Experimental Samples Research Articles

Machine learning-based design of double corrugated steel plate shear walls

PurposeIn this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.Design/methodology/approachIn this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.FindingsThe proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.Originality/valueIn this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

PERFORMANCE CHARACTERISTICS OF A MODIFIED COMPOSITE MATERIAL CONTAINING INORGANIC LIGHTWEIGHT AGGREGATES

A modified thermal and sound insulation composite material, potentially applicable in the field of modern construction, has been developed. Experimental samples based on foam glass aggregate (made from recycled waste glass), perlite (with fraction size up to 3 mm) and hydraulic binders were obtained. The acoustic and mechanical characteristics and the thermal conductivity coefficient of the prepared series of composite samples were studied. Values for Rw in the range from 38 to 46 dB and for λ from 0.04 to 0.15 W m-1 K-1 were established. The influence of the different components of the composition on the values of the studied performance indicators and the structure of the composite was analysed.

Water Quality Analysis of a 300 Mvar Large-Scale Dual Internal Water Cooling Synchronous Condenser External Cooling System and Exploration of Optimal Water Treatment Agent Dosage at Different Temperatures

The external cooling water system of a 300 Mvar dual internal water cooling synchronous condenser at a certain ultra-high voltage converter station continued to exhibit significant scaling and corrosion, even with regular addition of scale and corrosion inhibitors. To solve this problem, the external cooling water of the synchronous condenser was sampled and tested periodically, with the main test items including conductivity, pH value, turbidity, hardness, alkalinity, and other water quality parameters directly related to corrosion and scaling. The trends of these parameters over time were also analyzed. The results showed that as the operation time increased, the cooling water became concentrated during multiple circulation cycles, and the various dissolved or suspended substances underwent a certain degree of enrichment. However, the addition of scale and corrosion inhibitors did not dynamically adjust according to the changes in water quality, and there was always an excessive dosage. Thus, using the external cooling water as the experimental sample, static scale inhibition tests and rotating coupon corrosion tests were conducted to evaluate the scale and corrosion inhibition performance of the commercial AS-582 scale and corrosion inhibitor used at this ultra-high voltage converter station under different conditions. Considering the more obvious corrosive tendency of this water sample, the focus was on testing its corrosion inhibition performance. When the dosage was 600 ppm, the scale inhibition effect was optimal, with an inhibition rate of 92.15%. The corrosion inhibition effect of this scale and the corrosion inhibitor were significantly related to water temperature. At 25 °C, when the dosage was 500 ppm, the corrosion inhibition effect was optimal, with an inhibition rate of 86.79%. However, when the temperature increased to 40 °C, the corrosion inhibition effect under each dosage was significantly worse, unable to meet the requirements, and the use of other corrosion inhibitors in combination was necessary. This work will provide a reference for the scientific use of scale and corrosion inhibitors.

A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure.

Porous ceramic composites play an important role in several applications. This is due to their unique properties resulting from a combination of various materials. Determination of the composite properties and structure is crucial for their further development and optimization. However, composite analysis often requires complex, expensive, and time-demanding experimental work. Mathematical modeling represents an effective tool to substitute experimental approach. The present study employs a Monte Carlo 3D equivalent electronic circuit network model developed to analyze a highly porous composite on the basis of minimum easily obtainable input parameters. Solid oxide cell electrodes were used as a model example, and this study focuses primarily on materials with a porosity of 55% and higher, characterized by deviation of behavior from those of lower void fraction share. This task is approached by adding to the original Monte Carlo model an additional parameter defining the void phase coalescence phenomenon. The enhanced model accurately simulates electrical conductivity for experimental samples of up to 75% porosity. Using sample composition, single-phase properties, and experimentally determined conductivity, this model allows us to estimate data of the internal structure of the material. This approach offers a rapid and cost-effective method to study material microstructure, providing insights into properties, such as electrical conductivity and heat conductivity. The present research thus contributes to advancing predictive capabilities in understanding and optimizing the performance of composite materials with potential in various technological applications.

DeepLPos: a comprehensive hybrid deep learning model for lying position recognition using a tactile sensor array system

Abstract Unpredictable limb movements or turning motions can significantly disrupt the accurate extraction of physiological signals, such as respiratory and heart rates. In clinical environments, reliable detection of lying positions is crucial for continuous patient monitoring, particularly during sleep. In this paper, a smart sleeping position recognition system is proposed, which employs a tactile pressure sensor array based on the unique structure of ‘the electrostatic double-layer capacitors’. The sensor array, comprising 64 rows and 32 columns (2048 nodes), captures four types of healthy lying positions using an 8-bit AD module. Despite challenges arising from limited experimental samples for accurate training, we propose DeepLPos, a hybrid deep learning approach combining generative adversarial networks and the you only look once network. To tackle the differentiation challenge between supine and prone positions, we introduce an SPD Conv attention module to enhance the resolution of detailed descriptions in pressure images. The model is further pruned to optimize both structure and parameters, enabling efficient real-time detection. Evaluated on the SLP dataset, the proposed system achieves an accuracy of 97.5% with a real-time processing speed of 0.069 s per frame, demonstrating its potential for practical, high-precision measurement and monitoring applications in healthcare.

Resistive Heating and Thermal Decomposition of an Additively Manufacturable Electrically Conductive Explosive

AbstractThe ability to transmit electrical signals through high explosive charges without using dense metal conductors could enable continuous monitoring of explosive charges with internally embedded sensors. With the sensor materials themselves being energetic, the impact on the intended detonation performance of the charge can be minimized. Embedded sensors enable real time, in‐situ measurements of conditions relevant to ageing assessments. Additionally, dynamic material property sensing can provide detonation performance data, enabling measurement of dynamic shock position. One proposed method to allow for electronic signal transfer through high explosives is to use electrically conductive explosive composites consisting of an electrically conductive polymer binder mixed with high explosive crystals. These conductive energetic pathways also serve as independently reactive elements and can provide additional functionality via dynamic sensor removal through resistive heating and subsequent decomposition. This work discusses the resistive heating of a previously developed, poly (3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) based, electrically conductive extrudable explosive composite. Experimental samples of the conductive explosive material were subjected to a range of direct current applications and the resulting heating and decomposition of the material is discussed.

Quantifying pigment features of Thangka Five Buddhas using hyperspectral imaging

Thangka, a masterpiece of Tibetan painting, is renowned for its adept use of natural mineral pigments such as gold, turquoise, and cinnabar, which imbue it with profound artistic and historical significance. Presently, chemical analysis methods relying on microscopic perspectives are prevalent in researching the pigment components of cultural artifacts. However, these methods suffer from quantization gaps and carry the risk of damaging the relics. Hence, in this study, we focus on Thangka Five Buddha as our experimental sample and propose a novel approach utilizing Linear Spectral Mixed Analysis (LSMA) based on Hyperspectral Imaging (HSI) technology to perform quantitative analysis of pigment components at a sub-pixel level. The results indicate the following. 1) A database of spectral curves for 25 representative Thangka pigments was established, covering 196 bands from 393 to 800 nm VIR-NIR range. 2) The LSMA model successfully separated the 13 pigment components of the Thangka at the sub-pixel level, achieving a Root Mean Square Error (RMSE) of 0.0186, which indicates high classification accuracy. 3) The quantitative analysis reveals that 33.07 % of the area is painted using a single pigment, while 56.01 % is painted using a combination of two pigments. Verdigris (18.56 %), malachite (17.52 %), and cinnabar (10.91 %) are the pigment types with the highest proportions among them. Out of the 521 pigment combinations, verdigris and turquoise (4.55 %), malachite and calcite (4.02 %), minium and cinnabar (2.87 %), and turquoise and malachite (2.82 %) are more commonly used. 4) The application of quantitative analysis methods demonstrates significant potential in painting techniques, authentication processes, and establishing historical dating, among other areas of study.

A novel infrared and visible image fusion network based on cross-modality reinforcement and multi-attention fusion strategy

Infrared and visible image fusion focuses on the integration of complementary information, whose fused results with abundant texture details and salient targets can cater for human visual observation well. However, the loss of some important details is still a challenge in the image fusion process. In this paper, a novel method based on cross-modality reinforcement module and multi-attention fusion strategy is proposed to boost the end-to-end CNN backbone. Specifically, a cross-modality architecture is applied to compensate the spectral differences from heterogeneous images; and the multi-scale strip pooling is utilized as a further feature representation tool to model long-rang independencies precisely; then, a detail injection block is devised to fulfill the enhancement requirements of texture-contrast and target intensity; sequentially, a multi-attention fusion module is proposed to integrate features progressively. Extensive comparative experiments are conducted on several datasets to demonstrate the superiority of the proposed method in both quantitative metrics and visual perception, such as the average of visual information fidelity metric based on all the experiment samples reach to 0.964.

The impact of 12 weeks of circuit body weight training on athletes’ physical fitness

The purpose of this study is to find out the impact of 12-week circuit body weight training on athletes’ physical fitness. This study used an experimental design with a control group. This experimental model consists of three stages. Firstly, pre-testing the dependent variable before implementing the treatment. Secondly, experimental participants are given experimental treatment, specifically the circuit body weight approach. Thirdly, provide a post-test to measure the dependent variable after treatment. The experimental sample included 25 athletes from various sports who were separated into two treatment groups. The circuit body weight group consisted of 13 samples, while the remaining 12 were trained using conventional direct teaching methods. The data analysis technique begins with calculating the data’s normality. The homogeneity test determines if two data sets contain homogeneous versions. Finally, the Mann-Whitney test compares two similar population means. Calculations using SPSS IBM 26 on data before treatment in the experimental class resulted in a valid sample size of 25 and a score of 48.40 ± 2,931. The experimental group had a valid sample size of 25, with a score of 49.57 ± 2,979. Pre-test data for the control class showed 25 valid samples with a score of 38.7 ± 6,607. Meanwhile, the control class post-test data obtained a valid sample size of 25 with a score 39.43 ± 6.796. The table shows a sig (2-tailed) value of 0.000 < 0.05, indicating a significant difference in physical fitness scores between the experimental and control groups. Additionally, there is a difference in average Physical Fitness results between the Circuit bodyweight training model and the conventional learning model.

Discover QTLs for the level of blood components in Shaoxing duck using GWAS and haplotype sharing analysis

Blood composition is indicative of health-related traits such as immunity and metabolism. The use of molecular genetics to investigate alterations in these attributes in laying ducks is a novel approach. Our objective was to employ genome − wide association studies (GWAS) and haplotype − sharing analysis to identify genomic regions and potential genes associated with 11 blood components in Shaoxing ducks. Our findings revealed 35 SNPs and 1 SNP associated with low-density lipoprotein cholesterol (LDL) and globulin (GLB), respectively. We identified 36 putative candidate genes for the LDL trait in close proximity to major QTLs and key loci. Based on their biochemical and physiological properties, TRA2A, NPY, ARHGEF26, DHX36, and AADAC are the strongest putative candidate genes. Through linkage disequilibrium analysis and haplotype sharing analysis, we identified three haplotypes and one haplotype, respectively, that were significantly linked with LDL and GLB. These haplotypes could be selected as potential candidate haplotypes for molecular breeding of Shaoxing ducks. Additionally, we utilized a bootstrap test to verify the reliability of GWAS with small experimental samples. The test can be accessed at https://github.com/xuwenwu24/Bootstrap-test.

ОПЫТ ПРИМЕНЕНИЯ ФУНКЦИИ ЖЕЛАТЕЛЬНОСТИ ХАРРИНГТОНА ПРИ ВЫБОРЕ ОПТИМАЛЬНОГО СОСТАВА ПЛЁНОК И ГЕЛЯ С НАФТОХИНОНОВЫМ КОМПЛЕКСОМ

When a new pharmaceutical product development comes to the stage of selecting the optimal combination of components, researchers often need a comprehensive assessment method for experimental samples. The difficulty is caused by overall complexity of choosing the correct qualitative and quantitative parameters expressed in different units of measurement and establishing priorities between the criteria for the combined assessment. In this case membership functions can help, for example the generalized Harrington desirability function, which allows to combine individual indicators that differ in value and dimension. The article provides examples of the practical application of the generalized Harrington desirability function in assessing several parameters of experimental samples of films and gel developed by us with a naphthoquinone complex of Lithospermum erythrorhizon in order to determine the best composition of these forms. The chosen method made it possible to calculate the generalized desirability for each experimental sample, taking into account all the studied criteria and to determine the most successful experimental samples for further work.

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

In the context of the close integration of computer network technology and daily life, big data network security faces many challenges. With its powerful information processing ability, this AI can effectively identify and handle potential threats in the network, thereby improving network security. This paper discusses the application of artificial intelligence (AI) in enhancing big data network security defense, especially in the threat detection link. This paper proposes a detection algorithm that combines support vector machine (SVM) and K nearest neighbor (KNN), namely SVM-KNN algorithm, for network attack detection and defense. When the hazard data is 60 under the test sample, the detection accuracy of KNN, SVM, and SVM-KNN is 86.88%, 89.51%, and 96.75% respectively; when the hazard data is 60 under the experimental sample, the detection accuracy of KNN, SVM, and SVM-KNN is 82.70%, 83.88%, and 89.26% respectively.response.

Development of oat malt production technology using plasma-chemically activated aqueous solutions

The object of the study was oat malt production. The result of the presented study is the development of a technology for gluten-free oat malt production using cold plasma-treated technological solutions. The material for the study was oat grain. The main technological task is to obtain high-quality oat malt, which, in turn, will be suitable for producing gluten-free beverages and highly nutritious foods. The expediency of using cold plasma-treated aqueous solutions as an activator of the oat malting process and an effective disinfectant of the technological process and the finished product (oat malt) was experimentally proved. It was confirmed that using cold plasma-treated aqueous solutions can accelerate the oat germination process: the germination energy increased by 6–14 %; germination capacity by 2–10 %. The moisture content of oats when moistened increases by 6–37 %. Analysis of the amylolytic enzymatic activity showed a dynamic increase of 17.6 %. The result was the breakdown of carbohydrates. Thus, the amount of starch decreased by 2.4 %, fiber did not undergo significant changes, and the content of simple sugars increased by 2.3 %. The proteolytic activity of oat malt increased by 18.1 %. An increase in the amount of amino acids in the experimental samples was noted. The total amount of amino acids is 793 mg/100 g of product higher than in the control (i.e. by 3 %). There was also an increase in the content of vitamins B (B1, B2), as well as vitamin C, the content of which increased by 5.6 %. Disinfecting properties of cold plasma-treated aqueous solutions in relation to oat malt were noted. The innovative technology of oat malt production can be implemented in the industrial production of gluten-free malts for the brewing industry and for producing gluten-free foods.

Development of kefir product with Bifidobacterium animalis subsp. lactis (BB-12) activated by Sanguisorba officinalis L. extract

This study addresses the challenge of enhancing the activation of Bifidobacterium animalis subsp. lactis (BB-12) to improve the quality and functionality of kefir-based products. The objective was to develop a kefir product enriched with Bifidobacterium animalis subsp. lactis, activated using an extract of Sanguisorba officinalis L., a natural antioxidant, to reduce activation time and boost probiotic efficacy. Two kefir samples were prepared: a control and an experimental product enriched with Bifidobacterium animalis subsp. lactis activated by Sanguisorba officinalis L. at 10⁻⁵ g/cm³ concentration. The experimental sample exhibited a significant increase in Bifidobacterium animalis subsp. lactis count, reaching 9.52 lg(CFU/cm3), within 30 minutes at 37±1 °C, outperforming the control. This rapid activation led to improved physicochemical properties, including enhanced flavor, consistency, and a slower increase in titratable acidity over 14 days, indicating prolonged shelf life. The amino acid profile was enriched, with higher levels of essential amino acids such as valine, leucine, methionine, as well as increased water-soluble vitamins including riboflavin, ascorbic acid, and pantothenic acid. The slower decline in Bifidobacterium animalis subsp. lactis population during storage further highlighted the probiotic stability of the enriched kefir. These findings demonstrate that activating Bifidobacterium animalis subsp. lactis with Sanguisorba officinalis L. extract enhances the nutritional and functional qualities of kefir. This product holds potential for commercial application in the functional food industry, offering consumers a probiotic-rich, nutritionally superior fermented dairy option

The effect of ultrasonic treatment of a cured monolayer formed by three-dimensional printing from a prepreg reinforced with continuous carbon fiber on the resistance to the flow of solid particles

—The effect of ultrasonic processing at resonant frequencies of 22 and 44 kHz of a monolayer formed by three-dimensional printing from prepregs reinforced with continuous carbon fiber on the resistance to the flow of solid particles by imitating it by jet-abrasive treatment is investigated. The increment of the weight of both control and experimental samples in comparison with the initial state was established. It is shown that the force effect of ultrasound in rational modes contributes to a decrease in weight gain by 31.4% when processed at a frequency of 22 kHz and by 9% when processed at a frequency of 44 kHz. The decrease in weight increment is determined by an increase in the density of the monolayer structure after ultrasonic exposure, which leads to an increase in surface hardness in units of Schor-D by 13.5% at a frequency of 22 kHz and by 10% at a frequency of 44 kHz.

Organizational conflict management scale for first division league football players in the Kurdistan Region of Iraq

The importance of the study is to crystallize the concept of organizational conflict management by developing an organization conflict management scale for the first-division club players in the Kurdistan Region who face conflict through the game and matches. In addition, the players of the first-division clubs under study may benefit from this study in developing their level and getting rid of the difficulties of negative competition, and thus ascending and raising their performance and achieving the best results which helps them increase scientific knowledge of organizational conflict management in matches and its effects and how to confront and address it in a positive way. This study aims to develop an organizational conflict management scale for the first-division club players in the Kurdistan Region, using the descriptive approach in the survey method for its suitability with the nature of the study. Eighteen clubs from the first- division league of Kurdistan Region- Iraq season (2021-2022) were selected as a community of the study. The building sample (292) players were selected in a simple random way (draw) from the basic research sample. The sample of the exploratory experiment was chosen (27) players from Koya sports Club using the intentional method. Mean (M), standard deviation (SD), percentage (%), simple correlation and Spearman's correlation coefficient were used as a statistical analysis by using (SPSS). Attaining the organizational conflict management scale that the researcher developed through the scientific procedures followed in building the standards, The number of paragraphs of the scale was (46) paragraphs and the distribution of paragraphs on (4) areas.

Investigation of aerodynamic processes in porous materials based on triply periodic minimal surfaces

RELEVANCE: The relevance of this work lies in the study of new porous materials for use in compact, highly efficient heat exchange devices. PURPOSE: To investigate the hydro-aerodynamic properties of flows passing through porous inserts based on triply periodic minimal surface (TPMS) topologies. To develop a methodology for studying porous materials with ordered structures. To identify potentially suitable TPMS-based porous materials for application in heat exchange equipment. METHODS: Numerical (CFD) and experimental methods were used to address the research objectives. Ansys Fluent 2019 R3 software was utilized for numerical modeling. Experimental samples for the physical experiments conducted on the VENT-08-7LR-01 laboratory setup were fabricated using SLA additive technologies. The porosity of the samples ranged from 0.73 to 0.89. The experiment was conducted with inlet velocities ranging from 0.3 to 4.5 m/s. RESULTS: New empirical dependencies of pressure drop on flow velocity were obtained for inserts based on the surfaces: Primitive (P), Fischer Koch S (FKS), Neovius (N), and Schoen's I-WP (IWP). The airflow through the N structure showed the highest pressure drop, while the P structure had 8 times less pressure drop at the same velocity. Stagnation zones, which can negatively impact heat transfer, were identified in the porous inserts. Changes in local flow velocity in the porous inserts were determined to correlate with the insert's transparency. CONCLUSION: The research results can be used for designing cooling systems with TPMS-based ribbing. Based on the analysis of the velocity vector field distribution and pressure drops, the FKS and IWP structures have potential applications in heat exchange equipment.

Estimation of the soluble solid content of citrus based on the fractional-order derivative and optimal band combination algorithm.

The soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, a total of 261 experimental samples, including 70 Murcott, 91 Clementine, and 100 Navel orange, were divided into prediction and validation sets in a 7:3 ratio. After obtaining the reflection spectra and SSCs, SNV-FOD (Standard Normal Variate-Fractional-Order Derivative) was used to process the spectra, and the optimal band combination algorithm was introduced to select SSC-sensitive bands. Then, the obtained optimal dual-band combination was input into eight regression models for comparison, and the best performing models stacked ensemble models was selected. Finally, the H-ELR (HyperOpt-optimized ensemble learning regression) model, optimized using a Bayesian function, was applied for the effective estimation of SSC for three common citrus varieties in Guangxi, Murcott, Clementine, and Navel oranges. The results show that (1) the SNV-FOD preprocessing method proposed in this study improved the correlation coefficient with the SSC from 0.546 to 0.836 compared to that of the original spectrum, (2) the optimal dual-band combination (969 and 1069nm) constructed by integrating the differential index and 1.2-order derivative yielded the most accurate results (RPD=2.13), and (3) the H-ELR model, based on HyperOpt optimization, achieved good estimated performance (RPD=2.46). PRACTICAL APPLICATION: This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application.

A deep transfer learning model for online monitoring of surface roughness in milling with variable parameters

Surface roughness is crucial for the functional and aesthetic properties of mechanical components and must be carefully controlled during machining. However, predicting it under varying machining parameters is challenging due to limited experimental data and fluctuating factors like tool wear and vibration. This study develops a deep transfer learning model that incorporates the correlation alignment method and tool wear to enhance model generalization and reduce data acquisition costs. It utilizes multi-sensor data and the ResNet18 with a convolutional block attention module (CBAM-ResNet) to extract features with improved generalization and accuracy for monitoring milled surface roughness under varying conditions. The performance of the model is evaluated from different perspectives. First, the proposed model achieves high accuracy with fewer than 500 experimental samples from the target domain by using the CORAL module in the CBAM-ResNet model. This demonstrates the model's strong generalization capability by minimizing second-order statistical discrepancies between different datasets. Second, ablation experiments reveal a significant reduction in test error when incorporating CORAL and tool wear, highlighting their contributions to improved model generalization. Integrating tool wear information significantly reduces test errors across various transfer conditions, as it reflects changes in cutting force, vibration, and built-up edge formation. Third, comparisons with existing deep transfer models further emphasize the advantages of the proposed approach in improving model generalization. In summary, the proposed surface roughness model, which incorporates tool wear and multi-sensor signal features as inputs and employs feature transfer and CBAM-ResNet, demonstrates superior generalization and accuracy across various machining parameters.

Linear Cross-Entropy Certification of Quantum Computational Advantage in Gaussian Boson Sampling

Validation of quantum advantage claims in the context of Gaussian boson sampling (GBS) currently relies on providing evidence that the experimental samples genuinely follow their corresponding ground truth, i.e., the theoretical model of the experiment that includes all the possible losses that the experimenters can account for. This approach to verification has an important drawback: it is necessary to assume that the ground truth distributions are computationally hard to sample, that is, that they are sufficiently close to the distribution of the ideal, lossless experiment, for which there is evidence that sampling, either exactly or approximately, is a computationally hard task. This assumption, which cannot be easily confirmed, opens the door to classical algorithms that exploit the noise in the ground truth to efficiently simulate the experiments, thus undermining any quantum advantage claim. In this work, we argue that one can avoid this issue by validating GBS implementations using their corresponding ideal distributions directly. We explain how to use a modified version of the linear cross-entropy, a quantity that we call the LXE score, to find reference values that help us assess how close a given GBS implementation is to its corresponding ideal model. Finally, we analytically compute the score that would be obtained by a lossless GBS implementation. Published by the American Physical Society 2024