Modeling Techniques Research Articles

Monitoring soil nutrients using machine learning based on UAV hyperspectral remote sensing

ABSTRACT Unmanned aerial vehicles (UAV) are rapidly evolving experimental platforms that play an important role in remote sensing. In this study, we investigated a machine learning method for monitoring soil nutrient content using UAV hyperspectral remote sensing. We employed machine learning techniques for feature extraction and soil hyperspectral information modelling. In contrast to traditional mathematical transformation methods, we adopted a combination of random forest and differential evolution algorithms to rank the weights of individual hyperspectral data, thereby obtaining a series of spectral feature subsets for soil organic matter, total nitrogen and available phosphorus and potassium. Furthermore, the analytic hierarchy process was used for weight analysis, and the characteristic bands of the four soil nutrients were successfully extracted. Next, a quantitative inversion model based on a back-propagation (BP) neural network was established to estimate soil nutrient content, with determination coefficients higher than 0.7 and 0.6 for the modelling and verification sets, respectively. The relative percent difference values were greater than 2, among which the highest was for available potassium, with determination coefficients of 0.95 and 0.84 for the modelling and verification sets, respectively. In addition, visualization distribution maps of soil nutrients were obtained by combining the BP model and original reflectance hyperspectral images, and the comparisons of content histograms showed a relatively consistent distribution between the sampling and inversion points. The results verified the effectiveness of the combined machine learning method for large-scale and high-precision monitoring and visualization of soil nutrient contents.

Monitoring soil nutrients using machine learning based on UAV hyperspectral remote sensing

ABSTRACT Unmanned aerial vehicles (UAV) are rapidly evolving experimental platforms that play an important role in remote sensing. In this study, we investigated a machine learning method for monitoring soil nutrient content using UAV hyperspectral remote sensing. We employed machine learning techniques for feature extraction and soil hyperspectral information modelling. In contrast to traditional mathematical transformation methods, we adopted a combination of random forest and differential evolution algorithms to rank the weights of individual hyperspectral data, thereby obtaining a series of spectral feature subsets for soil organic matter, total nitrogen and available phosphorus and potassium. Furthermore, the analytic hierarchy process was used for weight analysis, and the characteristic bands of the four soil nutrients were successfully extracted. Next, a quantitative inversion model based on a back-propagation (BP) neural network was established to estimate soil nutrient content, with determination coefficients higher than 0.7 and 0.6 for the modelling and verification sets, respectively. The relative percent difference values were greater than 2, among which the highest was for available potassium, with determination coefficients of 0.95 and 0.84 for the modelling and verification sets, respectively. In addition, visualization distribution maps of soil nutrients were obtained by combining the BP model and original reflectance hyperspectral images, and the comparisons of content histograms showed a relatively consistent distribution between the sampling and inversion points. The results verified the effectiveness of the combined machine learning method for large-scale and high-precision monitoring and visualization of soil nutrient contents.

Computational 3D Models of Fe2+/3+-Aβ1-42 Complexes Associated with Alzheimer's Disease.

The interaction between iron and amyloid-beta (Aβ) peptides has received significant attention in Alzheimer's disease (AD) research due to its potential implications in developing this pathology. However, the coordination preferences of iron and Aβ1-42 have not been thoroughly investigated or remain unknown. This study employs a computational protocol that combines homology modeling techniques with quantum mechanics (DTF-xTB) calculations to build and evaluate several 3D models of Fe2+/3+-Aβ1-42. Our results reveal well-defined complexes for both the metal and peptide moieties, and we discuss the molecular interactions stabilizing these complexes by elucidating the coordinating environments and binding preferences. These proposed models offer valuable insights into the role of iron in Alzheimer's disease (AD) pathology.

Unraveling the post-adoptive hybrid learning experience: perspectives from information systems

Purpose Hybrid learning has become a reality due to the onslaught of the COVID-19 pandemic. Students world over had to switch to this new learning format. This study aims to analyze the impact of innovation attributes of the Unified Theory of Acceptance and User Technology (UTAUT2) model and community of inquiry (COI) framework on the hybrid learning experience and the continued intention for it. Design/methodology/approach Using a cross-sectional research design, the study has adapted a scale from past studies and collected data using purposive sampling from the student community. The research has used the structural equation modeling technique using SMART-PLS to study the hypothesized relationships. Findings The study’s findings are that performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, teaching presence, cognitive presence and social presence influence hybrid learning experience and continued intention. Further hybrid learning experience mediates the continued intention. Practical implications This study has several academic and practical implications for improving the hybrid learning experience. Various stakeholders can get insights on improving the user’s desire to pursue learning in a hybrid environment. Originality/value Hybrid learner experience is an upcoming area of research and yet unexplored in India as well as in other countries. A new hybrid experience model was developed by extending the UTAUT2 to include the COI framework and learner experience frameworks.

Assessing the Impact of Climate Change on Glacial Lake Outburst Flood (GLOF) in Eastern Hindu Kush Region Using Integrated Geo-Statistical and Spatial Hydrological Approach

Glacier retreat, a major impact of climate change that continues to occur in many parts of the world, continues to increase the risk of glacial lake outburst floods (GLOFs) in northern Pakistan. The rapid melting of glaciers in the mountains of Northern Pakistan, including the Hindu Kush, the Himalayas and the Karakoram, the rapid melting of glacier has led to the formation of 3044 glacial lakes, with 33 identified as particularly vulnerable to GLOFs. This study uses remote sensing and geographic information systems (GIS) methods for mapping and representing GLOFs. Based on the observational data of lake area, volume, and depth, empirical equations are developed through statistical methods. Only two lakes, Chitral-GL2 and Swat-G31, are classified as lakes with high potential for GLOF. Through modeling techniques using HEC-RAS and HEC-GeoRAS spatial hydrological models integrated with GIS remote sensing, the spatial extent and depth of inundations under different lake volumes are assessed. The analysis reveals that a total area of 20.56 km2 is susceptible to submersion by GLOFs, with Chitral-GL2 flooding area of 14.80 km2 and Swat-GL31 5.79 km2. Different land types are impacted by critical water depths, with built-up and agricultural lands 2.7 km2 totally, and barren lands 8.93 km2 under different flood depths ranging from less than 5 m to over 15 m.

Assessment of groundwater fluoride and human health effects in a hard rock province of south India: Implications from Pollution Index Model (PIM) and GeographicalInformationSystem (GIS) techniques.

This research examines whetherthe groundwater in the Sivakasi Region of South India is suitable for consumption, and assesses the possible health hazards for various age demographics including infants, children, teenagers, and adults. A totalof 77 groundwater samples were gathered, covering a total area of 580km2 and analyzed for major and minor ions. The hydrogen ion concentration (pH) of the samples indicates neutral to marginally alkaline. The total dissolved solids (TDS) fluctuate from 255 to 2701mg/l and electrical conductivity varies from 364 to 3540µS/cm. A wide range of fluoride concentration was detected (0.1 to 3.2mg/l) with nearly 38% groundwater samples surpassing the proposed limit (1.5 mg/l) suggestedby the World Health Organization in 2017. Gibbs plot analysis suggested that most of thesamples were influenced by geogenic factors, primarily rock weathering in this region. Correlation analysis showed that most of the samples were impacted by both natural and human sources. The pollution index of groundwater (PIG) fluctuated from 0.67 to 2.60 with approximately 30% and 53% of samples falling into insignificant and low pollution categories, respectively. Furthermore, 10% and 5% of total samples were characterized as moderate and high pollution levels, and 2% asvery high pollution category. Spatial analysis using GIS revealed that 440.63km2 were within safe fluoride levels according to theWHO standards, while 139.32km2 were identified as risk zone. The principal component analysis (PCA1) showedstrong positive loadings on EC (0.994), TDS (0.905), Mg2+ (0.910), Cl- (0.903) and HCO3- (0.923) indicating rock water interaction. PCA2 accounts the high positive factor loading on HCO3- (0.864) indicating ion exchange and mineral leaching. The PCA1 and PCA2 indicated that variables such as mineral leaching and rock water interaction are themajor mechanisms contributing to the chemical signatures in groundwater, which may support for theelevated fluoride levels in certain areas. Risk assessments, including Hazard Quotient results showed that 71%, 61% 38%, and 34% of groundwater samples exceededthe permissible THI limit (THI > 1) for infants, children, teenagers, and adults, respectively. The study recommends implementing measures such as denitrification, defluorination, rainwater harvesting, and improved sanitation infrastructure to enhance the health conditions in the study region. Additionally, it suggests introducing educational programs in rural areas to create awarenessabout the health dangers due to consumptionof water with high fluoride levels.

Study and Analysis of Methods and Techniques for Control-command Applications

After an introduction to systemic analysis and the OOPP (oriented objectives project planning) method, we present a systems analysis methodology based on the OOPP method applied to various modeling methods and techniques: we particularly cite Petri Nets (RdP), Logic Fuzzy and the UML object-oriented modeling language This methodology made it possible to describe the exchanges of information between the different components of a system and to define the different parameters involved in the constitution of the models.

Determine Intraday Trading Currency's Trend Framework Evidence From Machine Learning Techniques

The purpose of this study is to determine the intraday hourly trading trends of currencies using predictive modeling techniques. The study encompasses two distinct intraday time intervals of 30 minutes and 1 hour, analyzing currencies from 8 different countries. It incorporates the use of wavelets MODWT to identify trends and noise in intraday currency analysis. Three predictive models, namely Support Vector Regression, Recurrent Neural Network, and Long Short-Term Memory, are applied to relative time series data to predict intraday trading currency trends. The study reveals significant noise presence in three currencies based on MODWT analysis. Additionally, it demonstrates that deep learning techniques, such as LSTM, outperform traditional machine learning approaches in accurately predicting intraday currency trends. This study contributes substantially to the theoretical understanding of international finance and provides practical insights for real-time problem-solving in currency markets. Further, this research adds to the discourse on leveraging sophisticated analytical methods within the domain of business intelligence to enhance decision-making processes in organizations operating within dynamic and complex financial environments.

Exploring the horizon of AI development: Navigating constraints of chips and power in the technological landscape

With the proliferation of AI technology, machine learning has emerged as a cornerstone of AI systems, facilitating pattern recognition and decision-making through robust data analysis. This encompasses various learning paradigms such as supervised, unsupervised, and reinforcement learning, all of which are indispensable for the advancement of artificial intelligence. Nevertheless, the development of AI necessitates substantial computational resources, with specialized chips serving as the linchpin, particularly in demanding tasks such as deep learning. Dedicated chip development, exemplified by GPUs and TPUs, plays a pivotal role in enhancing the performance of AI systems, notwithstanding challenges related to costs and market monopolies. Moreover, AI systems require significant power support, especially during the training of large-scale models. To address these challenges, this paper reviews the existing literature on modeling techniques aimed at enhancing the efficiency of machine learning and reducing energy consumption. This review encompasses optimal algorithm design, hardware optimization, and spatial modeling. Through the implementation of these approaches, the challenges posed by resource constraints in machine learning scenarios can be effectively mitigated, thereby fostering the continued development and application of AI technology.

3D electromagnetic assessment of bended CORC® cables

Conductor on round core (CORC®) cables have emerged as a leading contender in high-temperature superconducting (HTS) cable designs, offering exceptional performance with current densities surpassing 300 A/mm2 and the ability to withstand high axial tensile and compressive strain. Despite their remarkable properties, optimizing CORC® cables remains a challenge, particularly in accurately estimating their AC losses under real-world conditions, which necessitates advanced numerical modeling techniques. Building upon recent advancements in simulating straight CORC® cables, where Bean’s-like current profiles were observed across the actual thickness of wound superconducting tapes, we introduce a tailored computational approach to enhance the processing speed of three-dimensional (3D) finite element models of wound HTS tapes. This tailored approach is specifically designed to address the complexities of bent CORC® cables, which exhibit helicoidal winding and are subjected to varying mechanical strain. We focus on analyzing their electromagnetic performance by transitioning from idealized straight-former designs to more realistic scenarios where cable-formers are bent to accommodate flexible cable routing or coil configurations. Our simulations consider a typical cable design comprising three 4 mm-wide SuperPower tapes (SCS4050) with a twist pitch of 40 mm. We demonstrate the capability to accurately model the full electromagnetic behavior of bent CORC® cables without the reduction of degrees of freedom, providing valuable insights into their performance under bending conditions. Our findings contribute to the ongoing optimization of CORC® cable designs for a wide range of practical applications in high-current and high-magnetic field environments.

Customer churn data analysis using data mining

Churn is the phenomenon of a customer terminating their relationship or subscription with a company or service provider[1]. It represents the rate at which customers stop using a company's product or service during a specific period of time. Attrition rate is an important metric for businesses as it directly impacts revenue, growth and customer retention. In the context of the churn data set, the churn label indicates whether a customer has been churn. Lost customers are those who decide to stop buying the company's products. On the other hand, non-churn customers are those who continue to buy the company's products. Understanding customer churn is critical for businesses to identify the patterns, factors, and metrics that lead to customer churn. By analyzing churn behavior and its associated characteristics, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer churn. Predictive modeling techniques can also be applied to anticipate and proactively address potential customer churn, enabling companies to take proactive steps to retain at-risk customers[2].

Research and implementation of three-dimensional modeling techniques for Chinese dragon

This paper delves into a new trend in the field of computer science, which involves a shift from complex code editing techniques of the past towards more intuitive visual software applications. Under this trend, modeling and rendering techniques have undergone long-term development and continuous iteration, becoming indispensable tools across various domains. Using the construction of a Chinese dragon model as a case study, this paper showcases the application and development of this new trend through a detailed description of the selected software tools' characteristics, classification of production steps, and the modeling process. Special emphasis was placed on shaping key details such as the dragon's body contour, scales, eyes, etc., during the dragon model construction process, as well as how these details are manifested in software applications. Software selection and utilization were carefully aligned with practical needs and technical features to ensure the efficiency and accuracy of the modeling process. In-depth research into the modeling process revealed the potential and advantages of this new technology, receiving widespread acclaim and recognition.

Shape- and frequency-dependent self-excited forces emulation for the aero-structural design of bluff deck bridges

The shape design and optimization of bluff decks prone to aeroelastic phenomena require emulating the fluid-structure interaction parameters as a function of the body shape and the oscillation frequency. This is particularly relevant for long- and medium-span bridges equipped with single-box decks that are far from being considered streamlined and for other girder typologies such as traditional truss decks and modern twin- and multi-box decks. The success of aero-structural design frameworks, which are inherently iterative, relies on the efficient and accurate numerical evaluation of the wind-induced responses. This study proposes emulating the fluid-structure interaction parameters of bluff decks using surrogate modeling techniques to integrate them into aero-structural optimization frameworks. The surrogate is trained with data extracted from forced-vibration CFD simulations of a typical single-box girder to emulate the values of the flutter derivatives as a function of the deck shape and reduced velocity. The focus is on deck configurations ranging from streamlined to bluff cross-sections and on low reduced velocities to capture eventual aerodynamic nonlinearities. The girder cross-section geometry is tailored based on its buffeting performance. This design tool is fundamental to finding the optimum balance between the structural and aeroelastic requirements that drive the design of bluff deck bridges.

Neural patient-specific 3D-2D registration in laparoscopic liver resection.

Augmented reality guidance in laparoscopic liver resection requires the registration of a preoperative 3D model to the intraoperative 2D image. However, 3D-2D liver registration poses challenges owing to the liver's flexibility, particularly in the limited visibility conditions of laparoscopy. Although promising, the current registration methods are computationally expensive and often necessitate manual initialisation. The first neural model predicting the registration (NM) is proposed, represented as 3D model deformation coefficients, from image landmarks. The strategy consists in training a patient-specific model based on synthetic data generated automatically from the patient's preoperative model. A liver shape modelling technique, which further reduces time complexity, is also proposed. The NM method was evaluated using the target registration error measure, showing an accuracy on par with existing methods, all based on numerical optimisation. Notably, NM runs much faster, offering the possibility of achieving real-time inference, a significant step ahead in this field. The proposed method represents the first neural method for 3D-2D liver registration. Preliminary experimental findings show comparable performance to existing methods, with superior computational efficiency. These results suggest a potential to deeply impact liver registration techniques.

Navigating the Dynamics of Brent Crude Oil Prices: Factors, Trends, and Insights

Accurate Brent Crude Oil price prediction is essential for informed decision-making and risk control in the global energy market. This paper examines the dynamics of Brent Crude Oil prices over the previous 35 years, utilizing predictive modelling techniques to project future prices and analyzing historical trends and relationships with the GDPs of major economies. The statistical methods used in this paper include the moving average and Pearson correlation coefficient. While correlation studies show significant positive links between Brent Crude Oil prices and the GDPs of major economies like China, Saudi Arabia, Europe, Russia, and the United States, historical data analysis reveals large price volatility. Highly accurate price forecasting using LSTM (Long Short-Term Memory) modelling approaches provides detailed risk management and decision-making information in the global energy market. The application of the Neural Network and the ARIMA model shows an increase in the price of Brent crude oil in the next year, 2024. Identifying the importance of the Brent crude oil price forecast and its effect on the international market is highly needed. This paper thus might help to increase economic growth. These results highlight the importance of advanced modelling techniques and economic indicators in managing oil price changes and help make informed decisions in the energy industry.

Advances in the Design of Renewable Energy Power Supply for Rural Health Clinics, Case Studies, and Future Directions

Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally.

Pengaruh Moderasi Gender terhadap Niat Penggunaan Aplikasi Seabank di Kalangan Mahasiswa Yogyakarta dengan Analisis Model UTAUT2

This study aims to analyze the moderating effect of gender on the intention to use the Seabank application among Yogyakarta university students using the Unified Theory Of Acceptance And Use Of Technology 2 (UTAUT2) model. The research method used is a quantitative survey by distributing questionnaires to 110 university students. The data obtained were analyzed using Structural Equation Modeling (SEM) techniques, while the analysis tool used was SmartPLS. The results showed that gender moderation was not significant in influencing the relationship between facilitating conditions, hedonic motivation, price value and habit on behavioral intention to use the Seabank application. This finding suggests that these factors influence usage intention independently of gender. The implication of this study is that digital financial application marketing strategies can be focused on increasing performance expectations and effort without the need to consider gender differences.

Effect of nanodiamonds surface deposition on hydrophilicity, bulk degradation and in-vitro cell adhesion of 3D-printed polycaprolactone scaffolds for bone tissue engineering

This study was designed to deposit nanodiamonds (NDs) on 3D-printed poly-ϵ-caprolactone (PCL) scaffolds and evaluate their effect on the surface topography, hydrophilicity, degradation, andin-vitrocell adhesion compared to untreated PCL scaffolds. The PCL scaffold specimens were 3D-printed by fused deposition modeling (FDM) technique with specific porosity parameters. The 3D-printed specimens' surfaces were modified by NDs deposition followed by oxygen plasma post-treatment using a plasma focus device and a non-thermal atmospheric plasma jet, respectively. Specimens were evaluated through morphological characterization by field emission scanning electron microscope (FESEM), microstructure characterization by Raman spectroscopy, chemical characterization by Fourier transform infrared (FTIR) spectroscopy, hydrophilicity degree by contact angle and water uptake measurements, andin-vitrodegradation measurements (n= 6). In addition,in-vitrobone marrow mesenchymal stem cells adhesion was evaluated quantitatively by confocal microscopy and qualitatively by FESEM at different time intervals after cell seeding (n= 6). The statistical significance level was set atp⩽ 0.05. The FESEM micrographs, the Raman, and FTIR spectra confirmed the successful surface deposition of NDs on scaffold specimens. The NDs treated specimens showed nano-scale features distributed homogeneously across the surface compared to the untreated ones. Also, the NDs treated specimens revealed a statistically significant smaller contact angle (17.45 ± 1.34 degrees), higher water uptake percentage after 24 h immersion in phosphate buffer saline (PBS) (21.56% ± 1.73), and higher degradation rate after six months of immersion in PBS (43.92 ± 0.77%). Moreover, enhanced cell adhesion at all different time intervals was observed in NDs treated specimens with higher nuclei area fraction percentage (69.87 ± 3.97%) compared to the untreated specimens (11.46 ± 1.34%). Surface deposition of NDs with oxygen-containing functional groups on 3D-printed PCL scaffolds increased their hydrophilicity and degradation rate with significant enhancement of thein-vitrocell adhesion compared to untreated PCL scaffolds.

Exploring PCSWMM for Large Mixed Land Use Watershed by Establishing Monitoring Sites to Evaluate Stream Water Quality

Extensive hydrologic and water quality modeling within a watershed benefits from long-term flow and nutrient data sets for appropriate model calibration and validation. However, due to a lack of local water quality data, simpler water quality modeling techniques are generally adopted. In this study, the monitoring sites were established at two different locations to collect hydraulic data for the hydraulic calibration and validation of the model. In addition, water quality samples were collected at eight monitoring sites and analyzed in the lab for various parameters for calibration. This includes total suspended solids (TSS), soluble phosphorus, five-day biochemical oxygen demand (BOD5), and dissolved oxygen (DO). The Personal Computer Storm Water Management Model (PCSWMM) 7.6 software was used to simulate all the pollutant loads using event mean concentrations (EMCs). The performance of the model for streamflow calibration at the two USGS gauging stations was satisfactory, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.51 to 0.54 and coefficients of determination (R2) ranging from 0.71 to 0.72. The model was also validated with the help of historical flow data with NSE values ranging from 0.5 to 0.79, and R2 values ranging from 0.6 to 0.95. The hydraulic calibration also showed acceptable results with reasonable NSE and R2 values. The water quality data recorded at the monitoring stations were then compared with the simulated water quality modeling results. The model reasonably simulated the water quality, which was evaluated through visual inspection using a scatter plot. Our analysis showed that the upstream tributaries, particularly from agricultural areas, were contributing more pollutants than the downstream tributaries. Overall, this study demonstrates that the PCSWMM, which was typically used for modeling urban watersheds, could also be used for modeling larger mixed land use watersheds with reasonable accuracy.

Translational pharmacokinetic-toxicodynamic model of myelosuppression for dose optimization in combination chemotherapy of capecitabine and oxaliplatin from rats to humans.

XELOX therapy, which comprises capecitabine and oxaliplatin, is the standard first-line chemotherapeutic regimen for colorectal cancer. However, its myelosuppressive effects pose challenges for its clinical management. Mathematical modeling combining pharmacokinetics (PK) and toxicodynamics (TD) is a promising approach for optimizing dosing strategies and reducing toxicity. This study aimed to develop a translational PK-TD model using rat data to inform dosing strategies and TD implications in humans. The rats were administered capecitabine, oxaliplatin, or XELOX combination regimen, and PK and TD data were collected. PK parameters were analyzed using sequential compartment analysis, whereas TD responses were assessed using Friberg's semi-physiological model. A toxicity intensity-based nomogram recommends optimal dosing strategies. Translational modeling techniques using the hybrid PK-TD model were employed to predict clinical responses. The PK-TD model successfully predicted the time-course profiles of hematological responses in rats following monotherapy and XELOX combination treatment. Interactive effects on lymphocytopenia were identified with the co-administration of capecitabine and oxaliplatin. A model-based recommended combination of the dose reduction rate for escaping severe lymphocytopenia was proposed as 40% and 60% doses of capecitabine and oxaliplatin, respectively. The current translational model techniques successfully simulated the time-course profiles of blood cell counts with confidence intervals in patients using rat data. Our study provides valuable insights into dose optimization strategies for each individual drug within the XELOX regimen and underscores the potential of translational modeling to improve patient outcomes. In addition to dose determination, these data will lay the groundwork for advancing drug development processes in oncology. Significance Statement This study introduced a novel translational modeling approach rooted in a rat PK-TD model to optimize dosing strategies for the XELOX regimen for colorectal cancer treatment. Our findings highlight the interactive effects on lymphocytopenia and suggest a toxicity intensity-based nomogram for dose reduction, thus advancing precision medicine. This translational modeling paradigm enhances our understanding of drug interactions, offering a tool to tailor dosing, minimize hematological toxicity, and improve therapeutic outcomes in patients undergoing XELOX therapy.