Search results for: accuracy estimate

Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: Harish Bisht, Bahadur Singh Kotlia, Kireet Kumar

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In order to reconstruct past retreating rates, total area loss, volume change and shift in snout position were measured through multi-temporal satellite data from 1989 to 2016 and kinematic GPS survey from 2015 to 2016. The results obtained from satellite data indicate that in the last 27 years, Chaturangi glacier snout has retreated 1172.57 ± 38.3 m (average 45.07 ± 4.31 m/year) with a total area and volume loss of 0.626 ± 0.001 sq. Km and 0.139 Km³, respectively. The field measurements through differential global positioning system survey revealed that the annual retreating rate was 22.84 ± 0.05 m/year. The large variations in results derived from both the methods are probably because of higher difference in their accuracy. Snout monitoring of the Gangotri glacier during the ablation season (May to September) in the years 2005 and 2015 reveals that the retreating rate has been comparatively more declined than that shown by the earlier studies. The GPS dataset shows that the average recession rate is 10.26 ± 0.05 m/year. In order to determine the possible causes of decreased retreating rate, a relationship between debris thickness and melt rate was also established by using ablation stakes. The present study concludes that remote sensing method is suitable for large area and long term study, while kinematic GPS is more appropriate for the annual monitoring of retreating rate of glacier snout. The present study also emphasizes on mapping of all the tributary glaciers in order to assess the overall changes in the main glacier system and its health.

Keywords: Chaturangi glacier, Gangotri glacier, glacier snout, kinematic global positioning system, retreat rate

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Authors: Surabhi Agrawal Kohli, Sunita Gupta, Esha Khanuja, Parul Garg, P. Gupta

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Background: Adenomyosis is a common gynaecological condition that affects the menstruating women. Uterine enlargement, dysmenorrhoea, and menorrhagia are regarded as the cardinal clinical symptoms of adenomyosis. Classically it was thought, compared with ultrasonography, when adenomyosis is suspected, MRI enables more accurate diagnosis of the disease. Materials and Methods: 172 subjects were enrolled after an informed consent that had complaints of HMB, dyspareunia, dysmenorrhea, and chronic pelvic pain. Detailed history of the enrolled subjects was taken, followed by a clinical examination. These patients were then subjected to TVS where myometrial echo texture, presence of myometrial cysts, blurring of endomyometrial junction was noted. MRI was followed which noted the presence of junctional zone thickness and myometrial cysts. After hysterectomy, histopathological diagnosis was obtained. Results: 78 participants were analysed. The mean age was 44.2 years. 43.5% had parity of 4 or more. heavy menstrual bleeding (HMB) was present in 97.8% and dysmenorrhea in 93.48 % of HPE positive patient. Transvaginal sonography (TVS) and MRI had a sensitivity of 89.13% and 80.43%, specificity of 90.62% and 84.37%, positive likelihood ratio of 9.51 and 5.15, negative likelihood ratio of 0.12 and 0.23, positive predictive value of 93.18% and 88.1%, negative predictive value of 85.29% and 75% and a diagnostic accuracy of 89.74% and 82.5%. Comparison of sensitivity (p=0.289) and specificity (p=0.625) showed no statistically significant difference between TVS and MRI. Conclusion: Prevalence of 30.23%. HMB with dysmenorrhoea and chronic pelvic pain helps in diagnosis. TVS (Endomyometrial junction blurring) is both sensitive and specific in diagnosing adenomyosis without need for additional diagnostic tool. Both TVS and MRI are equally efficient, however because of certain additional advantages of TVS over MRI, it may be used as the first choice of imaging. MRI may be used additionally in difficult cases as well as in patients with existing co-pathologies.

Keywords: adenomyosis, heavy menstrual bleeding, MRI, TVS

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Authors: Lu Song Ma Li Zhi

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With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: John Tarus, Zhendong Niu, Bakhti Khadidja

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In recent years, e-learning recommender systems has attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. E-learning recommenders continue to play an increasing educational role in aiding learners to find appropriate learning materials to support the achievement of their learning goals. Although general recommender systems have recorded significant success in solving the problem of information overload in e-commerce domains and providing accurate recommendations, e-learning recommender systems on the other hand still face some issues arising from differences in learner characteristics such as learning style, skill level and study level. Conventional recommendation techniques such as collaborative filtering and content-based deal with only two types of entities namely users and items with their ratings. These conventional recommender systems do not take into account the learner characteristics in their recommendation process. Therefore, conventional recommendation techniques cannot make accurate and personalized recommendations in e-learning environment. In this paper, we propose a recommendation technique combining collaborative filtering and ontology to recommend personalized learning materials to online learners. Ontology is used to incorporate the learner characteristics into the recommendation process alongside the ratings while collaborate filtering predicts ratings and generate recommendations. Furthermore, ontological knowledge is used by the recommender system at the initial stages in the absence of ratings to alleviate the cold-start problem. Evaluation results show that our proposed recommendation technique outperforms collaborative filtering on its own in terms of personalization and recommendation accuracy.

Keywords: collaborative filtering, e-learning, ontology, recommender system

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Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

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Authors: Mustafa Metin Donma

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Circumference measurements are important because they are easily obtained values for the identification of the weight gain without determining body fat. They may give meaningful information about the varying stages of obesity. Besides, some formulas may be derived from a number of body circumference measurements to estimate body fat. Waist (WC), hip (HC) and neck (NC) circumferences are currently the most frequently used measurements. The aim of this study was to develop a formula derived from these three anthropometric measurements, each giving a valuable information independently, to question whether their combined power within a formula was capable of being helpful for the differential diagnosis of morbid obesity without metabolic syndrome (MetS) from MetS. One hundred and eighty seven children were recruited from the pediatrics outpatient clinic of Tekirdag Namik Kemal University Faculty of Medicine. The parents of the participants were informed about asked to fill and sign the consent forms. The study was carried out according to the Helsinki Declaration. The study protocol was approved by the institutional non-interventional ethics committee. The study population was divided into four groups as normal-body mass index (N-BMI), obese (OB), morbid obese (MO) and MetS, which were composed of 35, 44, 75 and 33 children, respectively. Age- and gender-adjusted BMI percentile values were used for the classification of groups. The children in MetS group were selected based upon the nature of the MetS components described as MetS criteria. Anthropometric measurements, laboratory analysis and statistical evaluation confined to study population were performed. Body mass index values were calculated. A circumference index, advanced Donma circumference index (ADCI) was introduced as WC*HC/NC. The statistical significance degree was chosen as p value smaller than 0.05. Body mass index values were 17.7±2.8, 24.5±3.3, 28.8±5.7, 31.4±8.0 kg/m2, for N-BMI, OB, MO, MetS groups, respectively. The corresponding values for ADCI were 165±35, 240±42, 270±55, and 298±62. Significant differences were obtained between BMI values of N-BMI and OB, MO, MetS groups (p=0.001). Obese group BMI values also differed from MO group BMI values (p=0.001). However, the increase in MetS group compared to MO group was not significant (p=0.091). For the new index, significant differences were obtained between N-BMI and OB, MO, MetS groups (p=0.001). Obese group ADCI values also differed from MO group ADCI values (p=0.015). A significant difference between MO and MetS groups was detected (p=0.043). The correlation coefficient value and the significance check of the correlation was found between BMI and ADCI as r=0.0883 and p=0.001 upon consideration of all participants. In conclusion, in spite of the strong correlation between BMI and ADCI values obtained when all groups were considered, ADCI, but not BMI, was the index, which was capable of differentiating cases with morbid obesity from cases with morbid obesity and MetS.

Keywords: anthropometry, body mass index, child, circumference, metabolic syndrome, obesity

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Authors: Oluwatosin M. A. Jesuyon

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In order to propose a simpler tool that eliminates the age-long problems associated with the traditional index method for selection of multiple traits in broilers, the Barttlet factor regression equation is being proposed as an alternative selection tool. 100 day-old chicks each of Arbor Acres (AA) and Annak (AN) broiler strains were obtained from two rival hatcheries in Ibadan Nigeria. These were raised in deep litter system in a 56-day feeding trial at the University of Ibadan Teaching and Research Farm, located in South-west Tropical Nigeria. The body weight and body dimensions were measured and recorded during the trial period. Eight (8) zoometric measurements namely live weight (g), abdominal circumference, abdominal length, breast width, leg length, height, wing length and thigh circumference (all in cm) were recorded randomly from 20 birds within strain, at a fixed time on the first day of the new week respectively with a 5-kg capacity Camry scale. These records were analyzed and compared using completely randomized design (CRD) of SPSS analytical software, with the means procedure, Factor Scores (FS) in stepwise Multiple Linear Regression (MLR) procedure for initial live weight equations. Bartlett Factor Score (BFS) analysis extracted 2 factors for each strain, termed Body-length and Thigh-meatiness Factors for AA, and; Breast Size and Height Factors for AN. These derived orthogonal factors assisted in deducing and comparing trait-combinations that best describe body conformation and Meatiness in experimental broilers. BFS procedure yielded different body conformational traits for the two strains, thus indicating the different economic traits and advantages of strains. These factors could be useful as selection criteria for improving desired economic traits. The final Bartlett Factor Regression equations for prediction of body weight were highly significant with P < 0.0001, R2 of 0.92 and above, VIF of 1.00, and DW of 1.90 and 1.47 for Arbor Acres and Annak respectively. These FSR equations could be used as a simple and potent tool for selection during poultry flock improvement, it could also be used to estimate selection index of flocks in order to discriminate between strains, and evaluate consumer preference traits in broilers.

Keywords: alternative selection tool, Bartlet factor regression model, consumer preference trait, linear and body measurements, live body weight

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Authors: Mohammed Ahmed Ahmed Odah

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DNA barcoding is a crucial method for assessing and monitoring species diversity amidst escalating threats to global biodiversity. The author explores DNA barcoding's potential as a robust and reliable tool for biodiversity assessment. It begins with a comprehensive review of existing literature, delving into the theoretical foundations, methodologies and applications of DNA barcoding. The suitability of various DNA regions, like the COI gene, as universal barcodes is extensively investigated. Additionally, the advantages and limitations of different DNA sequencing technologies and bioinformatics tools are evaluated within the context of DNA barcoding. To evaluate the efficacy of DNA barcoding, diverse ecosystems, including terrestrial, freshwater and marine habitats, are sampled. Extracted DNA from collected specimens undergoes amplification and sequencing of the target barcode region. Comparison of the obtained DNA sequences with reference databases allows for the identification and classification of the sampled organisms. Findings demonstrate that DNA barcoding accurately identifies species, even in cases where morphological identification proves challenging. Moreover, it sheds light on cryptic and endangered species, aiding conservation efforts. The author also investigates patterns of genetic diversity and evolutionary relationships among different taxa through the analysis of genetic data. This research contributes to the growing knowledge of DNA barcoding and its applicability for biodiversity assessment. The advantages of this approach, such as speed, accuracy and cost-effectiveness, are highlighted, along with areas for improvement. By unlocking the genetic code, DNA barcoding enhances our understanding of biodiversity, supports conservation initiatives and informs evidence-based decision-making for the sustainable management of ecosystems.

Keywords: DNA barcoding, biodiversity assessment, genetic code, species identification, taxonomic resolution, next-generation sequencing

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Authors: Numair Ahmed Siddiqui, Abdul Hadi bin Abd Rahman, Chow Weng Sum, Wan Ismail Wan Yousif, Asif Zameer, Joel Ben-Awal

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Advances in data capturing from outcrop studies have made possible the acquisition of high-resolution digital data, offering improved and economical reservoir modelling methods. Terrestrial laser scanning utilizing LiDAR (Light detection and ranging) provides a new method to build outcrop based reservoir models, which provide a crucial piece of information to understand heterogeneities in sandstone facies with high-resolution images and data set. This study presents the detailed application of outcrop based sandstone facies connectivity model by acquiring information gathered from traditional fieldwork and processing detailed digital point-cloud data from LiDAR to develop an intermediate small-scale reservoir sandstone facies model of the Miocene Sandakan Formation, Sabah, East Malaysia. The software RiScan pro (v1.8.0) was used in digital data collection and post-processing with an accuracy of 0.01 m and point acquisition rate of up to 10,000 points per second. We provide an accurate and descriptive workflow to triangulate point-clouds of different sets of sandstone facies with well-marked top and bottom boundaries in conjunction with field sedimentology. This will provide highly accurate qualitative sandstone facies connectivity model which is a challenge to obtain from subsurface datasets (i.e., seismic and well data). Finally, by applying this workflow, we can build an outcrop based static connectivity model, which can be an analogue to subsurface reservoir studies.

Keywords: LiDAR, outcrop, high resolution, sandstone faceis, connectivity model

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Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

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The study uses an image analysis technique employing thermal imaging to measure the percentage of areas with various temperatures on a freezing surface. An image segmentation method using threshold values is applied to a sequence of image recording the freezing process. The phenomenon is transient and temperatures vary fast to reach the freezing point and complete the freezing process. Freezing salt water is subjected to the salt rejection that makes the freezing point dynamic and dependent on the salinity at the phase interface. For a specific area of freezing, nucleation starts from one side and end to another side, which causes a dynamic and transient temperature in that area. Thermal cameras are able to reveal a difference in temperature due to their sensitivity to infrared radiance. Using Experimental setup, a video is recorded by a thermal camera to monitor radiance and temperatures during the freezing process. Image processing techniques are applied to all frames to detect and classify temperatures on the surface. Image processing segmentation method is used to find contours with same temperatures on the icing surface. Each segment is obtained using the temperature range appeared in the image and correspond pixel values in the image. Using the contours extracted from image and camera parameters, stratified areas with different temperatures are calculated. To observe temperature contours on the icing surface using the thermal camera, the salt water sample is dropped on a cold surface with the temperature of -20°C. A thermal video is recorded for 2 minutes to observe the temperature field. Examining the results obtained by the method and the experimental observations verifies the accuracy and applicability of the method.

Keywords: ice contour boundary, image processing, image segmentation, salt ice, thermal image

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Shidvash Vakilipour, Scott Ormiston, Masoud Mohammadi, Rouzbeh Riazi, Kimia Amiri, Sahar Barati

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Two-phase and multi-phase flows are common flow types in fluid mechanics engineering. Among the basic and applied problems of these flow types, two-phase parallel flow is the one that two immiscible fluids flow in the vicinity of each other. In this type of flow, fluid properties (e.g. density, viscosity, and temperature) are different at the two sides of the interface of the two fluids. The most challenging part of the numerical simulation of two-phase flow is to determine the location of interface accurately. In the present work, a coupled interface tracking algorithm is developed based on Arbitrary Lagrangian-Eulerian (ALE) approach using a cell-centered, pressure-based, coupled solver. To validate this algorithm, an analytical solution for fully developed two-phase flow in presence of gravity is derived, and then, the results of the numerical simulation of this flow are compared with analytical solution at various flow conditions. The results of the simulations show good accuracy of the algorithm despite using a nearly coarse and uniform grid. Temporal variations of interface profile toward the steady-state solution show that a greater difference between fluids properties (especially dynamic viscosity) will result in larger traveling waves. Gravity effect studies also show that favorable gravity will result in a reduction of heavier fluid thickness and adverse gravity leads to increasing it with respect to the zero gravity condition. However, the magnitude of variation in favorable gravity is much more than adverse gravity.

Keywords: coupled solver, gravitational force, interface tracking, Reynolds number to Froude number, two-phase flow

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Authors: Salim Yaren, Nergis Canturk

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Signatures are signs that are handwritten by person in order to confirm values such as information, amount, meaning, time and undertaking that bear on a document. It is understood that the signature of a document and the accuracy of the information on the signature is accepted and approved. Forged signatures are formed by forger without knowing and seeing original signature of person that forger will imitate and as a result of his/her effort for hiding typical characteristics of his/her own signatures. Forged signatures are often signed by starting with the initials of the first and last name or persons of the persons whose fake signature will be signed. The similarities in the signatures are completely random. Within the scope of the study, forged signatures are collected from 100 people both their original signatures and forged signatures signed referring to 5 imaginary people. These signatures are compared for 14 signature analyzing criteria by 2 signature analyzing experts except the researcher. 1 numbered analyzing expert who is 9 year experience in his/her field evaluated signatures of 39 (39%) people right and of 25 (25%) people wrong and he /she made any evaluations for signatures of 36 (36%) people. 2 numbered analyzing expert who is 16 year experienced in his/her field evaluated signatures of 49 (49%) people right and 28 (28%) people wrong and he /she made any evaluations for signatures of 23 (23%) people. Forged signatures that are signed by 24 (24%) people are matched by two analyzing experts properly, forged signatures that are signed by 8 (8%) people are matched wrongfully and made up signatures that are signed by 12 (12%) people couldn't be decided by both analyzing experts. Signatures analyzing is a subjective topic so that analyzing and comparisons take form according to education, knowledge and experience of the expert. Consequently, due to the fact that 39% success is achieved by analyzing expert who has 9 year professional experience and 49% success is achieved by analyzing expert who has 16 year professional experience, it is seen that success rate is directly proportionate to knowledge and experience of the expert.

Keywords: forensic signature, forensic signature analysis, signature analysis criteria, forged signature

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Authors: Adrian Forca, Simeon Cainday III

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The Electronic Payment Recording with Payment History Retrieval Module is developed intendedly for the College of Science and Technology. This system software innovates the manual process of recording the payments done in the department through the development of electronic payment recording system software shifting from the slow and time-consuming procedure to quick yet reliable and accurate way of recording payments because it immediately generates receipts for every transaction. As an added feature to its software process, generation of recorded payment report is integrated eliminating the manual reporting to a more easy and consolidated report. As an added feature to the system, all recorded payments of the students can be retrieved immediately making the system transparent and reliable payment recording software. Viewing the whole process, the system software will shift from the manual process to an organized software technology because the information will be stored in a logically correct and normalized database. Further, the software will be developed using the modern programming language and implement strict programming methods to validate all users accessing the system, evaluate all data passed into the system and information retrieved to ensure data accuracy and reliability. In addition, the system will identify the user and limit its access privilege to establish boundaries of the specific access to information allowed for the store, modify, and update making the information secure against unauthorized data manipulation. As a result, the System software will eliminate the manual procedure and replace with an innovative modern information technology resulting to the improvement of the whole process of payment recording fast, secure, accurate and reliable software innovations.

Keywords: collection, information system, manual procedure, payment

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Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

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Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Ahmed M. Debela, Mayreli Ortiz , Ciara K. O´Sullivan

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The completion of the human genome Project (HGP) has paved the way for mapping the diversity in the overall genome sequence which helps to understand the genetic causes of inherited diseases and susceptibility to drugs or environmental toxins. Arrayed primer extension (APEX) is a microarray based minisequencing strategy for screening disease causing mutations. It is derived from Sanger DNA sequencing and uses fluorescently dideoxynucleotides (ddNTPs) for termination of a growing DNA strand from a primer with its 3´- end designed immediately upstream of a site where single nucleotide polymorphism (SNP) occurs. The use of DNA polymerase offers a very high accuracy and specificity to APEX which in turn happens to be a method of choice for multiplex SNP detection. Coupling the high specificity of this method with the high sensitivity, low cost and compatibility for miniaturization of electrochemical techniques would offer an excellent platform for detection of mutation as well as sequencing of DNA templates. We are developing an electrochemical APEX for the analysis of SNPs found in the MYH7 gene for group of cardiomyopathy patients. ddNTPs were labeled with four different redox active compounds with four distinct potentials. Thiolated oligonucleotide probes were immobilised on gold and glassy carbon substrates which are followed by hybridisation with complementary target DNA just adjacent to the base to be extended by polymerase. Electrochemical interrogation was performed after the incorporation of the redox labelled dedioxynucleotide. The work involved the synthesis and characterisation of the redox labelled ddNTPs, optimisation and characterisation of surface functionalisation strategies and the nucleotide incorporation assays.

Keywords: array based primer extension, labelled ddNTPs, electrochemical, mutations

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Authors: Endang Lukitaningsih, Fathul Jannah, Arief R. Hakim, Ratna D. Puspita, Zullies Ikawati

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Rifampicin is a semisynthetic antibiotic derivative of rifamycin B produced by Streptomyces mediterranei. RIF has been used worldwide as first line drug-prescribed throughout tuberculosis therapy. This study aims to develop and to validate an HPLC method couple with a UV detection for determination of rifampicin in spiked human plasma and its application for bioequivalence study. The chromatographic separation was achieved on an RP-C18 column (LachromHitachi, 250 x 4.6 mm., 5μm), utilizing a mobile phase of phosphate buffer/acetonitrile (55:45, v/v, pH 6.8 ± 0.1) at a flow of 1.5 mL/min. Detection was carried out at 337 nm by using spectrophotometer. The developed method was statistically validated for the linearity, accuracy, limit of detection, limit of quantitation, precise and specifity. The specifity of the method was ascertained by comparing chromatograms of blank plasma and plasma containing rifampicin; the matrix and rifampicin were well separated. The limit of detection and limit of quantification were 0.7 µg/mL and 2.3 µg/mL, respectively. The regression curve of standard was linear (r > 0.999) over a range concentration of 20.0 – 100.0 µg/mL. The mean recovery of the method was 96.68 ± 8.06 %. Both intraday and interday precision data showed reproducibility (R.S.D. 2.98% and 1.13 %, respectively). Therefore, the method can be used for routine analysis of rifampicin in human plasma and in bioequivalence study. The validated method was successfully applied in pharmacokinetic and bioequivalence study of rifampicin tablet in a limited number of subjects (under an Ethical Clearance No. KE/FK/6201/EC/2015). The mean values of Cmax, Tmax, AUC(0-24) and AUC(o-∞) for the test formulation of rifampicin were 5.81 ± 0.88 µg/mL, 1.25 hour, 29.16 ± 4.05 µg/mL. h. and 29.41 ± 4.07 µg/mL. h., respectively. Meanwhile for the reference formulation, the values were 5.04 ± 0.54 µg/mL, 1.31 hour, 27.20 ± 3.98 µg/mL.h. and 27.49 ± 4.01 µg/mL.h. From bioequivalence study, the 90% CIs for the test formulation/reference formulation ratio for the logarithmic transformations of Cmax and AUC(0-24) were 97.96-129.48% and 99.13-120.02%, respectively. According to the bioequivamence test guidelines of the European Commission-European Medicines Agency, it can be concluded that the test formulation of rifampicin is bioequivalence with the reference formulation.

Keywords: validation, HPLC, plasma, bioequivalence

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Authors: Ricardo Torcato, Helder Morais

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The manufacture of crystal glass parts is based on obtaining the rough geometry by blowing and/or injection, generally followed by a set of manual finishing operations using cutting and grinding tools. The forming techniques used do not allow the obtainment, with repeatability, of parts with complex shapes and the finishing operations use intensive specialized labor resulting in high cycle times and production costs. This work aims to explore the digital manufacture of crystal glass parts by investigating new subtractive techniques for the automated, flexible finishing of these parts. Finishing operations are essential to respond to customer demands in terms of crystal feel and shine. It is intended to investigate the applicability of different computerized finishing technologies, namely milling and grinding in a CNC machining center with or without ultrasonic assistance, to crystal processing. Research in the field of grinding hard and brittle materials, despite not being extensive, has increased in recent years, and scientific knowledge about the machinability of crystal glass is still very limited. However, it can be said that the unique properties of glass, such as high hardness and very low toughness, make any glass machining technology a very challenging process. This work will measure the performance improvement brought about by the use of ultrasound compared to conventional crystal grinding. This presentation is focused on the mechanical characterization and analysis of the cutting forces in CNC machining of superior crystal glass (Pb ≥ 30%). For the mechanical characterization, the Vickers hardness test provides an estimate of the material hardness (Hv) and the fracture toughness based on cracks that appear in the indentation. Mechanical impulse excitation test estimates the Young’s Modulus, shear modulus and Poisson ratio of the material. For the cutting forces, it a dynamometer was used to measure the forces in the face grinding process. The tests were made based on the Taguchi method to correlate the input parameters (feed rate, tool rotation speed and depth of cut) with the output parameters (surface roughness and cutting forces) to optimize the process (better roughness using the cutting forces that do not compromise the material structure and the tool life) using ANOVA. This study was conducted for conventional grinding and for the ultrasonic grinding process with the same cutting tools. It was possible to determine the optimum cutting parameters for minimum cutting forces and for minimum surface roughness in both grinding processes. Ultrasonic-assisted grinding provides a better surface roughness than conventional grinding.

Keywords: CNC machining, crystal glass, cutting forces, hardness

Procedia PDF Downloads 148

Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

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Authors: Sukriti Sabharwal, Sonali Bhattar, Shikhar Saxena

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Introduction: COVID-19 molecular diagnostic laboratory is the cornerstone of the COVID-19 disease diagnosis as the patient’s treatment and management protocol depend on the molecular results. For this purpose, it is extremely important that the laboratory conducting these results adheres to the quality management processes to increase the accuracy and validity of the reports generated. We started our own molecular diagnostic setup at the onset of the pandemic. Therefore, we conducted this study to generate our quality management data to help us in improving on our weak points. Materials and Methods: A total of 14561 samples were evaluated by the retrospective observational method. The quality variables analysed were classified into pre-analytical, analytical, and post-analytical variables, and the results were presented in percentages. Results: Among the pre-analytical variables, sample leaking was the most common cause of the rejection of samples (134/14561, 0.92%), followed by non-generation of SRF ID (76/14561, 0.52%) and non-compliance to triple packaging (44/14561, 0.3%). The other pre-analytical aspects assessed were incomplete patient identification (17/14561, 0.11%), insufficient quantity of samples (12/14561, 0.08%), missing forms/samples (7/14561, 0.04%), samples in the wrong vials/empty VTM tubes (5/14561, 0.03%) and LIMS entry not done (2/14561, 0.01%). We are unable to obtain internal quality control in 0.37% of samples (55/14561). We also experienced two incidences of cross-contamination among the samples resulting in false-positive results. Among the post-analytical factors, a total of 0.07% of samples (11/14561) could not be dispatched within the stipulated time frame. Conclusion: Adherence to quality control processes is foremost for the smooth running of any diagnostic laboratory, especially the ones involved in critical reporting. Not only do the indicators help in keeping in check the laboratory parameters but they also allow comparison with other laboratories.

Keywords: laboratory quality management, COVID-19, molecular diagnostics, healthcare

Procedia PDF Downloads 155

Authors: Yepeng Cheng, Yasuhiko Morimoto

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Customer relationship analysis is vital for retail stores, especially for supermarkets. The point of sale (POS) systems make it possible to record the daily purchasing behaviors of customers as an identification point of sale (ID-POS) database, which can be used to analyze customer behaviors of a supermarket. The customer value is an indicator based on ID-POS database for detecting the customer loyalty of a store. In general, there are many supermarkets in a city, and other nearby competitor supermarkets significantly affect the customer value of customers of a supermarket. However, it is impossible to get detailed ID-POS databases of competitor supermarkets. This study firstly focused on the customer value and distance between a customer's home and supermarkets in a city, and then constructed the models based on logistic regression analysis to analyze correlations between distance and purchasing behaviors only from a POS database of a supermarket chain. During the modeling process, there are three primary problems existed, including the incomparable problem of customer values, the multicollinearity problem among customer value and distance data, and the number of valid partial regression coefficients. The improved customer value, Huff’s gravity model, and inverse attractiveness frequency are considered to solve these problems. This paper presents three types of models based on these three methods for loyal customer classification and competitors’ influence analysis. In numerical experiments, all types of models are useful for loyal customer classification. The type of model, including all three methods, is the most superior one for evaluating the influence of the other nearby supermarkets on customers' purchasing of a supermarket chain from the viewpoint of valid partial regression coefficients and accuracy.

Keywords: customer value, Huff's Gravity Model, POS, Retailer

Procedia PDF Downloads 117

Authors: Tugba Bayoglu

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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

Procedia PDF Downloads 270

Authors: Oskar Sekowski

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Current global warming causes changes in the traditional zones of viticulture worldwide. During 20th century, the average global air temperature increased by 0.89˚C. The models of climate change indicate that viticulture, currently concentrating in narrow geographic niches, may move towards the poles, to higher geographic latitudes. Global warming may cause changes in traditional viticulture regions. Therefore, there is a need to estimate the climatic conditions and climate change in areas that are not traditionally associated with viticulture, e.g., Poland. The primary objective of this paper is to prepare methodology to evaluate the climatic and meteorological potential for viticulture in Poland based on a case study. Moreover, the additional aim is to evaluate the climatic potential of a mesoregion where a university vineyard is located. The daily data of temperature, precipitation, insolation, and wind speed (1988-2018) from the meteorological station located in Łazy, southern Poland, was used to evaluate 15 climatological parameters and indices connected with viticulture. The next steps of the methodology are based on Geographic Information System methods. The topographical factors such as a slope gradient and slope exposure were created using Digital Elevation Models. The spatial distribution of climatological elements was interpolated by ordinary kriging. The values of each factor and indices were also ranked and classified. The viticultural potential was determined by integrating two suitability maps, i.e., the topographical and climatic ones, and by calculating the average for each pixel. Data analysis shows significant changes in heat accumulation indices that are driven by increases in maximum temperature, mostly increasing number of days with Tmax > 30˚C. The climatic conditions of this mesoregion are sufficient for vitis vinifera viticulture. The values of indicators and insolation are similar to those in the known wine regions located on similar geographical latitudes in Europe. The smallest threat to viticulture in study area is the occurrence of hail and the highest occurrence of frost in the winter. This research provides the basis for evaluating general suitability and climatologic potential for viticulture in Poland. To characterize the climatic potential for viticulture, it is necessary to assess the suitability of all climatological and topographical factors that can influence viticulture. The methodology used in this case study shows places where there is a possibility to create vineyards. It may also be helpful for wine-makers to select grape varieties.

Keywords: climatologic potential, climatic classification, Poland, viticulture

Procedia PDF Downloads 96

Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

Procedia PDF Downloads 175

Authors: Migael van Zyl, Stefanie Visser, Awelani Phaswana

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The electricity landscape in South Africa is characterized by frequent occurrences of loadshedding, a measure implemented by Eskom to manage electricity generation shortages by curtailing demand. Loadshedding, classified into stages ranging from 1 to 8 based on severity, involves the systematic rotation of power cuts across municipalities according to predefined schedules. However, this practice introduces distortions in recorded electricity demand, posing challenges to accurate forecasting essential for budgeting, network planning, and generation scheduling. Addressing this challenge requires the development of a methodology to quantify the impact of loadshedding and integrate it back into metered electricity demand data. Fortunately, comprehensive records of loadshedding impacts are maintained in a database, enabling the alignment of Loadshedding effects with hourly demand data. This adjustment ensures that forecasts accurately reflect true demand patterns, independent of loadshedding's influence, thereby enhancing the reliability of electricity supply management in South Africa. This paper presents a methodology for determining the hourly impact of load scheduling and subsequently adjusting historical demand data to account for it. Furthermore, two forecasting models are developed: one utilizing the original dataset and the other using the adjusted data. A comparative analysis is conducted to evaluate forecast accuracy improvements resulting from the adjustment process. By implementing this methodology, stakeholders can make more informed decisions regarding electricity infrastructure investments, resource allocation, and operational planning, contributing to the overall stability and efficiency of South Africa's electricity supply system.

Keywords: electricity demand forecasting, load shedding, demand side management, data science

Procedia PDF Downloads 53

Authors: B. Petrović, M. Petrović, L. Rutonjski, I. Djan, V. Ivanović

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Objective: Cardiac pacemakers are very sensitive to radiotherapy treatment from two sources: electromagnetic influence from the medical linear accelerator producing ionizing radiation- influencing electronics within the pacemaker, and the absorption of dose to the device. On the other hand, patients with cardiac pacemakers at the place of a tumor are rather rare, and single clinic hardly has experience with the management of such patients. The widely accepted international guidelines for management of radiation oncology patients recommend that these patients should be closely monitored and examined before, during and after radiotherapy treatment by cardiologist, and their device and condition followed up. The number of patients having both cancer and pacemaker, is growing every year, as both cancer incidence, as well as cardiac diseases incidence, are inevitably growing figures. Materials and methods: Female patient, age 69, was diagnozed with valvular cardiomyopathy and got implanted a pacemaker in 2005 and prosthetic mitral valve in 1993 (cancer was diagnosed in 2012). She was stable cardiologically and came to radiation therapy department with the diagnosis of right breast cancer, with the tumor in upper lateral quadrant of the right breast. Since she had all lymph nodes positive (28 in total), she had to have irradiated the supraclavicular region, as well as the breast with the tumor bed. She previously received chemotherapy, approved by the cardiologist. The patient was estimated to be with the high risk as device was within the field of irradiation, and the patient had high dependence on her pacemaker. The radiation therapy plan was conducted as 3D conformal therapy. The delineated target was breast with supraclavicular region, where the pacemaker was actually placed, with the addition of a pacemaker as organ at risk, to estimate the dose to the device and its components as recommended, and the breast. The targets received both 50 Gy in 25 fractions (where 20% of a pacemaker received 50 Gy, and 60% of a device received 40 Gy). The electrode to the heart received between 1 Gy and 50 Gy. Verification of dose planned and delivered was performed. Results: Evaluation of the patient status according to the guidelines and especially evaluation of all associated risks to the patient during treatment was done. Patient was irradiated by prescribed dose and followed up for the whole year, with no symptoms of failure of the pacemaker device during, or after treatment in follow up period. The functionality of a device was estimated to be unchanged, according to the parameters (electrode impedance and battery energy). Conclusion: Patient was closely monitored according to published guidelines during irradiation and afterwards. Pacemaker irradiated with the full dose did not show any signs of failure despite recommendations data, but in correlation with other published data.

Keywords: cardiac pacemaker, breast cancer, radiotherapy treatment planning, complications of treatment

Procedia PDF Downloads 432

Authors: Warsi A. Ibrahim, Qureshi A. Ambrina, Younus M. Anjum

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Introduction: Oral lesions related to commercially available Smokeless Tobacco (ST), such as, Pan, Gutka, Mahwa, Naswar is considered a serious challenge for dental health care providers in Pakistan. Majority of labored Pakistani population consume ST, where public transporters and drivers are no exception. It was necessary to identify individuals of this particular population group and screen their oral health and early signs of pre-cancerous lesions so that appropriate preventive measures could be taken to reduce the burden on health providers. Aim of Study: To estimate Prevalence of ST consumption and perception of use, and to evaluate Oral Health status among public drivers of Karachi. Material & methods: A cross-sectional study survey was conducted over duration of 2 months, through convenient sampling. Sample size (n=615) of public drivers (age > 18 years) all over Karachi was gathered. A structured proforma was used to record socio-demographics, addiction profile, perception of use and oral health status (oral lesions, oral sub-mucosal fibrosis and dental caries) of study participants. Data was entered and analyzed using SPSS version 16.0 using descriptive statistics only. Results: Prevalence of ST consumption among the study participants was figured to 92.5%. Out of these almost 70% suffered from one or the other form of oral lesion(s). Four major types of ST consumption were observed out of which 60 % of oral lesion were related to Gutka chewers showing early signs of oral cancer. In addition, occurrence of Oral sub-mucosal fibrosis (OSF) was found to be significantly high around 54.8%. Overall dental caries status was also high, showing on an average 5 teeth of an individual were decayed, missing or filled deviating from WHO normal criteria (mean < 3). It was thus proven from the study that public drivers relied on oral tobacco consumption because it helps them ‘Improve consciousness’ (p-value: < 0.01; using chi-square test). Multivariate analysis showed that there were higher prevalence of smokeless tobacco among highway drivers versus local drivers (A.O.R: 2.82 [0.83-9.61], p-value: < 0.01) Conclusion: Smokeless tobacco (ST) consumption has a direct effect on oral health. However, the type of ST, the duration of consumption are factors which are directly related to the severity. Moreover, Gutka may be considered as having most lethal effects on oral health which may lead to oral cancer and affect individual’s quality of life. Specific preventive programs must be undertaken to reduce the consumption of Gutka among public transporters and drivers.

Keywords: smokeless tobacco, oral lesions, drivers, public transporters

Procedia PDF Downloads 304

Authors: Meriem Taleb, Ghania Tail, Fatma Zohra Kara, Brahim Djedouani, T. Moussa

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Forensic entomology has grown immensely as a discipline in the past thirty years. The main purpose of forensic entomology is to establish the post mortem interval or PMI. Three days after the death, insect evidence is often the most accurate and sometimes the only method of determining elapsed time since death. This work presents the estimation of the PMI in an experiment to test the reliability of the accumulated degree days (ADD) method and the application of this method in a real case. The study was conducted at the Laboratory of Entomology at the National Institute for Criminalistics and Criminology of the National Gendarmerie, Algeria. The domestic rabbit Oryctolagus cuniculus L. was selected as the animal model. On 08th July 2012, the animal was killed. Larvae were collected and raised to adulthood. Estimation of oviposition time was calculated by summing up average daily temperatures minus minimum development temperature (also specific to each species). When the sum is reached, it corresponds to the oviposition day. Weather data were obtained from the nearest meteorological station. After rearing was accomplished, three species emerged: Lucilia sericata, Chrysomya albiceps, and Sarcophaga africa. For Chrysomya albiceps species, a cumulation of 186°C is necessary. The emergence of adults occured on 22nd July 2012. A value of 193.4°C is reached on 9th August 2012. Lucilia sericata species require a cumulation of 207°C. The emergence of adults occurred on 23rd, July 2012. A value of 211.35°C is reached on 9th August 2012. We should also consider that oviposition may occur more than 12 hours after death. Thus, the obtained PMI is in agreement with the actual time of death. We illustrate the use of this method during the investigation of a case of a decaying human body found on 03rd March 2015 in Bechar, South West of Algerian desert. Maggots were collected and sent to the Laboratory of Entomology. Lucilia sericata adults were identified on 24th March 2015 after emergence. A sum of 211.6°C was reached on 1st March 2015 which corresponds to the estimated day of oviposition. Therefore, the estimated date of death is 1st March 2015 ± 24 hours. The estimated PMI by accumulated degree days (ADD) method seems to be very precise. Entomological evidence should always be used in homicide investigations when the time of death cannot be determined by other methods.

Keywords: forensic entomology, accumulated degree days, postmortem interval, diptera, Algeria

Procedia PDF Downloads 289