Centroid Method Research Articles

Various defuzzification and ranking techniques for the heptagonal fuzzy number to prioritize the vulnerable countries of stroke disease

The idea of fuzzy numbers is determined according to their uncertainty points, which include interval values, triangular, trapezoidal, and heptagonal fuzzy numbers, etc. This article contains two types of fuzzy numbers: the heptagonal fuzzy number (HpFN) and heptagonal interval-valued fuzzy numbers (HpIVFN). For both the HpFN and HpIVFNs, the new distinct rational perspective and viewpoints are presented. First, the membership function (MF) is generalized to the linear and non-linear for observing the symmetric and asymmetric HpFN and HpIVFN. In light of this, α-cut is constructed for both linear and non-linear MF of HpFN and HpIVFN. The various types of HpIVFNs are classified along with the graphical representation. Then, new defuzzification under HpFN is determined using three methods, namely the centroid method, mean of α-cut method, and removal of area methods, and a comparison is demonstrated. A ranking method is also performed for HpFN. Using the proposed defuzzification approach in the weighted aggregates sum product assessment (WASPAS) method, an analysis is conducted to identify the countries with the highest vulnerability to stroke disease. The results indicated that world Bank upper-middle-income countries is significantly affected by stroke disease. The obtained outcome is validated through comparative and sensitivity analyses.

COMPARATIVE STUDY OF CENTROID DEFUZZIFICATION AND MAXIMUM DEFUZZIFIER MODEL MEAN OF MAXIMA ALGORITHM FOR CORONARY ARTERY DISEASE DIAGNOSIS

Objective: Cardiovascular, especially coronary artery disease (CAD), is the number one cause of death globally. Every year, more people die from cardiovascular disease than any other disease. There are several factors and symptoms of coronary artery disease that are easily recognized. To find out the severity of CAD, a diagnosis of CAD is needed to facilitate medical treatment. This study aims to explain the diagnostic process for CAD using a fuzzy system and to determine the accuracy of the fuzzy system. Design and method: This study uses a fuzzy system to diagnose the severity of coronary artery disease. Input variables used in the study were gender, age, pulse, systolic blood pressure, cholesterol, blood sugar, triglycerides, electrocardiogram (ECG), chest pain, shortness of breath, and cough. In making the system, 90 data were used which were then divided into two types of data, namely 70 training data and 20 testing data. The fuzzy inference system used in this study is the Mamdani inference system which uses centroid defuzzification and maximum defuzzifier mean of maxima (MOM). This defuzzification process is used to diagnose types of coronary artery disease, namely: CAD level 1 (Asymptomatic), CAD level 2 (Angina Pectoris), and CAD level 3 (Acute Myocardial Infarction). Results: The results of the research on the application of the fuzzy system for diagnosing coronary artery disease were the accuracy of the defuzzification centroid method of 98.5% for training data and 95% for testing data, while for the MOM defuzzification method, the accuracy level of training data was 98.5% and 90% for testing data. Conclusions: Based on the research results, it can be concluded that the centroid defuzzification method is better than the MOM defuzzification system for the coronary artery disease diagnosis system, so, it can be said that by using a fuzzy system with centroid defuzzification, the probability of correctly diagnosing one patient is 95%.

Penerapan Metode SWOT-MAGIQ Sebagai Strategi Peningkatan Pelayanan Jasa Laundry

Small and Medium-sized Laundry Service Enterprises face stiff competition and rapidly changing market dynamics in the contemporary business era. This study aims to understand the key factors influencing the success of Small and Medium-sized Laundry Service Enterprises through a SWOT analysis, focusing on strengths, weaknesses, opportunities, and threats. Using the Rank Order Centroid (ROC) method for criteria evaluation, this research found that "Employee Friendliness" is the primary strength. At the same time, "Lack of Human Resources" is a significant weakness. On the other hand, "Good Customer Relations" was identified as the most significant opportunity, and "Emergence of Competitors" was the primary threat. These findings highlight the importance of human interaction in service businesses and the need to enhance HR capabilities. This study provides valuable insights for Small and Medium-sized Laundry Service Enterprises on leveraging strengths, addressing weaknesses, capitalizing on opportunities, and confronting threats for sustainable business success

Measurement of Residual Stress Distribution in the Depth Direction of Annealed Materials of Lapped Bearing Steel Using Weighted Averaging Analysis Method

This paper reports the results of an experimental examination using X-rays to test annealing materials for lapped bearing steel (STB2), to confirm the validity of the weighted averaging analysis method. The distribution behavior for the αψ-sin2ψ diagram and the presence or absence of differences in the peak method, half-value breadth method, and centroid method were investigated. When lapping the annealed bearing steel (STB2) material, a residual stress state with a non-directional steep gradient appeared in the surface layer, and it was found that the weighted averaging analysis method was effective. If there is a steep stress gradient, the sin2ψ diagram is curved and the diffraction intensity distribution curve becomes asymmetric, resulting in a difference between the peak method, half-value breadth method, and centroid method. This phenomenon was evident when the stress gradient was more than 2~3 kg/mm2/µm. In this case, if the position of the diffraction line is determined using the centroid method and the weighted averaging analysis method is applied, the stress value on the surface and the stress gradient under the surface can be obtained more accurately. When the stress gradient becomes a problem, since the curvature of the sin2ψ diagram appears clearly in the region of sin2ψ > 0.5, it is necessary to increase the inclination angle ψ as much as possible. In the case of a lapping layer, a more accurate value can be obtained by considering σ3 in the weighted averaging analysis method. In an isotropic biaxial residual stress state, the presence or absence of σ3 can be determined as the presence or absence of strain for sin2ψ≈0.4.

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

Velocity estimation of space moving targets is a key part of space situational awareness. However, most of the existing methods do not consider the satellite observation process, and the performance mainly depends on the preset target motion state, which has great limitations. To accurately obtain the motion characteristics of space infrared dim targets in space-based infrared detection, a velocity estimation method based on multi-satellite observation and robust locally weighted regression is proposed. Firstly, according to parameters such as satellite position, satellite attitude angle, and sensor line of sight, the overall target observation model from the sensor coordinate frame to the Earth-centered inertial coordinate frame is established, and the pixel coordinates of the target imaging point are extracted using the gray-weighted centroid method. Then, combined with the least squares criterion, the position sequence of the space target is obtained. Finally, a robust locally weighted regression operation is performed on the target position sequence to estimate the velocity. This study verified the feasibility of the proposed method through simulation examples, with the results showing that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the method were only 0.0733 m/s and 1.6640 m/s without measurement error. Moreover, the velocity estimation accuracy was better than that of other methods in most scenarios. In addition, the estimation accuracy under the impact of various measurement errors was analyzed, and it was found that the pixel coordinate extraction error had the greatest impact on velocity estimation accuracy. The proposed method provides a technical basis for the recognition of space infrared dim moving targets.

Fuzzy Technology Design for Early Detection of Diseases in Tobacco Plants

Tobacco is an agricultural product that uses leaves to be processed into pesticides, medicines and cigarettes. Tobacco quality is determined by plant maintenance and reduced pest and disease attacks. To avoid these disturbances, control is needed quickly, precisely and accurately so that the tobacco plant disease cannot spread throughout agricultural land. In making fuzzy, diseases and symptoms in tobacco plants are used as a rule base in making a fuzzy expert system. The expert system created in this research is an expert system using the concept of fuzzy logic to diagnose tobacco plant diseases, using the Mamdani inference method and the defuzzification process using the centroid method (firmness value) to get the right conclusions in diagnosing tobacco plant diseases. From the results of Mamdani's design and manual fuzzy calculations, it can be concluded that the design is ready to be further implemented into the required programming language. From the sample calculation results, it was found that damping off disease has a moderate degree of risk with a value of 41.54. With the construction of this system, it will provide easy information for farmers to carry out and find out what symptoms are contracting diseases in tobacco plants.

Postoperative stability of bioresorbable plates made of 85:15 poly (L-lactide-co-glycolide) in Le Fort I osteotomy

Recently, RapidSorb plates (DePuy Synthes) made of 85.15 poly (L-lactide-co-glycolide) have been used for orthognathic surgery; however, reports regarding their effectiveness are limited. We aimed to compare the postoperative stability of RapidSorb plates, RapidSorb combined with titanium (MOJ plates), and MOJ plates in patients who underwent Le Fort I osteotomy at Tokyo Medical and Dental University Hospital. The use of RapidSorb in the maxilla is a load-sharing application and therefore constitutes an approved indication. Discrepancies in the maxillary positions were measured using postoperative computed tomography data at 1 week and 1 year using the centroid method 3-dimensionally. Treatment with RapidSorb alone showed a more vertical discrepancy in the maxilla treatment with MOJ and RapidSorb+MOJ. The RapidSorb4 group was subdivided into 2 groups (under and over 1.0-mm) based on the change in the maxillary centroid. The bone gap at the lateral border of the piriform aperture was significantly larger in the over-1.0-mm group than in the 1.0-mm group. The fixation of RapidSorb alone is not appropriate in load-bearing and unstable applications but is not contraindicated for load-sharing indications. Fixation with RapidSorb combined with MOJ was clinically effective, with results similar to titanium plate-only fixation regarding postoperative stability.

Hybrid Approach to Detect Prolonged Speech Segments

In the last 10 decades various methods have been introduced to detect prolonged speech segments automatically for stuttered speech signals. However less attention has been paid by researches in the detection of prolongation disorder at the parametric level. The aim of this study is to propose a hybrid approach to detect the prolonged speech segments by combining various spectral parameters with their recognition accuracies for the reconstructed speech signal. The paper presents prolonged segments detection by considering the parameters individually, combining various spectral parameters, validation of prolongation detection system, MFCC feature extraction process, basic model accuracies for the reconstructed signals. The proposed methods are simulated and experimented on UCLASS derived dataset. Obtained results are compared with the existing works of prolongation detection at parametric and word level. It is observed that hybrid parameters yield 92% of recognition rate for larger frame sizes of 200ms when modeled with SVM. The results are also tabulated and discussed for various metrics like sensitivity, specificity and accuracy metrics in detecting the prolonged segments. The study also focuses on the prolongation characteristics of vocalized and non-vocalized sounds at phoneme level. The detection accuracy of 71% is observed for Vocalized prolonged vowel phonemes over non-vocalized prolonged signal. Objectives: The objective of this work is to propose a hybrid algorithm to detect prolonged segments automatically for speech signal with prolongation disorder. The other objective is to evaluate the obtained spectral parameters performances by applying to various evaluation metrics and models to compute the recognition accuracy of a reconstructed signal. The objective is further extended to bring out the importance of variable frame size concept and to analyze the variations in vocalized and non-vocalized sounds. Methods: The methods adopted to detect prolonged speech segments are discussed at two levels namely at the preprocessing and modeling levels. The Preprocessing level is discussed by applying various parameters at an individual level, hybrid level by combing the Centroid, Entropy, Energy, ZCR parameters and MFCC feature extraction method. A new method has been applied using Specificity, Sensitivity and accuracy metrics to validate the prolongation detection model performance. In modeling level, the above parameters are discussed by applying evaluation metrics for the clustering and classification models like K-means, FCM and SVM. The performance of these methods is considered for evaluating and estimating the prolonged segment detection accuracy of the reconstructed speech signals of vocalized and non-vocalized sounds. All these methods are discussed in detail in the following sections. Findings: Hybridizing the spectral parameters to detect the prolonged speech segment automatically is a major finding of this work. It is also found that Specificity, sensitivity and accuracy metrics plays a major role in designing and validating the prolongation detection model. From the further experiments it is identified that the hybrid and verification metrics suits better for vocalized and non-vocalized sounds when larger frame lengths are considered. SVM has been found to perform better for all the above considerations. Novelty: As per Literature survey it is observed that individual and few parameters are applied to detect the prolongation. But works are not addressed on applying or combining more than two parameters to detect the prolonged speech segments. The novelty of this work lies in selecting and combining the spectral parameters at the preprocessing stage to detect the prolongation disorder. Spectral centroid and entropy are considered as appropriate parameters along with ZCR and Energy parameters. Hence hybridizing these parameters results in a novelty to propose an automatic prolongation detection system. Novelty is further brought by applying Specificity, sensitivity and accuracy metrics to build and evaluate the detection system for vocalized and non-vocalized prolonged sounds.

Achieving Sub-Parts-per-Million Mass Measurement Accuracy on an Orbitrap Mass Spectrometry Imaging Platform without Automatic Gain Control

The collection of profile data is standard practice within the field of mass spectrometry (MS). However, profile data collection often results in large data files that require extensive processing times, especially in mass spectrometry imaging (MSI) studies where thousands of high-resolution scans are recorded. Natively collecting centroid MS data is an alternative that effectively reduces both the resulting file size and the data processing time. Herein, high-resolution accurate mass (HRAM) Orbitrap MSI data on mouse liver tissue sections without automatic gain control (AGC) were natively collected in both profile and centroid modes and compared based on the file size and processing time. Additionally, centroid data were evaluated against the profile data with regard to the spectra integrity, mass measurement accuracy (MMA), and the number of lipid annotations to ensure that centroid data did not compromise the data quality. For both native and postacquisition centroided data, the variation in mass measurement accuracy decreased relative to the profile data collection. Furthermore, centroid data collection increased the number of METASPACE database annotations indicating higher sensitivity and greater accuracy for lipid annotation compared to native profile data collection. Profile MSI data was shown to have a higher likelihood of false positive identifications due to an increased number of data points on either side of the peaks, whereas the same trend was not observed in data collected in native centroid data collection. This publication explores and explains the importance in properly centroiding MSI data, either natively or by adequate centroiding methods, to obtain the most accurate information and come to the best conclusions. These data support that natively collecting centroid data significantly improves MMA to sub-ppm levels without AGC and reduces false positive annotations.

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

AbstractCommunication and networking plays a crucial role in data transmission in UASN. The sensor nodes in UASN are intermittently deployed randomly in the three‐dimension scenario. As a consequence, the three‐dimension based localization algorithm is the need for an hour. To fulfill the objective, we have proposed a localization algorithm I‐LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) in three‐dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes. The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA‐ESSP (Gauss‐newton algorithm‐extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA‐ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represents that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time and convergence rate.

Pulse Lidar imaging algorithm based on adaptive triangle window-width centroid discrimination

The pulse light detecting and ranging (Lidar) can obtain rich environmental information of the target, and the centroid algorithm can obtain the distance and intensity information of the target from the echo pulse. However, there are problems of sensitivity to noise and fixed window width. Therefore, this paper proposes an adaptive triangle window-width centroid algorithm (ATWC) to improve the ranging accuracy and robustness of Lidar. This algorithm combines the characteristics of the steep front and slow back of the pulse echo waveform. The centroid calculation point is selected by an isosceles triangle envelope in the time domain and a non-isosceles triangle envelope in the intensity domain with a longer front and a shorter back. Further combined with intensity weighting, the distance and intensity values of ATWC are obtained. On the one hand, the simulation verifies the influence of window width selection on the accuracy of the centroid algorithm. On the other hand, real environment comparison experiments are conducted with traditional waveform centroid (TWC), peak ranging (PR), and adaptive threshold methods (ATM) under a pulse Lidar system constructed by our method, and our method achieves significant improvements. At different distances, when the peak signal-to-noise ratio (PSNR) is above 20 dB, the ranging error of ATWC is below 0.3 ns, which is much better than the compared algorithms. When the target is at a distance of 25 m, which is a weak echo signal environment with a PSNR of 15.6 dB, our algorithm can still achieve a ranging error of 0.56 ns, and the detection success rate can still reach 88.8%. In addition, our method can obtain a clearer intensity image, which further demonstrates the research potential and practical utility of the proposed method.

Rekomendasi Kelayakan Penerima Kredit Menggunakan Metode TOPSIS dengan Pembobotan ROC

The process of applying for credit (loan funds) by customers is one of the many forms of service at the Bank. To approve credit applications, the bank, especially the credit manager, must carry out a complex assessment in accordance with the standard requirements that apply to the bank, this is due to minimizing the risk of bad credit because customers cannot make installment payments in the future. Due to the complexity of the considerations in determining the eligibility of customers who apply for credit, limited employees (labor) and also the evaluation process that is still done manually so it takes a long time, so to overcome these problems, we need a system that can provide recommendations for customer decisions that are acceptable. in applying for credit using a decision support system method, one of which is used in this study is the Technique For Others Reference by Similarity to Ideal Solution (TOPSIS) method with the weighting Rank Order Centroid (ROC) method. The criteria used in this appraisal process include the price of the vehicle, down payment (DP), income, customer character and housing status. Based on the calculation results according to predetermined criteria, the highest value is 0,86 and the lowest value is 0,10.

The Weighted Product Method and the Multi-Objective Optimization on the Basis of Ratio Analysis Method for Determining the Best Customer


 The objective of this study is to compare the effectiveness of the Weighted Product (WP) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods in determining the best customers. Onesnet, the case study service provider, provides discounts and rewards to eligible customers to support this objective. The problem addressed in this study is how to determine the most relevant method for selecting eligible customers for bonuses. To achieve this, sensitivity testing was conducted by altering the weights of each criterion in both methods and observing the percentage changes of the results. The Weighted Product method multiplies the rating of each connected attribute, which is raised to the appropriate attribute weight, to decide. Data for this study was collected through interviews and observations at Onesnet and processed using the Rank Order Centroid (ROC) method for weighting, and the WP and MOORA methods for evaluating and selecting a decision. The WP and MOORA methods produced different total values and rankings, but the modeling with either method can be used equally for selecting the best customers. While there was a 60% similarity in data between the two methods, the WP method is recommended over MOORA, as it prioritizes customers with high loyalty criteria as the best customers.

Influence of synthetic jet on uniform momentum zones

Based on a set of streamwise-wall-normal experimental databases of turbulent boundary layer (TBL) acquired by time-resolved particle image velocimetry (TR-PIV) system at the Reynolds number Reτ=537, which includes the velocity fields with and without synthetic jet, the influence of the synthetic jet on uniform momentum zones (UMZs) was studied. The turbulent/non-turbulent (T/NT) interface of each instantaneous frame was identified by local turbulent kinetic energy (L-TKE) method and the area below the interface was divided into UMZs by probability density function (P.d.f.) histogram of streamwise velocity. The relationship between the T/NT interface average height EYi and the UMZs number NUMZ is analyzed. The spatial position and velocity of UMZs are extracted by centroid method, and the basic statistical characteristics within UMZs under two working conditions are investigated. After processing the velocity fields corresponding to each NUMZ separately, it is found that NUMZ is highly correlated with Q events. The synthetic jet reduces the streamwise and wall-normal length scales of the fluid induced by Q events, and under the same externa conditions, the two scales are also positively related to the intensity of Q events. The shear stress in the flow field promotes the generation of the forward vortices, which drives the stratification of UMZs. The hairpin vortices generated by synthetic jet result in a large number of forward vortices in the region with y/δ<0.3, and the turbulent kinetic energy (TKE) and Reynolds shear stress are input into the vortex region in varying degrees.

Penerapan Metode MABAC dengan Pembobotan ROC Dalam Sistem Pendukung Keputusan Pemilihan Pengajar dengan Kinerja Terbaik

Selection of the best teacher is one of the problems in a decision making according to semi-structured criteria. In this research, the decision support system explains how the process of selecting alternative teachers who have the best performance in elementary schools applies the MABAC method and weights it using the Rank Order Centroid (ROC) method. The use of this method can solve the problem of determining the best teacher that occurs in elementary schools. The final result of completing the method will produce the highest ranking value which will be determined to be the highest alternative. The data used in this study were 10 alternative teacher data using 5 criteria, resulting in 1 highest ranking of the 10 alternatives obtained by alternative 1 named Servin Manullang with a value of 0.6086 as a recommendation for the teacher with the best performance

A novel fuzzy approach to identify the phenotypic adaptability of common bean lines

The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotypes in a growing region. Some of these methodologies incorporate previous information for recommending an extra group of genotypes denominated as specific ideotypes under certain cultivation conditions. Based on this strategy, the centroid method and its modifications have been widely used due to the simplicity of classification of the evaluated genotypes. However, these methodologies present problems in identifying adaptability patterns of some genotypes. Artificial intelligence techniques, such as fuzzy C-means, can be an alternative to reduce these difficulties, since they use, in addition to distance information between genotypes, memberships (measures quantifying how much an observation belongs to a particular class) to increase discriminatory power. Therefore, our aim was to propose and evaluate the phenotypic adaptability method by fuzzy clustering to assist cultivar recommendations. The adaptation of the fuzzy C-Means method to classify the genotypes was implemented in BioFuzzy software. The grain yield data of black common bean genotypes were used to evaluate the potential of the method. The results obtained by this method were compared with those obtained by the centroid method. The phenotypic adaptability method by fuzzy clustering was effective in identifying the adaptability patterns of common bean genotypes. Moreover, the discriminatory power was higher than that observed with the centroid method.

A fuzzy rule‐based system with decision tree for breast cancer detection

AbstractBreast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human‐understandable terms‐linguistic variables. The Mamdani Fuzzy Rule‐Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels‐ benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state‐of‐the‐art dataset.

A Decision Model for Ranking Asian Higher Education Institutes Using an NLP-Based Text Analysis Approach

Identification of the best institute for higher education has become one of the most challenging issues in the present education system. It has become more complicated as more institutes exist with extraordinary infrastructural facilities. Therefore, a decision model is required to identify the best institute for higher education based on multiple criteria. This article proposes a Natural Language Processing (NLP) -based decision model for the identification of the best higher education institute using MCDM methods. The existing decision models for the selection of the best higher education institutions consider a limited number of criteria for decision-making. In this proposed model, 17 criteria and 15 institute datasets have been identified for the development of the decision model through extensive research and experts opinion. The NLP-based text analysis approach is applied to extract the relevant information and convert it to a suitable format. As the relative importance of the criteria plays a crucial role in decision-making, CRITIC and Rank centroid methods are applied for the calculation of relative weights of criteria. TOPSIS method is used to generate the ranking grades of alternatives for each criterion. An objective function is defined to calculate the evaluation scores and select the best institute for higher education. It has been observed that the ranks obtained from the developed model match pretty well with the ranks obtained from other MCDM methods and the experts.

Novel Centroid Method for Robust Evaluation of Return Time of Reflected Waves in the Systemic Arterial Network

A diastolic-to-systolic shift in the return time (RT) of backward waves to central arteries is expected with ageing. However, current methods of estimating RT-inflection point, zero crossing, and foot-depend on a single waveform feature and produce systolic RT throughout life. We propose a novel centroid method that accounts for the entire backward pressure waveform. We assess the accuracy of the various methods against a ground truth RT (GTRT) and their sensitivity to diastolic/systolic RT. Linear wave tracking was implemented in a one-dimensional systemic arterial tree model and GTRT was calculated as the amplitude-weighted mean RT of backward waves at the ascending aorta. The sensitivity of the methods to diastolic/systolic RT was also assessed in ten sheep. A balloon catheter in the descending thoracic aorta generated a backward-running pulse that arrived at the ascending aorta at different times during diastole or systole, allowing the 'bulk' RT of the backward-running wave ensemble to be manipulated. Using a virtual cohort of 1200 patients, the centroid RT was closest to GTRT compared to the zero crossing, inflection point, and foot methods; mean differences (limits of agreement) were -8 (-47, 30), vs -42 (-136, 52), -78 (-305, 149), and -197 (-379,-15) ms, respectively. Furthermore, only the centroid method was sensitive to both diastolic and systolic RT; other methods were only sensitive to systolic RT. The centroid method had the highest accuracy and robustness in estimating RT. This can provide insight into the diastolic-to-systolic shift in RT of backward waves with ageing.

DETERMINATION OF GEOTHERMAL POTENTIAL IN YANKARI GAME RESERVE, BAUCHI STATE, NIGERIA

The study deals with the determination of geothermal potential of Yankari Game reserve which involves; estimate of the Curie point depth, heat flow and geothermal gradient from spectral analysis of aeromagnetic data covering an area located approximately at longitude 9.7567 oN and Latitude 10.5094 oE. Radially power spectrum was applied to the aeromagnetic data of the study area divided into 4 square blocks and each block analyzed using the spectral centroid method to obtain depth to the top, centroid and bottom of magnetic sources. The depth values were subsequently used to evaluate the Curie-point depth (CPD), geothermal gradient and near-surface heat flow in the study area. The values of the curie point depths (Zb), range from 8.91 km to 10.11 km, with a mean value of 9.71 km, geothermal gradient, range from 57.37 oCkm-1 to 65.10 oCkm-1, with mean value of 60.10 oCkm-1 and heat flow (q), range from 143.43 mWm-2 to 162.75 mWm-2, with a mean value of 150.24 mWm-2. These results entails that the area under study has good geothermal potential for exploration.