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Recently, artificial intelligence technologies have received considerable attention because of their practical applications in various fields. The key factor in this prosperity is deep learning which is inspired by the information processing in biological brains. In this study, we apply one of the representative deep learning techniques multi-channel convolutional neural networks (CNNs) to predict the fluctuation of the stock index. Furthermore, we optimize the network topology of CNN to improve the model performance. CNN has many hyper-parameters that need to be adjusted for constructing an optimal model that can learn the data patterns efficiently. In particular, we focus on the optimization of feature extraction part of CNN, because this is the most important part of the computational procedure of CNN. This study proposes a method to systematically optimize the parameters for the CNN model by using genetic algorithm (GA). To verify the effectiveness of our model, we compare the prediction result with standard artificial neural networks (ANNs) and CNN models. The experimental results show that the GA-CNN outperforms the comparative models and demonstrate the effectiveness of the hybrid approach of GA and CNN.

With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.

Most mitochondrial precursor polypeptides are imported from the cytosol into the mitochondrion, where they must efficiently undergo folding. Mitochondrial precursors are imported as unfolded polypeptides. For proteins of the mitochondrial matrix and inner membrane, two separate chaperone systems, HSP60 and mitochondrial HSP70 (mtHSP70), facilitate protein folding. We show that LONP1, an AAA+ protease of the mitochondrial matrix, works with the mtHSP70 chaperone system to promote mitochondrial protein folding. Inhibition of LONP1 results in aggregation of a protein subset similar to that caused by knockdown of DNAJA3, a co-chaperone of mtHSP70. LONP1 is required for DNAJA3 and mtHSP70 solubility, and its ATPase, but not its protease activity, is required for this function. In vitro, LONP1 shows an intrinsic chaperone-like activity and collaborates with mtHSP70 to stabilize a folding intermediate of OXA1L. Our results identify LONP1 as a critical factor in the mtHSP70 folding pathway and demonstrate its proposed chaperone activity. Most mitochondrial proteins are imported from the cytosol and must fold in the mitochondria. Here, the authors show that the mitochondrial protease LONP1 plays a critical role in the mtHSP70 chaperone system independently of its protease activity.

Iron-chalcogenide superconductors have emerged as a promising Majorana platform for topological quantum computation. By combining topological band and superconductivity in a single material, they provide significant advantage to realize isolated Majorana zero modes. However, iron-chalcogenide superconductors, especially Fe(Te,Se), suffer from strong inhomogeneity which may hamper their practical application. In addition, some iron-pnictide superconductors have been demonstrated to have topological surface states, yet no Majorana zero mode has been observed inside their vortices, raising a question of universality about this new Majorana platform. In this work, through angle-resolved photoemission spectroscopy and scanning tunneling microscopy/spectroscopy measurement, we identify Dirac surface states and Majorana zero modes, respectively, for the first time in an iron-pnictide superconductor, CaKFe 4 As 4 . More strikingly, the multiple vortex bound states with integer-quantization sequences can be accurately reproduced by our model calculation, firmly establishing Majorana nature of the zero mode. Iron-pnictide superconductors share similar topological band structure with iron-chalcogenide superconductors, but no Majorana modes have been observed in the former. Here, the authors observe both the superconducting Dirac surface states and Majorana zero modes inside its vortex cores in CaKFe 4 As 4 .

A promising path toward topological quantum computing involves exotic quasiparticles called the Majorana bound states (MBSs). MBSs have been observed in heterostructures that require careful nanofabrication, but the complexity of such systems makes further progress tricky. Zhang et al. identified a topological superconductor in which MBSs may be observed in a simpler way by looking into the cores of vortices induced by an external magnetic field. Using angle-resolved photoemission, the researchers found that the surface of the iron superconductor FeTe 0.55 Se 0.45 satisfies the required conditions for topological superconductivity. Science , this issue p. 182 Angle-resolved photoemission spectroscopy indicates that FeTe 0.55 Se 0.45 harbors Dirac-cone–type spin-helical surface states. Topological superconductors are predicted to host exotic Majorana states that obey non-Abelian statistics and can be used to implement a topological quantum computer. Most of the proposed topological superconductors are realized in difficult-to-fabricate heterostructures at very low temperatures. By using high-resolution spin-resolved and angle-resolved photoelectron spectroscopy, we find that the iron-based superconductor FeTe 1– x Se x ( x = 0.45; superconducting transition temperature T c = 14.5 kelvin) hosts Dirac-cone–type spin-helical surface states at the Fermi level; the surface states exhibit an s-wave superconducting gap below T c . Our study shows that the surface states of FeTe 0.55 Se 0.45 are topologically superconducting, providing a simple and possibly high-temperature platform for realizing Majorana states.

A quantum spin Hall (QSH) insulator hosts topological states at the one-dimensional (1D) edge, along which backscattering by nonmagnetic impurities is strictly prohibited. Its 3D analogue, a weak topological insulator (WTI), possesses similar quasi-1D topological states confined at side surfaces. The enhanced confinement could provide a route for dissipationless current and better advantages for applications relative to strong topological insulators (STIs). However, the topological side surface is usually not cleavable and is thus hard to observe. Here, we visualize the topological states of the WTI candidate ZrTe 5 by spin and angle-resolved photoemission spectroscopy (ARPES): a quasi-1D band with spin-momentum locking was revealed on the side surface. We further demonstrate that the bulk band gap is controlled by external strain, realizing a more stable WTI state or an ideal Dirac semimetal (DS) state. The highly directional spin-current and the tunable band gap in ZrTe 5 will provide an excellent platform for applications. Topological side surface, characterization of a weak topological insulator (WTI), has rarely been investigated. Here, Zhang et al. visualize a quasi-one dimensional, spin-momentum locked band on the side surface of the WTI candidate ZrTe 5 , and manipulate the bulk band gap by strain.

As noted by Warburg, many cancer cells depend on the consumption of glucose. We performed a genetic screen to identify factors responsible for glucose addiction and recovered the two subunits of the xCT antiporter (system x c − ), which plays an antioxidant role by exporting glutamate for cystine. Disruption of the xCT antiporter greatly improves cell viability after glucose withdrawal, because conservation of glutamate enables cells to maintain mitochondrial respiration. In some breast cancer cells, xCT antiporter expression is upregulated through the antioxidant transcription factor Nrf2 and contributes to their requirement for glucose as a carbon source. In cells carrying patient-derived mitochondrial DNA mutations, the xCT antiporter is upregulated and its inhibition improves mitochondrial function and cell viability. Therefore, although upregulation of the xCT antiporter promotes antioxidant defence, it antagonizes glutamine metabolism and restricts nutrient flexibility. In cells with mitochondrial dysfunction, the potential utility of xCT antiporter inhibition should be further tested. The factors that limit the nutrient flexibility of cells remain largely unknown. Here, the authors identify the glutamate/cysteine antiporter xCT in a genetic screen for glucose dependency and show it determines the ability of cells to survive under conditions of low glucose by limiting the utilization of glutamine.

Background The 2015 outbreak of Middle East Respiratory Syndrome (MERS) in the Republic of Korea is a recent and representative occurrence of nationwide outbreaks of Emerging Infectious Diseases (EIDs). In addition to physical symptoms, posttraumatic stress disorder (PTSD) and depression are common following outbreaks of EID. Methods The present study investigated the long-term mental health outcomes and related risk factors in survivors of MERS. A prospective nationwide cohort study was conducted 12 months after the MERS outbreak at multi-centers throughout Korea. PTSD and depression as the main mental health outcomes were assessed with the Impact of Event Scale-Revised Korean version (IES-R-K) and the Patient Health Questionnaire-9 (PHQ-9) respectively. Results 42.9% of survivors reported PTSD (IES-R-K ≥ 25) and 27.0% reported depression (PHQ-9 ≥ 10) at 12 months post-MERS. A multivariate analysis revealed that anxiety (adjusted odds ratio [aOR], 5.76; 95%CI, 1.29–25.58; P  = 0.021), and a greater recognition of stigma (aOR, 11.09, 95%CI, 2.28–53.90; P  = 0.003) during the MERS-affected period were independent predictors of PTSD at 12 months after the MERS outbreak. Having a family member who died from MERS predicted the development of depression (aOR, 12.08, 95%CI, 1.47–99.19; P  = 0.020). Conclusion This finding implies that psychosocial factors, particularly during the outbreak phase, influenced the mental health of patients over a long-term period. Mental health support among the infected subjects and efforts to reduce stigma may improve recovery from psychological distress in an EID outbreak.

Spin-momentum locking is essential to the spin-split Fermi surfaces of inversion-symmetry broken materials, which are caused by either Rashba-type or Zeeman-type spin-orbit coupling (SOC). While the effect of Zeeman-type SOC on superconductivity has experimentally been shown recently, that of Rashba-type SOC remains elusive. Here we report on convincing evidence for the critical role of the spin-momentum locking on crystalline atomic-layer superconductors on surfaces, for which the presence of the Rashba-type SOC is demonstrated. In-situ electron transport measurements reveal that in-plane upper critical magnetic field is anomalously enhanced, reaching approximately three times the Pauli limit at T = 0. Our quantitative analysis clarifies that dynamic spin-momentum locking, a mechanism where spin is forced to flip at every elastic electron scattering, suppresses the Cooper pair-breaking parameter by orders of magnitude and thereby protects superconductivity. The present result provides a new insight into how superconductivity can survive the detrimental effects of strong magnetic fields and exchange interactions. The effect of Rashba spin orbit coupling (SOC) on superconductivity remains elusive. Here, the authors report largely enhanced in-plane upper critical magnetic field due to Rashba SOC induced dynamic spin-momentum locking on the surfaces of an atomic-layer superconductor.

Low-dimensional van der Waals materials have been extensively studied as a platform with which to generate quantum effects. Advancing this research, topological quantum materials with van der Waals structures are currently receiving a great deal of attention. Here, we use the concept of designing topological materials by the van der Waals stacking of quantum spin Hall insulators. Most interestingly, we find that a slight shift of inversion centre in the unit cell caused by a modification of stacking induces a transition from a trivial insulator to a higher-order topological insulator. Based on this, we present angle-resolved photoemission spectroscopy results showing that the real three-dimensional material Bi 4 Br 4 is a higher-order topological insulator. Our demonstration that various topological states can be selected by stacking chains differently, combined with the advantages of van der Waals materials, offers a playground for engineering topologically non-trivial edge states towards future spintronics applications. Angle-resolved photoemission evidence for a three-dimensional higher-order topological insulator is presented. This work demonstrates that stacking configurations can be utilized to realize different topological phases.