Search

The high and growing penetration of inverter-based resources (IBR) in power systems challenges the way that system strength is assessed. It has been noticed that the standard indicator of system strength, short-circuit ratio (SCR), is not fully effective in anticipating the sub/super-synchronous oscillation phenomena that can arise from interactions of the control system of an IBR and the rest of the system. In this paper, system strength is first viewed from two perspectives, small-signal system strength and large-signal system strength, which is a natural separation considering the markedly different response of an IBR in normal operation and under faults. For assessing the onset of small-signal instability, a new metric for small-signal system strength is proposed and named Impedance Margin Ratio (IMR). IMR is the ratio between the allowed variation of the impedance of an IBR and the original value of the impedance at a particular point of interconnection. IMR is assessed at the oscillatory frequency of the dominant mode or taken as the minimum value across several modes. It is shown that as a localised frequency-domain indicator, IMR can successfully reveal weak points in the system towards various causes of oscillations.

End-to-End (E2E) learning has increasingly become a dominant method for enhancing the performance of communication systems. In this paper, an E2E Radio Over Fiber (RoF) transmission system utilizing self-supervised learning (SSL) is proposed. This E2E approach enables automatic optimization of the transmission system's transmitter modulator and receiver demodulator based on specific channel characteristics. The SSL architecture comprises four deep neural networks: TransNN for symbol mapping, SamplNN for upsampling, ChannelNN for channel modeling, and ReceivNN for demodulation, collectively replacing traditional components in the RoF link. Indeed, the E2E system adjusts the geometric shape of the constellation and upsampling rules according to RoF channel properties, facilitating transmission with enhanced received sensitivity. Perturbation noise is incorporated during the training phase to improve the ability to generalize the SSL. Notably, traditional demodulation methods cannot demodulate the RF signals transmitted in the E2E system, thereby introducing an additional layer of confidentiality to the transmission process. Numerical simulations have been conducted in 10 GHz 2 Gsym/s RoF transmission systems. The results indicate that compared to traditional approaches, the received sensitivity of the E2E system improved by 3.5 dB, under a BER limit of 2.4e-2. Compared to optimized only at the receiver side, achieved a sensitivity improvement of at least 3 dB. The numerical experiments also validate the importance of the SamplNN and perturbation training for the E2E system. These elements can improve the system's transmission performance and generalization ability, as well as enhance the system's security.

Manganese single atoms implanted in nitrogen-doped porous carbon nanosheets, featuring Mn–N structures and abundant charge storage sites, have been developed as a cathode in zinc ion hybrid capacitors, exhibits remarkable electrochemical performance.

The technology of injecting carbon dioxide into coal seams to reduce greenhouse gas emissions and has brought attraction on the wettability of coal. As a fast and non-destructive method, nuclear magnetic resonance (NMR) is widely used in the evaluation of coal wettability, but the inconsistency of analytical basis makes the quantitative evaluation of coal wettability changes uncertain. In this study, based on NMR combined with centrifugal experiments, a novel analytical basis using irreducible water defined by T2cutoff value was proposed to quantitatively analyze the dynamic wetting mechanism under different CO2pressures. The results show that when T2< 72.32 ms, the water in coal is irreducible water; when T2> 72.32 ms, the water in coal is movable water. CO2pressure hindered free water conversion to adsorbed and capillary water, but impacted adsorbed water more. Increased CO2pressure caused the wetting porosity of coal to decrease from 1.62 % to 1.08 %, and coal gradually changed from water-wet to CO2-wet. The increase in CO2pressure enlarged the CO2aggregations in the seepage channel, thus weakening the wetting rate of water to coal. CO2preferentially occupied small pores, which will hinder the migration of water into small pore spaces. Molecular diffusion controls water to achieve wetting equilibrium more than gravity/pressure difference and capillary force. As CO2pressure increases, the wetting equilibrium time decreases.

Eighteen nitrogen-containing compounds (1–18) were isolated from cultures of the lichen-associated Streptomyces flavidovirenscollected from the Qinghai-Tibet Plateau, including seven phenazine derivatives with three new ones, named subphenazines A–C (2–4), two new furan pyrrolidones (8–9), and nine known alkaloids. The structures were elucidated by spectroscopic data analysis, and absolute configurations were determined by single-crystal X-ray diffraction and ECD calculations. The phenazine-type derivatives, in particular compound 3, exhibited significantly better antineuroinflammatory activity than other isolated compounds (8–18). Compound 3inhibited the release of proinflammatory cytokines including IL-6, TNF-α, and PGE2, and the nuclear translocation of NF-κB; it also reduced the oxidative stress and activated the Nrf2 signaling pathway in LPS-induced BV2 microglia cells. In vivo anti-inflammatory activity in zebrafish indicated that 3inhibited LPS-stimulated ROS generation. These findings suggested that compound 3might be a potent antineuroinflammatory agent through the regulation of the NF-κB/Nrf2 signaling pathways.

Metabolic dysfunction is recognized as a contributing factor in the pathogenesis of wet age-related macular degeneration (wAMD). However, the specific metabolism-related proteins implicated in wAMD remain elusive. In this study, we assessed the expression profiles of 92 metabolism-related proteins in aqueous humor (AH) samples obtained from 44 wAMD patients and 44 cataract control patients. Our findings revealed significant alterations in the expression of 60 metabolism-related proteins between the two groups. Notably, ANGPTL7 and METRNL displayed promising diagnostic potential for wAMD, as evidenced by area under the curve values of 0.88 and 0.85, respectively. Subsequent validation studies confirmed the upregulation of ANGPTL7 and METRNL in the AH of wAMD patients and in choroidal neovascularization (CNV) models. Functional assays revealed that increased ANGPTL7 and METRNL played a pro-angiogenic role in endothelial biology by promoting endothelial cell proliferation, migration, tube formation, and spouting in vitro. Moreover, in vivostudies revealed the pro-angiogenic effects of ANGPTL7 and METRNL in CNV formation. In conclusion, our findings highlight the association between elevated ANGPTL7 and METRNL levels and wAMD, suggesting their potential as novel predictive and diagnostic biomarkers for this condition. These results underscore the significance of ANGPTL7 and METRNL in the context of wAMD pathogenesis and offer new avenues for future research and therapeutic interventions.

The fruit of Alpinia oxyphyllaMiq is an important food spice in southern China and has been used in the treatment of kidney disorders for centuries. In order to discover the natural products with potent renoprotective activities in A. oxyphyllaand provide some references for its usage, systematic phytochemical studies were carried out and 24 new diverse sesquiterpenoids, including seven guaiane sesquiterpenoids (1–7), 10 eudesmane sesquiterpenoids (9–13, 18, 19, and 21–23), six cadinane sesquiterpenoids (31–35and 38), and an eremophilane sesquiterpenoid (40), along with 24 known analogues were isolated and elucidated by analysis of spectroscopic data and quantum-chemical calculations. Biological evaluation showed that 6 sesquiterpenoids could significantly inhibit the expression of extracellular matrix components, α-SMA in TGF-β1 induced kidney proximal tubular cells (NRK-52e) at low concentrations, and 9 sesquiterpenoids could also downregulate fibronectin and collagen I in a concentration-dependent manner, showing their potential in renal fibrosis. Further action mechanism study displayed that TGF-β1/Smads pathway might be involved in the antifibrotic effects of active sesquiterpenoids 15and 43. These studies suggest that A. oxyphyllamay have a potential to serve as a functional food in preventing renal fibrosis-associated diseases.

•Enasidenib treatment may be associated with DS in patients with AML, yet diagnosis of DS is challenging because of nonspecific symptoms.•IDH inhibition-associated DS is manageable by identifying risk factors, recognizing symptoms, and initiating treatment in a timely manner.

Hypertrophic scars (HS) still remain an urgent challenge in the medical community. Traditional Chinese medicine (TCM) has unique advantages in the treatment of HS. However, due to the natural barrier of the skin, it is difficult for the natural active components of TCM to more effectively penetrate the skin and exert therapeutic effects. Therefore, the development of an efficient drug delivery system to facilitate enhanced transdermal absorption of TCM becomes imperative for its clinical application. In this study, we designed a compound Salvia miltiorrhiza–Blumea balsamiferananoemulsion gel (CSB-NEG) and investigated its therapeutic effects on rabbit HS models. The prescription of CSB-NEG was optimized by single-factor, pseudoternary phase diagram, and central composite design experiments. The results showed that the average particle size and PDI of the optimized CSB-NE were 46.0 ± 0.2 nm and 0.222 ± 0.004, respectively, and the encapsulation efficiency of total phenolic acid was 93.37 ± 2.56%. CSB-NEG demonstrated excellent stability and skin permeation in vitroand displayed a significantly enhanced ability to inhibit scar formation compared to the CSB physical mixture in vivo. After 3 weeks of CSB-NEG treatment, the scar appeared to be flat, pink, and flexible. Furthermore, this treatment also resulted in a decrease in the levels of the collagen I/III ratio and TGF-ß1and Smad2 proteins while simultaneously promoting the growth and remodeling of microvessels. These findings suggest that CSB-NEG has the potential to effectively address the barrier properties of the skin and provide therapeutic benefits for HS, offering a new perspective for the prevention and treatment of HS.

Mobile ions at mineral surfaces can respond to an applied electric field, adopting a new distribution that effectively represents polarization of the electrical double layer. When the field is released, the ions relax to their equilibrium distribution. In both cases, the dynamics are characteristic of the interface. However, current models of electrokinetic phenomena are not sufficiently robust to accurately predict collective ion dynamics at structurally and chemically specific mineral–water interfaces. In this study, we use electrostatic force microscopy (EFM) to investigate the dynamics of ion relaxation at hydrated calcite (104) surfaces at controlled relative humidity (RH). Electrically biased probes are used to polarize the distributions of calcium and carbonate ions that are intrinsic to this interface across a range of RH values. Polarization kinetics are tracked by monitoring the tip–sample force gradient during charging, and EFM imaging is used to characterize the spatial relaxation dynamics after the applied field is released. Electrostatic finite element modeling of the sample/probe system across length-scales from nanometers to millimeters reproduces the observed stretched exponential charging response. Together, these results allow us to estimate the ion diffusivities at the interface across a wide range of RH values. These diffusivities increase by roughly 5 orders of magnitude as the RH is increased from 5 to 90%, highlighting the critical role of adsorbed water for surface ion solvation that enables ion mobility.

As an important alarmone nucleotide, guanosine 3′-diphosphate-5′-diphosphate (ppGpp) can regulate the survival of bacteria under strict environmental conditions. Direct detection of ppGpp in bacteria with high sensitivity and selectivity is crucial for elucidating the role of ppGpp in bacterial stringent response. Herein, the terbium–carbon dots nanocomposite (CDs-Tb) modified glass nanopipet was developed for the recognition of ppGpp. The CDs-Tb in glass nanopipette preserved their fluorescence properties as well as the coordination capacity of Tb3+toward ppGpp. The addition of ppGpp not only led to the fluorescence response of CDs-Tb but also triggered variations of surface charge inside the glass nanopipet, resulting in the ionic current response. Compared with nucleotides with similar structures, this method displayed good selectivity toward ppGpp. Moreover, the dual signals (fluorescence and ionic current) offered a built-in correction for potential interference. Apart from the high selectivity, the proposed method can determine the concentration of ppGpp from 10–13to 10–7M. Taking advantage of the significant analytical performance, we monitored ppGpp in Escherichia coliunder different nutritional conditions and studied the relationship between ppGpp and DNA repair, which is helpful for overcoming antibiotic resistance and promoting the development of potential drugs for antibacterial treatment.

Participation analysis based on whole-system impedance models enables the root-cause stability analysis of large-scale power systems when state-space models are unavailable. To extend participation analysis to the hybrid AC/DC system, especially for the interlinking converter, this article proposes two complementary methods, namely the direct method and the indirect method. The direct method treats the interlinking converter as hybrid AC-DC port apparatus and the participation factor can be defined with the terminal hybrid AC-DC impedance matrix correspondingly. As for the indirect method, system equivalence can be made at the AC or DC terminal of the interlinking converter when impedance models within subsystems are unavailable such that preliminary knowledge of systems can be simplified. Importantly, the equivalence of these two methods on the observability of system components is revealed through theoretical analysis. The proposed impedance participation analysis methods are illustrated with a 4-bus hybrid AC/DC system, a modified 28-bus hybrid AC/DC system and a modified 96-bus hybrid AC/DC system. Numerical calculations and time-domain simulations are performed to validate the theoretical analysis.

Wearable devices, interactive human-machine interface equipment, wireless sensors, and small-scale cleaning devices play crucial roles in biomedical implantation, disease treatment, health monitoring, environmental purification, etc. These devices require a sustainable energy source to work effectively. With the consideration of the global energy crisis and environmental pollution, researchers are exploring new, stable, and environmentally friendly methods to power these low-powered devices. Mechanical energy is one of the most abundant natural energy sources. Converting mechanical energy from the ambient environment or host structures into electrical energy via the direct piezoelectric effect is an efficient energy harvesting technique. This paper reviews the application of advanced piezoelectric materials, and small-scale self-powered and self-sensing piezoelectric devices at the cubic centimeter scale in energy harvesting and health monitoring of human, animal, machinery, roads, bridges, as well as the pollutant degradation of the environment. Some of these devices have the capability to not only harvest mechanical energy but also enable real-time monitoring and analysis of the electrical signals generated by the direct piezoelectric effect, facilitating prompt decision-making and appropriate responses. In addition, potential challenges and future prospects of small-scale self-powered and self-sensing piezoelectric devices are discussed.

Real-time emergency event processing for mega-constellation management requires an approach that can quickly obtain a satisfactory satellite range rescheduling scheme. In this article, a deep reinforcement learning (DRL) approach is proposed for rapid rescheduling on the basis of established satellite range scheduling schemes. Three rescheduling principles with different impacts on established scheduling schemes are first analyzed. A mathematical model for both maximizing the completion profits of dynamic missions and minimizing their impacts on scheduled missions is formulated. An efficient DRL-based approach to satellite range rescheduling is then developed. A mask processing mechanism is integrated into the reinforcement learning framework to effectively eliminate the conflicts accurately expressed by the conflict space, and a modified pointer network (Ptr-Net) based on the mask processing mechanism is employed to approximate the action-value function for optimal decision-making. Experimental results show that the proposed approach significantly outperforms the rule-based heuristic and another DRL-based method in terms of computational efficiency and optimization performance, and the total time spent during the training and testing phases of the proposed approach is approximately 13% of the total running time required by the search-based methods.

Multipath self-interference (MSI) between the transmitter and receiver is an inevitable problem in realistic in-band full-duplex (IBFD) transmissions. Due to the dynamic and intricate MSI channels, it is crucial for the MSI cancellation system to quickly obtain the optimal configuration of multi-dimensional parameters. To overcome this problem, a photonics-assisted adaptive MSI cancellation scheme using deep reinforcement learning (DRL) is proposed and experimentally demonstrated. Multiple local reference signals are obtained using optical wavelength division multiplexing to achieve MSI cancellation. An adaptive algorithm based on DRL is applied for adaptive optimization of multi-dimensional optical parameters to achieve real-time MSI cancellation. To manipulate the amplitude and time delay of the multiple-path local reference signals, a multipath optical tunable delay line (MOTDL) module is introduced. A proof-of-concept experiment was carried out to verify the feasibility of the proposed scheme. By superimposing three-path local reference signals, a MSI cancellation depth of 24 dB with a bandwidth of 1 GHz was achieved. After undergoing random exploration, the adaptive algorithm learns the state relationship between the MSI and multiple-path local reference signal. This facilitates achieving optimal cancellation within just 5 steps from most random starting point. In the experiment, a 16-QAM OFDM signal with a bandwidth of 600 MHz was successfully recovered from the MSI signal with a 1 GHz bandwidth in an IBFD transmission. Furthermore, the adaptive capability of the proposed algorithm is also validated in response to varying MSI conditions.

A data-driven digital demodulator based on a Fourier layer Transformer network (FTnet) for radio over fiber (RoF) transmission system with quadrature amplitude modulation (QAM) is developed and experimentally demonstrated. The FTnet combines the Transformer encoder with the Fourier layer to learn the waveforms and directly recover the bitstreams from the impaired received signals. The FTnet-based demodulator does not rely on a series of digital demodulation algorithms such as frequency offset compensation, down-conversion, equalization, and decoding, making the process more efficient and accurate. The 10 GHz 2 Gsym/s 25 km RoF transmission systems are established to evaluate the proposed FTnet-based digital demodulator experimentally. The results show that the bit error rates (BERs) performance of the proposed demodulator for the 16-QAM RoF is better than the ones based on a fully connected neural network, Transformer, and traditional digital demodulator with the least mean square error equalizer (TDD-LMS). The optical receiving sensitivity for the 64-QAM RoF system based on the proposed demodulator is improved by 3 dB compared to TDD-LMS under a BER limit of 3.8 × 10−3. Furthermore, our proposed demodulator outperforms other demodulators for the RoF system with wireless transmission at different received optical powers and wireless distances.

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security. While the data-driven deep learning approach has shown great capacity in predicting yield patterns, its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown. In this study, we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018, with a special focus on the extreme yield loss in 2012. We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018 (R2 = 0.81) and well reproduced the extreme yield anomalies in 2012 (R2 = 0.79). Further attribution analysis indicated that extreme heat stress was the major cause for yield loss, contributing to 72.5% of the yield loss, followed by anomalies of vapor pressure deficit (17.6%) and precipitation (10.8%). Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012. Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

Developing an understanding of the response of mineral/water interfaces to applied electric fields is central to detecting and interpreting signatures of interfacial processes in the subsurface. Here, we focus density functional theory calculations on understanding K+cation binding and migration across the (001) surface of orthoclase feldspar under various applied electric fields with and without surface hydration. The calculations reveal how water ligands labilize surface K+cations for migration while also increasing their sensitivity to electric field effects on the binding energy at different surface sites. The calculations also show how the direction and strength of the electric field systematically affect surface cation mobility, sorption, and hydration behavior. Specifically, electric fields directed toward the surface reduce the energy gap between the different surface potassium sites, favor hydration, and shorten K+residence time at their crystallographic site. The findings help fill a major knowledge gap in the impact of electric fields on mineral/water interface structure and dynamics more generally, featuring, in this case, a commonly found type of feldspar involved in a multitude of atmospheric and geochemical processes.

A palladium-catalyzed olefination of meta-C–H bonds in arenes containing oxyamides using a nitrile template as the directing group has been established. The methodology exhibited high meta-selectivity and tolerated different functional groups such as benzyloxyamides and olefin substrates. The desired products were obtained in good yields. This approach enabled the modification of natural products and drugs and was also applicable on the gram-scale. Furthermore, the directing template was readily removed by selective cleavage of the amide bond or the O–N bond to obtain meta-functionalized hydroxylamines and benzyl alcohols. The proposed method holds great potential for the design of novel drugs.

In this paper, DOA and subarray-interval estimation are considered and applied to arbitrarily distributed unmanned aerial vehicle (UAV) swarm system, in which multiple small UAVs containing uniform linear array (ULA) are divided by unknown intervals because of dynamic moving. Three parameters are taken to indicate the steering vector, namely, the direction of arrivals (DOAs) of target users, the intervals of UAVs, and the orientation angles of UAVs. The orientation angles are first estimated with an auxiliary user and the DOAs are obtained through a search free rooting method, despite the intervals among the UAVs. Afterwards, the intervals among UAVs can also be calculated via exhaustive when the number of target users are no less than three. We further develop a low-complex method to reduce the computational complexity during subarray-interval estimation. The deterministic Cramer-Rao bound (CRB) of the DOA, orientation angle and subarray-interval can be inferred in a closed form. Eventually, numerical instances are cited to verify the research results.

Impedance measurement techniques based on small-signal injections in ac power system have been well developed but a gap in the analysis is that the selection of an injection amplitude is based only on experience. This letter develops an analytical process to analyze the noise existing in power systems and thereby determines the minimum injection amplitude according to an allowable error. The process is based on knowledge of stochastic processes and statistics, where the Monte Carlo method is also employed to simplify the process. A power hardware-in-the-loop system with a grid-following inverter is employed in experimental verification of the results.

High-fidelity modeling is of great importance for digital twin (DT) technology. A Transformer-based modeling method for radio over fiber (RoF) link is developed and experimentally demonstrated, in which the Transformer is a kind of deep learning network. The modeling performance of the constructed model by using the proposed method is evaluated, which is better than the ones based on fully connected neural network (FCNN), convolution neural network (CNN), and generative adversarial network (GAN). In addition, the modeling consistency with different laser launch power, fiber length, carrier frequency, and signal-to-noise ratio (SNR) is also investigated. Experimental results show that the fitting waveforms predicted by the proposed method agree well with the actual waveforms at the receiver end. The normalized mean square errors of the fitting waveforms are lower than the acceptable bound of 0.02.

BACKGROUND:: Our previous studies found that the mechanical stimulation promote chondrogenic differentiation of bone marrow mesenchymal stem cells (BMSCs), along with up-regulation of thrombospondin-2 (TSP-2). The aim of this study was to explore the effect of thrombospondin-2 (TSP-2) on the mechanical pressure-stimulated chondrogenic differentiation of BMSCs and the possible role of NF-κB signaling in the mechano-chemical coupling regulation toward chondrogenesis. METHODS:: Rat BMSCs were isolated, cultured and identified. The time-dependent expressions of TSP-2 and Sox9 in BMSCs under a dynamic mechanical pressure of 0–120 kPa at 0.1 Hz for 1 h were tested by qPCR and Western blotting. The role of TSP-2 in chondrogenic differentiation of BMSCs under mechanical pressure was validated by using small interfering RNA. The impact of TSP-2 and mechanical pressure on chondrogenesis were detected and the downstream signaling molecules were explored using Western blotting. RESULTS:: Mechanical pressure stimulation of 0–120 kPa for 1 h significantly upregulated the expression of TSP-2 in BMSCs. The expression of the chondrogenesis markers Sox9, Aggrecan, and Col-II were all upregulated under dynamic mechanical pressure or TSP-2 stimulation. Additional exogenous TSP-2 may potentiate the chondrogenic effect of mechanical stimulation. After knock down TSP-2, the upregulation of Sox9, Aggrecan and Col-II under mechanical pressure was inhibited. The NF-κB signaling pathway responded to both dynamic pressure and TSP-2 stimulation, and the cartilage-promoting effect was blocked by an NF-κB signaling inhibitor. CONCLUSION:: TSP-2 plays an essential role in the chondrogenic differentiation of BMSCs under mechanical pressure. NF-κB signaling is involved in the mechano-chemical coupling of TSP-2 and mechanical pressure for the chondrogenic differentiation of BMSCs.

Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box impedance models are available. For tracing the root causes of poor damping and tuning modes of the system, the sensitivity of the modes to components and parameters are needed. The so-called critical admittance-eigenvalue sensitivity based on nodal admittance model has provided a partial solution but omits meaningful directional information. The alternative whole-system impedance model yields participation factors of shunt-connected apparatus with directional information that allows separate tuning for damping and frequency, yet do not cover series-connected components. This paper formalises the relationships between the two forms of impedance models and between the two forms of root-cause analysis. The calculation of system eigenvalue sensitivity in impedance models is further developed, which fills the gaps of previous research and establishes a complete theory of impedance-based root-cause analysis. The theoretical relationships and the tuning of parameters have been illustrated with a three-node passive network, a modified IEEE 14-bus network and a modified NETS-NYPS 68-bus network, showing that tools can be developed for tuning of IBR-rich power systems where only black-box impedance models are available.