Transformation Technique Research Articles

Alternating-bias assisted annealing of amorphous oxide tunnel junctions

Superconducting quantum bits (qubits) rely on ultra-thin, amorphous oxide tunneling barriers that can have significant inhomogeneities and defects as grown. This can result in relatively large uncertainties and deleterious effects in the circuits, limiting the scalability. Finding a robust solution to the junction reproducibility problem has been a long-standing goal in the field. Here, we demonstrate a transformational technique for controllably tuning the electrical properties of aluminum-oxide tunnel junctions. This is accomplished using a low-voltage, alternating-bias applied individually to the tunnel junctions, with which resistance tuning by more than 70% can be achieved. The data indicates an improvement of coherence and reduction of two-level system defects. Transmission electron microscopy shows that the treated junctions are predominantly amorphous, albeit with a more uniform distribution of alumina coordination across the barrier. This technique is expected to be useful for other devices based on ionic amorphous materials.

Radical Cation Alkene Functionalization Using Anodic Oxidation

Oxidative functionalization of alkenes is a promising molecular transformation technique, where intramolecular reactions enable the synthesis of saturated heterocycles1), which are often found in natural products and pharmaceuticals. In addition to the widely studied palladium-catalyzed oxidative functionalization of alkenes2), electrochemical3), 4) and photocatalytic5) methods have recently been developed. These one-electron oxidation-based methods have been applied to trisubstituted and tetrasubstituted alkenes with large steric hindrance, which is difficult to achieve with conventional palladium-catalyzed reactions. In many cases, these methods reported previously form rings as neutral radicals under basic conditions. On the other hand, radical cationic cyclization is relatively rare and have not yet been fully discussed. In this study, we developed an alkene oxidative functionalization using electrolysis for radical cationic cyclization.After optimizing the reaction conditions using trisubstituted alkene (1a) as a model substrate, we succeeded in obtaining the target compound (2a) in 90% yield (Scheme 1). In this reaction, a six-membered ring is formed as a byproduct, but the anion species of the supporting salt affects the reaction selectivity, and it was found that a five-membered ring was formed with high selectivity when ClO4- or TfO- were used. Furthermore, using a 1:9 mixture of highly polar CH3CN and low-polar 1,2-DCE as the reaction solvent improved the solubility and conductivity of the supporting salt, resulting in a significant increase in yield. The use of carbon felt, which has a large surface area, as the working electrode also contributed to the yield improvement by suppressing the voltage increase.For sulfonamides (1b-d) with various substituents on the aromatic ring, we were able to obtain five-membered ring compounds (2b-d) in good yields in all cases. In addition to these results, the details of the condition studies and other substrates will be discussed.1) Nakamura, I.; Yamamoto, Y. Chem. Rev. 2004, 104, 2127-2198.2) McDonald, R. I.; Liu, G.; Stahl, S, S. Chem. Rev. 2011, 111, 2981-3019.3) Xu, H.-C.; Moeller, K. D. J. Am. Chem. Soc. 2010, 132, 2839–2844.4) Huang, C.; Li, Z.-Y.; Song, J.; Xu, H.-C. Angew. Chem. Int. Ed. 2021, 60, 11237–11241.5) Reed, N. L.; Lutovsky, G. A.; Yoon, T. P. J. Am. Chem. Soc. 2021, 143, 6065-6070. Figure 1

Iterative learning resilient consensus of uncertain nonlinear multi-agent systems vulnerable to false data injection attacks

This work investigates the iterative learning resilient consensus of uncertain nonlinear high-order multi-agent systems (MASs) against false data injection (FDI) attacks. First, in order to reduce the impact of FDI attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked, and the Nussbaum gain technique is adopted to address the problem of unknown attack gains resulting from FDI attacks. Then, by employing compromised state variables, a fuzzy adaptive iterative learning resilient control method is presented based on the composite energy function, where unknown nonlinear functions are handled using fuzzy logic systems. The presented approach can guarantee that the outputs of all followers precisely track the leader in a limited time interval while ensuring that all closed-loop signals are bounded. Finally, a numerical simulation is provided to demonstrate the efficacy of the designed control strategy.

A High-Order Numerical Method Based on a Spatial Compact Exponential Scheme for Solving the Time-Fractional Black–Scholes Model

This paper investigates a high-order numerical method based on a spatial compact exponential scheme for solving the time-fractional Black–Scholes model. Firstly, the original time-fractional Black–Scholes model is converted into an equivalent time-fractional advection–diffusion reaction model by means of a variable transformation technique. Secondly, a novel high-order numerical method is constructed with (2−α) accuracy in time and fourth-order accuracy in space based on a spatial compact exponential scheme, where α is the fractional derivative. The uniqueness of solvability of the derived numerical method is rigorously discussed. Thirdly, the unconditional stability and convergence of the derived numerical method are rigorously analyzed using the Fourier analysis technique. Finally, numerical examples are presented to test the effectiveness of the derived numerical method. The proposed numerical method is also applied to solve the time-fractional Black–Scholes model, whose exact analytical solution is unknown; numerical results are illustrated graphically.

High Efficiency in Planta Agrobacterium-Mediated Transformation in Lilium cv. Pavia

Absence of an efficient genetic transformation technique acts as a major barrier for development of new lily cultivars with desirable characters. Here we report a callus freein planta Agrobacterium mediated transformation protocol for the Lilium cv. Pavia. Tiny shoot buds obtained directly from the basal portion of the Lililum scales were used for Agrobacterium infection. Agrobacterium tumefaciens strain LB4404 harboring pCAMBIA 3301 vector was used for transformation. Putative transformants were selected on a selection media containing L-phosphinithricin. Further these putative transformants were confirmed by PCR analysis using primers specific for gus and bar gene. An average transformation efficiency of 31% was obtained using in planta transformation of Lilium shoot buds. The high efficiency and rapidity of this protocol make it suitable for commercial application and for future research on this valuable ornamental plant.

Analysis of Caputo Sequential Fractional Differential Equations with Generalized Riemann–Liouville Boundary Conditions

This paper delves into a novel category of nonlocal boundary value problems concerning nonlinear sequential fractional differential equations, coupled with a unique form of generalized Riemann–Liouville fractional differential integral boundary conditions. For single-valued maps, we employ a transformation technique to convert the provided system into an equivalent fixed-point problem, which we then address using standard fixed-point theorems. Following this, we evaluate the stability of these solutions utilizing the Ulam–Hyres stability method. To elucidate the derived findings, we present constructed examples.

ClickHouse - Lightning Fast Analytics for Everyone

Over the past several decades, the amount of data being stored and analyzed has increased exponentially. Businesses across industries and sectors have begun relying on this data to improve products, evaluate performance, and make business-critical decisions. However, as data volumes have increasingly become internet-scale, businesses have needed to manage historical and new data in a cost-effective and scalable manner, while analyzing it using a high number of concurrent queries and an expectation of real-time latencies (e.g. less than one second, depending on the use case). This paper presents an overview of ClickHouse, a popular open-source OLAP database designed for high-performance analytics over petabyte-scale data sets with high ingestion rates. Its storage layer combines a data format based on traditional log-structured merge (LSM) trees with novel techniques for continuous transformation (e.g. aggregation, archiving) of historical data in the background. Queries are written in a convenient SQL dialect and processed by a state-of-the-art vectorized query execution engine with optional code compilation. ClickHouse makes aggressive use of pruning techniques to avoid evaluating irrelevant data in queries. Other data management systems can be integrated at the table function, table engine, or database engine level. Real-world benchmarks demonstrate that ClickHouse is amongst the fastest analytical databases on the market.

28 GHz Compact LNAs With 1.9 dB Minimum NF Using Folded Three-Coil Transformer and Dual-Feedforward Techniques for Phased Array Systems

28 GHz Compact LNAs With 1.9 dB Minimum NF Using Folded Three-Coil Transformer and Dual-Feedforward Techniques for Phased Array Systems

Plant Adaptation and Tolerance to Heat Stress: Advance Approaches and Future Aspects.

Heat stress impacts plant growth at all phases of development, although the particular threshold for heat tolerance varies significantly across different developmental stages. During seed germination, elevated temperatures can either impede or completely halt the process, contingent upon the plant type and the severity of the stress. During advanced stages, high temperatures can have a negative impact on photosynthesis, respiration, water balance, and membrane integrity. Additionally, they can also influence the levels of hormones and primary and secondary metabolites. In addition, during the growth and development of plants, there is an increased expression of various heat shock proteins, as well as other proteins related to stress, and the generation of reactive oxygen species (ROS). These are significant plant responses to heat stress. Plants employ several strategies to deal with heat stress, such as maintaining the stability of their cell membranes, removing harmful reactive oxygen species (ROS), producing antioxidants, accumulating and adjusting compatible solutes, activating mitogen-activated protein kinase (MAPK) and calcium-dependent protein kinase (CDPK) cascades, and, crucially, signaling through chaperones and activating transcription. These molecular-level systems boost the ability of plants to flourish in heat stress. Potential genetic methods to enhance plant heat stress resistance encompass old and modern molecular breeding techniques and transgenic approaches, all of which rely on a comprehensive comprehension of these systems. Although several plants exhibit enhanced heat tolerance through traditional breeding methods, the effectiveness of genetic transformation techniques has been somewhat restricted. The latter results from the current constraints in our understanding and access to genes that have known impacts on plant heat stress tolerance. However, these challenges may be overcome in the future. Besides genetic methods, crops' heat tolerance can be improved through the pre-treatment of plants with various environmental challenges or the external application of osmoprotectants such as glycine betaine and proline. Thermotolerance is achieved through an active process in which plants allocate significant energy to maintain their structure and function to avoid damage induced by heat stress. The practice of nanoparticles has been shown to upgrade both the standard and the quantity of produce when crops are under heat stress. This review provides information on the effects of heat stress on plants and explores the importance of nanoparticles, transgenics, and genomic techniques in reducing the negative consequences of heat stress. Furthermore, it explores how plants might adapt to heat stress by modifying their biochemical, physiological, and molecular reactions.

Dynamic Event-Based Adaptive Fixed-Time Control for Uncertain Strict-Feedback Nonlinear Systems With State Constraints.

In this article, the event-triggered fixed-time tracking control is investigated for uncertain strict-feedback nonlinear systems involving state constraints. By employing the universal transformed function (UTF) and coordinate transformation techniques into backstepping design procedure, the proposed control scheme ensures that all states are constrained within the time-varying asymmetric boundaries, and meanwhile, the undesired feasibility condition existing in other constrained controllers can be removed elegantly. Different from the existing static event-triggered mechanism, a dynamic event-triggered mechanism (DETM) is devised via constructing a novel dynamic function, so that the communication burden from the controller to actuator is further alleviated. Furthermore, with the aid of adaptive neural network (NN) technique and generalized first-order filter, together with Lyapunov theory, it is proved that the states of closed-loop system converge to small regions around zero with fixed-time convergence rate. The simulation results confirm the benefits of developed scheme.

Continuum topological derivative - a novel application tool for denoising CT and MRI medical images

BackgroundCT and MRI modalities are important diagnostics tools for exploring the anatomical and tissue properties, respectively of the human beings. Several advancements like HRCT, FLAIR and Propeller have advantages in diagnosing the diseases very accurately, but still have enough space for improvements due to the presence of inherent and instrument noises. In the case of CT and MRI, the quantum mottle and the Gaussian and Rayleigh noises, respectively are still present in their advanced modalities of imaging. This paper addresses the denoising problem with continuum topological derivative technique and proved its trustworthiness based on the comparative study with other traditional filtration methods such as spatial, adaptive, frequency and transformation techniques using measures like visual inspection and performance metrics.MethodsThis research study focuses on identifying a novel method for denoising by testing different filters on HRCT (High-Resolution Computed Tomography) and MR (Magnetic Resonance) images. The images were acquired from the Image Art Radiological Scan Centre using the SOMATOM CT and SIGNA Explorer (operating at 1.5 Tesla) machines. To compare the performance of the proposed CTD (Continuum Topological Derivative) method, various filters were tested on both HRCT and MR images. The filters tested for comparison were Gaussian (2D convolution operator), Wiener (deconvolution operator), Laplacian and Laplacian diagonal (2nd order partial differential operator), Average, Minimum, and Median (ordinary spatial operators), PMAD (Anisotropic diffusion operator), Kuan (statistical operator), Frost (exponential convolution operator), and HAAR Wavelet (time–frequency operator). The purpose of the study was to evaluate the effectiveness of the CTD method in removing noise compared to the other filters. The performance metrics were analyzed to assess the diligence of noise removal achieved by the CTD method. The primary outcome of the study was the removal of quantum mottle noise in HRCT images, while the secondary outcome focused on removing Gaussian (foreground) and Rayleigh (background) noise in MR images. The study aimed to observe the dynamics of noise removal by examining the values of the performance metrics.In summary, this study aimed to assess the denoising ability of various filters in HRCT and MR images, with the CTD method being the proposed approach. The study evaluated the performance of each filter using specific metrics and compared the results to determine the effectiveness of the CTD method in removing noise from the images.ResultsBased on the calculated performance metric values, it has been observed that the CTD method successfully removed quantum mottle noise in HRCT images and Gaussian as well as Rayleigh noise in MRI. This can be evidenced by the PSNR (Peak Signal-to-Noise Ratio) metric, which consistently exhibited values ranging from 50 to 65 for all the tested images. Additionally, the CTD method demonstrated remarkably low residual values, typically on the order of e−09, which is a distinctive characteristic across all the images. Furthermore, the performance metrics of the CTD method consistently outperformed those of the other tested methods. Consequently, the results of this study have significant implications for the quality, structural similarity, and contrast of HRCT and MR images, enabling clinicians to obtain finer details for diagnostic purposes.ConclusionContinuum topological derivative algorithm is found to be constructive in removing prominent noises in both CT and MRI images and can serve as a potential tool for recognition of anatomical details in case of diseased and normal ones. The results obtained from this research work are highly inspiring and offer great promise in obtaining accurate diagnostic information for critical cases such as Thoracic Cavity Carina, Brain SPI Globe Lens 4th Ventricle, Brain-Middle Cerebral Artery, Brain-Middle Cerebral Artery and neoplastic lesions. These findings lay the foundation for implementing the proposed CTD technique in routine clinical diagnosis.

Controlling DNA Fragments Translocation across Nanopores with the Synergic Use of Site-Directed Mutagenesis, pH-Dependent Charge Tuning, and Electroosmotic Flow.

Biological and solid-state nanopores are at the core of transformative techniques and nanodevices, democratizing the examination of matter and biochemical reactions at the single-molecule level, with low cost, portability, and simplicity in operation. One of the crucial hurdles in such endeavors is the fast analyte translocation, which limits characterization, and a rich number of strategies have been explored over the years to overcome this. Here, by site-directed mutagenesis on the α-hemolysin protein nanopore (α-HL), sought to replace selected amino acids with glycine, electrostatic binding sites were induced on the nanopore's vestibule and constriction region and achieved in the most favorable case a 20-fold increase in the translocation time of short single-stranded DNA (ssDNA) at neutral pH, with respect to the wild-type (WT) nanopore. We demonstrated an efficient tool of controlling the ssDNA translocation time, via the interplay between the nanopore-ssDNA surface electrostatic interactions and electroosmotic flow, all mediated by the pH-dependent ionization of amino acids lining the nanopore's translocation pathway. Our data also reveal the nonmonotonic, pH-induced alteration of ssDNA average translocation time. Unlike mildly acidic conditions (pH ∼ 4.7), at a pH ∼ 2.8 maintained symmetrically or asymmetrically across the WT α-HL, we evidenced the manifestation of a dominant electroosmotic flow, determining the speeding up of the ssDNA translocation across the nanopore by counteracting the ssDNA-nanopore attractive electrostatic interactions. We envision potential applications of the presented approach by enabling easy-to-use, real-time detection of short ssDNA sequences, without the need for complex biochemical modifications to the nanopore to mitigate the fast translocation of such sequences.

Classification of Alzheimer’s Disease Based on Deep Learning Using Medical Images

Neurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector.

Oscillatory behavior of solutions of second-order non-linear differential equations with mixed non-linear neutral terms

This study primarily seeks to expand upon these developments by encompassing neutral differential equations of mixed type, incorporating both delay and advanced terms, particularly in the case of the canonical operator. The presented results are derived from the application of the comparison method, Riccati transformation, and integral averaging technique. These methodologies lead to substantial improvements and extensions of existing results found in the literature. Additionally, illustrative examples are provided to demonstrate the practical implications of the developed criteria.

Fractional derivative modeling of heat transfer and fluid flow around a contracting permeable infinite cylinder: Computational study

Understanding the complex interplay between the contracting behavior of the cylinder and the fluid flow dynamics has implications for the design of porous structures for heat exchange and filtration systems. In this study, we investigate the dynamics and thermal behavior of fluid flow past a contracting permeable infinite cylinder. First, we developed a mathematical model based on the Navier–Stokes equations to describe the fluid dynamics around the contracting permeable infinite cylinder. A new simple well-behaved definition of fractional derivative called conformal fractional derivative introduced by authors Khalil et al., (2014) is employed to generalize the PDE’s of momentum and energy. The similarity transformation technique is utilized to transform the proposed mathematical problem into a non-linear ODE’s. Then, we utilized IPS technique to numerically solve the governing equations and obtain detailed insights into the flow patterns and thermal characteristics. We observed significant changes in the flow velocity and temperature distribution as the cylinder contracted, highlighting the intricate thermal behavior of the system. Additionally, the changes in the fraction order was found to have a notable impact on the overall flow patterns and heat transfer processes.

Turbulent and non-turbulent analysis of thermomagnetic convection and heat transfer of darcian radiative nanofluid flow across inclined stretching surface in microgravity environment

Turbulent and non-turbulent analysis of thermomagnetic convection, heating rate and mass transport of Darcian radiating nanofluid flow through porous slanted sheet is the aim of present study. Influence of microgravity is more useful for the movement of thermophoresis nanoparticles with maximum temperature and density. Joule heating, porous medium, magnetic field and thermal radiations are incorporated for the performance of thermal convection. Governing equations are reduced in dimensionless form. Oscillatory stokes conditions are applied to separate the steady, real and imaginary equations. Finite difference, Primitive transformation, and Gaussian elimination techniques are applied for numerical outputs. For asymptotic results, the appropriate range of parameters such as 0.1≤JH≤15.0, 0.1≤Pr≤12.0, 0.1≤Rd≤25.0, 0.0≤λT≤5.0, 0.1≤NT≤1.0, 0.1≤Fr≥6.0, and 0.1≤δ≤1.0 is utilized. Main novelty of work is to examine the steady state and oscillatory behavior of friction-rate, heat/mass transport over slanted two-angles π/6 and π/4. Maximum amplitude in fluid velocity is observed by increasing radiations and buoyant forces. Temperature distribution and nanomaterial concentrations enhance as Joule-heating and Prandtl number increases under microgravity region. Amplitude and oscillation of heat and mass rate is increased as reaction rate, Joule heating and Forchheimer parameter increases. Enhancing behavior of energy transport is observed for maximum choice of Prandtl index with small magnetic effects. It is depicted that high rate of oscillating frequency in heat transmission is detected with maximum radiation effects.

Dynamic inspection data-based analysis of rail base metal irregularities for engineering failure prevention

With the continual increase in train speeds, the requirement for track smoothness has become increasingly critical. Within the railway system of China, the prevalence of short-wave defects, attributed to the rail base metal irregularity, adversely affects the stability of train movement and constitutes a considerable safety risk. However, research into the rail base metal irregularity remains markedly scant, with a conspicuous absence of effective evaluation and identification methodologies. This deficiency critically restricts the capability to mitigate short-wave disturbances arising from such irregularities, negatively impacting the safety and operational stability of railway systems. Therefore, this paper proposes a method for evaluating and identifying the rail base metal irregularity based on dynamic inspection data. Initially, this paper conducts a comprehensive assessment of rail base metal irregularities by analysing wavelength characteristics, frequency amplitude features, and dynamic responses of vehicles. Subsequently, this paper utilises the Synchrosqueezed Wavelet Transform (SWT) technique, which refines the frequency resolution by compressing frequency components within the frequency domain, eliminating cross-frequency interference, and minimising energy dispersion across scale domains, thus accurately delineating the time–frequency attributes of rail base metal irregularity. Building upon the foundation provided by SWT, this paper introduces an identification method employing the Summed Wavelet Power (SWP) across various frequency bands. This approach evaluates the energy variations of different wavelength components relative to changes in mileage, facilitating the identification of track sections with rail base metal irregularity. The efficacy of the proposed methodology is corroborated through the analysis of actual measurement data. The results show that the SWT and SWP techniques can effectively identify rail base metal irregularity, thereby offering substantial support for enhancing the safety and comfort of railway operations.

A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images

Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

Exploring the Potential of Generative Adversarial Networks in Enhancing Urban Renewal Efficiency

As Chinese cities transition into a stage of stock development, the revitalization of industrial areas becomes increasingly crucial, serving as a pivotal factor in urban renewal. The renovation of old factory buildings is in full swing, and architects often rely on matured experience to produce several profile renovation schemes for selection during the renovation process. However, when dealing with a large number of factories, this task can consume a significant amount of manpower. In the era of maturing machine learning, this study, set against the backdrop of the renovation of old factory buildings in an industrial district, explores the potential application of deep learning technology in improving the efficiency of factory renovation. We establish a factory renovation profile generation model based on the generative adversarial networks (GANs), learning and generating design features for the renovation of factory building profiles. To ensure a balance between feasibility and creativity in the generated designs, this study employs various transformation techniques on each original profile image during dataset construction, creating mappings between the original profile images and various potential renovation schemes. Additionally, data augmentation techniques are applied to expand the dataset, and the trained models are validated and analyzed on the test set. This study demonstrates the significant potential of the GANs in factory renovation profile design, providing designers with richer reference solutions.

Twin Boundaries-Induced Centrosymmetric Breaking of Hollow CaTiO3 Nanocuboids for Piezocatalytic Hydrogen Evolution.

Piezocatalysis, a transformative mechanochemical energy conversion technique, has received considerable attention over the past decade for its role in processes such as hydrogen evolution from water. Despite notable progress in the field, challenges remain, particularly in the areas of limited piezocatalysis efficiency and limited availability of materials requiring a non-centrosymmetric structure. Here, a pioneering contribution is presented by elucidating the piezocatalytic properties of hollow CaTiO3 nanocuboids, a centrosymmetric material with a nominally nonpolar state. Remarkably, CaTiO3 nanocuboids exhibit an impressive hydrogen production rate of 3.44mmolg-1h-1 under ultrasonic vibrations, surpassing the performance of the well-established piezocatalyst BaTiO3 (2.23mmolg-1h-1). In contrast, commercial CaTiO3 nanoparticles do not exhibit piezocatalytic performance. The exceptional performance of hollow CaTiO3 nanocuboids is attributed to the abundance presence of twin boundaries on the {110} facet within its crystal structure, which can impart significant polarization strength to CaTiO3. Extending the investigation to other centrosymmetric materials, such as SrZrO3 and BaZrO3, the experimental results also demonstrate their commendable properties for piezocatalytic hydrogen production from water. This research underscores the significant potential of centrosymmetric materials in piezocatalysis.