Hasil untuk "Information technology"

T. Malone, J. Yates, R. Benjamin

W. Orlikowski, S. Iacono

C. Chong, Srikanta P. R. Kumar

G. Huber

Heeseok Lee, Byounggu Choi

S. Star

Cameron D. Norman, H. Skinner

Background Electronic health resources are helpful only when people are able to use them, yet there remain few tools available to assess consumers’ capacity for engaging in eHealth. Over 40% of US and Canadian adults have low basic literacy levels, suggesting that eHealth resources are likely to be inaccessible to large segments of the population. Using information technology for health requires eHealth literacy—the ability to read, use computers, search for information, understand health information, and put it into context. The eHealth Literacy Scale (eHEALS) was designed (1) to assess consumers’ perceived skills at using information technology for health and (2) to aid in determining the fit between eHealth programs and consumers. Objectives The eHEALS is an 8-item measure of eHealth literacy developed to measure consumers’ combined knowledge, comfort, and perceived skills at finding, evaluating, and applying electronic health information to health problems. The objective of the study was to psychometrically evaluate the properties of the eHEALS within a population context. A youth population was chosen as the focus for the initial development primarily because they have high levels of eHealth use and familiarity with information technology tools. Methods Data were collected at baseline, post-intervention, and 3- and 6-month follow-up using control group data as part of a single session, randomized intervention trial evaluating Web-based eHealth programs. Scale reliability was tested using item analysis for internal consistency (coefficient alpha) and test-retest reliability estimates. Principal components factor analysis was used to determine the theoretical fit of the measures with the data. Results A total of 664 participants (370 boys; 294 girls) aged 13 to 21 (mean = 14.95; SD = 1.24) completed the eHEALS at four time points over 6 months. Item analysis was performed on the 8-item scale at baseline, producing a tight fitting scale with α = .88. Item-scale correlations ranged from r = .51 to .76. Test-retest reliability showed modest stability over time from baseline to 6-month follow-up (r = .68 to .40). Principal components analysis produced a single factor solution (56% of variance). Factor loadings ranged from .60 to .84 among the 8 items. Conclusions The eHEALS reliably and consistently captures the eHealth literacy concept in repeated administrations, showing promise as tool for assessing consumer comfort and skill in using information technology for health. Within a clinical environment, the eHEALS has the potential to serve as a means of identifying those who may or may not benefit from referrals to an eHealth intervention or resource. Further research needs to examine the applicability of the eHEALS to other populations and settings while exploring the relationship between eHealth literacy and health care outcomes.

Dimitrios Buhalis

E. Swanson

W. Orlikowski

In this paper, I outline a perspective on organizational transformation which proposes change as endemic to the practice of organizing and hence as enacted through the situated practices of organizational actors as they improvise, innovate, and adjust their work routines over time. I ground this perspective in an empirical study which examined the use of a new information technology within one organization over a two-year period. In this organization, a series of subtle but nonetheless significant changes were enacted over time as organizational actors appropriated the new technology into their work practices, and then experimented with local innovations, responded to unanticipated breakdowns and contingencies, initiated opportunistic shifts in structure and coordination mechanisms, and improvised various procedural, cognitive, and normative variations to accommodate their evolving use of the technology. These findings provide the empirical basis for a practice-based perspective on organizational transfor...

Suhong Li, Binshan Lin

Abdulkafi Albirini

J. Peppard, J. Ward

Youngjin Yoo, O. Henfridsson, K. Lyytinen

Björn Niehaves, Ralf Plattfaut

Information technology (IT) allows members of the growing elderly population to remain independent longer. However, while technology becomes more and more pervasive, an age-related underutilisation of IT remains observable. For instance, elderly people (65 years of age and older) are significantly less likely to use the Internet than the average population (see, for instance, European Commission, 2011). This age-related digital divide prevents many elderly people from using IT to enhance their quality of life through tools, such as Internet-based service delivery. Despite the significance of this phenomenon, the information systems (IS) literature lacks a comprehensive consideration and explanation of technology acceptance in general and more specifically, Internet adoption by the elderly. This paper thus studies the intentions of the elderly with regard to Internet use and identifies important influencing factors. Four alternative models based on technology acceptance theory are tested in the context of comprehensive survey data. As a result, a model that explains as much as 84% of the variance in technology adoption among the elderly is developed. We discuss the contribution of our analyses to the research on Internet adoption (and IT adoption in general) by the elderly, on the digital divide, and on technology acceptance and identify potentially effective paths for future research and theoretical development.

Jae-Sung Lim, Haesong Cho, Do-Hoon Kwon et al.

Diagnosing system faults is essential for ensuring the safety and reliability of Medium-Voltage Direct Current (MVDC) systems. In this regard, this study proposes a highly accurate Artificial Intelligence (AI)-based fault diagnosis scheme for MVDC systems. The proposed scheme pre-processes the measured voltage and current data using a Discrete Wavelet Transform (DWT), considering a 60 × 100 2D window size. Subsequently, a bi-directional long short-term memory (Bi-LSTM) network is employed to diagnose and classify fault types and locations accurately. A stack method is applied in the data post-processing stage to achieve 100 % fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis scheme was verified by comparing its accuracy in 4-terminal MVDC system with that of existing schemes that employ other AI algorithms, such as CNN and LSTM. The proposed fault diagnosis scheme shows improved accuracy by 1.6 %, 3.8 %, and 2.9 %, 2.4 %, respectively, compared to existing schemes such as Bi-LSTM without stack method, LSTM, and CNN, GRU. Moreover, the scalability of the fault diagnosis scheme was verified by training and testing the scheme on a 5-terminal system and 4-terminal system, respectively. To a limited extent, the results demonstrate that the proposed fault diagnosis scheme improves accuracy even when the training and testing systems differ.

Pengfei Hao, Jianpeng An, Qing Cai et al.

Accurate, high-throughput estimation of Above-Ground Biomass (AGB), a key predictor of yield, is a critical goal in rapeseed breeding. However, this is constrained by two key challenges: (1) traditional measurement is destructive and laborious, and (2) modern deep learning approaches require vast, costly labeled datasets. To address these issues, we present a data-efficient deep learning framework using smartphone-captured top-down RGB images for AGB estimation (Fresh Weight, FW, and Dry Weight, DW). Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated dataset of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning (SSL) method. Subsequently, this pre-trained model is fine-tuned on a small, custom-labeled rapeseed dataset (N = 833) using a Multi-Task Learning (MTL) framework to simultaneously regress both FW and DW. This MTL approach acts as a powerful regularizer, forcing the model to learn robust features related to the 3D plant structure and density. Through rigorous 5-fold cross-validation, our proposed model achieved strong predictive performance for both Fresh Weight (Coefficient of Determination, R² = 0.842) and Dry Weight (R² = 0.829). The model significantly outperformed a range of baselines, including models trained from scratch and those pre-trained on the generic ImageNet dataset. Ablation studies confirmed the critical and synergistic contributions of both domain-specific SSL (vs. ImageNet) and the MTL framework (vs. single-task training). This study demonstrates that an SSL+MTL framework can effectively learn to infer complex 3D plant attributes from 2D images, providing a robust and scalable tool for non-destructive phenotyping to accelerate the rapeseed breeding cycle.

Fabin Dong, Qiang Yin, Wen Hong

Multiaspect polarimetric synthetic aperture radar (SAR) captures the polarimetric properties of targets from various observational aspects. The comprehensive multiaspect scattering characteristics are valuable for man-made structure detection and classification. Typically, the anisotropic scattering of targets could be characterized by the differences in the statistical properties of polarimetric data across aspects. However, both the statistical similarities in man-made structures and variabilities in natural targets at different aspects can negatively impact the ability to distinguish between them. Consequently, relying solely on anisotropic analysis may not yield favorable man-made structure detection results. Since man-made structures usually include special shapes, such as dihedral angle, there are significant variations in scattering power across different aspects. Therefore, this article proposes an improved man-made structure detection method that integrates scattering power characteristics and anisotropic features. First, to highlight differences between aspects, this article introduces a similarity matrix to perform azimuth sequence filtering. Subsequently, anisotropic features are extracted through differences in statistical distribution, and scattering power characteristics at individual aspects, along with their variations, are extracted using the fuzzy C-means clustering combined with spatial neighborhood. Two different features are fused to distinguish man-made structures from natural targets. Finally, the most significant azimuth aspect is determined by comparing the scattering contributions of individual subapertures. Experimental verification with airborne circular polarimetric SAR data confirms that the multifeature fusion method, following azimuth sequence filtering, effectively improves the detection of man-made structures and their most anisotropic subapertures.

Hassan Al Garni, Arunachalam Sundaram, Anjali Awasthi et al.

A major design challenge for a grid-integrated photovoltaic power plant is to generate maximum power under varying loads, irradiance, and outdoor climatic conditions using competitive algorithm-based controllers. The objective of this study is to review experimentally validated advanced maximum power point tracking algorithms for enhancing power generation. A comprehensive analysis of 14 of the most advanced metaheuristics and 17 hybrid homogeneous and heterogeneous metaheuristic techniques is carried out, along with a comparison of algorithm complexity, maximum power point tracking capability, tracking frequency, accuracy, and maximum power extracted from PV systems. The results show that maximum power point tracking controllers mostly use conventional algorithms; however, metaheuristic algorithms and their hybrid variants are found to be superior to conventional techniques under varying environmental conditions. The Grey Wolf Optimization, in combination with Perturb & Observe, and Jaya-Differential Evolution, is found to be the most competitive technique. The study shows that standard testing and evaluation procedures can be further developed for comparing metaheuristic algorithms and their hybrid variants for developing advanced maximum power point tracking controllers. The identified algorithms are found to enhance power generation by grid-integrated commercial solar power plants. The results are of importance to the solar industry and researchers worldwide.

Minghua Zhao, Yahui Xiao, Jiaqi Zhang et al.

Optical molecular tomography (OMT) is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas, which can provide non-invasive quantitative three-dimensional (3D) information regarding tumor distribution in living animals. The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results, resulting in problems such as low accuracy, poor robustness, and long-time consumption. Here, a gates joint locally connected network (GLCN) method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly, thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy. Moreover, gates module was composed of the concatenation and multiplication operators of three different gates. It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates. To evaluate the performance of the proposed method, numerical simulations were conducted, whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.