Resistivity, magnetoresistance, and Mössbauer effect of metallic spiral antiferromagnet SrFeO 2.95 have been examined in the temperature range 4.5-300 K. A large negative magnetoresistance below 50 K is observed. We find hysteresis in resistivity in 0 and 9 T due to the coexistence of antiferromagnetic and paramagnetic domains in the temperature region 50-80 K. Our result shows two contributions to magnetoresistance: one below 50 K that is due to helical-conical spin transformation and another close to T N that is due to reduced spin fluctuation under magnetic field.Colossal magnetoresistance ͑CMR͒-a huge decrease in resistance in response to a magnetic field-has recently been observed in manganese oxide with perovskite structure. This effect has attracted considerable interest from both fundamental and practical points of view. 1 But the criteria for achieving ͑and hence optimizing͒ CMR is not clear, presenting a challenge for materials scientists. The accepted description of ferromagnetic and metallic behavior in the manganite perovskite invokes the ''double-exchange'' mechanism, whereby ferromagnetic coupling between localized Mn t 2g 3 spins is mediated by the hopping of e g electrons. 2 Furthermore, recent theoretical and experimental evidence indicates that the important feature of the manganites is the competition between double-exchange ferromagnetism and another instability associated with electron-lattice coupling, partially the Jahn-Teller-type, leading to ͑JT͒ polaron formation. 3,4 Most recently, Uehara et al. 5 have shown that the instability relevant for colossal magnetoresistance is a static chargeordered ͑CO͒ state with a particular modulation, resulting in the large-scale coexistence of this CO phase with a ferromagnetic ͑FM͒ metallic phase. Large magnetoresistance has also been found in Ln 1Ϫx A x CoO 3Ϫ␦ , where LnϭY or La and AϭPb, Ca, Sr, or Ba. 6 The magnetoresistance of the Co-containing samples increases as the size of the alkaline earth ions increase, in sharp contrast with Mn-containing compounds, in which the magnetoresistance effect increases as the size of the alkaline earth ion decreases. The question arises whether these effect are unique to Mn-and Co-based perovskite oxides or can be found in other Fe-based perovskite materials.The Fe͑IV͒ perovskite SrFeO 3 has long been known as a conducting antiferromagnet with a magnetic transition temperature of 134 K and resistivity of about 10 Ϫ3 ⍀ cm. 7,8 It has a proper screw spin structure with the propagation vector parallel to a ͓111͔ direction and does not show a cooperative Jahn-Teller distortion down to 4.2 K. 9 The absence of JahnTeller distortion in this high spin 3d 4 state was explained by high electrical conductivity where the e g * orbitals are broadened into an itinerant electronic conduction band. The low electron density at the iron nucleus observed by the Möss-bauer spectroscopy, 7 as well as molecular orbital calculation and analysis of photoelectron spectra, which is also evidence that Fe 4ϩ is in a high-spin state, eluci...

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Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen’s kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.

Diabetic retinopathy (DR) is an eye disease that damages the blood vessels of the eye. DR causes blurred vision or it may lead to blindness if it is not detected in early stages. DR has five stages, i.e., 0 normal, 1 mild, 2 moderate, 3 severe, and 4 PDR. Conventionally, many hand-on projects of computer vision have been applied to detect DR but cannot code the intricate underlying features. Therefore, they result in poor classification of DR stages, particularly for early stages. In this research, two deep CNN models were proposed with an ensemble technique to detect all the stages of DR by using balanced and imbalanced datasets. The models were trained with Kaggle dataset on a high-end Graphical Processing data. Balanced dataset was used to train both models, and we test these models with balanced and imbalanced datasets. The result shows that the proposed models detect all the stages of DR unlike the current methods and perform better compared to state-of-the-art methods on the same Kaggle dataset.

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