Short-term Instability Research Articles

Quasi-static transverse mode degradation in an erbium–ytterbium co-doped fiber amplifier

Long-term stable operation of high-power fiber amplifiers is required to realize high-speed communication in non-terrestrial networks. Recently, quasi-static transverse mode degradation (QS-TMD) was found in ytterbium-doped fiber amplifiers on a few-hour time scale at 1064 nm. Here we demonstrate experimentally that QS-TMD also occurs in erbium–ytterbium co-doped fibers (EYDFs) that amplify light at 1550 nm. QS-TMD was observed by measuring the power of fundamental and higher-order modes separately. QS-TMD progressed as short-term transverse mode instability increased. A persistent long-period grating also formed in the EYDF. This work will aid design of stable high-power fiber amplifiers.

Moderators of test-retest reliability in implicit and explicit attitudes.

A great deal of research in dual-process models has been devoted to highlighting differences in the structure and function of the implicit and explicit attitude constructs. However, the two forms of attitudes can also demonstrate important shared properties, and prior work suggests that one similarity may be in factors that determine measurement reliability. To better explore this issue, Study 1 analyzed the test-retest reliability in measures of both implicit and explicit attitudes within a single study session across 75 topics (N > 35,000). Explicit attitudes had greater test-retest reliability than implicit attitudes, but each showed considerable heterogeneity across topics even when measured within a single study session. Analyses also included several candidate moderator variables, such as attitude certainty or familiarity. While results were not identical, the moderators associated with greater test-retest reliability for implicit and explicit attitudes exhibited more similarities than differences. Specifically, attitudes experienced as more distinctive, more relevant to one's self-concept, more certain, and more accessible had higher test-retest reliability for both forms of evaluation. Variation in short-term reliability for implicit and explicit attitudes was replicated in Study 2, and Study 3 revealed that topics low in short-term reliability were also lower in a longitudinal sample that completed attitude measures separated by several weeks. These results advance our understanding of each attitude construct and are consistent with a more dynamic relationship between an attitude and its measure, as even attitudes measured with high levels of conscious control could show remarkable short-term instability when assessed only minutes apart. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Study of the Arrhythmogenic Profile of Dogs with Myxomatous Mitral Valve Disease in Stages B1 and B2.

Myxomatous mitral valve disease (MMVD) is the most prevalent cardiac disease in dogs. This study aimed to compare the arrhythmogenic profile and heart rate variability (HRV) of dogs with MMVD in stages B1 and B2. Electrocardiographic exams and the medical records of 60 dogs were analyzed, and HRV, P wave dispersion, QT interval dispersion, and QT interval instability parameters were determined. The results showed significantly increased values in stage B2 compared with stage B1 (p < 0.05) regarding P wave maximum and minimum duration (Pmax and Pmin) and short-term instability (STI). In contrast, no statistically significant differences were observed regarding HRV parameters, P wave dispersion, or QT interval dispersion. Our findings showed that cardiac remodeling in stage B2 could not significantly alter the sympathovagal balance and showed little interference with the predisposition of arrhythmias in dogs with MMVD.

Field trial of stable radio frequency transfer system in 200 km metropolitan optical fiber link.

In this paper, we demonstrate stable radio frequency (RF) transfer via metropolitan optical fiber link in the Beijing area. The phase variation of the RF signal is compensated by a phase conjugation method incorporating two high-performance phase-locked loops. The wavelength conversion module extends the transmission length to 200 km with only two parallel 50 km dark optical fibers available. We optimize the configuration of dispersion compensation and optical amplification due to the high loss (0.31 dB/km) of the optical fiber link. At the same time, comparative experiments verify the short-term instability limitation that arises from the group velocity dispersion of the optical fiber link. The measured standard Allan deviation of the 2.4GHz RF transmission system with dispersion compensation is 4.5 × 10-14/1 and 2.6 × 10-17/20 000s, which is superior to that of the reference rubidium clock. The short-term instability of the system is deteriorated to 2.5 × 10-13/1s without dispersion compensation.

From innovation to stability: Evaluating the ripple influence of digital payment systems and capital adequacy ratio on a bank’s Z-score

This study investigated the influence of digital payment systems on banks’ stability by exploring their effect on the Z-score of the Jordanian banking sector during the period from 2004 until 2022. It specifically focused on liquidity risks generated from e-payment transactions and how sufficient capital adequacy ratios enhance banking sector stability over both short-term and long-term periods by standing against sudden volatilities yielded from large amounts of transactions executed through digital payment systems. To achieve this objective, the study utilizes time series dual regression analyses of vector autoregression and vector error correction models on E-views 12 to cover the time variation influences of digital payment on the banking sector Z-score. The regression results indicate varied effects between the benefits and risks of digital payment systems on a bank’s Z-score that influence the immediate sector’s stability, indicating that while digital payment systems can initially hold liquidity risks, leading to short-term instability; the strategic implementation of robust capital adequacy ratio stands as a protective buffer by fostering long-term banking sector resilience. The results also suggest future predictions and insights for financial sector legislators and regulators emphasizing the need for monitoring strategies that stimulate continuous innovations in the digital payment infrastructure while constantly ensuring the stability and resilience of the banking sector. Thus, prudent liquidity management and the reinforcement of capital buffers are encouraged to pilot the dual challenges and opportunities that appeared at the stages of the digital payment process, ultimately guiding the sector toward continuous growth and sustainability. AcknowledgmentThe author is grateful to the Middle East University, Amman, Jordan for the financial support granted to cover the publication fee of this research.

Our Experience with Primary Trapeziometacarpal Implant Arthroplasty in Severely Comminuted Rolando Fractures

Abstract Background The concept of primary joint replacement in severely comminuted fractures in elderly patients has proven to be a suitable treatment option in the shoulder, elbow and knee joint. Comminuted intra-articular fractures of the first metacarpal base can pose significant technical challenges, and no established gold standard treatment currently exists. Conventional methods like open reduction and internal fixation or external fixation are commonly employed, emphasizing the importance of anatomical reduction for restoring the articular surface. However, achieving articular surface congruity is often unattainable in severely comminuted intra-articular fractures, leading to short-term joint stiffness and instability, as well as long-term complications like secondary osteoarthritis. Aims This case report aims to share our experience with the use of primary trapeziometacarpal implant arthroplasty in the treatment of two patients with severely comminuted Rolando fractures. Methods In two cases of comminuted Rolando fractures, a primary trapeziometacarpal prosthesis (MAÏA™) was implanted based on preoperative decision-making facilitated by 3D-CT analysis. Patient’s visual analogue score for pain, range of motion, pinch and grip strength compared to the contralateral side were asked and examined. Results Both patients exhibited successful adaptation to their daily routines within a three-month postoperative period. No complications, such as cup or stem loosening or dislocation, were identified. Both patients almost reached equal range of motion, pinch and grip strength compared to the contralateral side. Conclusion The use of primary trapeziometacarpal implant arthroplasty seems to be a viable alternative for managing severely comminuted Rolando fractures, particularly in elderly patients and those with pre-existing osteoarthritis. This approach offers promising outcomes. Studies with other implant arthroplasties have shown better outcomes in patients treated primarily, than after several failed surgeries. We suggest the same is true for the trapeziometacarpal joint. Further studies are necessary to validate the efficacy of this intervention in a larger cohort of patients.

Laser frequency stabilization by modulation transfer spectroscopy and balanced detection of molecular iodine for laser cooling of 174Yb.

We report laser frequency stabilization by the combination of modulation transfer spectroscopy and balanced detection of a relatively weak hyperfine transition of the R(158)25-0 line of molecular iodine (127I2), which is used as a new frequency reference for laser trapping and cooling of 174Yb on the 1S0 - 3P1 transition. The atomic cloud is characterized by time-of-flight measurements, and an on-resonance optical depth of up to 47 is obtained. We show laser noise reduction and characterize the short-term laser frequency instability by the Allan deviation of the laser fractional frequency. The minimum measured value is 3.9 ×10-13 at 0.17 s of averaging time.

Temporary Financial Assistance Reduced The Probability Of Unstable Housing Among Veterans For More Than 1 Year.

The Department of Veterans Affairs (VA) aims to reduce homelessness among veterans through programs such as Supportive Services for Veteran Families (SSVF). An important component of SSVF is temporary financial assistance. Previous research has demonstrated the effectiveness of temporary financial assistance in reducing short-term housing instability, but studies have not examined its long-term effect on housing outcomes. Using data from the VA's electronic health record system, we analyzed the effect of temporary financial assistance on veterans' housing instability for three years after entry into SSVF. We extracted housing outcomes from clinical notes, using natural language processing, and compared the probability of unstable housing among veterans who did and did not receive temporary financial assistance. We found that temporary financial assistance rapidly reduced the probability of unstable housing, but the effect attenuated after forty-five days. Our findings suggest that to maintain long-term housing stability for veterans who have exited SSVF, additional interventions may be needed.

Mitochondrial point heteroplasmy: insights from deep-sequencing of human replicate samples

BackgroundHuman mitochondrial heteroplasmy is an extensively investigated phenomenon in the context of medical diagnostics, forensic identification and molecular evolution. However, technical limitations of high-throughput sequencing hinder reliable determination of point heteroplasmies (PHPs) with minor allele frequencies (MAFs) within the noise threshold.ResultsTo investigate the PHP landscape at an MAF threshold down to 0.1%, we sequenced whole mitochondrial genomes at approximately 7.700x coverage, in multiple technical and biological replicates of longitudinal blood and buccal swab samples from 11 human donors (159 libraries in total). The results obtained by two independent sequencing platforms and bioinformatics pipelines indicate distinctive PHP patterns below and above the 1% MAF cut-off. We found a high inter-individual prevalence of low-level PHPs (MAF < 1%) at polymorphic positions of the mitochondrial DNA control region (CR), their tissue preference, and a tissue-specific minor allele linkage. We also established the position-dependent potential of minor allele expansion in PHPs, and short-term PHP instability in a mitotically active tissue. We demonstrate that the increase in sensitivity of PHP detection to minor allele frequencies below 1% within a robust experimental and analytical pipeline, provides new information with potential applicative value.ConclusionsOur findings reliably show different mutational loads between tissues at sub-1% allele frequencies, which may serve as an informative medical biomarker of time-dependent, tissue-specific mutational burden, or help discriminate forensically relevant tissues in a single person, close maternal relatives or unrelated individuals of similar phylogenetic background.

Short-term voltage instability prediction using pre-identified voltage templates and machine learning classifiers

System operators use different procedures to detect disturbances which can lead to short-term voltage instability. These schemes are designed using the domain knowledge and generally specific for the corresponding system. The design procedure of such schemes is heuristic and cannot be directly applied to any other power system. This paper proposes a data-driven approach to predict short-term voltage stability using set of post-disturbance voltage templates and a machine learning-based classifier. In this scheme, a set of voltage templates corresponding to stable/unstable events is identified for selected buses using a suitably generated training data set. The proximity of real-time voltage trajectory to each of the template is computed and input to a trained machine learning classifier to predict the short-term voltage stability status. The proposed approach is validated using IEEE Nordic test system. The investigations show that this method provides accurate short-term voltage stability predictions. Furthermore, impact of adverse phenomena such as measurement noise and missing data on the proposed method is analyzed to confirm the robustness of this scheme in particle environments. © 2017 Elsevier Inc. All rights reserved.

A Novel Data-Driven Approach for Managing Renewable Energy Systems During Short-Term Voltage Instability

A Novel Data-Driven Approach for Managing Renewable Energy Systems During Short-Term Voltage Instability

Quantification and Classification of Short-term Instability Voltage Deviations

Quantification and Classification of Short-term Instability Voltage Deviations

Improvement of the quantitativeness of the thermal desorption-GC/MS method and development of polystyrene certified reference material for the quantification of decabromodiphenyl ether (BDE 209) by using isotope dilution mass spectrometry.

A polystyrene (PS) certified reference material (CRM) for the analysis of decabromodiphenyl ether (BDE 209) was issued. PS disk was prepared by injection molding of the mixture of versine PS and BDE 209. The certification of the PS CRM was conducted by two analytical methods with different sample preparation methods using isotope dilution mass spectrometry (IDMS). The certified value, wCRM, was 978 mg/kg, and this value coincided with the regulation value of BDE 209 in the Restriction of Hazardous Substances directive (1000 mg/kg). The uncertainties related to certification, uwmean, inhomogeneity, uhom, and long- and short-term instability, usts and ults, respectively, were evaluated based on the mass fraction of BDE 209. The uwmean, uhom, usts, and ults were 0.0265, 0.0046, 0.0061, and 0.0099 (relative), respectively, and the expanded uncertainty for this CRM was determined as 57 mg/kg (coverage factor is 2). Additionally, the quantitative capability of the thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) method was evaluated. In TD-GC/MS, the analytical values of the developed CRM obtained by the external and internal standard methods with matrix-free calibrants were out of the range of the wCRM (almost 10% larger or smaller), whereas those with matrix-matched calibrants agreed with the wCRM. In contrast to these results, the analytical values obtained by TD-GC/MS using IDMS were consistent with the wCRM no matter if matrix-free or matrix-matched calibrants were used. These results indicated that, for quantification of BDE 209 in PS, the trueness and precision of TD-GC/MS can be enhanced by applying IDMS without matrix-matched calibrants.

Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance

Most existing data-driven power system short-term voltage stability assessment (STVSA) approaches presume class-balanced input data. However, in practical applications, the occurrence of short-term voltage instability following a disturbance is minimal, leading to a significant class imbalance problem and a consequent decline in classifier performance. This work proposes a Transformer-based STVSA method to address this challenge. By utilizing the basic Transformer architecture, a stability assessment Transformer (StaaT) is developed as a classification model to reflect the correlation between the operational states of the system and the resulting stability outcomes. To combat the negative impact of imbalanced datasets, this work employs a conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for synthetic data generation, aiding in the creation of a balanced, representative training set for the classifier. Semi-supervised clustering learning is implemented to enhance clustering quality, addressing the lack of a unified quantitative criterion for short-term voltage stability. Numerical tests on the IEEE 39-bus test system extensively demonstrate that the proposed method exhibits robust performance under class imbalances up to 100:1 and noisy environments, and maintains consistent effectiveness even with an increased penetration of renewable energy. Comparative results reveal that the CWGAN-GP generates more balanced datasets than traditional oversampling methods and that the StaaT outperforms other deep learning algorithms. This study presents a compelling solution for real-world STVSA applications that often face class imbalance and data noise challenges.

Node-downloadable frequency transfer system based on a mode-locked laser with over 100 km of fiber.

To meet the requirements of time-frequency networks and enable frequency downloadability for nodes along the link, we demonstrated the extraction of stable frequency signals at nodes using a mode-locked laser under the condition of 100 km laboratory fiber. The node consists of a simple structure that utilizes widely used optoelectronic devices and enables plug-and-play applications. In addition, the node can recover frequency signals with multiple frequencies, which are useful for scenarios that require different frequencies. Here, we experimentally demonstrated a short-term frequency instability of 2.83 × 10-13@1 s and a long-term frequency instability of 1.18 × 10-15@10,000 s at the node, which is similar to that at the remote site of the frequency transfer system. At the same time, frequency signals with different frequencies also achieved stable extraction with the same performance at the node. Our results can support the distributed application under large-scale time-frequency networks.

Encoding Time Series as Images: A Robust and Transferable Framework for Power System DIM Identification Combining Rules and VGGNet

Digital simulation is very important for the safe and economic operation of power systems. Power system dominant instability mode (DIM) identification is an intractable problem in large-scale transient simulation analysis, because transient instability and short-term voltage instability are often intertwined. Deep learning is a promising way to achieve accurate DIM identification. By encoding time series as images, this paper proposes a robust and transferable DIM identification framework combining curve filtering rules and a dual-channel VGGNet (DCVGG) image recognition network. The curve filtering rules can effectively reduce the redundancy of massive time-domain features, the model parameters, and training time. A skillfully designed method is proposed to encode time series as images and improve the feature extraction capability of convolutional neural networks. The proposed DCVGG network is capable of processing rotor angle and voltage images simultaneously and has the potential to become a pre-trained model, making transfer learning between different power systems possible. Case studies conducted on 8-machine 36-bus system and Northeast China Power Grid demonstrate the proposed framework has better performance, scalability, robustness, and transferable ability than contrastive machine learning methods.

An ultra-stable laser based on molecular iodine with a short-term instability of 3.3 × 10−15 for space based gravity missions

Many space based gravity missions require frequency stabilized lasers with stringent requirements. Toward those requirements, we develop a compact frequency-stabilized laser which is referenced to the R(56)32−0: a1 transition of molecular iodine based on the modulation transfer spectroscopy technique. The stability of the laser is limited by the beam pointing noise, the electronic servo noise, and the residual amplitude modulation (RAM) noise. To improve the beam pointing stability, the system is constructed by gluing most components of the optical system on an ultra-low expansion glass base. We use a pre-amplifier to suppress the electronic servo noise, and use a wedged electro-optic phase modulator to suppress the RAM noise. The fractional frequency instability of the system is evaluated to be 3.3 × 10−15 at 2 s and 4 s averaging time, and is lower than 6 × 10−15 at averaging times from 1 s to 10 000 s. To our knowledge, this is the best short-term (1–4 s) instability reported so far for an iodine stabilized laser. The stability fully meets the requirements of next generation gravity mission and laser interferometer space antenna mission.

Design of Energy Storage for Frequency Stability in Low-Inertia Power Grid

Short-term frequency instability is one of the major concerns in power systems with high percentage of converter-interfaced renewable energy sources. Energy storage system (ESS) has proven to be a viable solution for the problem of short-term frequency instability by fast frequency response (FFR). However, the appropriate location, size, and operating strategy of ESS are the main challenges for FFR. Power injection at some buses in large grids may lead to angular separation and instability. In addition, oversizing ESS could lead to huge investments without appropriate returns and under sizing may jeopardizes grid stability. Capacity estimation of ESS for FFR considering the suitable location and overall deployment strategies are missing in the current literature for large power grids. Hence, this research proposes a technique to place and size ESS for better FFR in power grids. The proposed technique consists of two steps. In the first step, a methodology based on frequency dynamic signature (FDS) is developed to identify the most suitable location. In the second step, the required capacity of the ESS is estimated based on a step reduction iterative algorithm (SRIA). SRIA and FDS consider the complete dynamics of power system components that affect the frequency dynamics of the system. The proposed methodology is thoroughly verified for various operating conditions in IEEE 39-bus using DIgSILENT PowerFactory.

Evaluation of a ^{88}Sr^{+} Optical Clock with a Direct Measurement of the Blackbody Radiation Shift and Determination of the Clock Frequency.

We report on an evaluation of an optical clock that uses the ^{2}S_{1/2}→^{2}D_{5/2} transition of a single ^{88}Sr^{+} ion as the reference. In contrast to previous work, we estimate the effective temperature of the blackbody radiation that shifts the reference transition directly during operation from the corresponding frequency shift and the well-characterized sensitivity to thermal radiation. We measure the clock output frequency against an independent ^{171}Yb^{+} ion clock, based on the ^{2}S_{1/2}(F=0)→^{2}F_{7/2}(F=3) electric octupole (E3) transition, and determine the frequency ratio with a total fractional uncertainty of 2.3×10^{-17}. Relying on a previous measurement of the ^{171}Yb^{+} (E3) clock frequency, we find the absolute frequency of the ^{88}Sr^{+} clock transition to be 444 779 044 095 485.277(59)Hz. Our result reduces the uncertainty by a factor of 3 compared with the previously most accurate measurement and may help to resolve so far inconsistent determinations of this value. We also show that for three simultaneously interrogated ^{88}Sr^{+} ions, the increased number causes the expected improvement of the short-term frequency instability of the optical clock without degrading its systematic uncertainty.

Deep Reinforcement Learning for Load Shedding Against Short-Term Voltage Instability in Large Power Systems.

We introduce an innovative solution approach to the challenging dynamic load-shedding problem which directly affects the stability of large power grid. Our proposed deep Q-network for load-shedding (DQN-LS) determines optimal load-shedding strategy to maintain power system stability by taking into account both spatial and temporal information of a dynamically operating power system, using a convolutional long-short-term memory (ConvLSTM) network to automatically capture dynamic features that are translation-invariant in short-term voltage instability, and by introducing a new design of the reward function. The overall goal for the proposed DQN-LS is to provide real-time, fast, and accurate load-shedding decisions to increase the quality and probability of voltage recovery. To demonstrate the efficacy of our proposed approach and its scalability to large-scale, complex dynamic problems, we utilize the China Southern Grid (CSG) to obtain our test results, which clearly show superior voltage recovery performance by employing the proposed DQN-LS under different and uncertain power system fault conditions. What we have developed and demonstrated in this study, in terms of the scale of the problem, the load-shedding performance obtained, and the DQN-LS approach, have not been demonstrated previously.