Intensity Variations Research Articles

Space-Time Variability of Maximum Daily Rainfall in Piura River Basin in Peru Related to El Niño Occurrence

This study analyzes hydrometeorological data (1950–2023) to examine the signatures of El Niño and La Niña events and assess their impact on rainfall distribution in the Piura Region, Peru. Using data from 23 stations, high-resolution gridded rainfall datasets (PISCO), and oceanic–atmospheric indices we investigated the frequency, intensity, and spatial variability of these events in the Piura River Basin (PRB). Return periods for very strong El Niño and La Niña events are 25 and 19 years, respectively, compared to 2 years for neutral conditions. Over the past 30 years, the recurrence of Coastal El Niño has significantly increased. This increased frequency contributes to the global rise in El Niño events, reducing the return period for very strong events from 5.2 to 3.4 years. This rise correlates with an increase in maximum daily precipitation across the basin centered in the middle PRB during El Niño years. Future rainfall projections, based on 20 CMIP6 GCMs under SSP2-4.5 and SSP5-8.5 scenarios, suggest continued intensification of rainfall events. These findings highlight the necessity of incorporating El Niño variability into infrastructure design, water resource management, and climate adaptation strategies to mitigate the impacts of these increasingly frequent and severe events in the PRB.

GAFchromic EBT film lateral resolution and contrast reproduction in the UV-blue range

The sensitivity of radiochromic films to UV-blue light is increasingly considered for light dosimetry purposes, owing to their bidimensional detection capabilities and ease of use. While film response to radiation intensity has been widely investigated by commercial scanners, spatial resolution studies remain scarce, especially for small field-of-view applications. These are of growing interest due to the antimicrobial or photo-bio-stimulating effects of UV-blue light sources in in vitro, ex vivo and in vivo models, where precise knowledge of irradiation conditions with adequate spatial resolution is crucial. In this study, we report the spatial lateral resolution and contrast reproduction of GAFchromic EBT2 and EBT3 models. Upon film irradiation by a 405 nm laser source or 365 nm LED, a confocal microscope setup was employed to read the film response at 405, 470, 488, 532 and 570 nm wavelengths, with radiant exposure of 10–70 J/cm2. The measured lateral resolution ranged from 8 to 33 μm. The film capability to reproduce contrast across various spatial frequencies (4–14 lines/mm) was evaluated using modulation transfer function analysis with irradiation performed at 365 nm and 405 nm, revealing a pronounced dependency on both radiant exposure and reading wavelength. These results confirm the film capacity to detect and resolve light intensity variability with a ~ 10 μm resolution, with notable applications in micro-beam profiling and light dosimetry.

Simulation and attribution analysis of agricultural nitrogen non-point source pollution in Huang-Huai-Hai Plain based on the InVEST model

The study of agricultural non-point source pollution (ANNSP) patterns and driving mechanisms is crucial for regional ecological management. This paper evaluates the sources of ANNSP in the Huang-Huai-Hai Plain for the years 2000, 2010, and 2020 using the source list method. Additionally, nitrogen source data for the InVEST model were supplemented and adjusted, and the nitrogen output load and proportions derived in farmland from both methods were compared. The temporal and spatial distribution of nitrogen emission intensity was analyzed based on InVEST model outputs. Key source areas were identified through hotspot analysis, and spatial driving factors were examined using geodetector. Key findings reveal: 1) While the source list method estimated a higher nitrogen output load, both methods concurred on the proportion of agricultural nitrogen. The main sources of nitrogen input were overuse of fertilizer and livestock breeding. 2) Over this period, nitrogen emission intensity declined, exhibiting a distinct spatial pattern of “northwest low, southeast high.” 3) The extent of critical source areas underwent a dynamic shift, initially contracting before expanding. 4) Rainfall emerged as the primary driver of spatial variability in agricultural TN emission intensity. These insights offer critical reference points for achieving efficient ANNSP management in the Huang-Huai-Hai Plain.

Comparative analysis of vision transformers and fine-tuned transfer learning models for brain tumor classification

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Hot pepper wilt intensity, management practices, and pathogenic variability of Fusarium oxysporum f. sp. capsici (FOC) isolates in northwest Ethiopia

Hot pepper (Capsicum annuum L.) is essential in Ethiopian agriculture but is increasingly threatened by Fusarium wilt, caused by Fusarium oxysporum f. sp. capsici, which severely affects yields and farmer livelihoods in Northwest Ethiopia. This study assessed wilt incidence, severity, and farmer practices across four districts in West Gojjam Zone (Womberma, Burie Zuria, Jabi Tehinan, and Dembecha Zuria). Surveys from 128 farmers and tests on 67 fungal isolates revealed that Burie Zuria faced the highest disease incidence (56.6%) and severity (52.5%), while Womberma had the lowest rates. The most pathogenic isolate, 4WBG1, showed 100% incidence and high severity (82.5%) and 100% AUDPC value. Agronomic practices like seed sourcing and crop rotation influenced disease control, yet only 23.4% of farmers used chemical or cultural controls, with 11% applying Mancozeb. These findings highlight the need for improved management strategies and resistant varieties to safeguard hot pepper production.

A Review on Lung and Colon Combine Cancer Detection using ML and DL Techniques

The detection of lung and colon cancer is a critical challenge in medical diagnosis, and machine learning (ML) and deep learning (DL) techniques are increasingly being used to enhance accuracy and efficiency. This review focuses on the integration of ML and DL methods for the combined detection of lung and colon cancer, emphasizing their strengths, limitations, and future potential. The motivation behind this study is to address the growing demand for accurate and early detection of these cancers, which significantly impacts treatment outcomes. Current methods often struggle with feature complexity, image variability, and computational intensity, which limit their real-world applicability. The aim is to consolidate various ML and DL techniques that have been employed for this purpose, highlighting how hybrid models can improve detection rates. The objective of this review is to provide a comprehensive analysis of different methodologies, their datasets, pre-processing techniques, feature extraction methods, and evaluation parameters. This review also explores recent advancements, such as transfer learning combined with fine-tuning techniques, which can further optimize performance in cancer detection. The findings suggest that while current methods show promise, further improvements in model generalization, interpretability, and computational efficiency are required to overcome existing limitations and expand clinical use.

Long-term persistency of a strong non-dipole field in the South Atlantic

Earth’s magnetic field exhibits a dominant dipole morphology. Notwithstanding, significant deviations from the dipole are evident today, particularly the South Atlantic Anomaly (SAA), characterized by anomalously low-field intensity and high directional variability, diminishing the field’s shielding effect. To assess the persistence of SAA-like features over multimillion-year scales, we combine paleomagnetic data from Trindade Island (20°30’S, 29°22’W) with an evaluation of paleosecular variation (PSV) over the past 10 Myr. We employ synthetic models to explore how the position and intensity of magnetic flux patches at the core-mantle boundary can influence the long-term field behavior. Here we present results that reveal anomalous field signatures in the South Atlantic and the Atlantic-Pacific hemispheric asymmetries are enduring features, likely linked to a bottom-up control of PSV by the inner core’s heterogeneities but with contributions from mantle anomalies in the long-time range.

SERS calibration substrate with a silent region internal standard for reliable simultaneous detection of multiple antibiotics in water

As the extensive use of antibiotics has led to the rapid spread of antibiotic resistance, there is an urgent need for quantitative assessment of antibiotic residues in the environment. Surface-enhanced Raman spectroscopy (SERS) has emerged as a rapid and cost-effective detection method, but it suffers from the high variability in signal intensities, its quantitative detection remains challenging. Herein, we have developed a SERS calibration substrate with a silent region internal standard, enabling simultaneous and reliable quantitative detection of three commonly antibiotics of penicillin potassium (PP), tetracycline hydrochloride (TCH) and levofloxacin (LEV). The calibration substrate is made by assembling Au @ 4-mercaptobenzonitrile (4-MBN) @ SiO2 on silicon wafer. The chemically-inert silica shell allows the substrate to remain SERS active for more than 24 weeks in the air. The vC ≡ N of 4-MBN in the silent region of 1800–2800 cm−1 provides an effective reference for correcting Raman signal fluctuations. The relative SERS intensity by normalizing to the internal standard (IS) of vC ≡ N shows a linear response against to the logarithmic concentration with a correlation coefficient of 0.997, 0.976, 0.998 and a limit of detection (LOD) of 26.9, 28.2, 2.4 nM for PP, TCH and LEV, respectively. Furthermore, simultaneous detection and principal component analysis (PCA) of these antibiotics in lake water with a concentration range of 1–100 mg/L can achieve a sensitivity and specificity of 100 %. This novel quantitative technique of using this SERS calibration substrate shows promises as a high-throughput platform for multiple trace antibiotics analysis.

A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation

Hydrological studies depend heavily on environmental variables, such as precipitation and resulting runoff, which exhibit highly seasonal and intermittent behaviour in semiarid basins. In these basins, the use of traditional methods to adjust biases in time series projections can lead to inaccurate results regarding the impacts of climate change. This study introduces a non-stationary bias adjustment methodology (NS) specifically designed for environmental variables characterized by sporadic events and substantial intensity variability, such as precipitation. The methodology involves establishing a probability threshold to adapt the occurrence of precipitation events and utilizes a quantile mapping method based on a non-stationary theoretical and parametric distribution to adjust biases associated with precipitation intensity. The NS method is applied to daily precipitation projections from seven regional climatic models under the RCP 8.5 scenario spanning 2006–2100, alongside historical concurrent data from 1970 to 2005. The present method is compared to the widely used quantile delta mapping approach (QDM), revealing significant differences in performance related to the distribution of precipitation events throughout the year and the behaviour of mean and extreme intensity values. Both approaches show reliable performance, with root mean square errors between the empirical distribution functions of the corrected hindcast time series and the observations being lower than or close to 1 mm for the percentiles smaller than 50th. The error increases with the percentiles, particularly at stations located at higher altitudes. In these locations, QDM doubles or triples the error obtained with the non-stationary approach for percentiles higher than 75th, reaching up to 34 mm. The proposed methodology demonstrates promising potential in reducing uncertainties associated with systematic errors in inter-annual precipitation variability. It is a first step to jointly apply a similar approach to all involved driving variables. Such methods are key to assessing hydrological responses and associated impacts in semi-arid mountainous basins all around the Globe.

The impact of COVID-19 restrictions on road traffic and noise pollution of the urban street, a case study from Krakow, Poland

The study determined the impact of restrictions introduced by the Ministry of Health in connection with the COVID-19 pandemic on traffic and sound pressure levels in the university building and its immediate vicinity. Mickiewicza Street was selected for the study. It is one of the city’s busiest streets, located relatively close to the old historic center, and is the main artery connecting the north and south of the city. There are residential buildings on this street, but primarily offices and institutions, as well as schools and universities. Noise near the street and in buildings located relatively close to the road can be a serious problem in places where peace is required (e.g., libraries, reading rooms, university halls). Therefore, the acoustic comfort needed in these places may be disturbed, and the perception of knowledge acquisition may be limited. The aim of the work was achieved through measurements and analysis of street traffic intensity and sound pressure levels, taking into account pandemic restrictions. Then, statistical analyses were performed, which showed significant variability in traffic intensity and sound pressure values in individual periods of the pandemic. The pandemic’s beginning was mainly characterized by a significant decrease in the above data, deviating from the norm. In the pre-pandemic period, an average of 47,733 vehicles passed through the street daily, and the median sound pressure was Leq.OUT = 62.0 dBA outside the building and Leq.INS = 44.0 dBA inside. During the greatest restrictions, a period called “lockdown,” the number of cars driving decreased a little to an average of 44,153 per day, but there were days with 10,000–20,000 cars passing. In turn, noise decreased to 59.9 dBA for Leq.OUT and 43.2 dBA for Leq.INS. Between the first and second waves of infections, traffic was comparable to the post-pandemic period (an average of 69,027 cars per day), and noise also increased to Leq.OUT = 64–65 dBA and close to Leq.INS = 47.5 dBA. Studies have shown that noise on Mickiewicza Street is mainly caused by traffic (correlation coefficient: r = 0.92 for Leq.OUT and r = 0.86 for Leq.INS). The sound pressure level is high outside (Leq.OUT = 63.9 dBA) but relatively low inside the building (Leq.INS = 47.0 dBA) and should not negatively affect university staff and students (Leq.WH < LWH.acc). Based on the analyses, three stages show society’s approach to the COVID-19 pandemic: (1) fear of the COVID-19 pandemic; (2) relaxation after restrictions; (3) getting used to the COVID-19 pandemic. These periods did not correlate with the development of the pandemic or the number of people infected and dead.

Hourly electricity CO[formula omitted] intensity profiles based on the real operation of large-scale natural gas combined cycle cogeneration plants

The decarbonization of energy systems is a major challenge that requires complex cross-sectoral strategies that need to be supported by energy modeling. As many technologies rely on electricity, an accurate estimation of its CO2 intensity is of utmost importance for the reliability of modeling results. When electricity is generated from fossil sources, its CO2 intensity depends on several parameters. This research paper presents an hourly calculation of the CO2 intensity of power generation from natural gas combined cycles, based on real data from several years of operation of three plants. As these plants are also operated in combined heat and power mode, two alternative allocation methods are compared. The results confirm the variability of CO2 intensity based on the different operation strategies of the plants and the share of heat and electricity generated. The hourly analysis shows average values in the range of 230–250 gCO2/kWh in winter, rising to around 330–370 gCO2/kWh in summer. The real CO2 intensity profiles presented in this paper can be integrated into energy planning models to improve their ability to estimate the potential benefits of different decarbonization solutions by including the effect of the operational profiles over the day and the year.

Physiological responses and agreement between two traditional lunging protocols for physical fitness training in untrained ponies

Lunging is a training technique commonly performed in a round pen or on a lunge line. This study investigated the effort intensity, autonomic responses, and method agreement in applying different lunging protocols to untrained ponies. Sixteen untrained ponies (aged 13.6 ± 6.3 years and weighing 358 ± 47.4 kg) were studied. Each pony underwent the same research-focused lunging program on a lunge line and, subsequently, in a round pen with a two-day interval between sessions. The heart rate variability (HRV) and effort intensity, indicated as a percentage of maximum heart rate (%HRmax), were determined pre-lunging, during lunging at distinct gaits, and at 30 min intervals for 120 min post-lunging. The correlation and method agreement between the two lunging methods were analysed. Ponies ran faster and covered longer distances during exercise on a lunge line than in a round pen. The effort intensity during cantering reached moderate levels (75.10 ± 2.39 % HRmax) with occasional high-intensity levels (88.07 ± 1.29 % HRmax) via both lunging methods. The HRV reached a minimum during cantering and returned to the baseline 120 min post-lunging. The HRV parameters (SDNN, RMSSD, LF, HF, SD1, and SD2) were strongly correlated (rp ≥ 0.97 and p < 0.001 for all) with a large correlation effect size (R2 > 0.85) and excellent agreement (average differences were within mean ± 1.96 SD) between the two lunging methods. In conclusion, lunging can elicit high physiological responses in untrained ponies, and both lunging methods demonstrate excellent agreement and yield similar physiological responses in these ponies.

Influence of velocity pulse directivity on seismic response of cross-fault bridges

Pulse-type ground motions have special destructive effects on structures, and directivity is one of the fundamental characteristics of velocity pulses. In this paper, the impact of the velocity pulse directivity of seismic motions on both sides of the fault on the seismic response of fault-crossing bridges was quantified using the original records from the Chi-Chi earthquake in Taiwan. First, an automatic baseline correction method was applied to restore the permanent ground displacement, and the direction corresponding to the strongest pulse energy on both sides of the fault was identified based on continuous wavelet transformation. On this basis, the variability of the peak intensity of ground motions on the hanging wall and footwall in different directions, as well as the differences in pulse characteristics between the strongest pulse component and the two initial horizontal components, were analysed. Finally, a typical 5 × 30 m continuous girder bridge was considered as the research object to reveal the influence of the velocity pulse directivity on the seismic response of key parts on both sides of the fault. The results showed that the variability in the peak ground-motion intensity on the hanging wall of the fault was higher than that on the footwall. The displacement variability was up to 863 %, velocity was 262 %, and acceleration was 124 %. The pulse characteristics of the strongest pulse component were stronger than those of the parallel and vertical fault components, and its acceleration, velocity, and displacement response spectra were larger. The amplification effects of the velocity pulse directivity on the transverse bending moment, longitudinal bending moment, and torque of the pier bottom reached 1.3, 1.1, and 1.6, respectively. However, the amplification effect of the velocity pulse directivity on the relative displacement of the pier top, displacement of the main beam, and displacement of the bearing was more than 1.6 times, which was more significant than the internal force of the pier bottom.

Reconstruction of temperature and hydroclimate in Serling Co (Central Tibet) since the last deglaciation

Understanding the past climatic conditions and their possible driving mechanism can contribute to better constrain future climate projection, and to provide potential insights into the disputed Holocene temperature variations. In this study, we quantitatively reconstruct the mean annual air temperature (MAAT) of Serling Co since 17.3 cal kyr BP using a novel terrestrial thermometer (ring index of OH-GDGTs, RI-OH), and reconstructed the hydroclimatic evolutionary history in combination with n-alkanes and their associated proxies. Our temperature records indicate that Serling Co experienced a period of reduced temperatures during the Younger Dryas event, approximately 6 °C cooler than the present conditions. Subsequently, there was a rapid warming phase, leading to peak temperatures in the early Holocene, roughly 5.5 °C warmer than the modern MAAT, followed by a protracted cooling trend during the subsequent middle and late Holocene. The temperature and hydroclimate trends at Serling Co exhibit a synchronized evolutionary pattern since the last deglaciation, featuring an optimal hydrothermal combination in the early Holocene. This period witnessed the peak productivity of terrestrial and aquatic ecosystems, followed by a gradual decline. Summer insolation emerges as a primary controlling factor for temperature variations, with Indian Summer Monsoon intensity and internal climate system variability exerting dominance over atmospheric moisture and precipitation variations in the region.

Changes in Tsushima warm current intensity variability in the last 400 kyr and its linkages to the Japan Sea surface productivity

Changes in Tsushima warm current intensity variability in the last 400 kyr and its linkages to the Japan Sea surface productivity

Numerical investigation on the behavior of thin concrete shells subjected to nonuniform and asymmetrical loads

AbstractConcrete shells, designed with near optimal shapes for uniform gravity loads, can be subjected to nonmembrane effects caused by nonuniform and asymmetrical loads. In this paper, a numerical study on the effect of normative loads on a thin triangular concrete shell with three supports is presented. Two actions were considered, wind and snow, which present high spatial variability in both intensity and direction, assuming the values established in Eurocode 1 for the worst possible location, in Portugal. Linear, nonlinear and stability analyses were performed using the ABAQUS software. Results suggest that the wind action, but especially the exceptional snow load in drifted arrangement, produce significant bending moments, leading to decompression, and eventually to the development of stresses above the tensile strength of current concrete classes. Therefore, the production of these shells by assembling prefabricated un‐reinforced modules, requires the use of ultrahigh‐performance fiber reinforced concrete, combined with prestressing.

Genetic Variability and Flowering Intensity of Sugarcane Genotypes Affected by Agro-Climatic Conditions of Alexandria

Genetic Variability and Flowering Intensity of Sugarcane Genotypes Affected by Agro-Climatic Conditions of Alexandria

Detection of High-Temperature Gas Leaks in Pipelines Using Schlieren Visualization

This paper investigates the application of Schlieren flow visualization for detecting leaks in pipelines carrying high-temperature fluids. Two experimental setups were constructed: one with a 25 mm PTFE tube featuring a 2 mm diameter perforation, and another with a 100 mm diameter pipe insulated with an aluminum jacket and featuring a 12 mm leak gap. A single-mirror-off-axis Schlieren system, employing a 150 mm diameter parabolic mirror, was used to visualize the leaks. The temperature of the leaking air varied between 20 and 100 °C, while the ambient temperature was maintained at 14 °C. To quantify the leaks, the coefficient of variation for pixel intensity within the leak region was calculated. Results showed that for the PTFE tube, leaks became detectable when the temperature difference exceeded 34 °C, with the coefficient of variation surpassing 0.1. However, in the insulated pipe, detecting clear leak patterns was challenging. This research demonstrates the potential of Schlieren visualization as a valuable tool in enhancing pipeline leak detection.

Environmental changes in the Fleuve Manche paleoriver drainage system (Western Europe) linked to North Atlantic sub-millennial climate variability across Heinrich Stadial 1: Palynological evidence from the Bay of Biscay

Marine microfossils (dinoflagellate cysts and planktonic foraminifera) and geochemical (XRF-Ti/Ca)-based climatic records from a core (MD13–3438) located off the Fleuve Manche (FM) paleo-mouth have revealed that sustained warm summer sea surface temperatures (SSTs) during sub-millennial climate changes within HS1 (∼18–14.7 ka) may have played a key role in the FM regime related to the European Ice Sheet (EIS) melting rate. In this study, we have analyzed the MD13–3438 pollen content over the HS1 at a mean resolution of ∼50 years to test whether vegetation-based air temperatures were coupled to SSTs face to this rapid climate variability. First, our results highlight two major phases of pollen sources at site MD13–3438, preventing the pollen record to be interpreted as a continuous record of the evolution of vegetation and climate occupying a single watershed across HS1. The first phase, i.e. the HS1-a interval (∼18–16.8 ka), is marked by strong occurrences of boreal pollen taxa (especially Picea-Abies). Considering their spatial distribution and the coalescence of the British and Scandinavian ice sheets into the North Sea during the Last Glacial Maximum, these taxa probably originated from the North European Plain, i.e., eastern FM tributaries (east of the Rhine River), where cool-humid conditions generally prevailed. Then, the second phase, i.e. the HS1-b interval (∼16.8–14.7 ka BP), is characterized by a deceleration of the EIS retreat and the drop of boreal pollen values at site MD13–3438 further signing a less influence of the upstream FM drainage system and thus a better characterization of pollen sources related with western FM tributaries. Superimposed to these two HS1 main phases, pollen fluctuations are concomitant with sub-millennial variability in the EIS deglaciation intensity. During the early HS1 (HS1-a), we discuss two short-term increases in the ratio between deciduous trees (Quercus-Corylus-Alnus) and herbaceous plants (Plantago-Amaranthaceae-Artemisia). These events are coeval with phases of increasing dinocyst-based SST seasonality (i.e. through summer SST amplification). We associate these events with lower contribution of the upstream FM catchment as well as, possibly, atmospheric warming and regional sea-level positive oscillations. The HS1-b is composed of three main phases that appear more influenced by the downstream FM drainage system. HS1-b1 (16.8–16.3 ka BP) corresponds to the driest and coldest conditions west of the Rhine River. HS1-b2 (16.3–15.5 ka BP) is coeval with large arrivals of iceberg from the Hudson strait in the Bay of Biscay and thus likely to a major sea-level positive oscillation associated with a phase of FM valley reworking. HS1-b3 (15.5–14.7 ka BP) corresponds to persistent arid conditions that preceded the subsequent more humid conditions recorded from 14.7 ka BP at the start of the Bölling-Alleröd.

Pain mechanistic networks: the development using supervised multivariate data analysis and implications for chronic pain

Abstract Chronic postoperative pain is present in approximately 20% of patients undergoing total knee arthroplasty. Studies indicate that pain mechanisms are associated with development and maintenance of chronic postoperative pain. The current study assessed pain sensitivity, inflammation, microRNAs, and psychological factors and combined these in a network to describe chronic postoperative pain. This study involved 75 patients with and without chronic postoperative pain after total knee arthroplasty. Clinical pain intensity, Oxford Knee Score, and pain catastrophizing were assessed as clinical parameters. Quantitative sensory testing was assessed to evaluate pain sensitivity and microRNAs, and inflammatory markers were likewise analyzed. Supervised multivariate data analysis with “Data Integration Analysis for Biomarker Discovery” using Latent cOmponents (DIABLO) was used to describe the chronic postoperative pain intensity. Two DIABLO models were constructed by dividing the patients into 3 groups or 2 defined by clinical pain intensities. Data Integration Analysis for Biomarker discovery using Latent cOmponents model explained chronic postoperative pain and identified factors involved in pain mechanistic networks among assessments included in the analysis. Developing models of 3 or 2 patient groups using the assessments and the networks could explain 81% and 69% of the variability in clinical postoperative pain intensity. The reduction of the number of parameters stabilized the models and reduced the explanatory value to 69% and 51%. This is the first study to use the DIABLO model for chronic postoperative pain and to demonstrate how different pain mechanisms form a pain mechanistic network. The complex model explained 81% of the variability of clinical pain intensity, whereas the less complex model explained 51% of the variability of clinical pain intensity.