Search results for: parameter variations

Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

Procedia PDF Downloads 358

Authors: Murtadha Kareem, Oliver Faust

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Wearable biosensor systems can be designed and developed for health monitoring. There is much interest in both scientific and industrial communities established since 2007. Fundamentally, the cost of healthcare has increased dramatically and the world population is aging. That creates the need to harvest technological improvements with small bio-sensing devices, wireless-communication, microelectronics and smart textiles, that leads to non-stop developments of wearable sensor based systems. There has been a significant demand to monitor patient's health status while the patient leaves the hospital in his/her personal environment. To address this need, there are numerous system prototypes which has been launched in the medical market recently, the aim of that is to provide real time information feedback about patient's health status, either to the patient himself/herself or direct to the supervising medical centre station, while being capable to give a notification for the patient in case of possible imminent health threatening conditions. Furthermore, wearable health monitoring systems comprise new techniques to address the problem of managing and monitoring chronic heart diseases for elderly people. Wearable sensor systems for health monitoring include various types of miniature sensors, either wearable or implantable. To be specific, our proposed system able to measure essential physiological parameter, such as heart rate signal which could be transmitted through Bluetooth to the cloud server in order to store, process, analysis and visualise the data acquisition. The acquired measurements are connected through internet of things to a central node, for instance an android smart phone or tablet used for visualising the collected information on application or transmit it to a medical centre.

Keywords: Wearable sensor, Heart rate, Internet of things, Chronic heart disease

Procedia PDF Downloads 161

Authors: Aphinan Phukaoluan, Surachai Dechkunakorn, Niwat Anuwongnukroh, Anak Khantachawana, Pongpan Kaewtathip, Julathep Kajornchaiyakul, Peerapong Tua-Ngam

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Aims: The objectives of this study was to determine the load-deflecting characteristics of a fabricated orthodontic wire with alloy composition of 50.6% (atomic weight) Ni and 49.4% (atomic weight) Ti and to compare the results with Ormco, a commercially available pre-formed NiTi orthodontic archwire. Materials and Methods: The ingots alloys with atomic weight ratio 50.6 Ni: 49.4 Ti alloy were used in this study. Three specimens were cut to have wire dimensions of 0.016 inch x0.022 inch. For comparison, a commercially available pre-formed NiTi archwire, Ormco, with dimensions of 0.016 inch x 0.022 inch was used. Three-point bending tests were performed at the temperature 36+1 °C using a Universal Testing Machine on the newly fabricated and commercial archwires to assess the characteristics of the load-deflection curve with loading and unloading forces. The loading and unloading features at the deflection points 0.25, 0.50, 0.75. 1.0, 1.25, and 1.5 mm were compared. Descriptive statistics was used to evaluate each variables, and independent t-test at p < 0.05 was used to analyze the mean differences between the two groups. Results: The load-deflection curve of the 50.6Ni: 49.4Ti wires exhibited the characteristic features of superelasticity. The curves at the loading and unloading slope of Ormco NiTi archwire were more parallel than the newly fabricated NiTi wires. The average deflection force of the 50.6Ni: 49.4Ti wire was 304.98 g and 208.08 g for loading and unloading, respectively. Similarly, the values were 358.02 g loading and 253.98 g for unloading of Ormco NiTi archwire. The interval difference forces between each deflection points were in the range 20.40-121.38 g and 36.72-92.82 g for the loading and unloading curve of 50.6Ni: 49.4Ti wire, respectively, and 4.08-157.08 g and 14.28-90.78 g for the loading and unloading curve of commercial wire, respectively. The average deflection force of the 50.6Ni: 49.4Ti wire was less than that of Ormco NiTi archwire, which could have been due to variations in the wire dimensions. Although a greater force was required for each deflection point of loading and unloading for the 50.6Ni: 49.4Ti wire as compared to Ormco NiTi archwire, the values were still within the acceptable limits to be clinically used in orthodontic treatment. Conclusion: The 50.6Ni: 49.4Ti wires presented the characteristics of a superelastic orthodontic wire. The loading and unloading force were also suitable for orthodontic tooth movement. These results serve as a suitable foundation for further studies in the development of new orthodontic NiTi archwires.

Keywords: 50.6 ni 49.4 Ti alloy wire, load deflection curve, loading and unloading force, orthodontic

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Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

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Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

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Authors: Narek Margaryan, Zhozef Panosyan

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Due to their unique properties, carbon nanofilms have become the object of general attention and intensive research. In this case it plays a very important role to study surface properties of these films. It is also important to study processes of forming of this films, which is accompanied by a process of self-organization at the nano and micro levels. For more detailed investigation, we examined diamond-like carbon (DLC) layers deposited by chemical vapor deposition (CVD) method on Ge substrate and hydro-generated grapheme layers obtained on surface of colloidal solution using grouping method. In this report surface transformation of these CVD nanolayers is studied by atomic force microscopy (AFM) upon deposition time. Also, it can be successfully used to study surface properties of self-assembled grapheme layers. In turn, it is possible to sketch out their boundary line, which enables one to draw an idea of peculiarities of formation of these layers. Images obtained by AFM are investigated as a mathematical set of numbers and fractal and roughness analysis were done. Fractal dimension, Regne’s fractal coefficient, histogram, Fast Fourier transformation, etc. were obtained. The dependence of fractal parameters on the deposition duration for CVD films and on temperature of solution tribolayers was revealed. As an important surface parameter for our carbon films, surface energy was calculated as function of Regne’s fractal coefficient. Surface potential was also measured with Kelvin probe method using semi-contacting AFM. The dependence of surface potential on the deposition duration for CVD films and on temperature of solution for hydro-generated graphene was found as well. Results obtained by fractal analysis method was related with purly esperimental results for number of samples.

Keywords: nanostructured films, self-assembled grapheme, diamond-like carbon, surface potential, Kelvin probe method, fractal analysis

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Authors: Kanchan Mondal, Dasharath Koulage, Dattatray Manerikar, Asmita Ghate

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This paper presents an additive reliability model using warranty data and Finite Element Analysis (FEA) data. Warranty data for any product gives insight to its underlying issues. This is often used by Reliability Engineers to build prediction model to forecast failure rate of parts. But there is one major limitation in using warranty data for prediction. Warranty periods constitute only a small fraction of total lifetime of a product, most of the time it covers only the infant mortality and useful life zone of a bathtub curve. Predicting with warranty data alone in these cases is not generally provide results with desired accuracy. Failure rate of a mechanical part is driven by random issues initially and wear-out or usage related issues at later stages of the lifetime. For better predictability of failure rate, one need to explore the failure rate behavior at wear out zone of a bathtub curve. Due to cost and time constraints, it is not always possible to test samples till failure, but FEA-Fatigue analysis can provide the failure rate behavior of a part much beyond warranty period in a quicker time and at lesser cost. In this work, the authors proposed an Additive Weibull Model, which make use of both warranty and FEA fatigue analysis data for predicting failure rates. It involves modeling of two data sets of a part, one with existing warranty claims and other with fatigue life data. Hazard rate base Weibull estimation has been used for the modeling the warranty data whereas S-N curved based Weibull parameter estimation is used for FEA data. Two separate Weibull models’ parameters are estimated and combined to form the proposed Additive Weibull Model for prediction.

Keywords: bathtub curve, fatigue, FEA, reliability, warranty, Weibull

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Authors: Sayed M. Ahmed, Sawsan S. Darwish, Nagib A. Elmarzugi, Mohammad A. Al-Dosari, Mahmoud A. Adam, Nadia A. Al-Mouallimi

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The preservation of cultural heritage is a worldwide problem. Stone monuments represent an important part of this heritage, but due to their prevalently outdoor location, they are generally subject to a complex series of weathering and decay processes, in addition to physical and chemical factors, also biological agents usually play an important role in deterioration phenomena. The aim of this paper is to experimentally verify applicability and feasibility of titanium dioxide (TiO2) nanoparticles for the preservation of historical (architectural, monumental, archaeological) stone surfaces which enables to reduce the deterioration behaviors mentioned above. TiO2 nanoparticles dispersed in an aqueous colloidal suspension were applied directly on travertine (Marble and limestone often used in historical and monumental buildings) by spray-coating in order to obtain a nanometric film on stone samples. SEM, coupled with EDX microanalysis. (SEM-EDX), in order to obtain information oncoating homogeneity, surface morphology before and after aging and penetration depth of the TiO2 within the samples. Activity of the coated surface was evaluated with UV accelerated aging test. Capillary water absorption, thermal aging and colorimetric measurements have been performed on on coated and uncoated samples to evaluate their properties and estimate change of appearance with colour variation. Results show Tio2 nanoparticles good candidate for coating applications on calcareous stone, good water-repellence was observed on the samples after treatment; analyses were carried out on both untreated and freshly treated samples as well as after artificial aging. Colour change showed negligible variations on the coated or uncoated stone as well as after aging. Results showed that treated stone surfaces seem to be not affected after 1000 hours of exposure to UV radiation, no alteration of the original features.

Keywords: architectural and archaeological heritage, calcareous stone, photocatalysis TiO2, self-cleaning, thermal aging

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Authors: C. C. Okafor

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The effect of different varieties of plantain (Horn, false horn and French) and fibre types (soy bean residue, cassava sievette and rice bran) on functional and organoleptic properties of plantain-based flour was assessed. Horn, false horn french were processed by washing, peeling with knife, slicing into 3mm thickness and steam blanched at 80℃ for 5minutes, oven dried at 65℃ for 48 hours and milled into flours with attrition mill, sieved with 60 mesh sieve, separately. Fibre sources were processed, milled and fractionated into 60, 40 & 20 mesh sizes. Both flours were blended as 80:20, 70:30 and 60:40. Results obtained indicated that water absorption capacity is highest (2.68) in French plantain variety irrespective of the fibre type used. And in all variety tested the swelling capacity is highest (2.93) when the plantain flour is blended with soy residue (SR) and lowest (1.25) when blended with rice brain (RB). The results show that there is significant variety and fibre type interaction effect at (P < : 0.05). Again the results showed that texture mold ability and overall acceptability were best (7.00) when soy residue was used where as addition of rice bran into plantain flour resulted in fufu with poor texture. This trend was observed in all the verities of plantain tested and in all of the particle size of flour. Using cassava serviette also yield fufu similar to that produced with soy residue in all the parameter tested (mold ability, texture and overall acceptability. Generally, plantain flours from french and false horn yielded better quality fufu in terms of texture mold ability, overall acceptability, irrespective of the fibre type used.

Keywords: functional, organoleptic, particle size, sieve mesh, variety

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Authors: Sungpil Ha, Ju Yong Kang, Sangbaek Park, Seung-Ju Lee, Soo-Won Chae

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The growing number of obese patients in aging societies has led to an increase in the number of patients with knee medial osteoarthritis (OA). Artificial joint insertion is the most common treatment for knee medial OA. Surgery is effective for patients with serious arthritic symptoms, but it is costly and dangerous. It is also inappropriate way to prevent a disease as an early stage. Therefore Non-operative treatments such as toe-in gait are proposed recently. Toe-in gait is one of non-surgical interventions, which restrain the progression of arthritis and relieves pain by reducing knee adduction moment (KAM) to facilitate lateral distribution of load on to knee medial cartilage. Numerous studies have measured KAM in various foot progression angle (FPA), and KAM data could be obtained by motion analysis. However, variations in stress at knee cartilage could not be directly observed or evaluated by these experiments of measuring KAM. Therefore, this study applied motion analysis to major gait points (1st peak, mid –stance, 2nd peak) with regard to FPA, and to evaluate the effects of FPA on the human lower extremity, the finite element (FE) method was employed. Three types of gait analysis (toe-in, toe-out, baseline gait) were performed with markers placed at the lower extremity. Ground reaction forces (GRF) were obtained by the force plates. The forces associated with the major muscles were computed using GRF and marker trajectory data. MRI data provided by the Visible Human Project were used to develop a human lower extremity FE model. FE analyses for three types of gait simulations were performed based on the calculated muscle force and GRF. We observed the maximum stress point during toe-in gait was lower than the other types, by comparing the results of FE analyses at the 1st peak across gait types. This is the same as the trend exhibited by KAM, measured through motion analysis in other papers. This indicates that the progression of knee medial OA could be suppressed by adopting toe-in gait. This study integrated motion analysis with FE analysis. One advantage of this method is that re-modeling is not required even with changes in posture. Therefore another type of gait simulation or various motions of lower extremity can be easily analyzed using this method.

Keywords: finite element analysis, gait analysis, human model, motion capture

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Authors: He Li Wen, Meng Qing Xiang, Li De Yong, Zhang Ya Wei, Ren Li Ping

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Stress always exerts some extent adverse effects on the animal production, food safety and quality concerns. Stress is commonly-seen in livestock industry, but there is rare literature focusing on the effects of nutrition stress. What’s more, the research always concentrates on the effect of single stress additionally, there is scarce information about the stress effect on waterfowl like goose as they are commonly thought to be tolerant to stress. To our knowledge, it is not always true. The object of this study was to evaluate the response of growing Greylag geese offered different fiber sources to the comprehensive stress, primarily involving the procedures of fasting, transport, capture, etc. The birds were randomly selected to rear with the diets differing in fiber source, being corn straw silage (CSS), steam-exploded corn straw (SECS), steam-exploded wheat straw (SEWS), and steam-exploded rice straw (SERS), respectively. Blood samples designated for the determination of stress status were collected before (pre-stress ) and after (post-stress ) the stressors carried out. No difference (P>0.05) was found on the pre-stress blood parameters of growing Greylags fed alternative fiber sources. Irrespective of the dietary differences, the comprehensive stress decreased (P<0.01) the concentration of SOD and increased (P<0.01) that of CK. Between the dietary treatments, the birds fed CSS had a higher (P<0.05)post-stress concentration of MDA than those offered SECS, along with a similarity to those fed the other two fiber sources. There was no difference (P>0.05) found on the stress response of the birds fed different fiber sources. In conclusion, SOD and CK concentration in blood may be more sensitive in indicating stress status and dietary fiber source exerted no effect on the stress response of growing Greylags. There is little chance to improve the stress status by ingesting different fiber sources.

Keywords: blood parameter, fiber source, Greylag goose, stress

Procedia PDF Downloads 518

Authors: Rotimi Sunday Ajani, Omowumi Femi-Akinlosotu

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Background: The usually paired human kidneys are retroperitoneal urinary organs with some endocrine functions. Standard text books of anatomy ascribe single value to each of the dimension of length, width and thickness. Research questions: These values do not give consideration to racial and genetic variability in human morphology. They may thus be erroneous to students and clinicians working on Nigerians. Objectives: The study aimed at establishing reference values of the kidney length, width and thickness for Nigerians using the cadaveric model. Methodology: The length, width, thickness and weight of sixty kidneys harvested from cadavers of thirty adult Nigerians (Male: Female; 27: 3) were measured. Respective volume was calculated using the ellipsoid formula. Results: The mean length of the kidney was 9.84±0.89 cm (9.63±0.88 {right}; 10.06±0.86 {left}), width- 5.18±0.70 cm (5.21±0.72 {right}; 5.14±0.70 {left}), thickness-3.45±0.56 cm (3.36±0.58 {right}, 3.53±0.55 {left}), weight-125.06±22.34 g (122.36±21.70 {right}; 127.76 ±24.02 {left}) and volume of 95.45± 24.40 cm3 (91.73± 26.84 {right}; 99.17± 25.75 {left}). Discussion: Though the values of the parameters measured were higher for the left kidney (except for the width), they were not statistically significant. The various parameters obtained by this study differ from those of similar studies from other continents. Conclusion: Stating single value for each of the parameter of length, width and thickness of the kidney as currently obtained in textbooks of anatomy may be incomplete information and hence misleading. Thus, there is the need to emphasize racial differences when stating the normal values of kidney dimensions in textbooks of anatomy. Implication for Research and Innovation: The results of the study showed the dimensions of the kidney (length, width and thickness) have interracial vagaries as they were different from those of similar studies and values stated in standard textbooks of human anatomy. Future direction: This is a preliminary report and the study will continue so that more data will be obtained.

Keywords: kidney dimensions, cadaveric estimation, adult nigerians, racial differences

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Authors: Karthik K. R, Viswanath V, Asraff A. K

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The first stage of a new-generation launch vehicle of ISRO makes use of large pressure vessels made of Aluminium alloy AA2219 to store fuel and oxidizer. These vessels have many weld joints that may contain cracks or crack-like defects during their fabrication. These defects may propagate across the vessel during pressure testing or while in service under the influence of tensile stresses leading to catastrophe. Though ductile materials exhibit significant stable crack growth prior to failure, it is not generally acceptable for an aerospace component. There is a need to predict the initiation of stable crack growth. The structural integrity of the vessel from fracture considerations can be studied by constructing the Failure Assessment Diagram (FAD) that accounts for both brittle fracture and plastic collapse. Critical crack sizes of the pressure vessel may be highly conservative if it is predicted from FAD alone. If the J-R curve for material under consideration is available apriori, the critical crack sizes can be predicted to a certain degree of accuracy. In this paper, a novel approach is proposed to predict the integrity of a weld in a pressure vessel made of AA2219 material. Fracture parameter ‘J-integral’ at the crack front, evaluated through finite element analyses, is used in the new procedure. Based on the simulation of tension tests carried out on SCT specimens by NASA, a cut-off value of J-integral value (J?ᵤₜ_ₒ??) is finalised. For the pressure vessel, J-integral at the crack front is evaluated through FE simulations incorporating different surface cracks at long seam weld in a cylinder and in dome petal welds. The obtained J-integral, at vessel level, is compared with a value of J?ᵤₜ_ₒ??, and the integrity of vessel weld in the presence of the surface crack is firmed up. The advantage of this methodology is that if SCT test data of any metal is available, the critical crack size in hardware fabricated using that material can be predicted to a better level of accuracy.

Keywords: FAD, j-integral, fracture, surface crack

Procedia PDF Downloads 187

Authors: Mandeep Saini

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The rapid advancement of Artificial Intelligence (AI) is transforming various fields, including intellectual property law. This paper explores the emerging role of AI in interpreting patent claims, a critical and highly specialized area within intellectual property rights. Patent claims define the scope of legal protection granted to an invention, and their precise interpretation is crucial in determining the boundaries of the patent holder's rights. Traditionally, this interpretation has relied heavily on the expertise of patent examiners, legal professionals, and judges. However, the increasing complexity of modern inventions, especially in fields like biotechnology, software, and electronics, poses significant challenges to human interpretation. Introducing AI into patent claim interpretation raises several legal and ethical concerns. This paper addresses critical issues such as the reliability of AI-driven interpretations, the potential for algorithmic bias, and the lack of transparency in AI decision-making processes. It considers the legal implications of relying on AI, particularly regarding accountability for errors and the potential challenges to AI interpretations in court. The paper includes a comparative study of AI-driven patent claim interpretations versus human interpretations across different jurisdictions to provide a comprehensive analysis. This comparison highlights the variations in legal standards and practices, offering insights into how AI could impact the harmonization of international patent laws. The paper proposes policy recommendations for the responsible use of AI in patent law. It suggests legal frameworks that ensure AI tools complement, rather than replace, human expertise in patent claim interpretation. These recommendations aim to balance the benefits of AI with the need for maintaining trust, transparency, and fairness in the legal process. By addressing these critical issues, this research contributes to the ongoing discourse on integrating AI into the legal field, specifically within intellectual property rights. It provides a forward-looking perspective on how AI could reshape patent law, offering both opportunities for innovation and challenges that must be carefully managed to protect the integrity of the legal system.

Keywords: artificial intelligence (ai), patent claim interpretation, intellectual property rights, algorithmic bias, natural language processing, patent law harmonization, legal ethics

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Authors: Maria Luiza D. Ramiento, Maria Lissette D. Lucas

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Electricity is said to serve as the backbone of modern technology. Considering this, electricity consumption has dynamically grown due to the continuous demand. An alternative producer of energy concerning electricity must therefore be given focus. Microbial fuel cell wholly characterizes a new method of renewable energy recovery: the direct conversion of organic matter to electricity using bacteria. Electricity is produced as fuel or new food is given to the bacteria. The study concentrated in determining the feasibility of electricity production from local benthic zones. Microbial fuel cells were constructed to harvest the possible electricity and to test the presence of electricity producing microorganisms. Soil samples were gathered from Calumpang River, Palawan Mangrove Forest, Rosario River and Batangas Port. Eleven modules were constructed for the different trials of the soil samples. These modules were made of cathode and anode chambers connected by a salt bridge. For 85 days, the harvested voltage was measured daily. No parameter is added for the first 24 days. For the next 61 days, acetic acid was included in the first and second trials of the modules. Each of the trials of the soil samples gave a positive result in electricity production.There were electricity producing microbes in local benthic zones. It is observed that the higher the organic content of the soil sample, the higher the electricity harvested from it. It is recommended to identify the specific species of the electricity-producing microorganism present in the local benthic zone. Complement experiments are encouraged like determining the kind of soil particles to test its effect on the amount electricity that can be harvested. To pursue the development of microbial fuel cells by building a closed circuit in it is also suggested.

Keywords: microbial fuel cell, benthic zone, electricity, reduction-oxidation reaction, bacteria

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Authors: Kumar Y., Suman D., Sumathi

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Introduction: Burns to the face account for between one-fourth and one-third of all burns. The loss of an eyebrow due to a burn or infection can have negative physical and psychological consequences for patients because eyebrows have a critical functional and aesthetic role on the face. Plastic surgeons face unique challenges in reconstructing eyebrows due to their complex anatomy and variations within genders. As a general rule, there are three techniques for reconstructing the eyebrow: superficial temporal artery island flap, a composite graft from the scalp, and mini or micro follicular grafts from the scalp. In situations where a sufficient amount of subcutaneous tissue is not available and the defect is big such as the case of burns, flaps like the superficial temporal artery scalp flap remain reliable options. In 2018, a 17-year-old female patient presented to the department of Burns Plastic and reconstructive Surgery of Guru Teg Bahadur Hospital, Delhi, India. A scald-burn injury to the face occurred two years before admission, resulting in bilateral eyebrow loss. We reconstructed the bilateral eyebrows using bilateral scalp island flaps based on the posterior branch of the superficial temporal artery. The reconstructed eyebrows successfully assumed a desirable shape and exhibited a natural appearance, which was consistent with preoperative expectations and the patient stated that she was more comfortable with her social relationships. Among the current treatment procedures, the superficial temporal artery island flap continues to be a versatile option for reconstructing the eyebrows after alopecia, especially in cases of burns. Results: During the 30 days follow-up period, the scalp island flap remained vascularised with normal hair growth, without complications. The reconstructed eyebrows successfully assumed a desirable shape and exhibited a natural appearance; the patient stated that she was more comfortable with her social relationships. Conclusion: In this case report, we demonstrated how scalp island flaps pedicled by the superficial temporal artery could be performed very safely and reliably to create new eyebrows.

Keywords: alopecia, burns, eyebrow, flap, superficial temporal artery

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Authors: Alafara A. Baba, Kuranga I. Ayinla, Folahan A. Adekola, Rafiu B. Bale

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Due to increasing demands and diverse applications of copper oxide as pigment in ceramics, cuprammonium hydroxide solution for rayon, p-type semi-conductor, dry cell batteries production and as safety disposal of hazardous materials, a study on the hydrometallurgical operations involving leaching, solvent extraction and precipitation for the recovery of copper for producing high grade copper oxide from a Nigerian chalcopyrite ore in chloride media has been examined. At a particular set of experimental parameter with respect to acid concentration, reaction temperature and particle size, the leaching investigation showed that the ore dissolution increases with increasing acid concentration, temperature and decreasing particle diameter at a moderate stirring. The kinetics data has been analyzed and was found to follow diffusion control mechanism. At optimal conditions, the extent of ore dissolution reached 94.3%. The recovery of the total copper from the hydrochloric acid-leached chalcopyrite ore was undertaken by solvent extraction and precipitation techniques, prior to the beneficiation of the purified solution as copper oxide. The purification of the leach liquor was firstly done by precipitation of total iron and manganese using Ca(OH)2 and H2O2 as oxidizer at pH 3.5 and 4.25, respectively. An extraction efficiency of 97.3% total copper was obtained by 0.2 mol/L Dithizone in kerosene at 25±2ºC within 40 minutes, from which ≈98% Cu from loaded organic phase was successfully stripped by 0.1 mol/L HCl solution. The beneficiation of the recovered pure copper solution was carried out by crystallization through alkali addition followed by calcination at 600ºC to obtain high grade copper oxide (Tenorite, CuO: 05-0661). Finally, a simple hydrometallurgical scheme for the operational extraction procedure amenable for industrial utilization and economic sustainability was provided.

Keywords: chalcopyrite ore, Nigeria, copper, copper oxide, solvent extraction

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Authors: Enrique González Pendás, Vidal Pérez Hernández, Jorge Ferro Díaz, Nelson Careaga Pendás

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The investigation is carried out on the Protected Area of San Ubaldo, toward the interior of an open pinegrove with palm trees in a dry plainness of quar zitic sands, belonging to the Floristic Managed Reservation San Ubaldo-Sabanalamar, Guane, Pinar del Río, Cuba. This area is characterized by drastic seasonal variations, high temperatures and water evaporation, strong solar radiation, with sandy soils of almost pure quartz, which are very acid and poor in nutrients. The objective of the present work is to determine evidence of facilitation and its relationship with the structure and composition of plant communities in these peculiar ecosystems. For this study six lineal parallel transepts of 100 m are traced, in those, a general recording of the flora is carried out. To establish which plants act as nurses, is taken into account a height over 1 meter, canopy over 1.5 meter and the occurrence of several species under it. Covering was recorded using the line intercept method; the medium values of species richness for the taxa under nurses is compared with those that are located in open spaces among them. Then, it is determined which plants are better recruiter of other species (better nurses). An experiment is made to measure and compare some parameters in pine seedlings under the canopy of the Byrsonima crassifolia (L.) Kunth. and in open spaces, also the number of individuals is counted by species to calculate the frequency and total abundance in the study area. As a result, it is offered an up-to-date floristic list, a phylogenetic tree of the plant community showing a high phylodiversity, it is proven that the medium values of species richness and abundance of species under the nurses, is significantly superior to those occurring in open spaces. Furthermore, by means of phylogenetic trees it is shown that the species which cohabit under the nurses are not phylogenetically related. The former results are cited evidences of facilitation among plants, as well as it is one more time shown the importance of the nurse effect in preserving plant diversity on extreme environments.

Keywords: facilitation, nurse plants, positive interactions, quarzitic sands

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Authors: Magu Raam Prasaad R., Venkatramani Jagadish

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Recent studies in the literature have shown that randomly fluctuating wind flows can give rise to a distinct regime of pre-flutter oscillations called intermittency. Intermittency is characterized by the presence of sporadic bursts of high amplitude oscillations interspersed amidst low-amplitude aperiodic fluctuations. The focus of this study is on investigating the energy harvesting potential of these intermittent oscillations. Available literature has by and large devoted its attention on extracting energy from flutter oscillations. The possibility of harvesting energy from pre-flutter regimes have remained largely unexplored. However, extracting energy from violent flutter oscillations can be severely detrimental to the structural integrity of airfoil structures. Consequently, investigating the relatively stable pre-flutter responses for energy extraction applications is of practical importance. The present study is devoted towards addressing these concerns. A pitch-plunge airfoil with cubic hardening nonlinearity in the plunge and pitch degree of freedom is considered. The input flow fluctuations are modelled using a sinusoidal term with randomly perturbed frequencies. An electromagnetic coupling is provided to the pitch-plunge equations, such that, energy from the wind induced vibrations of the structural response are extracted. With the mean flow speed as the bifurcation parameter, a fourth order Runge-Kutta based time marching algorithm is used to solve the governing aeroelastic equations with electro-magnetic coupling. The harnessed energy from the intermittency regime is presented and the results are discussed in comparison to that obtained from the flutter regime. The insights from this study could be useful in health monitoring of aeroelastic structures.

Keywords: aeroelasticity, energy harvesting, intermittency, randomly fluctuating flows

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Authors: Yun Chen, Qihang Liu, Catherine Ticehurst, Chandrama Sarker, Fazlul Karim, Dave Penton, Ashmita Sengupta

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The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect with water. The ICESAT-2 track generates multiple VSs as it crosses the different water bodies. The difficulties are particularly pronounced in large river basins where there are many tributaries and meanders often adjacent to each other. One challenge is to split photon segments along a beam to accurately partition them to extract only the true representative water height for individual elements. As far as we can establish, there is no automated procedure to make this distinction. Earlier studies have relied on human intervention or river masks. Both approaches are unsatisfactory solutions where the number of intersections is large, and river width/extent changes over time. We describe here an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparison with river water level observations at 10 different stations on 37 different dates along the Lower Murray River, Australia. The congruence is very high and without detectable bias. In addition, we compared different outlier removal methods on the mean WSE calculation at VSs post the auto-segmentation process. All four outlier removal methods perform almost equally well with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189m) and MAE (0.130–0.142m). Overall, the auto-segmentation method developed here is an effective and efficient approach to deriving accurate mean WSE at river VSs. It provides a much better way of facilitating the application of ICESAT-2 ATL13 altimetry to rivers compared to previously reported studies. Therefore, the findings of our study will make a significant contribution towards the retrieval of hydraulic parameters, such as water surface slope along the river, water depth at cross sections, and river channel bathymetry for calculating flow velocity and discharge from remotely sensed imagery at large spatial scales.

Keywords: lidar sensor, virtual station, cross section, mean water surface elevation, beam/track segmentation

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Authors: Andrée-Anne Parent, Alain-Steve Comtois

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The Antarctica environment can be a great challenge for human exploration. Explorers need to be focused on the task and require the physical abilities to succeed and survive in complete autonomy in this hostile environment. The aim of this study was to observe cognitive performance and physiological stress with a biomarker (cortisol) and hand grip strength during an expedition in Antarctica. A total of 6 explorers were in complete autonomous exploration on the Forbidden Plateau in Antarctica to reach unknown summits during a 30 day period. The Stroop Test, a simple reaction time, and mood scale (PANAS) tests were performed every week during the expedition. Saliva samples were taken before sailing to Antarctica, the first day on the continent, after the mission on the continent and on the boat return trip. Furthermore, hair samples were taken before and after the expedition. The results were analyzed with SPSS using ANOVA repeated measures. The Stroop and mood scale results are presented in the following order: 1) before sailing to Antarctica, 2) the first day on the continent, 3) after the mission on the continent and 4) on the boat return trip. No significant difference was observed with the Stroop (759±166 ms, 850±114 ms, 772±179 ms and 833±105 ms, respectively) and the PANAS (39.5 ±5.7, 40.5±5, 41.8±6.9, 37.3±5.8 positive emotions, and 17.5±2.3, 18.2±5, 18.3±8.6, 15.8±5.4 negative emotions, respectively) (p>0.05). However, there appears to be an improvement at the end of the second week. Furthermore, the simple reaction time was significantly lower at the end of the second week, a moment where important decisions were taken about the mission, vs the week before (416±39 ms vs 459.8±39 ms respectively; p=0.030). Furthermore, the saliva cortisol was not significantly different (p>0.05) possibly due to important variations and seemed to reach a peak on the first day on the continent. However, the cortisol from the hair pre and post expedition increased significantly (2.4±0.5 pg/mg pre-expedition and 16.7±9.2 pg/mg post-expedition, p=0.013) showing important stress during the expedition. Moreover, no significant difference was observed on the grip strength except between after the mission on the continent and after the boat return trip (91.5±21 kg vs 85±19 kg, p=0.20). In conclusion, the cognitive performance does not seem to be affected during the expedition. Furthermore, it seems to increase for specific important events where the crew seemed to focus on the present task. The physiological stress does not seem to change significantly at specific moments, however, a global pre-post mission measure can be important and for this reason, for long-term missions, a pre-expedition baseline measure is important for crewmembers.

Keywords: Antarctica, cognitive performance, expedition, physiological adaptation, reaction time

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Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Santram Lodhi, Alok Pal Jain, Awesh K. Yadav, Gopal Rai

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Rhodiola rosea L. (Crassulaceae) is commonly known as golden root or rose root. It is a perennial herbaceous plant and most investigated species of the genus Rhodiola. Rhodiola rosea contains flavonoids, terpenoids, phenylpropanoid glycosides and phenylethanol derivatives in the roots of the plant. The objective of present study was to investigate the protective effect of hydroalcoholic extract from Rhodiola rosea roots in DSS induced colitis in mice. The ulcerative colitis was induced by DSS (3%, w/v) in mice and estimated weight loss and stool consistency. Various parameters including Colon length, spleen weights and ulcer index were also measured. The histological observations were observed by H&E staining. Effect of hydroalcoholic extract on various antioxidant parameter of rat colon such as tissue myeloperoxidase (MPO), reduced GSH, SOD concentrations and lipid peroxidation were determined. Pro-inflammatory mediators, such as tumour necrosis factor-α (TNF-α) and nitric oxide (NO) were determined by ELISA. In DSS induced group, mice body weight decreased gradually as compared to the control group. Redness and edema were observed in the colons intensely and scores representing inflammation in this group. The extract treated showed with tissue levels of TNF-α, IL-6 and MPO activity were significantly (p<0.05) increased. The mice treated with higher doses of hydroalcoholic extract (300 mg/kg) significantly reduced the activity compared with standard drug sulfasalazine (100 mg/kg. B.wt). Conclusion: Results of this study were suggested that the efficacy of hydroalcoholic extract, especially at the higher dose, was similar to that of standard drug, which concerned its potential application as a natural medicine for the treatment of ulcerative colitis.

Keywords: phenylpropanoid, Rhodiola rosea, sulfasalazin, ulcerative colitis

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Authors: Bernice Boafoaa Gyapong, Anne Jones, Sam Bassett, Janet Anderson

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Background: Maternal mortality and morbidity are global concerns, especially in sub-Saharan Africa (SSA). Most maternal mortalities occur at the time of birth. Quality intrapartum care is essential for improving maternal and newborn health outcomes. This scoping review aimed to assess and describe the quality of care during childbirth in SSA to provide an overview of the regional trend of the quality of intrapartum care, the challenges to quality care provision, and identify research gaps. Methods: A scoping review based on Arksey and O’Malley’s scoping review framework was conducted. Medline, CINAHL, PsycINFO, and maternal-infant databases were searched to identify the relevant studies for this review. A narrative summary was presented using themes based on the Donabedian structure, process, and outcome quality of care model. Results: A total of five hundred and forty-seven (547) publications were identified. Fifty-six (56) studies conducted in twenty (20) countries were included in the review. Thirty-four (34) were quantitative, sixteen (16) were qualitative, and six (6) were mixed methods. Most of the studies were related to the process component of quality of care. The provision of emergency obstetric care services, infrastructure, and availability of essential staff and equipment for perinatal care was inadequate in many facilities, particularly rural and peripheral health facilities. Many women experienced disrespectful care during childbirth. Routine care during labour and delivery was observed to be sub-optimal, yet some women reported high satisfaction with care. The use of health facilities for delivery was lower in health centres compared to hospitals. Conclusion: There are variations in the quality of maternity care provided in SSA. Intrapartum care quality is generally deficient in SSA, particularly in peripheral health facilities, health centres, and community clinics. Many of the quality-of-care issues identified are related to the structure component. Stakeholders must develop interventions that comprehensively address these interrelated issues to improve maternal healthcare quality, especially in primary healthcare facilities.

Keywords: quality of care, maternity health, Sub-Saharan Africa, intrapartum

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Authors: Eyob Messele Sefene, Atinkut Atinafu Yilma

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The highest strength-to-weight ratio criterion has fascinated increasing curiosity in virtually all areas where weight reduction is indispensable. One of the recent advances in manufacturing to achieve this intention endears friction stir welding (FSW). The process is widely used for joining similar and dissimilar non-ferrous materials. In FSW, the mechanical properties of the weld joints are impelled by property-selected process parameters. This paper presents verdicts of optimum process parameters in attempting to attain enhanced mechanical properties of the weld joint. The experiment was conducted on a 5 mm 6061 aluminum alloy sheet. A butt joint configuration was employed. Process parameters, rotational speed, traverse speed or feed rate, axial force, dwell time, tool material and tool profiles were utilized. Process parameters were also optimized, making use of a mixed L18 orthogonal array and the Grey relation analysis method with larger is better quality characteristics. The mechanical properties of the weld joint are examined through the tensile test, hardness test and liquid penetrant test at ambient temperature. ANOVA was conducted in order to investigate the significant process parameters. This research shows that dwell time, rotational speed, tool shape, and traverse speed have become significant, with a joint efficiency of about 82.58%. Nine confirmatory tests are conducted, and the results indicate that the average values of the grey relational grade fall within the 99% confidence interval. Hence the experiment is proven reliable.

Keywords: friction stir welding, optimization, 6061 AA, Taguchi

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Authors: Semayat Fanta, P.M. Mohite, C.S. Upadhyay

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Damage meso-model for laminates is one of the most widely applicable approaches for the analysis of damage induced in laminated fiber-reinforced polymeric composites. Damage meso-model for laminates has been developed over the last three decades by many researchers in experimental, theoretical, and analytical methods that have been carried out in micromechanics as well as meso-mechanics analysis approaches. It has been fundamentally developed based on the micromechanical description that aims to predict the damage initiation and evolution until the failure of structure in various loading conditions. The current damage meso-model for laminates aimed to act as a bridge between micromechanics and macro-mechanics of the laminated composite structure. This model considers two meso-constituents for the analysis of damage in ply and interface that imparted from low-velocity impact. The damages considered in this study include fiber breakage, matrix cracking, and diffused damage of the lamina, and delamination of the interface. The damage initiation and evolution in laminae can be modeled in terms of damaged strain energy density using damage parameters and the thermodynamic irreversible forces. Interface damage can be modeled with a new concept of spherical micro-void in the resin-rich zone of interface material. The damage evolution is controlled by the damage parameter (d) and the radius of micro-void (r) from the point of damage nucleation to its saturation. The constitutive martial model for meso-constituents is defined in a user material subroutine VUMAT and implemented in ABAQUS/Explicit finite element modeling tool. The model predicts the damages in the meso-constituents level very accurately and is considered the most effective technique of modeling low-velocity impact simulation for laminated composite structures.

Keywords: mesomodel, laminate, low-energy impact, micromechanics

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

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Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

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Authors: T. Poppendieker, H. Maurer, C. Segieth

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Over the past ten years, foam rolling has become a new trend in the fitness and health market. It is also a frequently used technique for self-massage. However, the scope of effects from foam rolling has only recently started to be researched and understood. The focus of this study is to examine the effects of prolonged foam rolling on muscle performance. Isometric muscle force was used as a parameter to determine an improving impact of the myofascial roller in two different application volumes. Besides the maximal muscle force, data were also collected on muscle activation during all tests. Twenty-four (17 females, 7 males) healthy students with an average age of 23.4 ± 2.8 years were recruited. The study followed a cross-over pre-/post design in which the order of conditions was counterbalanced. The subjects performed a one-minute and three-minute foam rolling application set on two separate days. Isometric maximal muscle force of the dominant calf was tested before and after the self-myofascial release application. The statistic software program SPSS 22 was used to analyze the data of the maximal isometric force of the calf muscle by a 2 x 2 (time of measurement x intervention) analysis of variance with repeated measures. The statistic significance level was set at p ≤ 0.05. Neither for the main effect of time of measurement (F(1,23) = .93, p = .36, f = .20) nor for the interaction of time of measurement x intervention (F(1,23) = 1.99, p = .17, f = 0.29) significant p-values were found. However, the effect size indicates a mean interaction effect with a tendency of greater pre-post improvements under the three-minute foam rolling condition. Changes in maximal force did not correlate with changes in EMG-activity (r = .02, p = .95 in the short and r = -.11, p = .65 in the long rolling condition). Results support findings of previous studies and suggest a positive potential for use of the foam roll as a means for keeping muscle force at least at the same performance level while leading to an increase in flexibility.

Keywords: application volume differences, foam rolling, isometric maximal force, self-myofascial release

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

Procedia PDF Downloads 727