The Research of Effective Factors on is Planning Capability of IT Organization

In fast-paced business environments, most businesses rely on IT. Business units continuously require planning, development and management of IS aligning with their business strategies. In this continuous process, an IS organization performs business analyses as well as planning and application functions for IS environments in a position of mediator between both business and IT units. In recent years, monitoring and evaluation of developed information systems has become an important tasks, which is inevitable and essential for making IT investment decision. This organization is generally referred to as an \u27IS strategic planning team\u27, \u27IT planning team\u27, \u27information strategic team\u27, and \u27IT strategy planning team\u27, etc., and is collectively referred to as an \u27information strategic organization\u27. This paper aims to identify ‘IS Planning Capability’ as the most important critical factor for information strategic organizations and examined how different factors that can affect planning capability, and further impacts on IS planning satisfaction in business units

Formation of polybromine anions and concurrent heavy hole doping in carbon nanotubes

Using density-functional theory calculations, we investigate the atomic and electronic structure of the bromine species encapsulated in carbon nanotubes. We find that the odd-membered molecular structures (Br3 and Br5) are energetically favored than the common Br2 molecule. The transformation from bromine molecules (Br2) into Br3 or Br5 is found to be almost barrierless. A strong electron transfer from the nanotube to the adsorbates, which has been doubtful in previous studies, is accompanied by the formation of such odd-membered polybromine anions. We suggest that the tip-opened carbon nanotube samples can be heavily hole-doped after exposure to Br2 gas.open3

A Similarity Index for Balance Assessment between Older Adults with and without Balance Deficits

Falls in older adults can cause disabling health even though falls are largely preventable. A combination of fall risk factors can be modified or predicted to minimize devastating complications. However, clinical balance assessment tools often have contradictory results since fall risks are individualized and multifactorial. The assessment tools are often practically limited to detecting sensitive changes between older adults with and without balance deficits. Recently, a similarity index (SI) has been developed to predict fall risks based on kinematic data during gait. The combined limb motions to those of a prototype derived from healthy individuals in the gait cycle might be differentiated from individuals with gait pathologies. The analyzed calculations result in response vectors that would be compared to controls-derived prototype response vectors. Furthermore, the normalized SI, based on the vector representing the data distribution, could be generated from the enhanced (dis)similarities dataset of subjects following an intervention (prototype response vectors). These quantified indices for compensatory patterns provide a further understanding of optimal injury prevention and specific rehabilitation strategies for older adults with balance deficits. This chapter will propose a novel sensitive measure, the SI, for older adults with orthopedic and neurologic dysfunction compared with control subjects

Realistic adsorption geometries and binding affinities of metal nanoparticles onto the surface of carbon nanotubes

Adsorption geometries and binding affinities of metal nanoparticles onto carbon nanotubes (CNTs) are investigated through density-functional-theory calculations. Clusters of 13 metal atoms are used as models for metal nanoparticles. Palladium, platinum, and titanium particles strongly chemisorb to the CNT surface. Unlike the cases of atomic adsorptions the aluminum particle has the weakest binding affinity with the CNT. Aluminum or gold nanoparticles accumulated on the CNT develop the triangular bonding network of the metal surfaces in which the metal-carbon bond is not favored. This suggests that the CNT-Al interface is likely to have many voids and thus susceptible to oxidation damages.open10

Ab initio study of the effect of water adsorption on the carbon nanotube field-effect transistor

We perform density-functional calculations to investigate the effect of adsorbed water molecules on carbon nanotubes (CNTs). Noting that the H2 O molecule has much wider energy gap than the CNT, we find that the charge transfer between them is negligible. We discuss that several recent publications, which claimed a substantial electron transfer from the water molecule to the CNT, have been based on incautious interpretations of the Mulliken population analysis. We suggest that the effect of humidity on nanotube devices may be attributed to various indirect effects enhanced by water vapors, rather than the carrier generations by the physisorbed H2 O molecules.open292

Effects of encapsulated metallofullerene on the Fermi level alignment at the metal-nanotube interface

We perform ab initio electronic structure calculations for the metal-carbon nanotube (CNT) interfaces with encapsulated fullerenes (C82) or metallofullerenes (La@C82). Gold and aluminum layers are chosen as typical examples of metals with a large work function and a small work function, respectively. It is found that the encapsulation of the fullerene species can affect the Schottky barrier height at the metal-CNT interface. We show that the fullerene-derived localized state could weakly pin the metal Fermi level in the gap of the nanotube. We suggest that the transport properties of the metallofullerene-encapsulated CNT should be explained in terms of the Schottky barrier adjustment rather than the band gap reduction model whose validity has been debated in recent publications.close4

Gait Asymmetry Comparison between Subjects with and without Nonspecific Chronic Low Back Pain

Individuals with chronic low back pain (LBP) report impaired somatosensory function and balance. However, there is a lack of investigation on limb motion similarities between subjects with and without LBP during gait. The aim of this study was to compare gait parameters as well as combined limb motions using the kinematic similarity index (KSI) between subjects with and without LBP. Twenty-two subjects with LBP and 19 age- and body mass index-matched control subjects participated in this study. The combined limb motions in the gait cycle of subjects with LBP were compared with those of a prototype derived from healthy subjects. The calculations resulted in response vectors that were analyzed in comparison to control-derived prototype response vectors for the normalized index at 5% increments in the gait cycle. The results of our study indicated that the KSI of the control group demonstrated higher similarities in the swing (t = 4.23, p = 0.001) and stance (t = 6.26, p = 0.001) phases compared to the LBP group. The index for the whole gait cycle was significantly different between the groups (t = 6.52, p = 0.001), especially in the midstance and swing phases. The LBP group could have adjusted the gait patterns during these specific phases. The KSI is useful for clinical outcome measures to differentiate kinematic changes and to demonstrate quantified similarities in the gait cycle between subjects with and without LBP. It is warranted to validate the KSI for the analysis of physiological gait asymmetry using a larger sample in future studies

Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

BackgroundMachine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an approach to overcome this issue. We focused on applying FL on vertically partitioned data, in which an individual’s record is scattered among different sites. ObjectiveThe aim of this study was to perform FL on vertically partitioned data to achieve performance comparable to that of centralized models without exposing the raw data. MethodsWe used three different datasets (Adult income, Schwannoma, and eICU datasets) and vertically divided each dataset into different pieces. Following the vertical division of data, overcomplete autoencoder-based model training was performed for each site. Following training, each site’s data were transformed into latent data, which were aggregated for training. A tabular neural network model with categorical embedding was used for training. A centrally based model was used as a baseline model, which was compared to that of FL in terms of accuracy and area under the receiver operating characteristic curve (AUROC). ResultsThe autoencoder-based network successfully transformed the original data into latent representations with no domain knowledge applied. These altered data were different from the original data in terms of the feature space and data distributions, indicating appropriate data security. The loss of performance was minimal when using an overcomplete autoencoder; accuracy loss was 1.2%, 8.89%, and 1.23%, and AUROC loss was 1.1%, 0%, and 1.12% in the Adult income, Schwannoma, and eICU dataset, respectively. ConclusionsWe proposed an autoencoder-based ML model for vertically incomplete data. Since our model is based on unsupervised learning, no domain-specific knowledge is required in individual sites. Under the circumstances where direct data sharing is not available, our approach may be a practical solution enabling both data protection and building a robust model

Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation

BackgroundPrivacy is of increasing interest in the present big data era, particularly the privacy of medical data. Specifically, differential privacy has emerged as the standard method for preservation of privacy during data analysis and publishing. ObjectiveUsing machine learning techniques, we applied differential privacy to medical data with diverse parameters and checked the feasibility of our algorithms with synthetic data as well as the balance between data privacy and utility. MethodsAll data were normalized to a range between –1 and 1, and the bounded Laplacian method was applied to prevent the generation of out-of-bound values after applying the differential privacy algorithm. To preserve the cardinality of the categorical variables, we performed postprocessing via discretization. The algorithm was evaluated using both synthetic and real-world data (from the eICU Collaborative Research Database). We evaluated the difference between the original data and the perturbated data using misclassification rates and the mean squared error for categorical data and continuous data, respectively. Further, we compared the performance of classification models that predict in-hospital mortality using real-world data. ResultsThe misclassification rate of categorical variables ranged between 0.49 and 0.85 when the value of ε was 0.1, and it converged to 0 as ε increased. When ε was between 102 and 103, the misclassification rate rapidly dropped to 0. Similarly, the mean squared error of the continuous variables decreased as ε increased. The performance of the model developed from perturbed data converged to that of the model developed from original data as ε increased. In particular, the accuracy of a random forest model developed from the original data was 0.801, and this value ranged from 0.757 to 0.81 when ε was 10-1 and 104, respectively. ConclusionsWe applied local differential privacy to medical domain data, which are diverse and high dimensional. Higher noise may offer enhanced privacy, but it simultaneously hinders utility. We should choose an appropriate degree of noise for data perturbation to balance privacy and utility depending on specific situations