Precise Model Research Articles

Neuroimaging Insights: Structural Changes and Classification in Ménière's Disease.

This study aimed to comprehensively investigate the neuroanatomical alterations associated with idiopathic Ménière's disease (MD) using voxel-based morphometry and surface-based morphometry techniques. The primary objective was to explore nuanced changes in gray matter volume, cortical thickness, fractal dimension, gyrification index, and sulcal depth in MD patients compared with healthy controls (HC). Additionally, we sought to develop a machine learning classification model utilizing these neuroimaging features to effectively discriminate between MD patients and HC. A total of 55 patients diagnosed with unilateral MD and 70 HC were enrolled in this study. Voxel-based morphometry and surface-based morphometry were employed to analyze neuroimaging data and identify structural differences between the two groups. The selected neuroimaging features were used to build a machine learning classification model for distinguishing MD patients from HC. Our analysis revealed significant reductions in gray matter volume in MD patients, particularly in frontal and cingulate gyri. Distinctive patterns of alterations in cortical thickness were observed in brain regions associated with emotional processing and sensory integration. Notably, the machine learning classification model achieved an impressive accuracy of 84% in distinguishing MD patients from HC. The model's precision and recall for MD and HC demonstrated robust performance, resulting in balanced F1-scores. Receiver operating characteristic curve analysis further confirmed the discriminative power of the model, supported by an area under the curve value of 0.92. This comprehensive investigation sheds light on the intricate neuroanatomical alterations in MD. The observed gray matter volume reductions and distinct cortical thickness patterns emphasize the disease's impact on neural structure. The high accuracy of our machine learning classification model underscores its diagnostic potential, providing a promising avenue for identifying MD patients. These findings contribute to our understanding of MD's neural underpinnings and offer insights for further research exploring the functional implications of structural changes.

Characterization of the Ictalurid herpesvirus 1 immediate-early gene ORF24 and its potential role in transcriptional regulation in yeast.

Herpesviruses adhere to a precise temporal expression model in which immediate-early (IE) genes play a crucial role in regulating the viral life cycle. However, there is a lack of functional research on the IE genes in Ictalurid herpesvirus 1 (IcHV-1). In this study, we identified the IcHV-1 ORF24 as an IE gene via a metabolic inhibition assay, and subcellular analysis indicated its predominant localisation in the nucleus. To investigate its function, we performed yeast reporter assays using an ORF24 fusion protein containing the Gal4-BD domain and found that BD-ORF24 was able to activate HIS3/lacZ reporter genes without the Gal4-AD domain. Our findings provide concrete evidence that ORF24 is indeed an IE gene that likely functions as a transcriptional regulator during IcHV-1 infection. This work contributes to our understanding of the molecular mechanisms underlying fish herpesvirus IE gene expression.

A Novel Internal Fixation Design for the Treatment of AO/OTA-31A3.3 Intertrochanteric Fractures: Finite Element Analysis.

AO/OTA 31-A3.3 intertrochanteric fracture is the most unstable type of intertrochanteric fracture, with a high rate of postoperative complications and implant failure. We have designed a new intramedullary fixation, proximal femoral totally bionic nail (PFTBN), for the treatment of A3.3 intertrochanteric fracture. To test its biomechanical performance, we adopted the method of finite element analysis and compared PFTBN with proximal femoral nail antirotation (PFNA) and proximal femoral bionic nail (PFBN, another internal fixation we previously designed for stable intertrochanteric fractures). Mimics, 3-matic, ANSYS, and other software were used to construct a highly precise and realistic 3D digital model of the human femur. An AO/OTA 31-A3.3 intertrochanteric fracture of the femur was constructed according to the 2018 classification of AO/OTA, and then assembled with PFNA, PFBN and PFTBN models, respectively. The stress distribution and displacement distribution of the three groups of constructs were tested under three times the body weight load and one-foot standing configuration. In terms of maximum stress and maximum displacement, the PFTBN group outperforms the PFBN group, and the PFBN group, in turn, surpasses the PFNA group. The maximum stress of PFTBN group was 408.5 Mpa, that of PFBN group was 525.4 MPa, and that of PFNA group was 764.3 Mpa. Comparatively, the maximum stress in the PFTBN group was reduced by 46.6% when contrasted with the PFNA group. Moreover, the stress dispersion within the PFTBN group was more evenly distributed than PFNA group. Regarding maximum displacement, the PFTBN group displayed the least displacement at 5.15 mm, followed by the PFBN group at 7.32 mm, and the PFNA group at 7.73 mm. Notably, the maximum displacement of the PFTBN group was 33.4% less than that observed in the PFNA group. Additionally, the relative displacement between the fragment and implant at the tip of pressure screw or helical blade was 0.22 mm in the PFTBN group, 0.34 mm in the PFBN group, and substantially higher 0.51 mm in the PFNA group. The "lever-reconstruction-balance" theory provides a new perspective for us to understand the mechanical conduction of the proximal femur. Compared with PFNA, in treating A3.3 intertrochanteric fractures PFTBN can better reconstruct the function of lateral wall, restore physiological mechanical conduction, increase postoperative stability, and finally reduce the risk of postoperative cut-out and implant failure. It might be a better alternative for the treatment of A3.3 intertrochanteric fracture.

Establishing equivalent circuits of mounted, high-power VCSEL arrays for iToF cameras.

Solid-state indirect time-of-flight (iToF) cameras are crucial to numerous short-to-medium-range applications, owing to their advantages in terms of system integrability and long-term reliability. However, due to the low light intensity, the sensing range of iToF cameras is generally limited to a few meters, which hinders their wide applications. Further increasing the sensing range requires not only higher-power laser diodes but also well-designed driver circuits, which are based on prior knowledge of the laser diodes' equivalent circuits (ECs). However, experimental studies on ECs of a mounted, high-power vertical-cavity surface-emitting laser (VCSEL) array that comprehensively incorporates all parasitic components, especially parasitic stemming from printed circuit boards (PCBs), remain absent. In this Letter, an 850 nm VCSEL array with a 15.3 W peak power and a 581 MHz bandwidth is fabricated, and more importantly, its EC is experimentally established. Leveraging the accurate EC, a compact iToF camera with a sensing range up to 11.50 m is designed. In addition, a modified precision model is proposed to better evaluate the iToF camera's performance.

Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

The Yeşilırmak River Basin in northern Türkiye is crucial for the region’s water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study’s findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.

#88 Establishment of machine learning-based risk prediction model for acute kidney injury after acute myocardial infarction

Abstract Background and Aims Acute kidney injury (AKI) is a common complication of acute myocardial infarction (AMI) with high morbidity, mortality and lack of effective treatment, so prevention is particularly important. This study intends to use machine learning to establish a precise prediction model for AKI after AMI. Method AMI patients were consecutively collected from July 2011 to December 2016 in Beijing Anzhen Hospital, Capital Medical University. First, SelectFromModel and Lasso regression model were used to select features. A predictive model (Model A) was then created using a multivariate logistic regression model in the training set and validated in the test set. At the same time, the prediction model (Model B) is created by machine learning algorithm in the training set. The process of model construction and evaluation is implemented by Python. The algorithm includes MLP, SVM, KNN and SimpleRNN, from which the model with the largest area under the ROC curve (Model B) is selected and verified in the test set. DeLong method was used to compare the model B with model A to compare whether the area under the ROC curve was better than multivariate logistic regression model and select the best model. Results A total of 6014 AMI patients were included in this study, 70% were randomly selected as the training set, and the remaining 30% were used as the test set. Males comprised 80.5% of the total population with a mean age of 58.4 ± 11.7 years. The incidence of AKI in the overall population was 11.2% (674/6014) (Fig. 1). A total of 12 important characteristics were included in the model, including the number of myocardial infarctions, ST-segment elevation myocardial infarction, ventricular tachycardia, third-degree atrioventricular block, decompensated heart failure at admission, admission serum creatinine value, admission urea nitrogen value, admission CK-MB peak value, whether diuretics were used, maximum daily dose of diuretics, days of diuretic use, and whether statins were used. Logistic regression analysis resulted in an area under the ROC curve of 0.80 (95% CI, 0.76-0.84) in the test set (Fig. 2). The models were constructed by MLP, SVM, KNN and SimpleRNN algorithms, respectively, and validated in the test set to calculate the area under the ROC curve of each model in the test set (Fig. 3), and finally the model constructed by MLP algorithm was selected as model B, and its area under the ROC curve was 0.82 (95% CI, 0.78–0.85). The area under the ROC curve of the two models was compared at p=0.363, but the machine learning algorithm constructed models with higher absolute values (Fig. 4). Conclusion The prediction model of AKI risk after AMI constructed based on machine learning is similar to that constructed by logistic regression analysis in terms of prediction ability, but the model constructed by machine learning algorithm has a better trend, indicating that the machine learning algorithm may improve the prediction ability of prediction model and provide an effective tool for early identification of these high-risk patients in clinical practice, early preventive measures, and reduction of morbidity.

#2886 Machine learning-based assessment of serum creatinine and NT-proBNP levels for predicting mortality in patients with heart failure and kidney injury

Abstract Background and Aims In a population hospitalized for acute heart failure, about 20% are affected by acute kidney injury, a significant independent factor linked to increased mortality both during hospitalization and within one year. While existing research offers insights, crafting a precise model to predict mortality in heart failure cases remains a complex task, primarily due to the difficulties in pinpointing critical determinants and achieving high accuracy. The aim of our research was to assess the efficacy of serum creatinine and NT-proBNP levels as predictors for mortality in heart failure patients, using machine learning. Method In our retrospective cross-sectional study, we examined the records of 1,042 patients hospitalized due to acute heart failure at the Institute of Cardiovascular Diseases of Vojvodina, spanning the period from January 1, 2021, to December 31, 2022. The data was initially cleaned, after which we employed a Random Forest model for our analysis. The dataset was partitioned into a training subset, constituting 70% of the data, and a testing subset, comprising the remaining 30%. To achieve optimal precision, we ran the model 100 times. Key performance metrics, including mean sensitivity, specificity, accuracy, F1 score, Matthew's correlation coefficient (MCC), and the area under the Receiver Operating Characteristic (ROC) curve (AUC), were derived from the model to assess its efficacy. Results Following data cleansing, the dataset encompassed a total of 389 patients, of which 58% were male. The median age in this cohort was 71 years, with an interquartile range (IQR) of 63 to 79 years. Key laboratory findings included a median serum creatinine level of 111 µmol/L (IQR: 85-167 µmol/L) and a median NT-proBNP level of 6,589 pg/mL (IQR: 2,822-15,608 pg/mL). After the partition, the median number of patients in the training set was 272 and the testing set comprised 117 patients. The performance of the Random Forest model was characterized by a sensitivity of 0.530, specificity of 0.916, an overall accuracy of 0.856, and an F1 score of 0.516. The MCC stood at 0.439 with the AUC under the ROC curve being 0.713. Conclusion While the relatively low sensitivity observed in our model could be attributed to the generally lower levels of serum creatinine upon admission and the retrospective nature of the study, which may have influenced the model's ability to identify all true positive cases effectively, serum creatinine and NT-proBNP levels hold promise as effective predictors for mortality in heart failure patients.

Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS

Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human hands. This paper introduces a data-driven approach that provides a learning-based planner for dexterous manipulation employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) fed by data obtained from an analytical manipulation planner. ANFIS captures the complex relationships between inputs and optimal manipulation parameters. Moreover, during a training phase, it is able to fine-tune itself on the basis of its experiences. The proposed planner enables a robot to interact with objects of various shapes, sizes, and material properties while providing an adaptive solution for increasing robotic dexterity. The planner is validated in a real-world environment, applying an Allegro anthropomorphic robotic hand. A link to a video of the experiment is provided in the paper.

A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising

Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating the precise modeling of real noise distributions while neglecting improvements in the noise modeling capabilities of learning models. To address this situation, a novel self-adaptive deformable-convolution-based U-Net (SD-UNet) model is proposed in this paper. Firstly, deformable convolution is employed to tackle noise patterns with different geometries, thus extracting more reliable noise representations. After that, a self-adaptive learning block is proposed to enable the network to automatically select appropriate learning branches for noise with different scales. Finally, a novel structural loss function is leveraged to evaluate the difference between denoised and clean images. The experimental results on multiple public datasets validate the effectiveness of the proposed method.

Machine learning models for predicting early hemorrhage progression in traumatic brain injury.

This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predict the risk of ICH progression using initial CT scans and identify clinical factors associated with this progression. A retrospective analysis of TBI patients between January 2010 and December 2021 was performed, focusing on initial CT evaluations and demographic, comorbid, and medical history data. ICH was categorized into intraparenchymal hemorrhage (IPH), petechial hemorrhage (PH), and subarachnoid hemorrhage (SAH). Within our study cohort, we identified a 22.2% progression rate of ICH among 650 TBI patients. The Random Forest algorithm identified variables such as petechial hemorrhage (PH) and countercoup injury as significant predictors of ICH progression. The XGBoost algorithm, incorporating key variables identified through SHAP values, demonstrated robust performance, achieving an AUC of 0.9. Additionally, an individual risk assessment diagram, utilizing significant SHAP values, visually represented the impact of each variable on the risk of ICH progression, providing personalized risk profiles. This approach, highlighted by an AUC of 0.913, underscores the model's precision in predicting ICH progression, marking a significant step towards enhancing TBI patient management through early identification of ICH progression risks.

Comparative Analysis of Machine and Deep Learning Techniques for Text Classification with Emphasis on Data Preprocessing

Physician-written discharge medical notes include vital details regarding their patients' health. Numerous deep learning algorithms have shown effective in gleaning crucial insights from unstructured medical notes data, leading to potentially useful outcomes in the medical field. The goal of this research is to determine how different deep learning algorithms perform as models for text classification issues in long short term memory (LSTM). Titanic Disaster Dataset has been used for pre-processing is essential since there is a lot of unnecessary information in textual data. Next, clean up the data by eliminating duplicate rows and filling in the blanks. Besides traditional machine learning algorithms such as naive bayes (NB), gradient boosting (GB), and support vector machine (SVM), we use deep learning algorithms to classify data, including bidirectional – LSTM using Conditional Random Fields (CRFs). BiLSTM is the most precise model compared to other models and baseline research, with a classification accuracy of 98.5%.

A Novel Approach to a Recurrent Fuzzy Logic Controller Design Applied to Systems with Multiple Disturbances

In any process, control plays a vital role in the quality of the product. The influence of a controller's operation on a process is linked to its success or failure. However, using a controller that works properly presents several obstacles, such as non-linearity or the lack of a model that adequately represents the behavior of the process. Different approaches have been devised and studied to deal with these difficulties. One of the most promising and widely applied and studied in recent years is fuzzy control due to its ability to operate systems with no precise plant model. Herein, a set of rules and a fuzzy control structure are proposed, with fewer design parameters than a conventional fuzzy controller and better performance. The global stochastic optimization tool Differential Evolution is used to obtain the design parameters of the proposed fuzzy controller.

Modeling and Control of Ejector-Based Hydrogen Circulation System for Proton Exchange Membrane Fuel Cell Systems

Ejector-based proton exchange membrane fuel cells (PEMFCs) are of great interest due to their simplicity and feasibility. Thus, proton exchange membrane fuel cells are considered the most suitable technology for in-vehicle systems, industrial applications, etc. Despite the passive characteristics of the ejector, active control of the hydrogen supply system is needed to ensure sufficient hydrogen, maintain the stack pressure, and ensure effective entrainment. In this research, a novel semi-empirical model is proposed to accurately predict the entrainment performance of the ejector with an 80 kW fuel cell system. According to the precise semi-empirical model, the hydrogen supply system and the anode channel are modeled. Then, a fuzzy logic controller (FLC) is developed to supply sufficient and adequate gas flow and maintain the rapid dynamic response. Compared to the conventional proportional–integral–derivative controller, the fuzzy logic controller could reduce the anode pressure variability by 5% during a stepped case and 2% during a dynamic case.

Improvement of augmented free-water flash algorithm based on HV mixing rule and simulated annealing optimization algorithm

The calculation of three-phase equilibrium in CO2-hydrocarbon-water mixtures holds significant importance within numerical simulations, particularly in applications such as CO2-enhanced oil recovery and carbon dioxide sequestration. However, due to the non-ideality of CO2 and the polarity of water, three-phase equilibrium calculations often encounter convergence challenges. The augmented free-water flash algorithm [6], specifically focusing on CO2 dissolution in the aqueous phase, addresses the convergence challenges encountered by traditional three-phase flash algorithms. Despite its accuracy diminishes under conditions of high pressure and high CO2 concentrations, limiting its capability to accurately describe CO2 dissolution in oil-gas-water multiphase systems. The GE (excess Gibbs free energy) type mixing rule can be effectively integrated with equations of state, including PR and SRK, by utilizing activity models like NRTL and UNIFAC, which can be applied to the calculation of thermodynamic parameters for both nonpolar and polar systems and high-pressure complex systems. In this study, based on the augmented free-water assumption, we have developed a new augmented free-water three-phase flash algorithm for CO2/hydrocarbon/water mixtures. This algorithm integrates the PR equation of state with the NRTL activity model, employing the HV mixing rule for CO2-water interactions and the Van der Waals rule for other non-polar components. Furthermore, to enhance computational efficiency, the algorithm incorporates the simulated annealing global optimization algorithm, replacing Newton iteration to more effectively identify the global minima of the complex objective function. The example calculations show that the improved augmented free-water flash algorithm has higher accuracy and efficiency than the traditional augmented free-water flash algorithm. The augmented free-water three-phase flash algorithm proposed in this paper offers a precise model for phase equilibrium calculations essential for numerical simulations in CO2-enhanced oil recovery and carbon dioxide sequestration.

Modeling of a PV/Thermal Hybrid Panel for Residential Hot Water System

Abstract Despite the extensive body of research on PV/thermal systems, a gap remains in evaluating their performance in residential settings. This study aims to bridge this gap by focusing on the energy modeling of a PV/Thermal (PVT) hybrid panel that incorporates heat pipe technology. The evaluation is conducted through MATLAB code to assess the system's capability to fulfill the electricity and heating demands of residential buildings. The model's reliability is affirmed by comparing it with experimental data from a PVT panel tested in Sydney, exploring the transient variations in both water heat gain rates and power generation. The model's precision is evident from the percentage of error in the estimated temperatures of the PV panel based on the test results under various weather conditions, which ranged from −8% to 6%. This method was also utilized to determine the overall energy efficiency of the PVT under different climatic conditions. The results reveal that the overall energy efficiency of the proposed PVT panel, on a typical day, is approximately 45%, significantly outperforming traditional PV panels by more than double. Furthermore, the payback period for a typical residential PVT system, providing both hot water and electricity, is found substantially shorter than that of installing separate PV and solar hot water systems, highlighting the economic and environmental benefits of the proposed hybrid system.

Optimising High-Pressure Processing (HPP) for Optimal Total Phenolic and Flavonoid Content in Kelulut (Stingless Bee) Honey

High-pressure processing (HPP) has the potential to enhance the total phenolic and flavonoid content (TPC and TFC) in Kelulut honey (KH). However, KH’s inherent variability possesses challenges in optimising these phytochemicals. This study hence explores the optimisation of KH through HPP, focusing on TPC and TFC. The Face-centered Central Composite Design (FC-CCD), involving two factors with three levels each (pressure of 200, 400, and 600 MPa, and time of 5, 10, and 15 min), was applied to investigate two response variables (total phenolic and flavonoid content). The optimisation identified the optimal parameters as 200 MPa for 15 min, with a desirability of 0.986, indicating precise modeling. Compared to other pressures (400 and 600 MPa) at common processing time (15 minutes), 200 MPa recorded higher TPC and TFC with differences of 25.71% to 30.92% and 13.78% to 14.19%, respectively. A verification step revealed that HPP-KH at 200 MPa for 15 minutes yielded a TPC of 15.278 ± 0.525 mg GAE/100 g and a TFC of 38.274 ± 1.980 mg RE/100 g, indicating the precise accuracy of the quadratic modeling. However, this discovery contradicts the consensus that pressures above 500 MPa increase TPC and TFC. Consequently, it underscores the need for tailored high-pressure strategies in KH processing, offering essential insights for industry applications and further research endeavors.

A long-horizon move-blocking based direct power model predictive control for dynamic enhancement of DC microgrids

This research paper presents a novel long-horizon move-blocking based direct power model predictive control (DPMPC) strategy, uniquely designed to enhance the dynamic performance of boost converters in the grid-connected mode within DC microgrids. A precise dynamic model is developed considering a boost converter connected to a DC-link supplying constant power and resistive loads. To predict the system's dynamic behavior over an extended interval and enhance its performance in the presence of constant power loads, the long-horizon based finite control set model predictive control (FCS-MPC) method is introduced. To address the computational complexity associated with the conventional long-horizon DPMPC approach, a move-blocking (MB) strategy is incorporated into FCS-MPC, reducing the computational burden while improving the dynamic performance of the boost converter. Unlike recent studies that utilize DPMPC with short prediction interval, this paper presents evidence that a longer prediction interval is crucial for maintaining system stable and enhancing the dynamic response and also utilizing MB to make the control implementable in the real-time. The simulation results conducted using MATLAB/Simulink demonstrate the effective performance of the proposed strategy across various operating conditions of the boost converter. Experimental results further validate the strategy's capability to enhance the dynamic performance of the boost converter. Importantly, the experimental findings confirm the feasibility of implementing the proposed long-horizon move-blocking DPMPC strategy in real-time applications.

Optimizing functionally graded tibial components for total knee replacements: a finite element analysis and multi-objective optimization study

The optimal design of complex engineering systems requires tracing precise mathematical modeling of the system’s behavior as a function of a set of design variables to achieve the desired design. Despite the success of current tibial components of knee implants, the limited lifespan remains the main concern of these complex systems. The mismatch between the properties of engineered biomaterials and those of biological materials leads to inadequate bonding with bone and the stress-shielding effect. Exploiting a functionally graded material for the stem of the tibial component of knee implants is attractive because the properties can be designed to vary in a certain pattern, meeting the desired requirements at different regions of the knee joint system. Therefore, in this study, a Ti6Al4V/Hydroxyapatite functionally graded stem with a laminated structure underwent simulation-based multi-objective design optimization for a tibial component of the knee implant. Employing finite element analysis and response surface methodology, three material design variables (stem’s central diameter, gradient factor, and number of layers) were optimized for seven objective functions related to stress-shielding and micro-motion (including Maximum stress on the cancellous bone, maximum and mean stresses on predefined paths, the standard deviation of mean stress on paths, maximum and mean micro-motions at the bone-implant interface and the standard deviation of mean micro-motion). Then, the optimized functionally graded stem with 6 layers, a central diameter of 5.59 mm, and a gradient factor of 1.31, was compared with a Ti6Al4V stem for various responses. In stress analysis, the optimal stem demonstrated a 1.92% improvement in cancellous bone stress while it had no considerable influence on the maximum, mean, and standard deviation of stresses on paths. In micro-motion analysis, the maximum, mean, and standard deviation of mean micro-motion at the interface were enhanced by 24.31%, 39.53%, and 19.77%, respectively.

ANALYSE DE L’INFLUENCE DES PARAMETRES METEOROLOGIQUES SUR LA PLUVIOMETRIE A KINSHASA

The understanding of meteorological parameters influencing rainfall is essential to effectively address environmental challenges and anticipate the consequences of extreme weather events. In this study, we analyze meteorological data provided by the Airways Authority of Kinshasa (RVA), covering the period from 1992 to 2019. This in-depth statistical analysis aims to better understand the factors that influence precipitation in the region and to identify relevant climate trends for anticipating future environmental changes. We used logistic regression to assess the impact of various climatic factors such as temperature (daily extremes), relative humidity, and atmospheric pressure on precipitation. The results indicate that minimum temperature, relative humidity, and atmospheric pressure show a strong correlation with rainfall in Kinshasa, highlighting their significant influence on the likelihood of precipitation. In contrast, maximum temperature has a limited impact, making it a less reliable indicator of rainfall in the city. The conclusions of this study provide valuable tools for local authorities in developing effective strategies for managing climate variations and predicting weather phenomena. These insights enable precise modeling of the impacts of meteorological fluctuations on ecosystems and urban infrastructure. Furthermore, the results of this research can guide local farmers in optimizing their agricultural practices by adjusting planting and harvesting schedules based on detailed weather forecasts, thereby minimizing economic losses and enhancing crop resilience to climate variations. Finally, this study provides climatologists with robust data for in-depth analysis of the effects of climate change on meteorological phenomena in Kinshasa. This knowledge is crucial for refining regional climate models and developing specific adaptation strategies for other urban areas in sub-Saharan Africa.

Advancement in Cancer Vasculogenesis Modeling through 3D Bioprinting Technology.

Cancer vasculogenesis is a pivotal focus of cancer research and treatment given its critical role in tumor development, metastasis, and the formation of vasculogenic microenvironments. Traditional approaches to investigating cancer vasculogenesis face significant challenges in accurately modeling intricate microenvironments. Recent advancements in three-dimensional (3D) bioprinting technology present promising solutions to these challenges. This review provides an overview of cancer vasculogenesis and underscores the importance of precise modeling. It juxtaposes traditional techniques with 3D bioprinting technologies, elucidating the advantages of the latter in developing cancer vasculogenesis models. Furthermore, it explores applications in pathological investigations, preclinical medication screening for personalized treatment and cancer diagnostics, and envisages future prospects for 3D bioprinted cancer vasculogenesis models. Despite notable advancements, current 3D bioprinting techniques for cancer vasculogenesis modeling have several limitations. Nonetheless, by overcoming these challenges and with technological advances, 3D bioprinting exhibits immense potential for revolutionizing the understanding of cancer vasculogenesis and augmenting treatment modalities.