Academic literature on the topic 'BTSIM model'

This paper delves into the dynamics of asset pricing within Bachelier’s market model (BMM), elucidating the representation of risky asset price dynamics and the definition of riskless assets. It highlights the fundamental differences between BMM and the Black–Scholes–Merton market model (BSMMM), including the extension of BMM to handle assets yielding a simple dividend. Our investigation further explores Bachelier’s term structure of interest rates (BTSIR), introducing a novel version of Bachelier’s Heath–Jarrow–Morton model and adapting the Hull–White interest rate model to fit BMM. This study concludes by examining the applicability of BMM in real-world scenarios, such as those involving environmental, social, and governance (ESG)-adjusted stock prices and commodity spreads.

Introduction: Brain tumors pose a significant danger to human health due to their complexity, variety and ability to progressive progress. Early and accurate diagnosis is important for effective treatment and improvement in survival rates. Traditional clinical processes are often dependent on time programs, subjective and expensive clinical equipment. The project presents an integrated, AI operated, multi-phase system for brain tumor detection and recommendations of personal treatment. By combining symptomatic evaluation, biomarker analysis, advanced imaging interpretation and machine learning models, this system aims to help health professionals with quick and more accurate clinical decisions.Objectives: The main objective of this project is to develop an integrated, multi -phase system for early detection and personal treatment using a combination of clinical symptoms, molecular biomarkers, imaging and artificial intelligence. The system begins to evaluate patient -reported symptoms and risk factors to calculate a functional weighted risk index (FWRI) for initial risk assessment. It then analyzes a blood -based biomarker to generate a blood -based tumor signal index (BTSI), which provides insight at molecular level. Then it explains the ATR-FTIR and the Multi-Omics report through rule-based arguments for determining nonconformities. The MRI scan is then classified into tumors-types-glioma, meningioma, pituitary or no tumor-a fine- model VGG19 uses deep learning models. Finally, the system integrates the results of all previous stages to offer individual means based on tumor type, severity and general risk profile.Methods: The proposed system follows a sequential five-stage methodology for comprehensive brain tumor detection and remedy making plans. In Stage 1, users input symptom severity and history through an interactive interface, which calculates a Functional Weighted Risk Index (FWRI) to assess preliminary tumor chance. Stage 2 entails the input of blood biomarker ranges, which might be analyzed to generate a Blood-Based Tumor Signal Index (BTSI), reflecting molecular signs of tumor presence. Stage 3 allows users to add ATR-FTIR or Multi-Omics reviews,that are interpreted using rule-primarily based logic to identify unusual styles that warrant imaging. In Stage 4, customers add brain MRI scans which can be processed through a pre-trained VGG19 convolutional neural network to classify the tumor into one among four classes: glioma, meningioma, pituitary, or no tumor. Finally, Stage five combines the outputs from all previous levels to generate customized treatment tips, taking into consideration the identified tumor type, severity, biomarker signals, and medical danger ratingsResults: The multi -phase system demonstrated promising consequences in all components. In phase 1, FWRI is effectively stable in categories with low, medium and high risks based on symptom input and severity. Phase 2 has shown that BTSI values have become close in line with a biomarker threshold installed, which strengthens the system's ability to reflect molecular tumor designs. Phase 3 explained ATR-FFIR and Multi-Omics data successfully, the exact flag of the unusual profile for further imaging analysis. In Step 4, VGG19 was based MR classification model accuracy of more than 92%, which distinguishes well between glioma, meningoom, pituitary tumor and no tumor. Finally, step 5 recommendations generated clinically relevant and similar relevant medical guidelines, which demonstrate the system's ability to provide integrated, computer-driven support for individual brain tumor care.Conclusions: Finally, integrated five-phase brain tumor detection and treatment system effectively combine the evaluation of clinical symptoms, biomorker analysis, omics data interpretation, advanced MRI classification and individual treatment form into an integrated diagnostic pipeline. By taking advantage of rule -based arguments and deep teaching techniques, the system increases early identification accuracy, reduces clinical delays and supports data driven clinical decision -making. The sequential current ensures that each step does on the previous one, reduces false positive and optimizing patient -specific consequences. With further clinical verification, this AI-assisted framework has a strong ability to become a reliable and skilled tool in medical environments in the real world for early diagnosis and handling of brain tumors.

This study investigates the efficiency of after-sales support provided by water service providers (WSPs) in Boracay Island as perceived by consumers, and examines whether the nature of the concern and consumer classification influence these perceptions. Boracay presents a unique context due to its duopolistic market structure—an uncommon characteristic in the Philippine public service sector. The study evaluates the efficiency using key performance indicators such as maximum response time, resolution rate, first response rate, customer effort, first call resolution, customer satisfaction, customer retention, and net promoter score. A quantitative, descriptive-inferential research design was employed, involving 324 customers of the Boracay Island Water Company Inc. (BIWC) and Boracay Tubi System Inc. (BTSI). Data were gathered using a validated questionnaire, with statistical analyses—including measures of association, rank-biserial correlation, and effect size—used to assess the relationships between after-sales efficiency metrics and independent variables (WSP, consumer classification, and nature of concern). Inferential methods such as Chi-Square Analysis, Wilcoxon Test, Mann-Whitney U Test, and Kruskal-Wallis Test revealed that, overall, WSPs provide efficient to highly efficient after-sales support (p > 0.01). However, disaggregated data showed variability in efficiency across consumer types and concern categories. Some key metrics demonstrated statistically significant differences (p < 0.01, effect size = 0.074), although most results indicate a generally uniform perception of service efficiency across providers and classifications. Based on these findings, the study recommends implementing a systematized and anonymous after-sales service process which incorporates consumer classification and nature of concern, leading to the development of the NECAR model. Additionally, the creation of a user-friendly digital platform is proposed to improve consumer access to support services. The results also underscore the need for standardized national performance benchmarks for public utilities, which can guide key performance indicators in after-sales service delivery.  

Abstract This study presents an innovative approach to fault diagnosis in sliding bearings, targeting the challenges posed by weak fault signals and heavy noise interference. The proposed method employs a generalized multi-scale permutation entropy (GMPE) algorithm, which utilizes a multi-scale mean coarse-graining strategy to effectively capture dynamic transitions in signals. To overcome the shortcomings of traditional binary tree support vector machine (BTSVM) classifiers—such as slow convergence and error accumulation due to early misclassifications—an enhanced BTSVM model is introduced to reduce error propagation. The effectiveness of the method is validated on both reciprocating compressor sliding bearings and automotive rolling bearings, achieving a fault diagnosis accuracy of over 99%. These results highlight a significant advancement in mechanical fault detection and demonstrate the strong potential of combining GMPE with an improved BTSVM for accurate fault diagnosis in complex machinery.