a valuable framework to visualize the integration of BRD pathogen and AMR laboratory data to support AMS and address any potential future testing requirements. Full article

This paper investigates the Building Information Modeling (BIM) adoption impact on the factors of Contract Cost Variance (CCV) over time. The study considers qualitative and quantitative data to identify the most common causes of CCV through pre-tendering. A partial least square-structure model (PLS-SEM) procedure was used to develop a causal model and rank CCV factors based on their effect, partially based on prior survey raw data conducted in 2022 and the data from 94 projects. Construction industry experts assessed the prior five-year rate of BIM adoption on construction projects to infer the expected trend in BIM adoption in the future (until 2037). Based on the causal model of CCV factors and the future rates of BIM adoption, the dynamic impact of BIM on CCV factors over time was modeled and analyzed. The analysis shows that BIM reduces CCV over time by improving Estimator Performance (EP), Information Quality (IQ), and contractual procedure (CP). The results showed that the CP, EP, and EF have directly impacted CCV, and the PC and IQ indirectly affect the CCV. This paper considers the temporal aspect, examining how the impact of BIM on CCV factors evolves. This dynamic analysis is crucial for long-term strategic planning in construction management. Full article

No-reference image quality assessment aims to evaluate image quality based on human subjective perceptions. Current methods face challenges with insufficient ability to focus on global and local information simultaneously and information loss due to image resizing. To address these issues, we propose a model that combines Swin-Transformer and natural scene statistics. The model utilizes Swin-Transformer to extract multi-scale features and incorporates a feature enhancement module and deformable convolution to improve feature representation, adapting better to structural variations in images, apply dual-branch attention to focus on key areas, and align the assessment more closely with human visual perception. The Natural Scene Statistics compensates information loss caused by image resizing. Additionally, we use a normalized loss function to accelerate model convergence and enhance stability. We evaluate our model on six standard image quality assessment datasets (both synthetic and authentic), and show that our model achieves advanced results across multiple datasets. Compared to the advanced DACNN method, our model achieved Spearman rank correlation coefficients of 0.922 and 0.923 on the KADID and KonIQ datasets, respectively, representing improvements of 1.9% and 2.4% over this method. It demonstrated outstanding performance in handling both synthetic and authentic scenes. Full article

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods’ intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods. Full article

With the rise of the metaverse, digital transformation is profoundly affecting the field of art exhibitions. Museums and galleries are actively adopting metaverse technologies to present artworks through virtual platforms, providing audiences with novel opportunities for immersive engagement and art experiences and shaping high-quality user experiences. However, the factors influencing user engagement in the metaverse art exhibition platform (MeAEP) remain unclear in the current research. This research combines the information systems success model (ISSM) and the hedonic motivation system adoption model (HMSAM) to construct a theoretical model that provides insights into the factors influencing MeAEP users’ intention to engage and their immersion behavior, with a focus on the sustainability of the art exhibition. We quantitatively analyzed 370 users that experienced MeAEP and analyzed the data and measurement model using SPSS 27 and partial least squares structural equation modeling (PLS-SEM). The results showed that information quality (IQ), system quality (SQ), and perceived ease of use (PEOU) significantly and positively influenced perceived usefulness (PU), curiosity (CUR), joy (JOY), and control (CON). PU, JOY, and CON have a positive and significant effect on Immersion (IM). Finally, PU, CUR, JOY, and CON had a positive effect on behavioral intention (BI). In conclusion, only one of the twenty hypotheses was not supported. The research findings not only enrich the academic and managerial theories related to the metaverse and art exhibition platforms, but also provide practical insights for administrators, developers, and MeAEP designers to create higher-quality and more immersive art content, as well as provide constructive ideas for the sustainability of art exhibitions to further enhance user experience. Full article

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS images. Based on these observations, this paper proposes a Mask2Former with an improved query (IQ2Former) for this task. The fundamental motivation behind the IQ2Former is to enhance the capability of the query of Mask2Former by exploiting the characteristics of RS images well. First, we propose the Query Scenario Module (QSM), which aims to learn and group the queries from feature maps, allowing the selection of distinct scenarios such as the urban and rural areas, building clusters, and parking lots. Second, we design the query position module (QPM), which is developed to assign the image position information to each query without increasing the number of parameters, thereby enhancing the model’s sensitivity to small targets in complex scenarios. Finally, we propose the query attention module (QAM), which is constructed to leverage the characteristics of query attention to extract valuable features from the preceding queries. Being positioned between the duplicated transformer decoder layers, QAM ensures the comprehensive utilization of the supervisory information and the exploitation of those fine-grained details. Architecturally, the QSM, QPM, and QAM as well as an end-to-end model are assembled to achieve high-quality semantic segmentation. In comparison to the classical or state-of-the-art models (FCN, PSPNet, DeepLabV3+, OCRNet, UPerNet, MaskFormer, Mask2Former), IQ2Former has demonstrated exceptional performance across three publicly challenging remote-sensing image datasets, 83.59 mIoU on the Vaihingen dataset, 87.89 mIoU on Potsdam dataset, and 56.31 mIoU on LoveDA dataset. Additionally, overall accuracy, ablation experiment, and visualization segmentation results all indicate IQ2Former validity. Full article

Objective: this study aims to identify and characterise genomic and phylogenetically isolated SARS-CoV-2 viral isolates in patients from Lambayeque, Peru. Methods: Nasopharyngeal swabs were taken from patients from the Almanzor Aguinaga Asenjo Hospital, Chiclayo, Lambayeque, Peru, which had been considered mild, moderate, and severe cases of COVID-19. Patients had to have tested positive for COVID-19, using a positive RT-PCR for SARS-CoV-2. Subsequently, the SARS-CoV-2 complete viral genome sequencing was carried out using Illumina MiSeq^®. The sequences obtained from the sequence were analysed in Nextclade V1.10.0 to assign the corresponding clades, identify mutations in the SARS-CoV-2 genes and perform quality control of the sequences obtained. All sequences were aligned using MAFFT v7.471. The SARS-CoV-2 isolate Wuhan NC 045512.2 was used as a reference sequence to analyse mutations at the amino acid level. The construction of the phylogenetic tree model was achieved with IQ-TREE v1.6.12. Results: It was determined that during the period from December 2020 to January 2021, the lineages s C.14, C.33, B.1.1.485, B.1.1, B.1.1.1, and B.1.111 circulated, with lineage C.14 being the most predominant at 76.7% (n = 23/30). These lineages were classified in clade 20D mainly and also within clades 20B and 20A. On the contrary, the variants found in the second batch of samples of the period from September to October 2021 were Delta (72.7%), Gamma (13.6%), Mu (4.6%), and Lambda (9.1%), distributed between clades 20J, 21G, 21H, 21J, and 21I. Conclusions: This study reveals updated information on the viral genomics of SARS-CoV-2 in the Lambayeque region, Peru, which is crucial to understanding the origins and dispersion of the virus and provides information on viral pathogenicity, transmission and epidemiology. Full article

To monitor objects of interest, such as wildlife and people, image-capturing devices are used to collect a large number of images with and without objects of interest. As we are recording valuable information about the behavior and activity of objects, the quality of images containing objects of interest should be better than that of images without objects of interest, even if the former exhibits more severe distortion than the latter. However, according to current methods, quality assessments produce the opposite results. In this study, we propose an end-to-end model, named DETR-IQA (detection transformer image quality assessment), which extends the capability to perform object detection and blind image quality assessment (IQA) simultaneously by adding IQA heads comprising simple multi-layer perceptrons at the top of the DETRs (detection transformers) decoder. Using IQA heads, DETR-IQA carried out blind IQAs based on the weighted fusion of the distortion degree of the region of objects of interest and the other regions of the image; the predicted quality score of images containing objects of interest was generally greater than that of images without objects of interest. Currently, the subjective quality score of all public datasets is in accordance with the distortion of images and does not consider objects of interest. We manually extracted the images in which the five predefined classes of objects were the main contents of the largest authentic distortion dataset, KonIQ-10k, which was used as the experimental dataset. The experimental results show that with slight degradation in object detection performance and simple IQA heads, the values of PLCC and SRCC were 0.785 and 0.727, respectively, and exceeded those of some deep learning-based IQA models that are specially designed for only performing IQA. With the negligible increase in the computation and complexity of object detection and without a decrease in inference speeds, DETR-IQA can perform object detection and IQA via multi-tasking and substantially reduce the workload. Full article

Pathogenic loss-of-function variants in the IQ motif and SEC7 domain containing protein 2 (IQSEC2) gene cause intellectual disability with Rett syndrome (RTT)-like features. The aim of this study was to obtain systematic information on the natural history and extra-central nervous system (CNS) manifestations for the Italian IQSEC2 population (>90%) by using structured family interviews and semi-quantitative questionnaires. IQSEC2 encephalopathy prevalence estimate was 7.0 to 7.9 × 10⁻⁷. Criteria for typical RTT were met in 42.1% of the cases, although psychomotor regression was occasionally evidenced. Genetic diagnosis was occasionally achieved in infancy despite a clinical onset before the first 24 months of life. High severity in both the CNS and extra-CNS manifestations for the IQSEC2 patients was documented and related to a consistently adverse quality of life. Neurodevelopmental delay was diagnosed before the onset of epilepsy by 1.8 to 2.4 years. An earlier age at menarche in IQSEC2 female patients was reported. Sleep disturbance was highly prevalent (60 to 77.8%), with mandatory co-sleeping behavior (50% of the female patients) being related to de novo variant origin, younger age, taller height with underweight, better social interaction, and lower life quality impact for the family and friends area. In conclusion, the IQSEC2 encephalopathy is a rare and likely underdiagnosed developmental encephalopathy leading to an adverse life quality impact. Full article

The Metaverse accelerates the development of the meta-human industry and human-AI interactions in both traditional media outlets and online platforms. As a typical application of meta-human, artificial intelligence (AI) news anchors have been gradually utilized for program reports instead of newscasters in China. In this paper, through the lens of expectation confirmation theory, we establish a conceptual model consisting of perceived anthropomorphism (ANT), perceived intelligence (PI), perceived attractiveness (PA), perceived novelty (PN), information quality (IQ), confirmation of expectation (CE), trust (TRU), and satisfaction (SAT) to explore continuous intention (CI) of watching news reported by AI anchors among online users. By leveraging on a sample of 598 eligible questionnaires, the partial least square structural equation model is employed and the results show that the holistic continuing intention for AI news anchor is positive but not robust. Further analysis indicates that SAT, PI, and TRU can predict CI directly, meanwhile CE, ANT, and PA associate with CI through the mediation of satisfaction. In addition, trust and satisfaction serve as serial mediators between IQ and CI. There is no direct relationship between CE & CI, ANT & CI, and PN & SAT. Nevertheless, user gender and previous experience can moderate the relationships of ANT & CI and PN & SAT, respectively. It can be seen that the proposed model can explain 80.1% of the variance in CI. The implications are intended to provide references for further commercialization of AI news anchors. Full article