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Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.
The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy.
Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively.
We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.
This paper presents a modularized autonomous pipeline inspection robot called MRINSPECT VII+, which we recently developed. MRINSPECT VII+ is aimed at inspect in-service urban gas pipelines with a diameter of 200 mm. The robot consists of five basic modules: driving, sensing, joint, and battery modules. For nondestructive testing (NDT), an NDT module can be added to the system. The driving module uses a multiaxial differential gear mechanism to provide traction forces to the robot. The sensor module recognizes the pipeline element using position-sensitive detector (PSD) sensors and a CCD camera. The control module contains a computing unit and manages the robot’s autonomous navigation. The battery module supplies power to the system. Each module is connected via backdrivable active joint modules, which provide flexibility while moving inside narrow pipelines. Additionally, the wireless communication module helps the system communicate with the ground station. We tested MRINSPECT VII+ in real pipeline environments and validated its feasibility successfully.
The incidence of restless legs syndrome (RLS) is presumed to be higher among people with schizophrenia who take antipsychotic medication, most of which blocks the dopamine D2 receptor. The purpose of this study was to determine whether the G-protein β3 subunit (GNB3) C825T polymorphism is associated with antipsychotic-induced RLS in schizophrenia.
We examined 178 Korean patients with schizophrenia. All of the subjects were evaluated using the diagnostic criteria of the International Restless Legs Syndrome Study Group and the International Restless Legs Scale. Genotyping was performed for the C825T polymorphism in the GNB3 gene.
The genotype distribution did not differ significantly between antipsychotic-induced RLS patients and patients who had no-RLS symptoms (χ2 = 4.30, p = 0.116). The genotypes of the C825T single-nucleotide polymorphism (SNP) were classified into two groups: C+ (CC and CT genotypes) and C– (TT genotype). The presence of the C allele (C+) was associated with an increased likelihood of RLS (χ2 = 4.14, p = 0.042; odds ratio = 2.56, 95% confidence interval = 1.02–6.47).
These results suggest that the GNB3 C825T SNP is associated with RLS in schizophrenia. However, confirming this association requires future larger scale studies in which the effects of medication are strictly controlled.
We have propsed MgO/AZO bi-layer transparent conducting oxide (TCO) for thin film solar cells. From XRD analysis, it was observed that the full width at half maximum of AZO decreased when it was grown on MgO precursor. The Hall mobility of MgO/AZO bi-layer was 17.5cm2/Vs, whereas that of AZO was 20.8cm2/Vs. These indicated that the crystallinity of AZO decreased by employing MgO precursor. However, the haze (=total diffusive transmittance/total transmittance) characteristics of highly crystalline AZO was significantly improved by MgO precursor. The average haze in the visible region increased from 14.3 to 48.2%, and that in the NIR region increased from 6.3 to 18.9%. The reflectance of microcrystalline silicon solar cell was decreased and external quantum efficiency was significantly improved by applying MgO/AZO bi-layer TCO. The efficiency of microcrystalline silicon solar cell with MgO/AZO bi-layer front TCO was 6.66%, whereas the efficiency of one with AZO single TCO was 5.19%.
We report successful application of a low-temperature-grown amorphous GaAs (a-GaAs) layer for stabilization of the fundamental transverse mode of InGaAs/GaAs vertical-cavity surface-emitting lasers. The maximum currents maintaining a stable fundamental transverse mode were increased by the antiguide effect of a-GaAs with a high refractive index. For 10-μm- and 15-μm-diameter devices, we attained a stable single-mode emission over a wide range of current. The antiguiding of transverse modes in vertical cavity buried in the high refractive cladding layer was calculated using a two-dimensional beam propagation method.
We report for the first time a successful application of semi-insulating amorphous GaAs layer for surface passivation of index-guided vertical-cavity surface-emitting lasers. The amorphous GaAs layers on ion-beam-etched active region and mirror layers provide a significant improvement, more than 20%, in the threshold current density and differential quantum efficiency. The improvement of these performances is attributed to low defect density at the surface of active layers induced by amorphous GaAs.
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