Experimental Analysis Research Articles

Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on Heuristic-Algorithm-Optimized Backpropagation Neural Network Model

A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of TCFDST members due to the variations in cross-section, this paper applied heuristic optimization algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO) to enhance a Backpropagation Neural Network (BPNN) model. A predictive model incorporating both global and local optimization strategies for the axial compressive capacity of a TCFDST structure is proposed. A comprehensive axial database for TCFDST members, comprising 1327 sets of experimental and finite element analysis results, was established, with ten types of component dimensions and material parameters selected as input variables and compressive bearing capacity as the output variable. This study developed and assessed four BPNN models, each optimized by a different heuristic algorithm, against various machine learning algorithms and standards. The heuristic-algorithm-optimized BPNN models demonstrated superior accuracy in predicting the axial compressive capacity of TCFDST members. Through parametric analysis, this study identified the relationship between the model’s bearing capacity predictions and each input parameter, confirming the model’s broad applicability. The optimized BPNN model, refined with heuristic algorithms, provides a significant reference for addressing the computational challenges associated with the load-bearing capacity of TCFDST structures and facilitating their application.

Experimental Analysis of Freehand Multi-object Selection Techniques in Virtual Reality Head-Mounted Displays

Object selection is essential in virtual reality (VR) head-mounted displays (HMDs). Prior work mainly focuses on enhancing and evaluating techniques for selecting a single object in VR, leaving a gap in the techniques for multi-object selection, a more complex but common selection scenario. To enable multi-object selection, the interaction technique should support group selection in addition to the default pointing selection mode for acquiring a single target. This composite interaction could be particularly challenging when using freehand gestural input. In this work, we present an empirical comparison of six freehand techniques, which are comprised of three mode-switching gestures (Finger Segment, Multi-Finger, and Wrist Orientation) and two group selection techniques (Cone-casting Selection and Crossing Selection) derived from prior work. Our results demonstrate the performance, user experience, and preference of each technique. The findings derive three design implications that can guide the design of freehand techniques for multi-object selection in VR HMDs.

Exploring the benefits of hydrogen-water injection technology in internal combustion engines: A rigorous experimental study

Previous hydrogen researchers have demonstrated the great potential of hydrogen as a zero-carbon fuel and its wider operational ability to directly replace the existing fossil-fuelled internal combustion engines (ICE). Hydrogen direct-injection technology can operate at stoichiometric combustion conditions, fully eliminating the intake backfire phenomena, which has the benefit of low airflow requirements and is a suitable solution for naturally aspirated ICE platforms. However, significant challenges must be addressed when operating an H2 ICE at or near stoichiometric. These include rapid pressure rise rate, high in-cylinder gas pressure and temperature and the consequential higher engine-out NOx emissions. This study provides a comprehensive experimental analysis of the effect of water injection on a light-duty H2 engine’s performance, efficiency, burn durations and NOx emission. The engine was modified to operate with hydrogen via a centrally-mounted direct H2 injector and an intake port-mounted water injection system. The study included water vapour measurements, NOx concentrations and other exhaust gases. The practical implications of these findings are significant. The results demonstrated that the NOx emissions were reduced by 79% when the water was injected at a rate of 5 kg/h at 10 bar IMEP and 2000 rpm. This reduction in emissions, coupled with the observed decrease in cylinder pressure and the rate of pressure rise, could substantially improve engine performance and efficiency. At higher load regions, the water injection extended the maximum engine load to 24 bar IMEP and increased the engine torque output by 10%. The maximum Indicated Thermal Efficiency (ITE) increased from 41% at 16.5 bar IMEP to 42.5% at 18 bar IMEP at a lower lambda, with a corresponding reduction in the intake-air-boost pressure requirement. Of particular note, the use of water injection decreased the engine-out NOx emissions under high-load conditions by more than 55%, even at richer AFR compared to pure hydrogen operation.

Asynchronous decentralized traffic signal coordinated control in urban road network

AbstractThis study introduces an asynchronous decentralized coordinated signal control (ADCSC) framework for multi‐agent traffic signal control in the urban road network. The controller at each intersection in the network optimizes its signal control decisions based on a prediction of the future traffic demand as an independent agent. The asynchronous framework decouples the entangled interdependence between decision‐making and state prediction among different agents in decentralized coordinated decision‐making problems, enabling agents to proceed with collaborative decision‐making without waiting for other agents’ decisions. Within the proposed ADCSC framework, each controller dynamically optimizes its signal timing strategy with a unique rolling horizon scheme. The scheme's individualized parameters for each controller are determined based on the vehicle travel time between the adjacent intersections, ensuring that controllers can make informed control decisions with accurate arrival flow information from upstream intersections. The signal optimization problem is formulated as a mixed integer linear program model, which adopts a flexible signal scheme without a fixed phase structure and sequence. Simulation results demonstrate that the proposed ADCSC strategy significantly outperforms the benchmark signal coordination methods in terms of average delay, travel speed, stop numbers, and energy consumption. Experimental analysis on computation time validates the applicability of the proposed optimization model for real‐time implementation. Sensitivity analysis on key parameters in the framework is conducted, offering insights for parameter selection in practice. Furthermore, the ADCSC framework is extended to a road network in Qinzhou City, China, with 45 signalized intersections, demonstrating its effectiveness and scalability in the real‐world road network.

3D reinforcement synergistic effect of embedded nickel‐foam skeleton of polyurethane based on x‐ray CT images: Experimental and finite element analysis

AbstractMonotonic shear tests were performed on Nickel Foam (NF)/Polyurethane (PU) and the results indicated that NF/PU was much greater than the sum of the individual phases in terms of strength and stiffness. Cyclic shear tests were carried out and it was found that shear evolution processes and failure modes of the composite were completely different from the individual phases and the damping properties were greatly improved. Revealing the synergistic mechanism of the 3D continuous metal skeleton on the PU is the key issue to explain the above phenomena. The geometric topology of NF was reproduced using CT technology and a fine‐scale finite element model was established. The mechanical behavior between NF and PU was investigated at the meso‐mechanical scale. The results show that the NF skeleton significantly improves the strength and damping properties of the PU. During cyclic shear, the effects of displacement and temperature on the mechanical and damping properties of the PU are significant. In comparison, the NF skeleton reduces the temperature sensitivity. Based on the analysis of experimental and FEA results, the monotonic and cyclic constitutive models of NF/PU were established. The shear constitutive model under the new loading/unloading criterion has good fitting accuracy and high reliability.Highlights NF/PU properties were investigated by experimental and finite element methods. NF skeleton improves PU performance through the synergistic effect. Synergistic effects were revealed by CT and finite element methods. The R‐O model can fit the shear constitutive model of NF/PU.

NUMA-aware parallel sparse LU factorization for SPICE-based circuit simulators on ARM multi-core processors

In circuit simulators that resemble the Simulation Program with Integrated Circuit Emphasis (SPICE), one of the most crucial steps is the solution of numerous sparse linear equations generated by frequency domain analysis or time domain analysis. The sparse direct solvers based on lower-upper (LU) factorization are extremely time-consuming, so their performance has become a significant bottleneck. Despite the existence of some parallel sparse direct solvers for circuit simulation problems, they remain challenging to adapt in terms of performance and scalability in the face of rapidly evolving parallel computers with multiple NUMA hardware based on ARM architecture. In this paper, we introduce a parallel sparse direct solver named HLU, which re-examines the performance of the parallel algorithm from the viewpoint of parallelism in pipeline mode and the computing efficiency of each task. To maximize task-level parallelism and further minimize the thread waiting time, HLU devises a fine-grained scheduling method based on an elimination tree in pipeline mode, which employs depth-first search (DFS-like) to iteratively search for parent tasks and then place dependent tasks in the same task queue. HLU also suggests two NUMA node affinity strategies: thread affinity optimization based on NUMA nodes topology to guarantee computational load balancing and data affinity optimization to enable effective memory placement when threads access data. The rationality and effectiveness of the sparse solver HLU are validated by the SuiteSparse Matrix Collection. In comparison with KLU and NICSLU, the experimental results and analysis show that HLU attains a speedup of up to 9.14× and 1.26x (geometric mean) on a Huawei Kunpeng 920 Server, respectively.

Research on music genre recognition method based on deep learning

In this paper, we explore music genre recognition using deep learning methods, examining the application of feature extraction, model construction, and performance evaluation for different music genres. During the data preparation and preprocessing stages, data augmentation and normalization techniques were employed to enhance the model’s generalization capabilities. By constructing multilayer convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we achieved automatic recognition of music genres. In the experimental results analysis, we compared the accuracy and training time of different models, validating the effectiveness of deep learning in the field of music genre recognition. The limitations of deep learning methods and future research directions are also discussed, providing a reference for further studies in music information processing. This study delves into the issue of music genre recognition and proposes a deep learning-based approach. This method leverages neural networks to extract features and learn from audio data, enabling accurate classification of different music genres. Extensive experiments have demonstrated that our method achieves highly satisfactory results in music genre recognition tasks. Furthermore, we optimized the deep learning models, improving their generalization capabilities and accuracy. Our research offers the music industry an efficient and accurate method for music genre recognition, providing new perspectives and technical support for research and applications in the music field.

Theoretical investigation on crash performance of bamboo-inspired energy absorption systems based on systematic experimental and numerical analysis

The need for energy-absorbing devices with adaptable crashworthiness is increasingly urgent in various engineering fields with diverse load environments. For this aim, bamboo-inspired modular energy absorption systems have recently been proposed to integrate mechanical efficiency, tunability, and robustness. However, their mechanical responses under varying load velocities have not been systematically studied, which limits the effective prediction and optimization of their crashworthiness. In this study, quasi-static and dynamic experiments were performed on bamboo-inspired systems, discretely assembled using 3D printed steel tube specimens. Matched finite element simulations were conducted using ABAQUS/Explicit. Specific energy absorption and efficiency of these systems respectively exceeded 12.9 J/g and 47 %, surpassing existing discretely assembled systems and rivaling typical cellular materials. Efficient deformation mode with self-locking capability was observed, and sharp impact peak was avoided when the crash velocity was less than 50 m/s. Building upon these findings, a modified spherical shell theory under crashing was developed to predict energy absorption performance of bamboo-inspired system based on the traveling plastic hinge model. The average relative error between the analytical solution and finite element results was found less than 6.5 %. The mean effective stress of the system could exceed 35 MPa under thickness-to-diameter ratio of 5 %. This work presents an efficient and effective approach for optimizing and estimating the crash performance of modular protective devices.

Analysis and Optimization of Super Duplex Stainless Steel Deposition in Wire Arc Additive Manufacturing Using Machine Learning Techniques

<div>This article presents experimental investigations and machine learning-based analysis on depositions of super duplex stainless steel (SDSS ER2594) material in wire arc additive manufacturing (WAAM) considering the process parameters namely voltage, wire feed rate, torch travel speed, and gas flow rate. Deposition efficiency and surface height values of the accumulated material were measured to build machine learning models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The developed ANN model could predict the deposition efficiency and surface height with mean absolute deviations (MADs) of 8.9% and 16.1%, respectively. The MAD for prediction of the two responses for ANFIS model was found to be 6.1% and 14.9% as compared to the experimental data. Multi-objective optimization was also performed to obtain optimal solutions to achieve desired deposition results. Mechanical properties and microstructures of the deposited materials with optimal processing parameters were found comparable to that of the base materials.</div>

Durability Evaluation of GGBS-RHA-Based Geopolymer Concrete Along with Lightweight Expanded Clay Aggregate Using SEM Images and EDAX Analysis

The durability of geopolymer concrete containing Ground Granulated Blast Furnace Slag (GGBS) and Rice Husk Ash (RHA), along with Lightweight Expanded Clay Aggregate (LECA), was investigated. Six different LWGPC mixtures were made with NaOH molarities of 8, 10, and 12M. For each molarity, two combinations of source materials were selected: 100% GGBS (G) and 80% GGBS with 20% RHA (RG). In all the mixtures, coarse aggregate was substituted with 35% LECA. LWGPC mixtures were exposed to 3% HCl, 5% MgSO4, and 3.5% NaCl for studying the durability properties. The test results demonstrate that 100% GGBS with 12M NaOH (12G) outperformed all other mixtures. The residual compressive strength of 12G mix LWGPC specimens after six months of exposure was found to be 86.4% in an acid environment, 90.6% in a sulfate environment, and 91.4% in a salt environment. The elemental composition analyzed using EDAX reveals that silica, alumina, calcium, and sodium are the predominant elements that form a dense microstructure with N-A-S-H, C-A-S-H, and C-S-H. Further, the inner properties of the specimens exposed to chemicals were examined using MATLAB R2023b and ImageJ 1.54f based on SEM images. The SEM image showed that the porosity of LWGPC specimens ranged from 0.5194 to 0.6748 µm, signifying an enhanced durability performance. The experimental results and microstructural analysis show that the LWGPC incorporating RHA and GGBS with LECA offers a superior performance, making it a promising solution for sustainable and durable construction.

Enhanced Collaborative Filtering: Combining Autoencoder and Opposite User Inference to Solve Sparsity and Gray Sheep Issues

In recent years, the study of recommendation systems has become crucial, capturing the interest of scientists and academics worldwide. Music, books, movies, news, conferences, courses, and learning materials are some examples of using the recommender system. Among the various strategies employed, collaborative filtering stands out as one of the most common and effective approaches. This method identifies similar active users to make item recommendations. However, collaborative filtering has two major challenges: sparsity and gray sheep. Inspired by the remarkable success of deep learning across a multitude of application areas, we have integrated deep learning techniques into our proposed method to effectively address the aforementioned challenges. In this paper, we present a new method called Enriched_AE, focused on autoencoder, a well-regarded unsupervised deep learning technique renowned for its superior ability in data dimensionality reduction, feature extraction, and data reconstruction, with an augmented rating matrix. This matrix not only includes real users but also incorporates virtual users inferred from opposing ratings given by real users. By doing so, we aim to enhance the accuracy of predictions, thus enabling more effective recommendation generation. Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.

Performance assessment of a 30.26 kW grid-connected photovoltaic plant in Egypt

Abstract This paper presents an experimental analysis and performance evaluation of a grid-connected photovoltaic plant installed on the rooftop of the Electronics Research Institute in Cairo, Egypt. Cairo is classified as a hot-desert climate region according to the standard Koppen-Geiger climate classification system. Over a year, we monitored real-time data to assess key system performance metrics, such as energy yield, efficiencies, performance ratio, capacity factor, and losses. Based on the obtained experimental results, the highest final yield of 5.2498 hr/day was observed in the summer, whereas the lowest yield of 3.439 hr/day occurred in the winter months. The photovoltaic plant had an average annual system efficiency of 15.8%, while the photovoltaic and inverter had mean yearly efficiencies of 17.1% and 97.2%, respectively. The average annual performance ratio is 83.03%, and the capacity factor is 18.72%. The monthly total loss exhibited a linear rise alongside increasing ambient temperature and solar irradiance. The ambient temperature affected the system efficiency, photovoltaic efficiency, and performance ratio. The findings can help strengthen forecasts of future large-scale photovoltaic plants in hot desert climates. Moreover, they can guide the design, optimization, operation, and maintenance of new grid-connected photovoltaic systems.

Improving solar panel performance using MPPT optimization algorithms

One of the primary challenges faced by solar energy systems is the issue of inconsistent lighting and uneven distribution of sunlight across the surface of solar panels. These conditions can result in a substantial reduction in the power output of the panels and, in some cases, the complete failure of certain solar cells. This paper examines the effects of shading on solar panel performance, focusing on how partial shading impacts energy production and efficiency through both experimental analysis and simulation. The findings emphasize the critical influence of shading patterns and configurations on the overall efficiency of photovoltaic systems. The study highlights the necessity of implementing shading mitigation strategies and optimization algorithms to enhance energy production and ensure the reliability of solar systems operating in environments with varying light conditions. This study showed that the use of optimization algorithms in photovoltaic systems exposed to shading increases their efficiency in terms of energy saving as well as performance stability. The simulation results shows that the GWO algorithm gives a higher performance than the other algorithms in the same partial shading conditions.

Doping effects in strontium hexaferrite: Theoretical and experimental analysis

Hexagonal ferrites based on SrFe12O19 (SFO) are vital materials in the field of permanent magnets, valued for their cost-effectiveness and reliable magnetic properties, making them a more affordable alternative to rare-earth permanent magnets. This study focuses on understanding the doping effects of commonly used elements in SFO system. DFT calculation results show that doping La or La-Co can enhance the phase stability of SFO, in which Co has a preference for the 4f1 sites of Fe. The total magnetic moment was observed to slightly decrease with La doping, whereas it exhibited an increasing trend with La-Co and La-Ca-Co co-doping and the largest increase of 6.5 % can be obtained in Sr2LaCaFe44Co4O76 (SLCFCO). The increase in the total magnetic moment is mainly attributed to the smaller antiferromagnetic moment of Co than that of Fe on 4f1 sites, as well as the reduction in the antiferromagnetic Fe moments at 4f1 and 4f2 sites. The total density of states indicates a transition from semiconductor behavior to a metallic character both in the La-Co and La-Ca-Co doped versions. The experimental results validate the theoretical predictions, confirming enhancements in magnetic performance. The saturation magnetization increased from 63.86 Am2/kg in SrFe11.7O19 (SFO) to 67.53 Am2/kg in Sr0.4La0.4Ca0.2Fe10.5Co0.5O19 composition. These findings provide a vital understanding of tuning the magnetic and electronic properties in doped SFO, which will enable the development of advanced applications in magnetic materials.

KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing

When using the Transformer model for wind power prediction, the presence of noise in wind power data and the model’s final layer relying solely on a simple linear output reduces the model’s ability to capture nonlinear relationships, leading to a decrease in prediction accuracy. To address these issues, this paper proposes an ultrashort-term wind power prediction model based on exponential weighted moving average (EWMA) data processing and Kolmogorov–Arnold Network (KAN)-Transformer. First, multiple variable features are smoothed using EWMA, which suppresses noise while preserving the original data trends. Then, the EWMA-processed data is input into the Encoder and Decoder modules of the Transformer model to extract features. The output from the Decoder layer is then passed through the KAN layer, built using a cubic B-spline function, to enhance the model’s ability to capture nonlinear relationships, thereby improving the prediction accuracy of the Transformer model for wind power. Finally, experimental analysis is conducted, and it shows that the proposed model achieves the highest prediction accuracy, with a mean absolute error of 4.38 MW, a root mean squared error of 7.37 MW, and a coefficient of determination of 98.73%.

Impact of collector aspect ratio on the energy and exergy efficiency of a louvered fin solar air heater

Solar air heaters have huge applications in renewable energy utilization segments concerning the issues of space heating, drying, and ventilation systems. Performance prediction of such systems largely depends upon the design of the collector itself. The present study has highlighted the influence of various aspect ratios of the collector on the thermal and exergy performance of louvered finned solar or air heaters. In this context, an experimental analysis has been carried out to compare the performance of LFSAH with conventional PSAH. These results show that an increase in aspect ratios enhances thermal efficiencies of heat transfer and the systems. An aspect ratio of 4:1 showed the maximum thermal efficiencies as 81.63 % for LFSAH and 68.13 % for PSAH. On the other hand, at larger mass flow rates, the exergetic efficiency of LFSAH becomes lower owing to the larger friction and exergy losses and sometimes even lower than that of PSAH. The novelties of the present study are in emphasizing the importance of aspect ratio in optimizing solar air heater design and providing relevant insight for better energy use.

A novel pedicle screw design to maximize screw-bone interface strength using finite element analysis and design of experiment techniques.

Basic study. This study aimed to utilize finite element (FE) analysis and design of experiment (DoE) techniques to propose and optimize a novel pedicle screw design and compare its pull-out force with that of a control device. Pedicle screw-based fixation is the gold-standard treatment for spine diseases, particularly in fusion procedures. However, pedicle screw loosening and breakage still occur in osteoporotic and non-osteoporotic patients. This research investigates screw design modifications to enhance screw-bone interface strength and reduce the likelihood of loosening. We conceptualized a novel pedicle screw considering vertebral bone morphology and strength differences. A validated FE model was developed and used in conjunction with DoE to determine the screw՚s optimum geometrical parameters. The FE model was validated through simulation and laboratory experiments using the control device. The optimized thread profiles for cortical bone and cancellous bone were determined, with pull-out force as the primary factor for screw design evaluation. FE analysis results for the control device closely matched experimental results, with less than 5% difference. The chosen unique pitch/depth ratio showed maximum pull-out force for cortical bone, while DoE enabled the optimization of design parameters for cancellous bone. The optimized pedicle screw exhibited a 15% increase in pull-out force compared to the control device. The study proposes a novel pedicle screw design with better pull-out strength than the control device. Combining FE analysis with DoE is an effective approach for screw design optimization, reducing the need for extensive prototyping tests. A two-variable analysis suffices for optimizing cortical bone design parameters, while a multi-variable analysis is more effective for optimizing cancellous bone design parameters.

Seismic isolation effect and parametric analysis of simply supported beam bridges with multi-level sliding friction adaptive isolation bearing

In response to the shortcomings of isolation bridge limit devices and friction dampers, a sliding friction bearing exhibiting multi-level working behavior (MSFB) is proposed. Its structure and mechanism are introduced, and a multi-condition quasi-static test is conducted using ABAQUS software. Through comparative analysis of theory and experiment, the theoretical model's accuracy and multi-level sliding friction mechanism of MSFB are verified. The mechanical parameters of MSFB are analyzed, clarifying the influence of various main mechanical parameters on the mechanical performance. The performance parameters of the MSFB are optimized based on a two-degree-of-freedom analysis model for a high-speed railway single-pier bridge equipped with MSFBs. The findings indicate that the seismic responses of MSFB is comprehensively affected by all the parameters, and the variation pattern is complex. By optimizing these performance parameters, MSFB can satisfy the multi-level performance demands of bridges subjected to varying earthquake intensities, and achieve the optimal control effect. The MSFB offers superior isolation efficiency during minor and moderate earthquakes and can substantially decrease the internal forces within the bridge substructure when utilized as a bridge isolation and limit device. During intense and infrequent earthquakes, the MSFB demonstrates remarkable displacement-limiting capacity and good self-centering and energy dissipation abilities.

Analysis of Interface Bonding Mechanism of Babbit Alloy/Steel Compound Castings with Different Surface Treatments

Tin-based Babbitt alloy/steel bimetallic castings were prepared by compound casting with different coating surface modification treatment. The defects of modified coating surface are effectively controlled, while the unmodified interface has a considerable number of micropores. In consequence, the additional composite surface modifier greatly increased the integrity of the bimetal interface and led to a significant improvement of shear strength to ~55.61 MPa, in comparison to its counterpart with no surface modifier of ~7.04 MPa. Based on extensive experimental investigation and theoretical analysis, the role of coating surface modifiers in the interfacial strengthening was revealed. Meanwhile, the diffusion behavior of the bimetal interface connection was analyzed by first-principles calculations. These results will shed new light on the understanding of the liquid–solid connection of bimetallic composites and improve defect control at the interface.

Corporate culture and shareholder litigation rights: evidence from a quasi-natural experiment and textual analysis

PurposeTaking advantage of an innovative measure of corporate culture obtained from advanced machine learning and textual analysis, we investigate how corporate culture is influenced by shareholder litigation rights, which are widely recognized as a crucial external governance mechanism. The innovative measure of corporate culture is based on a textual analysis of over 200,000 earnings call transcripts.Design/methodology/approachTo mitigate endogeneity and thus demonstrate causality, we exploit a quasi-natural experiment based on the staggered passage of universal demand laws, which reduce shareholder litigation rights. The enactment of state-level legislation is likely exogenous to individual firms’ characteristics as it is beyond the control of any given firm. Following the literature, we employ a difference-in-difference analysis, supplemented by several robustness checks, i.e. propensity score matching and entropy balancing.FindingsOur difference-in-difference estimates show that an exogenous reduction in shareholder litigation rights weakens corporate culture considerably. Specifically, corporate culture is 12.74–14.41% weaker after the implementation of universal demand laws. Our results corroborate the hypothesis that a decline in litigation risk exacerbates agency problems, discouraging self-interested managers from taking actions that enhance shareholder value in the long run, such as cultivating a strong corporate culture.Originality/valueOur study is the first to explore how corporate culture is affected by shareholder litigation risk, which constitutes a vital external governance mechanism. Moreover, we utilize an innovative measure of corporate culture based on sophisticated textual analysis. Finally, we employ a quasi-natural experiment based on an exogenous shock, making it more likely that our conclusion reflects a causal influence rather than merely a correlation.