Deep States Research Articles

Deep Temporal State Perception Toward Artificial Cyber–Physical Systems

Cyber-Physical systems, as the cornerstone of smart city, has been attracting great interest from academia and industry. It aims to monitor/ control physical components via communication and computation, while ensuring effectiveness, intelligence, and security. The related research has pointed that the state perception on physical device is the prerequisite for boosting overall CPS performance. Towards this end, we present an effective deep temporal perception networks to achieve classification based state detection. Namely, we first design a multi-feature encoding network for multi-view time series representation. Concretely, on the one hand, we utilize two piecewise aggregate representation strategies to obtain the key temporal trends; on the other hand, we adopt a temporal symbolic representation strategy to capture the necessary contextual semantic correlations. Thereafter, we develop a comprehensive representation enhancement module to improve feature comprehension capability, and thus boosting the overall performance and interpretability. Corresponding comparison experiments, ablation studies, and data visualization analyses on benchmark datasets have verified the effectiveness of our model.

Identification and characterization of deep nitrogen acceptors in β-Ga2O3 using defect spectroscopies

The ability to achieve highly resistive beta-phase gallium oxide (β-Ga2O3) layers and substrates is critical for β-Ga2O3 high voltage and RF devices. To date, the most common approach involves doping with iron (Fe), which generates a moderately deep acceptor-like defect state located at EC-0.8 eV in the β-Ga2O3 bandgap. Recently, there has been growing interest in alternative acceptors, such as magnesium (Mg) and nitrogen (N), due to their predicted deeper energy levels, which could avoid inadvertent charge modulation during device operation. In this work, a systematic study that makes direct correlations between the introduction of N using ion implantation and the observation of a newly observed deep level at EC-2.9 eV detected by deep-level optical spectroscopy (DLOS) is presented. The concentration of this state displayed a monotonic dependence with N concentration over a range of implant conditions, as confirmed by secondary ion mass spectrometry (SIMS). With a near 1:1 match in absolute N and EC-2.9 eV trap concentrations from SIMS and DLOS, respectively, which also matched the measured removal of free electrons from capacitance-voltage studies, this indicates that N contributes a very efficiently incorporated compensating defect. Density functional theory calculations confirm the assignment of this state to be an N (0/−1) acceptor with a configuration of N occupying the oxygen site III [NO(III)]. The near ideal efficiency for this state to compensate free electrons and its location toward the midgap region of the β-Ga2O3 bandgap demonstrates the potential of N doping as a promising approach for producing semi-insulating β-Ga2O3.

The Effects of Progressive Muscle Relaxation on Blood Pressure and Headache in Hypertension

Aims: Hypertension is a condition of increased blood pressure that causes the heart to work harder to circulate blood throughout the body. Patients with hypertension more frequently complain of headaches. Progressive Muscle Relaxation (PMR) is a non-pharmacological treatment to overcome hypertension, headache symptoms, and maintain a deep relaxation state. Objective: The study aimed to determine PMR's effects on blood pressure and headache in hypertensive patients. Methods: This study utilized a quasi-experimental design with one group pretest and posttest design. A sample of 30 respondents with hypertension and headaches was obtained through convenience sampling. Data were then analyzed using paired t-test. Results: The results showed a decrease in blood pressure and headache intensity after administrated PMR. The number of respondents with stage 2 hypertension decreased from nine (30%) in the pretest to zero respondents (0%) in the posttest. There was a significant effect of giving PMR to clients with increased blood pressure accompanied by headaches (p-value of 0.000). Conclusion: PMR is a significant measure that can be used to reduce blood pressure and headache. It can be done independently from ten to 15 minutes daily for a more effective outcome. Therefore, it is suggested that clients with increased blood pressure can do the PMR exercise independently every day for 10-15 minutes to reduce blood pressure and headache intensity.

Homeostatic Control of Deep Sleep and Molecular Correlates of Sleep Pressure in Drosophila.

Homeostatic control of sleep is typically addressed through mechanical stimulation-induced forced wakefulness and the measurement of subsequent increases in sleep. A major confound attends this approach: biological responses to deprivation may reflect a direct response to the mechanical insult rather than to the loss of sleep. Similar confounds accompany all forms of sleep deprivation and represent a major challenge to the field. Here we describe a new paradigm for sleep deprivation in Drosophila that fully accounts for sleep-independent effects. Our results reveal that deep sleep states are the primary target of homeostatic control and establish the presence of multi-cycle sleep rebound following deprivation. Furthermore, we establish that specific deprivation of deep sleep state results in state-specific homeostatic rebound. Finally, by accounting for the molecular effects of mechanical stimulation during deprivation experiments, we show that serotonin levels track sleep pressure in the fly's central brain. Our results illustrate the critical need to control for sleep-independent effects of deprivation when examining the molecular correlates of sleep pressure and call for a critical reassessment of work that has not accounted for such non-specific effects.

Modifying subgap states with hydrogen incorporation from source/drain alloys for oxide phototransistors

Phototransistors with amorphous oxide semiconductors (AOSs) have attracted considerable attention owing to their low leakage current density and process compatibility with the active array. In this study, we introduce hydrogen (H) from the source/drain electrodes to the active layer using a Ti-based alloy, specifically MoTi. The device with MoTi electrodes exhibits a more negatively shifted Von value of −3.34 V compared to the device with MoAl electrodes. The H in the oxide semiconductor passivates the oxygen vacancy states and generates additional deep states above the oxygen vacancy states. Consequently, phototransistors with MoTi electrodes demonstrate a higher photoresponse under green and blue light.

Anion optimization for bifunctional surface passivation in perovskite solar cells.

Pseudo-halide (PH) anion engineering has emerged as a surface passivation strategy of interest for perovskite-based optoelectronics; but until now, PH anions have led to insufficient defect passivation and thus to undesired deep impurity states. The size of the chemical space of PH anions (>106 molecules) has so far limited attempts to explore the full family of candidate molecules. We created a machine learning workflow to speed up the discovery process using full-density functional theory calculations for training the model. The physics-informed machine learning model allowed us to pinpoint promising molecules with a head group that prevents lattice distortion and anti-site defect formation, and a tail group optimized for strong attachment to the surface. We identified 15 potential bifunctional PH anions with the ability to passivate both donors and acceptors, and through experimentation, discovered that sodium thioglycolate was the most effective passivant. This strategy resulted in a power-conversion efficiency of 24.56% with a high open-circuit voltage of 1.19 volts (24.04% National Renewable Energy Lab-certified quasi-steady-state) in inverted perovskite solar cells. Encapsulated devices maintained 96% of their initial power-conversion energy during 900 hours of one-sun operation at the maximum power point.

DHESN: A deep hierarchical echo state network approach for algal bloom prediction

A deep hierarchical echo state network (DHESN) is designed for rectifying the shortcomings of the shallow coupled structure with less reservoir dynamics. This design is with reference to algal bloom which is a complex ecological phenomenon. Accurate prediction of algal bloom can reduce the environmental impact and economic loss. Since the formation of algal bloom has chaotic characteristics, the ESN has been employed to realize its prediction function. First, the candidate variables with strong causal relationship have been screened by transfer entropy, and the redundant variables is eliminated. Then, a hierarchical reservoir structure is established that is inspired by the hierarchical characteristics from the brain. The hierarchical reservoir has realized the connection between the representative nodes of each subreservoir, and improved the information processing ability of the reservoir. Finally, the pruning and compression of the output weights have been realized by the elastic regularization method, which improves the robustness of the prediction model. The simulation results demonstrate that the DHESN has appreciable prediction accuracy in both the chaotic and the public algal bloom datasets. The DHESN contains richer dynamic characteristics, and can realize the self-organization of the network structure. It provides a novel idea to realize the prediction model of algal bloom with a high accuracy and low complexity.

Investigation of the effects of trap-assisted tunneling and temperature variations on the sensitivity metrics of a tunnel FET biosensor

The study presented in this paper discusses Trap-Assisted Tunneling (TAT) and temperature variation as the subjects of concern regarding the sensitivity metrics of the presented TFET biosensor. In order to have a minimum amount of process-related variability, the concept of a dopingless TFET structure is used in the development of the suggested device. However, considering the possibility of traps in the doped Silicon–Germanium pocket, an investigation has been conducted using a certain set of models during simulation. The analysis of deep and shallow trap states incorporated to assess TAT current prevails that deep trap affects the biosensors' sensitivity by a significant rate. In addition to this, temperature variations also affect the bandgap narrowing owing to changes in the biosensor's sensitivity.

Electrical transport properties and impedance analysis of Pt/TiO2 Nanorods/FTO heterojunction device

Presented study focuses on the hydrothermal growth of Titanium Oxide (TiO2) vertical nanorods on a FTO substrate, leading to the realization of a Schottky junction through the evaporation of platinum onto the TiO2 nanorods. I-V (current-voltage), EIS (electrochemical impedance spectroscopy) measurements were carried out to characterize the fabricated heterojunction devices. The barrier height of 0.6 eV and ideality factor value of 16 indicate the presence of an interfacial layer in the heterojunction devices. Effective charge carrier mobility of ∼ 0.01842 cm2/Vs is determined through the application of Mott-Gurney's law. Conductivity and density of deep trap states (ntrap) were found to be ∼ 1.5595 × 10−8 (Ω cm)−1 and ∼ 1.4 × 1022 m−3 respectively. Additionally, the scanning Kelvin probe (SKP) technique is employed to investigate the contact potential difference (CPD) and work function of TiO2, while the mechanical behaviour of TiO2 nanorod arrays is studied using nanoindentation.

The Synergetic Effects of Ru and Al Co‐Doping for Improved Cyclability of LiCoO2 at High Voltages

AbstractLiCoO2 is one of the most extensively deployed cathode materials in commercial lithium‐ion batteries. However, it remains a challenge to fulfill the potential specific capacity of LiCoO2 during long‐term cycling, due to the lattice degradations in deep delithiation states. Herein, a dual doping scheme based on Ru and Al is explored, which improves the cyclability of LiCoO2 at high voltages through synergistically reconfiguring electronic structure and stabilizing lattice structure. It is well known that the doped Al will serve as a strong positively charged center and restrain the lattice distortion. The doping of Ru suppresses the occupied O:2p states below Fermi level and stabilizes oxygen redox during cycling. The proposed dual doping strategy improves the accessible energy density and cyclability of LiCoO2 at increased voltages significantly. For example, the dual doped LiCoO2 performs 19% higher energy density at 4.57 V versus Li+/Li than the pristine one. In addition, an initial capacity of 197 mAh g−1 and 86% capacity retention after 100 cycles are achieved from 3.00 to 4.53 V versus Li+/Li. This study sheds a light on the fundamental principles for the development of high‐voltage LiCoO2 and other layered oxide cathode materials with high energy density and excellent cyclability.

Light soaking effect on defect states distribution of Hydrogenated amorphous silicon investigated By means of constant photocurrent technique

In the present paper we have investigated, by using the constant photocurrent method in dc-mode (dc-CPM), the effect of the light soaking (LS) on the deep defect density (Nd) and the slope of the Urbach tail (E0) of a slightly phosphorus-doped hydrogenated amorphous silicon (a-Si:H) film prepared by Plasma-Enhanced Chemical Vapour Deposition (PECVD). By applying the derivative method, we have converted the measured data into a density of states (DOS) distribution in the lower part of the energy gap. The evolution of the sub-band-gap absorption coefficient and the CPMdetermined density of gap-states distribution within the gap versus the illumination time leads to: (i) an increase in the deep defect absorption without any significant changes in the Urbach tail (exponential part), (ii) a presence of more charged than neutral defects as predicted by the defect pool model, and (iii) a saturation point of the degradation of both optical absorption coefficient and density of deep states of slightly P-doped sample measured by dc-CPM. The constant photocurrent technique in dc-mode as a spectroscopy method for the defect distribution determination is, therefore, most reliable to study the light soaking effect on the stability of hydrogenated amorphous silicon layers used in solar cells manufacturing.

Demonstration of near-ideal Schottky contacts to Si-doped AlN

Near-ideal behavior in Schottky contacts to Si-doped AlN was observed as evidenced by a low ideality factor of 1.5 at room temperature. A temperature-independent Schottky barrier height of 1.9 eV was extracted from temperature-dependent I–V measurements. An activation energy of ∼300 meV was observed in the series resistance, which corresponded to the ionization energy of the deep Si donor state. Both Ohmic and Schottky contacts were stable up to 650 °C, with around four orders of magnitude rectification at this elevated temperature. These results demonstrate the potential of AlN as a platform for power devices capable of operating in extreme environments.

Threshold Modulative Artificial GABAergic Nociceptor.

Gamma-aminobutyric acid (GABA) is a crucial inhibitory neurotransmitter of the central nervous system. It modifies the signal threshold of the nociceptor, allowing it to react to external stimuli in various circumstances. Thus, GABAergic behaviors are critical characteristics of adaptive behavior in life. Here, a threshold-modulative artificial GABAergic nociceptor is reported for the first time at a Pt/Ti/Nb2 O5- x /Al2 O3- y /Pt/Ti (top to bottom) of the double charge trapping structure. The Al2 O3- y layer contains deep defect states that function similarly to the GABA neurotransmitter in modulating the signal threshold. Meanwhile, the Nb2 O5- x layer traps volatile charges and produces nociceptive behaviors. The combined dynamics of the two layers readily offer threshold-modulative GABAergic nociceptive behaviors. Based on these GABAergic behaviors, a method of implementing hot- and cold-sensitive thermoreceptors is demonstrated and shows its potential applications in advanced sensory devices.

Sequence prediction with different dimensions based on two novel deep echo state network models

The echo state network (ESN) is a typical reservoir computation model, which was first proposed by Jaeger et al. It was widely used in various fields and achieved excellent results for a long time, especially in time series prediction. In recent years, there are few improvements to the ESN structure, and the more famous is the deep echo state network (DESN) model. However, a DESN will cause the loss of input data. How to effectively optimize the structure of ESN and how to scientifically add input data to deep echo are urgent problems to be solved. In this paper, we propose multi-reservoir ESN models based on how the input data participate in the system. Then, we use complex nonlinear chaotic systems with different dimensions to test our model. Finally, we compare it with the traditional model and the recently proposed model, and then find that our models have better predictive performance.

Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks

Short-term traffic flow prediction is a significant and challenging research topic as it is closely related to the application of intelligent transportation systems. Due to the variable and random characteristics of the transportation system, raw traffic flow data often contain noise, and predicting the raw data directly may reduce the accuracy and effectiveness of the prediction models. Therefore, a hybrid method is established in this research which combines denoising schemes and deep learning models to improve the prediction accuracy. The time series denoising schemes include two parts: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and wavelet packet decomposition (WPD). Firstly, the raw traffic flow data are decomposed by CEEMDAN to obtain intrinsic mode functions (IMFs) and a residual. Then the IMFs are divided into anti-persistent and persistent components through the Hurst Exponent index. The anti-persistent components are re-decomposed by the WPD algorithm, and persistent components are aggregated into one component. Finally, these components and residual are forecasted by the deep echo state network (DeepESN) model. In the experiment, to investigate the prediction performance of the proposed CEEMDAN-WPD123456–7a11-DeepESN model, the LSTM, CEEMDAN-LSTM, CEEMDAN-WPD-LSTM, DeepESN, CEEMDAN-DeepESN, CEEMDAN-WPD1-DeepESN, CEEMDAN-WPD123456-DeepESN and CEEMDAN-WPD1a6–7a11-DeepESN models are considered to be comparison models. The experimental results demonstrate that the proposed model has superior performance on both efficiency and accuracy.

Development of a novel WO3 self-powered electrochromic window technology with a self-rechargeable battery application (ChromoVolt)

For zero-energy buildings, electrochromic smart windows are one of the most essential components, but the external power demand makes this purpose ambiguous. Here, we report a self-powered electrochromic smart window (ECSW) operated by internal electrochemical voltage (chromoVolt). This WO3-based ECSW provides a unique structured device as the 2 mm width Al strip (counter electrode) is placed at one side, making the total window transparent from the front or back point. The WO3 working electrode was fabricated by the large-scale roll-to-roll (R2R) slot-die wet coating method. This device goes into coloured (blue) or bleached (transparent) states spontaneously simply by connecting and disconnecting WO3 and Al electrodes respectively. Going into a deep blue stable-colored state takes approximately 2 min and shows a maximum 56.5 % transmittance change at 600 nm wavelength. Also, this ECSW can provide output potential through internal chemical reactions in a transparent state and can be charged up through disconnection: a self-rechargeable transparent battery application. Three small cells of an active area of 30 mm × 15 mm each provide an output voltage of around 3 V and can power up a small white LED light for up to 1 h. All these phenomena make this electrochromic smart window perfect for a zero-energy building.

Sonochemical-assisted preparation of mesoporous ZnS quantum dots: Optical, electrical, and temperature-dependent dielectric characteristics

Mesoporous ZnS quantum dots (QDs) were prepared by the sonochemical technique that indicated bi-structural cubic-hexagonal phases with an average particle size of 2.5 nm, a pore size of 4 nm, and 28.85 m2 g−1 surface area. ZnS QDs exhibited a high bandgap of 3.85 eV with a large Urbach energy of 280 meV. The presence of localized defects was verified by the photoluminescence emission that was ascribed to the recombination of trapped carriers in shallow and deep trapping states with photogenerated holes. ZnS QDs revealed a high DC conductivity of 5.37 × 10−6 Ω−1 m−1 and a small activation energy of 248 meV that facilitated the tunnelling of thermionically excited carriers. The frequency-dependent behavior of AC conductivity (σAC), dielectric constant (εr), and dielectric loss (ε′) was examined at various temperatures. It was observed that both σAC, εr, ε′ were enhanced with increasing temperature up to 333 K followed by a gradual decrease at higher temperatures.

Sleep deprivation soon after recovery from synthetic torpor enhances tau protein dephosphorylation in the rat brain.

Neuronal Tau protein hyperphosphorylation (PPtau) is a hallmark of tauopathic neurodegeneration. However, a reversible brain PPtau occurs in mammals during either natural or "synthetic" torpor (ST), a transient deep hypothermic state that can be pharmacologically induced in rats. Since in both conditions a high sleep pressure builds up during the regaining of euthermia, the aim of this work was to assess the possible role of post-ST sleep in PPtau dephosphorylation. Male rats were studied at the hypothermic nadir of ST, and 3-6h after the recovery of euthermia, after either normal sleep (NS) or total sleep deprivation (SD). The effects of SD were studied by assessing: (i) deep brain temperature (Tb); (ii) immunofluorescent staining for AT8 (phosphorylated Tau) and Tau-1 (non-phosphorylated Tau), assessed in 19 brain structures; (iii) different phosphorylated forms of Tau and the main cellular factors involved in Tau phospho-regulation, including pro- and anti-apoptotic markers, assessed through western blot in the parietal cortex and hippocampus; (iv) systemic factors which are involved in natural torpor; (v) microglia activation state, by considering morphometric variations. Unexpectedly, the reversibility of PPtau was more efficient in SD than in NS animals, and was concomitant with a higher Tb, higher melatonin plasma levels, and a higher frequency of the microglia resting phenotype. Since the reversibility of ST-induced PPtau was previously shown to be driven by a latent physiological molecular mechanism triggered by deep hypothermia, short-term SD soon after the regaining of euthermia seems to boost the possible neuroprotective effects of this mechanism.

Medial Septal Glutamatergic Neurons Modulate States of Consciousness during Sevoflurane Anesthesia in Mice.

Multiple neural structures involved in maintaining wakefulness have been found to promote arousal from general anesthesia. The medial septum is a critical region that modulates arousal behavior. This study hypothesized that glutamatergic neurons in the medial septum play a crucial role in regulating states of consciousness during sevoflurane general anesthesia. Adult male mice were used in this study. The effects of sevoflurane anesthesia on neuronal activity were determined by fiber photometry. Lesions and chemogenetic manipulations were used to study the effects of the altered activity of medial septal glutamatergic neurons on anesthesia induction, emergence, and sensitivity to sevoflurane. Optogenetic stimulation was used to observe the role of acute activation of medial septal glutamatergic neurons on cortical activity and behavioural changes during sevoflurane-induced continuous steady-state of general anesthesia and burst suppression state. We found that medial septal glutamatergic neuronal activity decreased during sevoflurane anesthesia induction and recovered in the early period of emergence. Chemogenetic activation of medial septal glutamatergic neurons prolonged the induction time (mean ± SD, hM3Dq-Clozapine N-oxide vs. hM3Dq-Saline, 297.5 ± 60.1 s vs. 229.4 ± 29.9 s, P < 0.001, n = 11) and decreased the emergence time (53.2 ± 11.8 s vs. 77.5 ± 33.5 s, P = 0.025, n = 11). Lesions or chemogenetic inhibition of these neurons produced the opposite effects. During steady-state of general anesthesia and deep anesthesia-induced burst suppression state, acute optogenetic activation of medial septal glutamatergic neurons induced cortical activation and behavioural emergence. Our findings reveal that activation of medial septal glutamatergic neurons has arousal-promoting effects during sevoflurane anesthesia in male mice. The activation of these neurons prolongs the induction and accelerates the emergence of anesthesia.

Place Visitation Data Reveals the Geographic and Racial Disparities of COVID-19 Impact on HIV Service Utilization in the Deep South.

The COVID-19 pandemic has posed unprecedented pressure to health care systems, and interrupted health care delivery and access including HIV care in the United States' Deep South, which endures a double epidemic of HIV and COVID-19. Ryan White programs cover HIV care services for over half of PLWH in the Deep South. Given the important role of Ryan White programs, examining the visitation changes to Ryan White facilities during the pandemic offers insights into the impact of the pandemic on HIV healthcare utilization. Analyze the geographic distribution of HIV facility visitors at the county level before and during the pandemic in the nine US states of Deep South (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas) to reveal the geographic and racial disparity in visitation disruption caused by the pandemic. We first extracted mobile device-based visitation data for Ryan White HIV facilities in the Deep South during 2019 and 2020. To quantify the disruption in visitations during 2020, we calculated the visitation reduction rate (VRR) for each county, using 2019 data as the baseline. Next, we conducted a spatial analysis of the VRR values to uncover geographical disparities in visitation interruptions. To investigate racial disparities, we performed spatial regression analyses with VRR as the dependent variable, and the percentages of Black, Hispanic, and Asian populations as the independent variables. In this analysis, we controlled for potential confounders. Geographic disparities in visitation reduction were observed, with all nine Deep South states experiencing significant drops. Georgia experienced the highest visitation loss (VRR = -0.58), followed by Texas (-0.47), Alabama (0.47), and Tennessee (-0.46), while South Carolina had the smallest reductions (-0.11). All the regression models consistently revealed racial disparities in visitation interruption. That is, counties with a higher proportion of Black population tended to have higher RW facility visitation reductions. Our analysis revealed distinct geographic disparities in visitation interruptions at Ryan White HIV facilities in the Deep South during the COVID-19 pandemic in 2020. Furthermore, we found that the Black/African American population experienced a greater disruption at the county level in the Deep South during this period.