Although computers and information technology (IT) have penetrated the field of social work, little research has systematically studied how users respond to this infusion. Information systems researchers have accumulated significant insights into IT acceptance in business organizations after decades of efforts. In this study, users in the social services were assessed for their acceptance of IT. A research model based on the decomposed theory of planned behavior was developed, arguing that attitudes toward using IT, subjective norms, and perceived behavioral control are important antecedents of intentions to use IT, which affect actual usage. In addition, users consider the benefits that they bring to their organizations and clients through using IT when forming their attitudes toward using IT. Data collected from users of a Homeless Management Information System in a northeastern U.S. state verified the research model. The results suggest new interventions to promote IT acceptance by users in the social services sector.

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Inspired by the synthesis of graphene with benzene as the precursor, a novel all-sp 2 hybridized two-dimensional (2D) carbon allotrope is proposed in this work. This 2D carbon allotrope is a poly-butadiene-cyclooctatetraeneframework and displays a P6/mmm space group with 24 carbon atoms in a hexagonal unit cell (denoted as PBCFgraphene). First-principles calculations show the presence of a honeycomb structure along the c-axis that possesses two kinds of holes with diameters of 4.88 and 2.39 Å. PBCFgraphene has a direct band gap of 1.355 eV at the Γ point as determined using the HSE06 hybrid functional. This structure is energetically preferable over E-graphene, C 33 -carbon sheets, cyclo[18]carbon and C 20 fullerenes and is the same to PCF-graphene. Its dynamical, thermal and mechanical stabilities are proven from the phonon dispersion, ab initio molecular dynamics (AIMD simulations and elastic constants, respectively. It can not only withstand equi-biaxial tensile strains as high as 17.6% but can also withstand temperatures at least 1000 K. PBCF-graphene exhibits a high, room temperature, in-plane, electron mobility, which is higher than the electron mobility of monolayer black phosphorus and 5.9 times higher than the hole mobility of monolayer MoS 2 . In addition, PBCF-graphene can absorb photons from the visible to near-ultraviolet regimes, giving it potential value in applications for nanoelectronic and optoelectronic devices.[a] Dr.

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PURPOSE Building well-performing machine learning (ML) models in health care has always been exigent because of the data-sharing concerns, yet ML approaches often require larger training samples than is afforded by one institution. This paper explores several federated learning implementations by applying them in both a simulated environment and an actual implementation using electronic health record data from two academic medical centers on a Microsoft Azure Cloud Databricks platform. MATERIALS AND METHODS Using two separate cloud tenants, ML models were created, trained, and exchanged from one institution to another via a GitHub repository. Federated learning processes were applied to both artificial neural networks (ANNs) and logistic regression (LR) models on the horizontal data sets that are varying in count and availability. Incremental and cyclic federated learning models have been tested in simulation and real environments. RESULTS The cyclically trained ANN showed a 3% increase in performance, a significant improvement across most attempts ( P < .05). Single weight neural network models showed improvement in some cases. However, LR models did not show much improvement after federated learning processes. The specific process that improved the performance differed based on the ML model and how federated learning was implemented. Moreover, we have confirmed that the order of the institutions during the training did influence the overall performance increase. CONCLUSION Unlike previous studies, our work has shown the implementation and effectiveness of federated learning processes beyond simulation. Additionally, we have identified different federated learning models that have achieved statistically significant performances. More work is needed to achieve effective federated learning processes in biomedicine, while preserving the security and privacy of the data.