Spectral Properties Research Articles

Adjuvant radiation improves survival outcomes in adrenocortical carcinoma: A population-based study.

The aim of this study was to evaluate the clinical benefits and outcomes of adjuvant radiation therapy on adrenocortical carcinoma (ACC) patients. All patients with ACC that were reported between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results database. A forward-stepwise Cox proportional hazards regression was used to identify independent risk factors. A new nomogram was created to predict overall and cancer-free survival probabilities at 1, 3, and 5 years for ACC patients. The concordance index, the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis were used to validate the accuracy and reliability of the model. A total of 426 ACC patients were enrolled in this study, of which 84 (19.7%) cases underwent adjuvant radiation therapy (RT). Six factors (age, T stage, type of surgery, radiation, and bone and liver metastases) were significantly related to overall survival (OS) (P < .05). Five factors (T stage, type of surgery, radiation, and bone and liver metastases) were significantly related to cancer-specific survival (CSS) (P < .05). A nomogram for OS and CSS was constructed. The area under the receiver operating characteristic curve values for 1-, 3-, and 5-year OS were 0.85, 0.818, and 0.814, respectively, and for CSS, they were 0.839, 0.803, and 0.796, respectively. C-indices were 0.756 and 0.749 for OS and CSS, respectively, indicating that the nomograms for OS and CSS had satisfactory discriminative power. A good consistency between the observed and the predicted survival was found in the calibration curves, and the nomogram has a good net clinical benefit, which is presented in the decision curve analysis curves. Our study demonstrates that adjuvant RT can produce a significant improvement in survival outcomes for ACC patients and suggests that adjuvant RT should routinely be applied in ACC patients.

Spectral properties of $$\mathcal {C}$$-graphs

Spectral properties of $$\mathcal {C}$$-graphs

Transient cortical beta-frequency oscillations associated with contextual novelty in high density mouse EEG

Beta-frequency oscillations (20–30 Hz) are prominent in both human and rodent electroencephalogram (EEG) recordings. Discrete epochs of beta (or Beta2) oscillations are prevalent in the hippocampus and other brain areas during exploration of novel environments. However, little is known about the spatial distribution and temporal relationships of beta oscillations across the cortex in response to novel contexts. To investigate this, mice fitted with 30-channel EEG-style multi-electrode arrays underwent a single recording session in a novel environment. While changes to spectral properties of cortical oscillations were minimal, there was a profound increase in the rate of beta bursts during the initial part of the recording session, when the environment was most novel. This was true across the cortex but most notable in recording channels situated above the retrosplenial cortex. Additionally, novelty was associated with greater connectivity between retrosplenial areas and the rest of the cortex, specifically in the beta frequency range. However, it was also found that the cortex in general, is highly modulated by environmental novelty. This data further suggests the retrosplenial cortex is an important hub for distinguishing environmental context and highlights the diversity of functions for beta oscillations across the brain, which can be observed using high-density EEG.

Diurnal and daily fluctuations in levels of the urinary oxidative stress marker 8-hydroxyguanosine in spot urine samples

BackgroundUrinary 8-hydroxyguanosine (8-OHGuo) levels serve as a biomarker for oxidative stress and hydroxyl radical–induced RNA damage. Evaluating the diurnal and daily fluctuations in urinary 8-OHGuo excretion levels is essential for understanding its implications. However, research in this area remains limited. In this study, we aim to investigate the diurnal and daily fluctuations in 8-OHGuo levels as well as the factors that influence these variations, using spot urine samples.MethodsUrine samples were collected from seven healthy participants during each urination from the time of awakening until 24:00 h to evaluate diurnal variations. To assess daily fluctuations, urine samples were collected from 18 healthy participants at the time of awakening for 23 consecutive days. The urinary 8-OHGuo levels were measured using an HPLC-ECD method.ResultsNo significant variations were observed in the diurnal levels of urinary 8-OHGuo among non-smokers. Conversely, the daily variation of 8-OHGuo in the urine of the smoker was significant, with a coefficient of variation of 18.71%. Each individual maintained a characteristic value despite some diurnal fluctuations. Furthermore, the daily levels of 8-OHGuo exhibited a range of variations influenced by lifestyle factors, including mental state, sleep duration, smoking, menstrual cycle, and dietary habits.ConclusionAs a specific marker of RNA oxidation, 8-OHGuo provides unique insights distinct from those provided by the widely used DNA oxidation marker 8-hydroxydeoxyguanosine as an indicator of oxidative stress. Urinary 8-OHGuo could serve as a valuable biomarker for managing and preventing oxidative stress–related diseases, provided that the specific range of daily variations is established. The high daily variation in urinary 8-OHGuo levels necessitates the use of multiple samples to accurately determine individual levels. However, further research with large sample sizes will help to validate these findings.

Investigating the factors influencing teachers’ adoption of gamification strategies in higher education: insights from Korthagen’s onion model

PurposeInterest in gamification as a relevant and innovative pedagogical tool has increased in recent years among academics and educators. While considerable literature has focused on the effectiveness of gamified learning, little is known about the motivations behind its adoption. By examining teachers’ perspectives, which are often overlooked, we investigated the factors influencing teachers’ adoption of gamification strategies.Design/methodology/approachWe adopted Korthagen’s onion model as the main theoretical lens to identify the influencing factors. A qualitative study involving interviews with 15 French higher education teachers was conducted to achieve our purpose.FindingsThe results were organized within the six layers of the onion model, through which we demonstrated the factors influencing teachers’ adoption of gamification. The institutional environment and teachers’ internal characteristics (beliefs, values, identity and mission) were identified as the determinants of gamification use.Originality/valueUnlike most gamification studies, this research contributes to the literature by considering teachers’ perspectives and integrating the Korthagen model into the educational setting. We also propose an integrative theoretical approach that uncovers synergies between the Korthagen model and the educational leadership literature.

Upstreamness and downstreamness in input–output analysis from local and aggregate information

Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.

New highly efficient phosphorescent manganese halides as green and blue emitters.

Three new manganese compounds on 5-(pyridin-2-yl)-3-phenyl-1,2,4-triazole (L) basis (HL)4[MnBr4]2·H2O (1), (HL)2[MnCl4] (2) and [MnL2Cl2]·H2O (3) have been synthesized and characterized in terms of their structure, photoluminescence (PL), and electroluminescence (EL) properties. Compounds 1 and 2 exhibit bright green luminescence (λem ≈ 550 nm) with high quantum yields of 75.1 and 71.2%, and 3 emits unusual blue emission at 438 nm with a quantum yield of 43.9%. Analysis of the spectral properties of complexes 1 and 2 indicates that luminescence is determined by the transfer of excitation energy from the triazole cation to the manganese cation. Electroluminescent devices based on complexes 1 and 3 show high brightness values of 3033 and 5230 Cd m-2, respectively.

Epigynum auritum-Derived Near-Infrared Carbon Dots for Bioimaging and Antimicrobial Applications.

The use of biomass feedstocks for producing high-value-added chemicals is gaining significant attention in the academic community. In this study, near-infrared carbon dots (NIR-CDs) with antimicrobial and bioimaging functions were prepared from Epigynum auritum branches and leaves using a novel green synthesis approach. The spectral properties of the synthesized NIR-CDs were characterized by ultraviolet-visible (UV-Vis) absorption and fluorescence spectroscopy. The crystal structures of the NIR-CDs were further characterized by high-resolution transmission electron microscopy (HR-TEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR), and X-ray diffraction (XRD). The NIR-CDs exhibited minimal toxicity, excellent biocompatibility, and high penetrability in both in vivo and in vitro environments, making them ideal luminescent probes for bioimaging applications. Moreover, the antimicrobial activity of NIR-CDs was tested against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), showing significant bacterial growth inhibition. The antimicrobial effect is likely attributed to the NIR-CDs disrupting the cell membrane integrity, leading to the leakage of the intracellular contents. Therefore, NIR-CDs hold promise as fluorescent bioimaging probes and antimicrobial agents.

Quantification of denitrification rate in shallow groundwater using the single-well, push-pull test technique.

Denitrification has been identified as a significant nitrate attenuation process in groundwater systems. Hence, accurate quantification of denitrification rates is consequently important for the better understanding and assessment of nitrate contamination of groundwater systems. There are, however, few studies that have investigated quantification of shallow groundwater denitrification rates using different analytical approaches or assuming different kinetic reaction models. In this study, we assessed different analytical approaches (reactant versus product) and kinetic reaction (zero-order and first-order) models analysing observations from a single-well, push-pull tests to quantify denitrification rates in shallow groundwater at two sites in the Manawatū River catchment, Lower North Island of New Zealand. Shallow groundwater denitrification rates analysed using the measurements of denitrification reactant (nitrate reduction) and zero-order kinetic models were quantified at 0.42-1.07mgNL-1h-1 and 0.05-0.12mgNL-1h-1 at the Palmerston North (PNR) and Woodville (WDV) sites, respectively. However, using first-order kinetic models, the denitrification rates were quantified at 0.03-0.09h-1 and 0.002-0.012h-1 at the PNR and WDV sites, respectively. These denitrification rates based on the measurements of denitrification reactant (nitrate reduction) were quantified significantly higher (6 to 60 times) than the rates estimated using the measurements of denitrification product (nitrous oxide production). However, the denitrification rate quantified based on the nitrate reduction may provide representative value of denitrification characteristics of shallow groundwater systems. This is more so when lacking practical methods to quantify all nitrogen species (i.e., total N, organic N, nitrite, nitrate, ammoniacal N, nitrous oxide, nitric oxide, and nitrogen gas) in a push-pull test. While estimates of denitrification rates also differed depending on the kinetic model used, both a zero-order and a first-order model appear to be valid to analyse and estimate denitrification rate from push-pull tests. However, a discrepancy in estimates of denitrification rates using either reactant or product and using zero- or first-order kinetics models may have implications in assessment of nitrate transport and transformation in groundwater systems. This necessitates further research and analysis for appropriate measurements and representation of spatial and temporal variability in denitrification characteristics of the shallow groundwater system.

Simultaneous Multiband Photometry of the Early Optical Afterglow of GRB 240825A with Mephisto

Abstract Gamma-ray bursts (GRBs) are the most luminous transients in the Universe. The interaction of the relativistic jet with the circumburst medium produces an afterglow and generates multiwavelength emission. In this work, we present simultaneous multiband photometry of GRB 240825A with the Multi-channel Photometric Survey Telescope (Mephisto) and analyze its temporal and spectral properties. The measurement began 128 s after the GRB trigger and continued until the fourth day, when the afterglow essentially diminished and the measured brightness was close to that of the host galaxy. Based on the multiband light curves in the uvgriz bands, we find that the optical flux density satisfies F ν,obs ∝ t −1.34 ν −2.48 with a spectral index of 2.48, much larger than those of most other GRBs. To reconcile the measured much softer spectral energy distribution (SED) with that predicted by the standard afterglow model, an extra host-galaxy extinction of E B−V ∼ (0.37–0.57) mag is required. We interpreted this excess as arising from a dense circumburst medium. We further find that the SED of the optical afterglow hardened as the afterglow decayed and the color excess E B−V decreased ∼0.26 mag from 100 to 3000 s after the GRB trigger. Finally, we analyze the properties of the host galaxy of GRB 240825A based on data from the Sloan Digital Sky Survey, the Pan-STARRS survey, and the HSC-SSP survey. For a host redshift of z = 0.659, the stellar mass and star formation rate of the host galaxy are estimated to be log ( M * / M ⊙ ) = 10 . 0 − 0.3 + 0.3 and log ( SFR / M ⊙ yr − 1 ) = 0 . 6 − 3.3 + 0.8 , respectively, pointing to a gas-rich, star-forming, medium-size galaxy.

Deciphering the Photophysical Properties of Nonplanar Heterocyclic Compounds in Different Polarity Environments.

Nonplanar (butterfly-shaped) phenothiazine (PTZ) and its derivative's (M-PTZ) photophysical and spectral properties have been tuned by varying the solvents and their polarity and investigated employing spectroscopic techniques such as UV-Vis, steady-state and time-resolved fluorescence, and TDDFT calculations. The UV-Vis absorption studies and TDDFT calculations reveal two distinct bands for both compounds: a strong π-π* transition at shorter wavelengths and a weaker n-π* transition, which displays a little bathochromic shift in polar solvents. The detailed emission studies reveal that such dual emission is a result of the photoinduced excited-state conjugation enhancement (ESCE) process. The band at a shorter wavelength corresponds to the locally excited (LE) state, while the longer wavelength band arises from the planarized excited state resulting from ESCE. With the increase in solvent polarity, the LE band is less affected, whereas strong positive solvatochromism is observed for the ESCE band. As the solvent polarity increases, the ESCE band demonstrates significant positive solvatochromism, while emission intensity decreases with higher solvent polarity, suggesting stabilization of the excited state. The biexponential decay of fluorescence lifetimes further corroborates the dual emission behavior of PTZ and M-PTZ. PTZ exhibits a higher photoluminescence quantum yield (PLQY) than that observed for M-PTZ, and the solvent viscosity influences the PLQY, indicating that nonradiative decay is activated during the planarization of the excited state, also known as excited-state conjugation enhancement. Furthermore, the (time-dependent) density functional theory (TD) DFT calculations performed to understand the geometrical parameters and the electronic transitions of these model molecules further corroborate experimental findings. These findings underscore the significant influence of solvent polarity and molecular structure on the dual emission and excited-state dynamics of PTZ and M-PTZ, which eventually hold substantial implications for advanced photophysical applications.

C 3 -GAN+: C omplex- C ondition- C ontrolled G enerative A dversarial N etworks with Enhanced Embedding

Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g. , new bus routes, rainfall intensity and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. Recently, we proposed a C omplex- C ondition- C ontrolled G enerative A dversarial N etwork ( \(C^{3}\) -GAN), which tackles both of the challenges and solves the urban traffic estimation problem under various complex conditions by adding a fixed embedding network and an inference network on top of the standard conditional GAN model. The randomly chosen embedding network transforms the complex conditions to latent vectors, and the inference network enhances the connections between the embedded vectors and the traffic data. However, a randomly chosen embedding network cannot always successfully extract features of complex urban conditions, which indicates \(C^{3}\) -GAN is unable to uniquely map different urban conditions to proper latent distributions. Thus, \(C^{3}\) -GAN would fail in certain traffic estimation tasks. Besides, \(C^{3}\) -GAN is hard to train due to vanishing gradients and mode collapse problems. To address these issues, in this paper, we extend our prior work by introducing a new deep generative model, namely, \(C^{3}\) -GAN+, which significantly improves the estimation performance and model stability. \(C^{3}\) -GAN+ has new objective, architecture and training algorithm. The new objective applies Wasserstein loss to the conditional generation case to encourage stable training. Shared convolutional layers between the discriminator and the inference network help to capture spatial dependencies of traffic more efficiently, part of the shared convolutional layers are used to update the embedding network periodically aiming to encourage good representation and avoid model divergence. Extensive experiments on real-world datasets demonstrate that our \(C^{3}\) -GAN+ produces high-quality traffic estimations and outperforms state-of-the-art baseline methods.

Generalization of Gaussian Mersenne numbers and their new families

In this article, we present the generalized Gaussian Mersenne numbers with arbitrary initial values and discuss two particular cases, namely, Gaussian Mersenne and Gaussian Mersenne-Lucas numbers. We present their various algebraic properties such as Binet’s formula, negatively subscripted elements, Catalans’s, Cassini’s, and d’Ocagne’s identities, partial sum, binomial sum, generating and exponential generating functions, etc. In addition, we study a new generalized sequence arising from the explicit expression made with the characteristic roots and refer to them as the k-generalized Gaussian Mersenne numbers. We present various identities of them and show their connections with the generalized Gaussian Mersenne numbers.

Malignant Risk Assessment of Cystic-Solid Thyroid Nodules Based on Multimodal Ultrasound Features: A Systematic Review and Meta-Analysis

Background: The malignant risk of cystic-solid thyroid nodules may be underestimated in the ultrasound assessment. Objective: This systematic review and meta-analysis aimed to evaluate the value of multimodal ultrasound characteristics in the malignant risk assessment of cystic-solid thyroid nodules. Methods: We conducted a comprehensive search of PubMed, Web of Science, and Cochrane Library databases for studies depicting the ultrasound characteristics of cystic-solid thyroid nodules published prior to October 2023. The Review Manager 5.4 software was utilized to evaluate the ultrasound features suggestive of malignancy and to determine their sensitivity and specificity. Additionally, the Sata 12.0 software was utilized to construct summary receiver operating characteristic curves (SROC), estimate the area under the curve (AUC), and evaluate any potential publication bias. Results: This review included 16 studies comprising 5,655 cystic-solid thyroid nodules. Nine ultrasound features were identified as risk factors for tumor malignancy. Among the ultrasound features, microcalcification in the solid portion, heterogeneous hypoenhancement on Contrast-Enhanced Ultrasound (CEUS), and sharp angles in the solid portion exhibited higher malignant predictive value in cystic-solid thyroid nodules, with AUC values of 0.91, 0.84, and 0.81, respectively. Conclusion: Our findings indicate that microcalcification and sharp angles in the solid part of the nodule, along with heterogeneous hypoenhancement on contrast-enhanced ultrasound (CEUS), can better predict malignant cystic-solid thyroid nodules. The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42024602893).

Control of Nanoparticle Size of Intrinsically Fluorescent PET (Polyethylene Terephthalate) Particles Produced Through Nanoprecipitation.

Plastics are widely produced due to their stability and ease of manufacturing, but many of them quickly become a waste, breaking down into microplastics and nanoplastics. While methods for the identification and characterization of plastic particles are well consolidated, the small size of nanoplastics presents challenges for their detection and analysis. Furthermore, due to the difficulty of identifying nanoplastics, analytical studies concerning their effect on cells and a comprehensive spectroscopic characterization are still lacking. In this paper, we overcome this obstacle by synthesizing and characterizing, for the first time, PET nanoparticles with specific, stable dimensions through a top-down approach. Using hexafluoroisopropanol-chloroform as a solvent, we prepared PET solutions at various concentrations and analyzed their spectral properties over time. Our results show that PET aggregates into nanoparticles, the quantity of which increases with concentration. These findings provide crucial insights for the detection of nanoplastics in environmental samples through fluorescence measurements and can potentially be used to produce stable PET nanoparticles to evaluate their cytotoxicity.

Effect of Tm3+ concentration on structure, spectral properties and cross relaxation of Tm:CaF2 crystal

Through the investigation of spectral characteristics, the evolution of cluster proportions and the cross-relaxation process in Tm:CaF2 crystals as a function of Tm doping concentration has been clarified. A quantitative model has been established to describe the relationship between these factors. At low concentrations (0.01-0.5 at.%), Tm ions rapidly form clusters with negligible cross-relaxation. As the concentration increases from 0.5 at.% to 2 at.%, the cluster ratio surpasses 90%, and its growth rate decelerates, while both the cross-relaxation rate and efficiency significantly increase. When the Tm concentration exceeds 2 at.%, the cluster ratio and cross-relaxation efficiency nearly reach 100% and remain stable. These findings provide valuable insights for the application of Tm:CaF2 in high-energy pulsed laser systems.

Weakly supervised pathological differentiation of primary central nervous system lymphoma and glioblastoma on multi-site whole slide images.

Differentiating primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is crucial because their prognosis and treatment differ substantially. Manual examination of their histological characteristics is considered the golden standard in clinical diagnosis. However, this process is tedious and time-consuming and might lead to misdiagnosis caused by morphological similarity between their histology and tumor heterogeneity. Existing research focuses on radiological differentiation, which mostly uses multi-parametric magnetic resonance imaging. By contrast, we investigate the pathological differentiation between the two types of tumors using whole slide images (WSIs) of postoperative formalin-fixed paraffin-embedded samples. To learn the specific and intrinsic histological feature representations from the WSI patches, a self-supervised feature extractor is trained. Then, the patch representations are fused by feeding into a weakly supervised multiple-instance learning model for the WSI classification. We validate our approach on 134 PCNSL and 526 GBM cases collected from three hospitals. We also investigate the effect of feature extraction on the final prediction by comparing the performance of applying the feature extractors trained on the PCNSL/GBM slides from specific institutions, multi-site PCNSL/GBM slides, and large-scale histopathological images. Different feature extractors perform comparably with the overall area under the receiver operating characteristic curve value exceeding 85% for each dataset and close to 95% for the combined multi-site dataset. Using the institution-specific feature extractors generally obtains the best overall prediction with both of the PCNSL and GBM classification accuracies reaching 80% for each dataset. The excellent classification performance suggests that our approach can be used as an assistant tool to reduce the pathologists' workload by providing an accurate and objective second diagnosis. Moreover, the discriminant regions indicated by the generated attention heatmap improve the model interpretability and provide additional diagnostic information.

Spectral properties of Levy Rosenzweig-Porter model via supersymmetric approach

Spectral properties of Levy Rosenzweig-Porter model via supersymmetric approach

Applications of Random Matrix Theory in Neuroscience and Neural Network Analysis: Unraveling High-Dimensional Connectivity

Random Matrix Theory (RMT) has emerged as a powerful mathematical framework to study large-scale, high-dimensional data across various scientific fields. In neuroscience, it provides unparalleled tools to elucidate the functional connectivity and dynamic properties underlying neural activity. By modeling connectivity matrices, synaptic weight distributions, and neural correlation structures through random matrices, researchers can dissect large-scale neural systems’ stability, detect phase transitions in neural firing patterns, and explain emergent phenomena such as criticality in brain networks. Similarly, in the realm of artificial neural networks, RMT guides the understanding of weight initialization, spectral properties of the network’s Jacobian matrices, generalization, and optimization landscapes. This article provides a comprehensive introduction to Random Matrix Theory’s fundamental principles, their application in contemporary neuroscience, and their intersection with modern neural network paradigms. We introduce the theoretical underpinnings and methodological steps for applying RMT to neuroscience data, including sequentially numbered didactic equations. We further present an illustrative code snippet to generate and visualize RMT-based graphs, discussing the results and connecting them to cutting-edge research. In a detailed discussion, we explore the benefits, limitations, and future directions of RMT methodologies in neuroscience, including ongoing challenges and emerging frontiers. This work aims to equip researchers with a robust theoretical and methodological framework, encouraging the integration of RMT into the neuroscience toolbox and catalyzing further breakthroughs in understanding complex neural systems.

Effect of Different Static Load Test Methods on the Performance of Combined Post-Grouted Piles: A Case Study in the Dongting Lake Area

To investigate the effect of combined end-and-shaft post-grouting on the vertical load-bearing performance of bridge-bored piles in the Dongting Lake area of Hunan, two post-grouted piles were subjected to bi-directional O-cell and top-down load tests before and after combined end-and-shaft grouting, based on the Wushi to Yiyang Expressway project. A comparative analysis was conducted on the bearing capacity, deformation characteristics, and load transfer behavior of the piles before and after grouting. This study also examined the conversion coefficient γ values of different soil layers obtained from the bi-directional O-cell test for bearing capacity calculations. Additionally, the characteristic values of the end bearing capacity, obtained from the bi-directional O-cell and top-down load tests, were compared with the values calculated using the relevant formulas in the current standards, which validated the accuracy of existing regulations and traditional loading methods. The results indicate that the stress distribution along the pile shaft differed between the two test methods. In the bi-directional O-cell test, the side resistance developed from the end to the head, while in the top-down load test, it developed from the head to the end. After combined post-grouting, the ultimate bearing capacity of the piles significantly increased, with side resistance increasing by up to 81.03% and end resistance by up to 105.66%. The conversion coefficients for the side resistance in silty sand and gravel before and after grouting are 0.86 and 0.80 and 0.81 and 0.69, respectively. The characteristic values of the end bearing capacity, as measured by the bi-directional O-cell and top-down load tests, were substantially higher than those calculated using the current highway bridge and culvert standards, showing increases of 133.63% and 86.15%, respectively. These findings suggest that the current standard formulas are overly conservative. Additionally, the measured values from the top-down load test may underestimate the actual bearing capacity of piles in engineering projects. Therefore, it is recommended that future pile foundation designs incorporate both bi-directional O-cell testing and combined post-grouting techniques to optimize design solutions.