General Method Research Articles

A broadband acoustic metamaterial lens for sub-wavelength imaging based on bandwidth enhancement

Acoustic metamaterials manufactured from natural materials can exhibit exotic characteristics that cannot be found in nature materials through the design of geometric structures and resonance effects, and acoustic metamaterials can be used to achieve sub-wavelength acoustic imaging as the acoustic metamaterial lens (AML). Currently, the AMLs based on the Fabry–Pérot (FP) resonance principle are all with single effective length hole, which is directly related to the thickness of its structure. They can only achieve acoustic imaging in a relatively narrow frequency range, and there is no AML which can achieve broadband sub-wavelength acoustic imaging. In this paper, a kind of broadband acoustic metamaterial lens (BAML) is introduced. While maintaining a constant overall thickness of the AML, a design which is composed of the supercell consisting of a cross distribution of different effective length holes is proposed. This design enables the AML formed by these supercells to encompass multiple operating frequencies corresponding to various holes, thereby broadening the frequency range of acoustic imaging. Finite element simulation and imaging experiments were conducted, and results demonstrated that the BAML consisting of multiple effective length holes has the ability to enhance bandwidth of acoustic imaging. Compared to AML with single effective length hole, it offers a broader range of imaging frequencies, this design offers a general method of bandwidth enhancement. BAML has potential application value in ultrasonic imaging, sound absorption, intelligent thermal control and medical diagnosis.

Economic crimes as a threat to Russia's socio-economic security

The security of the State, society and the individual is a fundamental task in ensuring the stability and prosperity of the country. This task implies the definition of rules and norms in relation to various social groups that correspond to the current institutional environment in Russia. The purpose of this study is to analyze the impact of economic crimes on the social security of citizens of the Russian Federation and ensuring economic growth in the country.To reveal the research topic, the paper considers the main basic indicators of Russia's socio-economic security. The methodological basis is general scientific methods: analysis, synthesis, comparison, generalization, ranking, etc. The main causes of the emergence and spread of economic crimes are presented in a systematic way. A retrospective analysis of economic crimes was carried out, and a trend line was drawn up. Using the ranking method, a rating of the leading regions in terms of socio-economic development was formed. It has been revealed that the scale of the spread of economic crimes has a fairly strong correlation with the institutional characteristics of the regions. The methods of combating economic offenses through the use of information technology are proposed.

Multiparameter cascaded quantum interferometer

We theoretically propose a multiparameter cascaded quantum interferometer in which a two-input and two-output setup is obtained by concatenating 50:50 beam splitters with n independent and adjustable time delays. A general method for deriving the coincidence probability of such an interferometer is given based on the linear transformation of the matrix of beam splitters. As examples, we analyze the interference characteristics of one-, two-, and three-parameter cascaded quantum interferometers with different frequency correlations and input states. Some typical interferograms of such interferometers are provided to reveal richer and more complicated two-photon interference phenomena. This work offers a general theoretical framework for designing versatile quantum interferometers and provides a convenient method for deriving the coincidence probabilities involved. In principle, arbitrary two-input and two-output experimental setups can be designed with the framework. Potential applications can be found in the complete spectral characterization of two-photon states, multiparameter estimation, and quantum metrology.

Self-catalysed breakdown of titanate nanotubes by graphitic carbon nitride resulting in enhanced hydrogen production

Efficient design of a photocatalyst is an important step in realizing real world applications. In this work, using in-situ catalysis we have prepared and investigated a titanate nanotube (TiNT)/ graphitic carbon nitride nanocomposite, which after optimization shows excellent hydrogen production efficiency of 2.3 mmolg−1h−1, much improved compared to GCN, which achieved a rate of 0.56 mmolg−1h−1. We can conclude that pyrolysis of urea to carbon nitride also self catalyses the breakdown of TiNT into anatase TiO2 nanoparticles, resulting in a nanocomposite material comprising TiO2 and heterojunctions with GCN. After heating and modification the TiO2 shows a conduction band edge with a more negative potential than the H+/H2 potential, which along with the ideal position of the GCN CB edge facilitates hydrogen production under light irradiation. This novel method can be viewed as a general method for improving catalysis synthesis and design, whilst simultaneously reducing the complexity and energy footprint of active catalyst synthesis.

SiMix: A domain generalization method for cross-site brain MRI harmonization via site mixing

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites.

Being good by doing good: an analysis of audit fees, philanthropic initiatives and board gender diversity among Chinese listed firms

Purpose Despite the acknowledged significance of the relationship between audit fees and corporate philanthropic initiatives, the existing literature has not yet reached the desired level of providing explicit evidence on how this relationship can be moderated by board gender diversity. This paper aims to contribute to the ongoing debate by using a panel data set comprising 905 Chinese listed firms over a five-year period from 2015 to 2019. Design/methodology/approach To generate solid findings and overcome the potential endogeneity bias, various econometric estimators, namely, ordinary least squares, two-step generalized method of moments, robust two-stage least squares and subsample analysis, have been carefully used. More interestingly, the study’s results remain consistent across different estimation methods. Findings The results reveal a statistically significant positive link between audit fees and corporate charitable giving. More interestingly, this connection strengthens with a higher representation of women directors on the board, particularly when there are three or more female directors. Furthermore, the results suggest that nonstate-owned firms exhibit greater motivation to participate in charitable giving initiatives compared to state-owned counterparts. Practical implications Stakeholders from various groups should attentively recognize the importance of gender-diverse boards as a dynamic factor impacting the association between audit fees and corporate charitable giving. Originality/value To the best of the authors’ knowledge, the crushing majority of the preceding research has not delved deeply into the critical role of board gender diversity in the relationship between audit fees and corporate charitable donations. Hence, this study provides a profound understanding of how audit fees predict corporate philanthropic initiatives.

Model-Based Inference of Flame Transfer Matrices from Acoustic Measurements in an Aero-Engine Test Rig

Abstract Flame dynamics, represented as a flame transfer matrix (FTM), is not directly measurable in test rigs and must be deduced from transfer matrix measurements of the combustion system. The burner-flame transfer matrix (BFTM) approach for FTM estimation is based on local pressure signals from microphones located upstream and downstream of the combustor. It combines acoustic measurements in non-reacting and reacting conditions, with the latter implicitly including flame dynamics. A simple matrix operation yields the FTM. However, this approach assumes loss-free wave propagation at constant speed of sound with no change in cross-sectional area between the microphones and the burner/flame. The present work demonstrates the limitations of these assumptions when applied to a test rig with effusion cooling, bypass annulus, and end contraction. This work proposes a method to infer the FTM for complex combustors by combining reactive transfer matrix measurements of the entire combustor with an accurate low-order model (LOM) of the test rig. This generalized method reduces to the BFTM approach as a special case. The Rolls-Royce SCARLET test rig, operating under realistic engine conditions, is used to analyze the capabilities of the proposed model-based inference method and the limitations of the BFTM approach. First, a LOM based on SCARLET's geometry and operating point is formulated using a generic FTM. This model visualizes the limitations of the BFTM approach concerning various physical and geometrical parameters. Finally, experimental data is used to infer the FTM of SCARLET using the proposed approach.

Dual-Source Cooperative Optimized Energy Management Strategy for Fuel Cell Tractor Considering Drive Efficiency and Power Allocation

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy source, and a dual-source cooperative optimization energy management strategy was proposed. Firstly, a general energy efficiency optimization method was designed for the power source composed of a traction motor and PTO motor, and the energy source was composed of a fuel cell and power battery. Secondly, the unified objective function and constraint conditions were established, and the instantaneous optimization algorithm was used to construct the weight factor. The instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were designed, respectively. Finally, with the demand power as the transfer parameter, the instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were integrated to form a dual-source collaborative optimal energy management strategy. In order to verify the effectiveness of the proposed strategy, a rule-based energy management strategy was developed as a comparison strategy and tested in an HIL test under plowing and rotary plowing conditions. The results show that the average fuel cell efficiency of the proposed strategy increased by 7.86% and 8.17%, respectively, and the proposed strategy’s equivalent hydrogen consumption decreased by 24.21% and 9.82%, respectively, compared with the comparison strategy under the two conditions. It can significantly reduce the SOC fluctuation of the power battery and extend the service life of the power battery.

Renewable Energy Transition and the Paris Agreement: How Governance Quality Makes a Difference?

This paper investigates whether the Paris Agreement affects renewable energy deployment and how institutional quality moderates this relationship. According to a generalized method of moments estimation for panel data for both developed and developing countries over the period 2000–2022, the Paris Agreement positively influences renewable energy deployment, suggesting that countries are promoting renewable energy to align with institutional expectations to maintain their reputations. The results further show that governance quality is the main determinant of renewable energy deployment. However, the moderating role of governance underscores the less-pronounced impact of the Paris Agreement on countries with high governance indicators, suggesting that these countries may have shifted their focus toward other avenues of climate management beyond the deployment of renewable energy. Furthermore, there is strong evidence of the relationship between forest area, CO2 emission, trade openness, domestic credit, and renewable energy deployment. The results are robust with the use of a dynamic panel threshold model.

Characterization of Strategic Deconflicting Service Impact on Very Low-Level Airspace Capacity

European airspace is poised for significant transformation as it prepares to accommodate a new class of unmanned traffic that will reshape the transport of people and goods. Unmanned aerial vehicle traffic will introduce a new level of services, but it remains unclear how safety and operators’ time flexibility in flight planning will impact capacity. This study focuses on the impact of strategic deconflicting services on the capacity of the very low-level airspace, a critical area in the future management of unmanned aerial vehicle traffic. The results validate the assumptions regarding the roles of airspace managers and drone operators through simulation studies; highlight the limitations of the first come, first served policy; and propose a batch policy as a potential optimization strategy for future airspace capacity management. The forecasting model developed using regression techniques provides a general method for predicting airspace capacity under specific conditions, contributing to the safe and efficient integration of unmanned aerial vehicles into European airspace.

Simulation data-driven attention fusion network with multi-similarity metric: A single-domain generalization diagnostic method for tie rod bolt loosening of a rod-fastening rotor system

The rod-fastening rotor system is the core rotating component of gas turbines and aero engines. The rod-disk joint structure of the rod-fastening rotor system is subjected to tie rod bolt loosening under complex operating conditions. Because of the uncertainty of fault occurrence, the model must possess diagnostic capability under unseen working conditions. Domain generalization methods are commonly employed to address such problems. They typically utilize multi-condition data or generate data from a single condition to train the model, enabling it to generalize to unknown operating conditions. However, difficulties in collecting complete multi-condition data and limitations in generating data, such as conflicting attributes and limited categories, make it challenging to ensure high-quality data for model training. To address these issues, this paper establishes a dynamic model of the rod-fastening rotor system to obtain high-fidelity simulation fault data, ensuring the physical accuracy, richness, and diversity of the source domain data. Meanwhile, a novel signal-domain generalization method called simulation data-driven attention fusion network with multi-similarity metric is proposed for tie rod bolt loosening in the rod-fastening rotor system. The attention feature fusion strategy is utilized to achieve the comprehensive representation of local and global features efficiently. Moreover, a multi-similarity loss based on deep metric learning is introduced to comprehensively consider the self-similarity and relative similarities between features, enabling class-level optimization of features. The proposed method is validated to have superior diagnostic performance in two cross-domain tasks compared with other methods. The proposed method can provide valuable insights into fault diagnosis in the rod-fastening rotor system.

Practical preparation of unsaturated very-long-chain fatty acids (VLCFAs) and very-long-chain alkene pollinator attractants

To prepare very-long-chain fatty acids and alkenes (VLCFAs and VLC alkenes) that are known pollinator attractants for sexually deceptive orchids, and biosynthetic precursors thereof, we applied a methodology allowing us to prepare monounsaturated VLCFAs with chain lengths up to 28 carbons and VLC alkenes up to 31 carbons. We implemented a coupling reaction between commercially available terminal alkynes and bromoalkanoic acids to prepare VLCFAs, allowing the products to be formed in two steps. For VLC alkenes, with many alkyltriphenylphosphonium bromides commercially available, we applied a Wittig reaction approach to prepare (Z)-configured monoenes in a single step. Using practical methods not requiring special reagents or equipment, we obtained 11 VLCFAs in > 90% isomeric purity, and 17 VLC alkenes in > 97% isomeric purity. Such general and accessible synthetic methods are essential for chemical ecology and biochemistry research to aid researchers in unambiguously identifying isolated semiochemicals and their precursors.

Domain generalization via causal fine-grained feature decomposition and learning

Domain generalization aims to accurately predict unknown data using models trained by known domain data. Learning domain-invariant representations based on causal inference is one of the popular directions in domain generalization. However, existing domain generalization models based on causal inference cannot correctly determine the invariance conditions of the data. It is because of the interdependent relationship between domain-invariant features and confounding factors that ultimately leads to difficulty in obtaining truly invariant representations by the model. In this paper, we propose a novel domain generalization method called causal fine-grained feature decomposition and learning (CFFDL), which aims to eliminate latent confounding factors and learn causal domain-invariant representations in image classification tasks. Specifically, we design a feature decomposition module based on mutual information, which can decompose deep features into fine-grained feature factors and achieve factor independence between different dimensions, thereby helping to eliminate confounding factors. Furthermore, we introduce a causal representations learning module that can effectively filter and extract relevant causal features of the prediction task while eliminating the influence of confounding factors, improving the model’s performance on domain generalization. Extensive experiments on three domain generalization datasets VLCS, PACS and Office–Home show that our method outperforms the current state-of-the-art models, proving its effectiveness and superiority on domain generalization tasks of image classification.

Reverse Quantum Annealing Assisted by Forward Annealing

Quantum annealers conventionally use forward annealing to generate heuristic solutions. Reverse annealing can potentially generate better solutions but necessitates an appropriate initial state. Ways to find such states are generally unknown or highly problem dependent, offer limited success, and severely restrict the scope of reverse annealing. We use a general method that improves the overall solution quality and quantity by feeding reverse annealing with low-quality solutions obtained from forward annealing. An experimental demonstration of solving the graph coloring problem using the D-Wave quantum annealers shows that our method is able to convert invalid solutions obtained from forward annealing to at least one valid solution obtained after assisted reverse annealing for 57% of 459 random Erdos–Rényi graphs. Our method significantly outperforms random initial states, obtains more unique solutions on average, and widens the applicability of reverse annealing. Although the average number of valid solutions obtained drops exponentially with the problem size, a scaling analysis for the graph coloring problem shows that our method effectively extends the computational reach of conventional forward annealing using reverse annealing.

Construction of axially chiral 2-arylpyrroles using catalytic asymmetric Suzuki-Miyaura cross-coupling: an efficient approach to esaxerenone.

A general and efficient method has been developed to access axially chiral 2-arylpyrroles using catalytic asymmetric Suzuki-Miyaura cross-coupling. A wide range of axially chiral arylpyrroles were obtained in high yields with good to excellent enantioselectivities. The key to success is the use of a combined catalytic system involving a palladium catalyst and chiral ferrocene diphosphine ligand for achieving effective enantiocontrol. More importantly, this axially chiral CF3-substituted 2-arylpyrrole serves as a key intermediate in the preparation of the anti-hypertensive and diabetic nephropathy drug esaxerenone. It was directly asymmetrically synthesized with high enantioselectivity (92% ee). Thus, a new strategy is provided for the catalytic asymmetric synthesis of esaxerenone.

Modifications to ArduSub That Improve BlueROV SITL Accuracy and Design of Hybrid Autopilot

Improvements to ArduSub for the BlueROV2 (BROV2) Heavy, necessary for accurate simulation and autonomous controller design, were implemented and validated in this work. The simulation model was made more accurate with new data obtained from real-world testing and values from the literature. The manual control algorithm in the BROV2 firmware was replaced with one compatible with automatic control. In a Robot Operating System (ROS), a proportional–derivative (PD) controller to assist augmented reality (AR) pilots in controlling angular degrees of freedom (DOF) of the vehicle was implemented. Open-loop testing determined the yaw hydrodynamic model of the vehicle. A general mathematical method to determine PD gains as a function of the desired closed-loop performance was outlined. Testing was carried out in the updated simulation environment. Step response testing found that a modified derivative gain was necessary. Comparable real-world results were obtained using settings determined in the simulation environment. Frequency response testing of the modified yaw control law discovered that the bandwidth of the nonlinear system had a one-to-one correspondence with the desired closed-loop natural frequency of a simplified linear approximation. The control law was generalized for angular DOF and linear DOF were operated with open-loop control. A full six-DOF simulated dive demonstrated excellent tracking.

Novel imbalanced fault diagnosis method based on generative adversarial networks with balancing serial CNN and Transformer (BCTGAN)

In industrial production engineering, machines cannot sustain faults for extended periods, resulting in limited fault data collection. This scarcity of data impedes the advancement of accurate fault diagnosis models, particularly in the context of small-sample, extremely imbalanced fault modeling, which poses a significant and challenging problem. In this paper, we introduce an innovative data augmentation approach for addressing the small-sample imbalanced problem: the balanced conditioning CNN (Convolutional Neural Networks)-Transformer architecture based Generative Adversarial Network (BCTGAN). Firstly, we employ a serial CNN and Transformer architecture as the foundation of the generator. Secondly, we introduce micro-enhancement and SMOTE (Synthetic Minority Oversampling Technique) enhancement of samples to facilitate model training. Micro-enhancement simply augments the sample size, while SMOTE enhancement enhances diversity. Together, these techniques balance the training process by introducing new loss function terms. Finally, outlier samples are mitigated through a designed multi-domain feature screening method. Despite the added time cost and computational complexity, results from three instances demonstrate that the proposed method holds superior diagnostic results with great potential compared to traditional and general GAN-based fault diagnosis methods. Our work provides theoretical and practical insights for modeling the field of small-sample blended disequilibrium fault diagnosis.

Pre-deformation assisted fabrication of bulk Nd2Fe14B/α-Fe nanocomposites with high energy density

For Nd2Fe14B/α-Fe nanocomposite magnets, which exhibit great superiority in theoretical energy products, it is a great challenge to obtain controlled nano-scale microstructural features while eliminating the harmful metastable phases due to the general metastable fabrication methods. Here, we report a strategy to control the nano-scale features and simultaneously eliminate metastable phases for Nd2Fe14B/α-Fe nanocomposites through low-temperature pre-deformation of amorphous alloys combined with subsequent thermal annealing. The resultant bulk Nd2Fe14B/α-Fe nanostructure exhibits desired ultrafine grain sizes, ∼20 nm for soft-magnetic α-Fe phase with a high fraction of Vα-Fe ≈ 30 wt% and ∼33 nm for hard-magnetic Nd2Fe14B phase. The desired microstructure results in a larger coercivity (Hci = 4.1 kOe) and higher saturation magnetization (Ms = 1.51 T) compared to those (Hci = 3.3 kOe and Ms = 1.35 T) of the counterpart without pre-deformation, contributing to a high energy density of 26.3 MGOe. This energy density is 200% larger than that of the counterpart without pre-deformation and far beyond that (around 20 MGOe) of previously reported bulk Nd2Fe14B/α-Fe nanocomposites with Vα-Fe ≥ 20 wt%. The superior property stems from the purposely introduced low-temperature pre-deformation that changed the structure and local chemistry of the amorphous alloy, facilitating the decomposition of harmful metastable phases and the formation of Nd2Fe14B phase during annealing processes, which lowers the temperature, from 750 to 700 °C for producing the Nd2Fe14B/α-Fe nanostructure and thus yields ultrafine nanograins. These results demonstrate an effective means to prepare high-performance bulk Nd2Fe14B/α-Fe nanocomposite magnets.

The Influence of Women on Boards on the Relationship between Executive and Employee Remuneration

The growing presence of women at the top of companies has sparked interest in examining their role in the remuneration gap between senior managers and employees. This article analyses the traditional Chief Executive Officer (CEO)-to-employee pay ratio but includes a new relation, the senior-management-to-employee pay ratio, and extends the research by including six positions for women in company management: on the board of directors, executive directors, CEOs, proprietary directors, independent directors, and senior managers. The study is based on a sample of 77 listed companies in Spain from 2015 to 2022 and the panel data models have been estimated using the Generalised Method of Moments (GMM). The main findings indicate that the proportion of women in different categories of board and senior management positions has a positive effect on the CEO-to-employee pay ratio, especially in companies with higher market capitalisation. In contrast, the proportion of women in senior management positions has a negative effect on the CEO-to-employee pay ratio in all the samples analysed. Government agencies should prioritise the participation of women in non-board senior management positions in order to at least reduce the pay gap between senior managers and employees.

General method to synthesize aggregation‐induced emission molecules via carbon‐sulfur bond activation

General method to synthesize aggregation‐induced emission molecules via carbon‐sulfur bond activation