Preparation of Spherical, Submicrometer Oxide Particles by Hydrolysis of Emulsified Alkoxide Droplets

A new method for forming spherical, submicrometer ceramic oxide particles by the hydrolysis of emulsified alkoxide droplets is reported. Emulsions are formed of alkoxide droplets dispersed in an inert, polar solvent. The alkoxide droplets are hydrolyzed to form oxide particles by adding water to the emulsion. It was shown that individual droplets acted as "microreactors" and controlled the powder size, shape, and composition. Both single-oxide and mixed-oxide powders were formed by this technique.     

The effect of molecular structure on the gas transmission rates of aromatic polyimides

A series of high-molecular weight condensation polyimides was evaluated to determine the effect of polymer molecular structure on the transmission rate of oxygen, carbon dioxide, and water vapor. The polyimide films were prepared from either 3,3′,4,4′-benzophenone tetracarboxylic dianhydride (BTDA) or pyromellitic dianhydride (PMDA) with various diamines. The study shows that molecular structure had a strong influence on gas transmission rates with results for some films varying three orders of magnitude from that of other polyimide films. In general, the BTDA series of polyimides had overall lower gas transmission rates than the PMDA-derived series. Polymers prepared with meta-oriented diamines characteristically displayed lower gas transmission than those prepared with para-oriented diamines.     

Preparation of Aluminum Nitride–Silicon Carbide Nanocomposite Powder by the Nitridation of Aluminum Silicon Carbide

Aluminum nitride (AlN)–silicon carbide (SiC) nanocomposite powders were prepared by the nitridation of aluminum-silicon carbide (Al₄SiC₄) with the specific surface area of 15.5 m²·g⁻¹. The powders nitrided at and above 1400°C for 3 h contained the 2H-phases which consisted of AlN-rich and SiC-rich phases. The formation of homogeneous solid solution proceeded with increasing nitridation temperature from 1400° up to 1500°C. The specific surface area of the AlN–SiC powder nitrided at 1500°C for 3 h was 19.5 m²·g⁻¹, whereas the primary particle size (assuming spherical particles) was estimated to be ∼100 nm.     

Associations between apolipoprotein E genotype and circulating F<Subscript>2</Subscript>-isoprostane levels in humans

Apolipoprotein E (apoE), an important determinant of plasma lipoprotein metabolism, has three common alleles (ε2, ε3, and ε4). Population studies have shown that the risk of diseases characterized by oxidative damage, such as coronary heart disease and Alzheimer’s disease, is significantly higher in ɛ4 carriers. We evaluated the association between apoE genotypes and plasma F₂-isoprostane levels, an index of lipid peroxidation, in humans. Two hundred seventy-four healthy subjects (104 males, 170 females; 46.9±13.0 yr; 200 whites, 74 blacks; 81 nonsmokers, 64 passive smokers, and 129 active smokers) recruited for a randomized clinical antioxidant intervention trial were included in this analysis. ApoE genotype was determined by PCR and restriction enzyme digestion. Free plasma F₂-isoprostane was measured by GC-MS. Genotype groups were compared using multiple regression analysis with adjustment for sex, age, race, smoking status, body mass index, plasma ascorbic acid, and β-carotene. Subjects with ε3/ε4 and ε4/ε4 genotype (ε4-carriers) and with ε2/ε3 and ε3/ε3 (non-ε4-carriers) were pooled for analysis. In subjects with high cholesterol levels (total cholesterol above 200 mg/dl), plasma F₂-isoprostane levels were 29% higher in ε4 carriers than in non-ε4-carriers (P=0.0056). High-cholesterol subjects that are ε4 carriers have significantly higher levels of lipid peroxidation as assessed by circulating F₂-isoprostane levels.     

What to do after a data breach? Examining apology and compensation as response strategies for health service providers

Innovative IT-enabled health services promise tremendous benefits for customers and service providers alike. Simultaneously, health services by nature process sensitive customer information, and data breaches have become an everyday phenomenon. The challenge that health service providers face is to find effective recovery strategies after data breaches to retain customer trust and loyalty. We theorize and investigate how two widely applied recovery actions (namely apology and compensation) affect customer reactions after a data breach in the specific context of fitness trackers. Drawing on expectation confirmation theory, we argue that the recovery actions derived from practice, apology, and compensation address the assimilation-contrast model’s tolerance range and, thus, always lead to satisfaction with the recovery strategy, which positively influences customers’ behavior. We employ an experimental investigation and collect data from fitness tracker users during a running event. In the end, we found substantial support for our research model. Health service providers should determine specific customer expectations and align their data breach recovery strategies accordingly.

     

Mobilising information systems scholarship for a circular economy: Review,synthesis, and directions for future research

One of today's grand societal challenges is to replace the current ‘take‐make‐waste’ economic model with a circular economic model that allows a gradual decoupling of economic activities from the consumption of finite virgin resources. While circular economy (CE) scholars have long lauded digital technologies such as sensors, distributed ledgers, or platforms as key enablers, our own community has not fully explored the potentials of information systems (IS) for a CE. Considering recent technological advances in software and hardware and our history of helping address wicked challenges, we believe the time is ripe to mobilise IS scholarship for a CE. Our findings from an interdisciplinary literature review show that research has primarily examined IS potentials for increasing efficiency of isolated intra‐organisational processes while neglecting the larger sustainability potential of IS to establish circular material flows—that is, slow down and close material loops across entire product lifecycles. In response, we propose directions for IS research that develop our knowledge of how IS can help understand and enact circular material flows to intensify and extend use of products and components and recycle waste materials. Our directions offer pathways to building and evaluating the problem‐solution pairing that could characterise a prolific CE‐IS relationship.     

Computational Decision Support System for ADHD Identification

Machine Intelligence Research - Attention deficit/hyperactivity disorder (ADHD) is a common disorder among children. ADHD often prevails into adulthood, unless proper treatments are facilitated to...     

Importance of Mullins effect in commercial silicone elastomer formulations for soft robotics

Silicone elastomers are used in a wide range of applications, including artificial muscles, biomedical devices, and soft robotics, for which chemical, thermal, and mechanical stability are important requirements that these elastomers must fulfill. However, to ensure that silicone elastomers' properties and performance remain constant under long-term deployment, it is necessary to examine and account for the Mullins effect, which has the potential to significantly alter certain elastomer properties of interest. In this article, the mechanical properties of soft and hard commercial silicone elastomers and two blends of commercial silicone elastomers are investigated—specifically their softening behavior due to the Mullins effect. Ultimate stresses, ultimate strains, and Young's moduli are obtained from uniaxial tensile tests. Results show that the point of softening greatly depends on both the elastomer type and its strain history. Furthermore, a significant permanent set is observed in the softest commercial formulations.     

Influence of motion correction on the visual analysis of cardiac magnetic resonance stress perfusion imaging

Objective

Image post-processing corrects for cardiac and respiratory motion (MoCo) during cardiovascular magnetic resonance (CMR) stress perfusion. The study analyzed its influence on visual image evaluation.

Materials and methods

Sixty-two patients with (suspected) coronary artery disease underwent a standard CMR stress perfusion exam during free-breathing. Image post-processing was performed without (non-MoCo) and with MoCo (image intensity normalization; motion extraction with iterative non-rigid registration; motion warping with the combined displacement field). Images were evaluated regarding the perfusion pattern (perfusion deficit, dark rim artifact, uncertain signal loss, and normal perfusion), the general image quality (non-diagnostic, imperfect, good, and excellent), and the reader’s subjective confidence to assess the images (not confident, confident, very confident).

Results

Fifty-three (non-MoCo) and 52 (MoCo) myocardial segments were rated as ‘perfusion deficit’, 113 vs. 109 as ‘dark rim artifacts’, 9 vs. 7 as ‘uncertain signal loss’, and 817 vs. 824 as ‘normal’. Agreement between non-MoCo and MoCo was high with no diagnostic difference per-patient. The image quality of MoCo was rated more often as ‘good’ or ‘excellent’ (92 vs. 63%), and the diagnostic confidence more often as “very confident” (71 vs. 45%) compared to non-MoCo.

Conclusions

The comparison of perfusion images acquired during free-breathing and post-processed with and without motion correction demonstrated that both methods led to a consistent evaluation of the perfusion pattern, while the image quality and the reader’s subjective confidence to assess the images were rated more favorably for MoCo.

     

Multi-label classification via learning a unified object-label graph with sparse representation

Automatic annotation is an essential technique for effectively handling and organizing Web objects (e.g., Web pages), which have experienced an unprecedented growth over the last few years. Automatic annotation is usually formulated as a multi-label classification problem. Unfortunately, labeled data are often time-consuming and expensive to obtain. Web data also accommodate much richer feature space. This calls for new semi-supervised approaches that are less demanding on labeled data to be effective in classification. In this paper, we propose a graph-based semi-supervised learning approach that leverages random walks and ? ₁ sparse reconstruction on a mixed object-label graph with both attribute and structure information for effective multi-label classification. The mixed graph contains an object-affinity subgraph, a label-correlation subgraph, and object-label edges with adaptive weight assignments indicating the assignment relationships. The object-affinity subgraph is constructed using ? ₁ sparse graph reconstruction with extracted structural meta-text, while the label-correlation subgraph captures pairwise correlations among labels via linear combination of their co-occurrence similarity and kernel-based similarity. A random walk with adaptive weight assignment is then performed on the constructed mixed graph to infer probabilistic assignment relationships between labels and objects. Extensive experiments on real Yahoo! Web datasets demonstrate the effectiveness of our approach.