Search results for: floor estimation algorithm

Authors: Young-Tae Lee, Hee-Chang Lim

Abstract:

This paper presents the performance characteristics of Darrieus-type vertical axis wind turbine (VAWT) with NACA airfoil blades. The performance of Darrieus-type VAWT can be characterized by torque and power. There are various parameters affecting the performance such as chord length, helical angle, pitch angle and rotor diameter. To estimate the optimum shape of Darrieustype wind turbine in accordance with various design parameters, we examined aerodynamic characteristics and separated flow occurring in the vicinity of blade, interaction between flow and blade, and torque and power characteristics derived from it. For flow analysis, flow variations were investigated based on the unsteady RANS (Reynolds-averaged Navier-Stokes) equation. Sliding mesh algorithm was employed in order to consider rotational effect of blade. To obtain more realistic results we conducted experiment and numerical analysis at the same time for three-dimensional shape. In addition, several parameters (chord length, rotor diameter, pitch angle, and helical angle) were considered to find out optimum shape design and characteristics of interaction with ambient flow. Since the NACA airfoil used in this study showed significant changes in magnitude of lift and drag depending on an angle of attack, the rotor with low drag, long cord length and short diameter shows high power coefficient in low tip speed ratio (TSR) range. On the contrary, in high TSR range, drag becomes high. Hence, the short-chord and long-diameter rotor produces high power coefficient. When a pitch angle at which airfoil directs toward inside equals to -2° and helical angle equals to 0°, Darrieus-type VAWT generates maximum power.

Keywords: darrieus wind turbine, VAWT, NACA airfoil, performance

Procedia PDF Downloads 373

Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro

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This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R² between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. The study has some limitations, such as the use of a single ANN architecture and two optimizers, the validation with data from only one producer, and the possible underestimation of the influence of seasonality and local climate variability. An irrigation management application using the most efficient models from this study is already under development. Future research can explore different ANN architectures and optimization techniques, validate models with data from multiple producers and regions, and investigate the model's response to different seasonal and climatic conditions.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

Procedia PDF Downloads 50

Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

Abstract:

Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

Procedia PDF Downloads 251

Authors: Ana Londral, Burcu Demiray, Marcus Cheetham

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Speech recording is a methodology used in many different studies related to cognitive and behaviour research. Modern advances in digital equipment brought the possibility of continuously recording hours of speech in naturalistic environments and building rich sets of sound files. Speech analysis can then extract from these files multiple features for different scopes of research in Language and Communication. However, tools for analysing a large set of sound files and automatically extract relevant features from these files are often inaccessible to researchers that are not familiar with programming languages. Manual analysis is a common alternative, with a high time and efficiency cost. In the analysis of long sound files, the first step is the voice segmentation, i.e. to detect and label segments containing speech. We present a comprehensive methodology aiming to support researchers on voice segmentation, as the first step for data analysis of a big set of sound files. Praat, an open source software, is suggested as a tool to run a voice detection algorithm, label segments and files and extract other quantitative features on a structure of folders containing a large number of sound files. We present the validation of our methodology with a set of 5000 sound files that were collected in the daily life of a group of voluntary participants with age over 65. A smartphone device was used to collect sound using the Electronically Activated Recorder (EAR): an app programmed to record 30-second sound samples that were randomly distributed throughout the day. Results demonstrated that automatic segmentation and labelling of files containing speech segments was 74% faster when compared to a manual analysis performed with two independent coders. Furthermore, the methodology presented allows manual adjustments of voiced segments with visualisation of the sound signal and the automatic extraction of quantitative information on speech. In conclusion, we propose a comprehensive methodology for voice segmentation, to be used by researchers that have to work with large sets of sound files and are not familiar with programming tools.

Keywords: automatic speech analysis, behavior analysis, naturalistic environments, voice segmentation

Procedia PDF Downloads 282

Authors: Ling Liyun

Abstract:

In the field of contemporary architecture, complex forms of architectural works continue to emerge in the world, along with some new terminology emerged: digital architecture, parametric design, algorithm generation, building information modeling, CNC construction and so on. Architects gradually mastered the new skills of mathematical logic in the form of exploration, virtual simulation, and the entire design and coordination in the construction process. Digital construction technology has a greater degree in controlling construction, and ensure its accuracy, creating a series of new construction techniques. As a result, the use of digital technology is an improvement and expansion of the practice of digital architecture design revolution. We worked by reading and analyzing information about the digital architecture development process, a large number of cases, as well as architectural design and construction as a whole process. Thus current developments were introduced and discussed in our paper, such as architectural discourse, design theory, digital design models and techniques, material selecting, as well as artificial intelligence space design. Our paper also pays attention to the representative three cases of digital design and construction experiment at great length in detail to expound high-informatization, high-reliability intelligence, and high-technique in constructing a humane space to cope with the rapid development of urbanization. We concluded that the opportunities and challenges of the shift existed in architectural paradigms, such as the cooperation methods, theories, models, technologies and techniques which were currently employed in digital design research and digital praxis. We also find out that the innovative use of space can gradually change the way people learn, talk, and control information. The past two decades, digital technology radically breaks the technology constraints of industrial technical products, digests the publicity on a particular architectural style (era doctrine). People should not adapt to the machine, but in turn, it’s better to make the machine work for users.

Keywords: artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

Procedia PDF Downloads 136

Authors: J. Ritonja, R. Brezovnik, M. Petrun, B. Polajžer

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Many modern synchronous generators in power systems are extremely weakly damped. The reasons are cost optimization of the machine building and introduction of the additional control equipment into power systems. Oscillations of the synchronous generators and related stability problems of the power systems are harmful and can lead to failures in operation and to damages. The only useful solution to increase damping of the unwanted oscillations represents the implementation of the power system stabilizers. Power system stabilizers generate the additional control signal which changes synchronous generator field excitation voltage. Modern power system stabilizers are integrated into static excitation systems of the synchronous generators. Available commercial power system stabilizers are based on linear control theory. Due to the nonlinear dynamics of the synchronous generator, current stabilizers do not assure optimal damping of the synchronous generator’s oscillations in the entire operating range. For that reason the use of the robust power system stabilizers which are convenient for the entire operating range is reasonable. There are numerous robust techniques applicable for the power system stabilizers. In this paper the use of sliding mode control for synchronous generator stability improvement is studied. On the basis of the sliding mode theory, the robust power system stabilizer was developed. The main advantages of the sliding mode controller are simple realization of the control algorithm, robustness to parameter variations and elimination of disturbances. The advantage of the proposed sliding mode controller against conventional linear controller was tested for damping of the synchronous generator oscillations in the entire operating range. Obtained results show the improved damping in the entire operating range of the synchronous generator and the increase of the power system stability. The proposed study contributes to the progress in the development of the advanced stabilizer, which will replace conventional linear stabilizers and improve damping of the synchronous generators.

Keywords: control theory, power system stabilizer, robust control, sliding mode control, stability, synchronous generator

Procedia PDF Downloads 225

Authors: Antonios Paraskevas, Michael Madas

Abstract:

For a university to maintain its international competitiveness in education, it is essential to recruit qualitative academic staff as it constitutes its most valuable asset. This selection demonstrates a significant role in achieving strategic objectives, particularly by emphasizing a firm commitment to the exceptional student experience and innovative teaching and learning practices of high quality. In this vein, the appropriate selection of academic staff establishes a very important factor of competitiveness, efficiency and reputation of an academic institute. Within this framework, our work demonstrates a comprehensive methodological concept that emphasizes the multi-criteria nature of the problem and how decision-makers could utilize our approach in order to proceed to the appropriate judgment. The conceptual framework introduced in this paper is built upon a hybrid neutrosophic method based on the Neutrosophic Analytical Hierarchy Process (N-AHP), which uses the theory of neutrosophy sets and is considered suitable in terms of a significant degree of ambiguity and indeterminacy observed in the decision-making process. To this end, our framework extends the N-AHP by incorporating the Neutrosophic Delphi Method (N-DM). By applying the N-DM, we can take into consideration the importance of each decision-maker and their preferences per evaluation criterion. To the best of our knowledge, the proposed model is the first which applies the Neutrosophic Delphi Method in the selection of academic staff. As a case study, it was decided to use our method for a real problem of academic personnel selection, having as the main goal to enhance the algorithm proposed in previous scholars’ work, and thus taking care of the inherent ineffectiveness which becomes apparent in traditional multi-criteria decision-making methods when dealing with situations alike. As a further result, we prove that our method demonstrates greater applicability and reliability when compared to other decision models.

Keywords: multi-criteria decision making methods, analytical hierarchy process, delphi method, personnel recruitment, neutrosophic set theory

Procedia PDF Downloads 117

Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

Procedia PDF Downloads 71

Authors: Amèdédjihundé H. J. Hounnou, Frédéric Dubas, François-Xavier Fifatin, Didier Chamagne, Antoine Vianou

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This paper presents the techno-economic evaluation of run-of-river small-hydropower plants. In this regard, a multi-objective optimization procedure is proposed for the optimal sizing of the hydropower plants, and NSGAII is employed as the optimization algorithm. Annual generated energy and investment cost are considered as the objective functions, and number of generator units (n) and nominal turbine flow rate (Q_T) constitute the decision variables. Site of Yeripao in Benin is considered as the case study. We have categorized the river of this site using its environmental characteristics: gross head, and first quartile, median, third quartile and mean of flow. Effects of each decision variable on the objective functions are analysed. The results gave Pareto Front which represents the trade-offs between annual energy generation and the investment cost of hydropower plants, as well as the recommended optimal solutions. We noted that with the increase of the annual energy generation, the investment cost rises. Thus, maximizing energy generation is contradictory with minimizing the investment cost. Moreover, we have noted that the solutions of Pareto Front are grouped according to the number of generator units (n). The results also illustrate that the costs per kWh are grouped according to the n and rise with the increase of the nominal turbine flow rate. The lowest investment costs per kWh are obtained for n equal to one and are between 0.065 and 0.180 €/kWh. Following the values of n (equal to 1, 2, 3 or 4), the investment cost and investment cost per kWh increase almost linearly with increasing the nominal turbine flowrate while annual generated. Energy increases logarithmically with increasing of the nominal turbine flowrate. This study made for the Yeripao river can be applied to other rivers with their own characteristics.

Keywords: hydropower plant, investment cost, multi-objective optimization, number of generator units

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Authors: Anu S. Nair, V. Anantha Subramanian

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This paper presents a surface development and fairing technique combining the features of a modern computer-aided design tool namely the Non-Uniform Rational Basis Spline (NURBS) with an algorithm to obtain a rapidly faired hull form. Some of the older series based designs give sectional area distribution such as in the Wageningen-Lap Series. Others such as the FORMDATA give more comprehensive offset data points. Nevertheless, this basic data still requires fairing to obtain an acceptable faired hull form. This method uses the input of sectional area distribution as an example and arrives at the faired form. Characteristic section shapes define any general ship hull form in the entrance, parallel mid-body and run regions. The method defines a minimum of control points at each section and using the Golden search method or the bisection method; the section shape converges to the one with the prescribed sectional area with a minimized error in the area fit. The section shapes combine into evolving the faired surface by NURBS and typically takes 20 iterations. The advantage of the method is that it is fast, robust and evolves the faired hull form through minimal iterations. The curvature criterion check for the hull lines shows the evolution of the smooth faired surface. The method is applicable to hull form from any parent series and the evolved form can be evaluated for hydrodynamic performance as is done in more modern design practice. The method can handle complex shape such as that of the bulbous bow. Surface patches developed fit together at their common boundaries with curvature continuity and fairness check. The development is coded in MATLAB and the example illustrates the development of the method. The most important advantage is quick time, the rapid iterative fairing of the hull form.

Keywords: computer-aided design, methodical series, NURBS, ship design

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Authors: Wajeeha Zahid, Shafquat Rozi, Farhan Raza, Masood Kadir

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Background: Dental caries affects the overall health and well-being of children. Findings from various international studies regarding the association of dental caries with asthma are inconsistent. With the increasing burden of caries and childhood asthma, it becomes imperative for an underdeveloped country like Pakistan where resources are limited to identify whether there is a relationship between the two. This study aims to identify an association between dental caries and asthma. Methods: A cross-sectional study was conducted on 544 children aged 12-15 years recruited from five private schools in Karachi. Information on asthma was collected through the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. The questionnaire addressed questions regarding child’s demographics, physician diagnoses of asthma, type of medication administered, family history of asthma and allergies, dietary habits and oral hygiene behavior. Dental caries was assessed using DMFT Index (Decayed, Missing, Filled teeth) index The data was analyzed using Cox proportional Hazard algorithm and crude and adjusted prevalence ratios with 95% CI were reported. Results: This study comprises of 306 (56.3%) boys and 238 (43.8%) girls. The mean age of children was 13.2 ± (0.05) years. The total number of children with carious teeth (DMFT > 0) were 166/544 (30.5%), and the decayed component contributed largely (22.8%) to the DMFT score. The prevalence of physician’s diagnosed asthma was 13%. This study identified almost 7% asthmatic children using the internationally validated International Study of Asthma and Allergies in Childhood (ISAAC) tool and 8 children with childhood asthma were identified by parent interviews. Overall prevalence of asthma was 109/544 (20%). The prevalence of caries in asthmatic children was 28.4% as compared to 31% among non-asthmatic children. The adjusted prevalence ratio of dental caries in asthmatic children was 0.8 (95% CI 0.59-1.29). After adjusting for carious food intake, age, oral hygiene index and dentist visit, the association between asthma and dental caries turned out to be non-significant. Conclusion: There was no association between asthma and dental caries among children who participated in this study.

Keywords: asthma, caries, children, school-based

Procedia PDF Downloads 246

Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

Procedia PDF Downloads 63

Authors: Imalka Wasana Rathnayaka, Rasheda Khanam, Mohammad Mafizur Rahman

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TheCOVID-19 pandemic has created unprecedented challenges to both the health and economic states in countries around the world. This study aims to evaluate the effectiveness of governments' decisions to mitigate the risks of COVID-19 through proposing policy directions to reduce its magnitude. The study is motivated by the ongoing coronavirus outbreaks and comprehensive policy responses taken by countries to mitigate the spread of COVID-19 and reduce death rates. This study contributes to filling the knowledge by exploiting the long-term efficacy of extensive plans of governments. This study employs a Panel autoregressive distributed lag (ARDL) framework. The panels incorporate both a significant number of variables and fortnightly observations from22 countries. The dependent variables adopted in this study are the fortnightly death rates and the rates of the spread of COVID-19. Mortality rate and the rate of infection data were computed based on the number of deaths and the number of new cases per 10000 people.The explanatory variables are fortnightly values of indexes taken to investigate the efficacy of government interventions to control COVID-19. Overall government response index, Stringency index, Containment and health index, and Economic support index were selected as explanatory variables. The study relies on the Oxford COVID-19 Government Measure Tracker (OxCGRT). According to the procedures of ARDL, the study employs (i) the unit root test to check stationarity, (ii) panel cointegration, and (iii) PMG and ARDL estimation techniques. The study shows that the COVID-19 pandemic forced immediate responses from policymakers across the world to mitigate the risks of COVID-19. Of the four types of government policy interventions: (i) Stringency and (ii) Economic Support have been most effective and reveal that facilitating Stringency and financial measures has resulted in a reduction in infection and fatality rates, while (iii) Government responses are positively associated with deaths but negatively with infected cases. Even though this positive relationship is unexpected to some extent in the long run, social distancing norms of the governments have been broken by the public in some countries, and population age demographics would be a possible reason for that result. (iv) Containment and healthcare improvements reduce death rates but increase the infection rates, although the effect has been lower (in absolute value). The model implies that implementation of containment health practices without association with tracing and individual-level quarantine does not work well. The policy implication based on containment health measures must be applied together with targeted, aggressive, and rapid containment to extensively reduce the number of people infected with COVID 19. Furthermore, the results demonstrate that economic support for income and debt relief has been the key to suppressing the rate of COVID-19 infections and fatality rates.

Keywords: COVID-19, infection rate, deaths rate, government response, panel data

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Authors: Yaolin Tian, Shanxiong Chen, Fujia Zhao, Xiaoyu Lin, Hailing Xiong

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Ancient books are significant culture inheritors and their background textures convey the potential history information. However, multi-style texture recovery of ancient books has received little attention. Restricted by insufficient ancient textures and complex handling process, the generation of ancient textures confronts with new challenges. For instance, training without sufficient data usually brings about overfitting or mode collapse, so some of the outputs are prone to be fake. Recently, image generation and style transfer based on deep learning are widely applied in computer vision. Breakthroughs within the field make it possible to conduct research upon multi-style texture recovery of ancient books. Under the circumstances, we proposed a network of layout analysis and image fusion system. Firstly, we trained models by using Deep Convolution Generative against Networks (DCGAN) to synthesize multi-style ancient textures; then, we analyzed layouts based on the Position Rearrangement (PR) algorithm that we proposed to adjust the layout structure of foreground content; at last, we realized our goal by fusing rearranged foreground texts and generated background. In experiments, diversified samples such as ancient Yi, Jurchen, Seal were selected as our training sets. Then, the performances of different fine-turning models were gradually improved by adjusting DCGAN model in parameters as well as structures. In order to evaluate the results scientifically, cross entropy loss function and Fréchet Inception Distance (FID) are selected to be our assessment criteria. Eventually, we got model M8 with lowest FID score. Compared with DCGAN model proposed by Radford at el., the FID score of M8 improved by 19.26%, enhancing the quality of the synthetic images profoundly.

Keywords: deep learning, image fusion, image generation, layout analysis

Procedia PDF Downloads 157

Authors: Robin Anderson, David Nisbet

Abstract:

Nitrate, 3-nitro-1-propionic acid (NPA) and 3-nitro-1-propanol (NPOH) are biologically active chemicals that can accumulate naturally in rangeland grasses forages consumed by grazing cattle, sheep and goats. While toxic to livestock if accumulations and amounts consumed are high enough, particularly in animals having no recent exposure to the forages, these chemicals are known to be potent inhibitors of methane-producing bacteria inhabiting the rumen. Consequently, there is interest in examining their potential use as anti-methanogenic compounds to decrease methane emissions by grazing ruminants. Presently, rumen microbes, collected freshly from a cannulated Holstein cow maintained on 50:50 corn based concentrate:alfalfa diet were mixed (10 mL fluid) in 18 x 150 mm crimp top tubes with 0.5 of high nitrate-containing barley (Hordeum vulgare; containing 272 µmol nitrate per g forage dry matter), and NPA- or NPOH- containing milkvetch forages (Astragalus canadensis and Astragalus miser containing 80 and 174 soluble µmol NPA or NPOH/g forage dry matter respectively). Incubations containing 0.5 g alfalfa (Medicago sativa) were used as controls. Tubes (3 per each respective forage) were capped and incubated anaerobically (using oxygen free carbon dioxide) for 24 h at 39oC after which time amounts of total gas produced were measured via volume displacement and headspace samples were analyzed by gas chromatography to determine concentrations of hydrogen and methane. Fluid samples were analyzed by gas chromatography to measure accumulations of fermentation acids. A completely randomized analysis of variance revealed that the nitrate-containing barley and both the NPA- and the NPOH-containing milkvetches significantly decreased methane production, by > 50%, when compared to methane produced by populations incubated similarly with alfalfa (70.4 ± 3.6 µmol/ml incubation fluid). Accumulations of hydrogen, which are typically increased when methane production is inhibited, by incubations with the nitrate-containing barley and the NPA- and NPOH-containing milkvetches did not differ from accumulations observed in the alfalfa controls (0.09 ± 0.04 µmol/mL incubation fluid). Accumulations of fermentation acids produced in the incubations containing the high-nitrate barley and the NPA- and NPOH-containing milkvetches likewise did not differ from accumulations observed in incubations containing alfalfa (123.5 ± 10.8, 36.0 ± 3.0, 17.1 ± 1.5, 3.5 ± 0.3, 2.3 ± 0.2, 2.2 ± 0.2 µmol/mL incubation fluid for acetate, propionate, butyrate, valerate, isobutyrate, and isovalerate, respectively). This finding indicates the microbial populations did not compensate for the decreased methane production via compensatory changes in production of fermentative acids. Stoichiometric estimation of fermentation balance revealed that > 77% of reducing equivalents generated during fermentation of the forages were recovered in fermentation products and the recoveries did not differ between the alfalfa incubations and those with the high-nitrate barley or the NPA- or NPOH-containing milkvetches. Stoichiometric estimates of amounts of hexose fermented similarly did not differ between the nitrate-, NPA and NPOH-containing incubations and those with the alfalfa, averaging 99.6 ± 37.2 µmol hexose consumed/mL of incubation fluid. These results suggest that forages containing nitrate, NPA or NPOH may be useful to reduce methane emissions of grazing ruminants provided risks of toxicity can be effectively managed.

Keywords: nitrate, nitropropanol, nitropropionic acid, rumen methane emissions

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Authors: S. Gokul Prassad, S. Aakash, K. Malar Mohan

Abstract:

In the process to cope with the challenges faced by the automobile industry in providing ride comfort, the electronics and control systems play a vital role. The control systems in an automobile monitor various parameters, controls the performances of the systems, thereby providing better handling characteristics. The automobile suspension system is one of the main systems that ensure the safety, stability and comfort of the passengers. The system is solely responsible for the isolation of the entire automobile from harmful road vibrations. Thus, integration of the control systems in the automobile suspension system would enhance its performance. The diverse road conditions of India demand the need of an efficient suspension system which can provide optimum ride comfort in all road conditions. For any passenger vehicle, the design of the suspension system plays a very important role in assuring the ride comfort and handling characteristics. In recent years, the air suspension system is preferred over the conventional suspension systems to ensure ride comfort. In this article, the ride comfort of the adaptive suspension system is compared with that of the passive suspension system. The schema is created in MATLAB/Simulink environment. The system is controlled by a proportional integral differential controller. Tuning of the controller was done with the Particle Swarm Optimization (PSO) algorithm, since it suited the problem best. Ziegler-Nichols and Modified Ziegler-Nichols tuning methods were also tried and compared. Both the static responses and dynamic responses of the systems were calculated. Various random road profiles as per ISO 8608 standard are modelled in the MATLAB environment and their responses plotted. Open-loop and closed loop responses of the random roads, various bumps and pot holes are also plotted. The simulation results of the proposed design are compared with the available passive suspension system. The obtained results show that the proposed adaptive suspension system is efficient in controlling the maximum over shoot and the settling time of the system is reduced enormously.

Keywords: automobile suspension, MATLAB, control system, PID, PSO

Procedia PDF Downloads 294

Authors: Antonios Paraskevas, Michael Madas

Abstract:

For a university to maintain its international competitiveness in education, it is essential to recruit qualitative academic staff as it constitutes its most valuable asset. This selection demonstrates a significant role in achieving strategic objectives, particularly by emphasizing a firm commitment to exceptional student experience and innovative teaching and learning practices of high quality. In this vein, the appropriate selection of academic staff establishes a very important factor of competitiveness, efficiency and reputation of an academic institute. Within this framework, our work demonstrates a comprehensive methodological concept that emphasizes on the multi-criteria nature of the problem and on how decision makers could utilize our approach in order to proceed to the appropriate judgment. The conceptual framework introduced in this paper is built upon a hybrid neutrosophic method based on the Neutrosophic Analytical Hierarchy Process (N-AHP), which uses the theory of neutrosophy sets and is considered suitable in terms of significant degree of ambiguity and indeterminacy observed in decision-making process. To this end, our framework extends the N-AHP by incorporating the Neutrosophic Delphi Method (N-DM). By applying the N-DM, we can take into consideration the importance of each decision-maker and their preferences per evaluation criterion. To the best of our knowledge, the proposed model is the first which applies Neutrosophic Delphi Method in the selection of academic staff. As a case study, it was decided to use our method to a real problem of academic personnel selection, having as main goal to enhance the algorithm proposed in previous scholars’ work, and thus taking care of the inherit ineffectiveness which becomes apparent in traditional multi-criteria decision-making methods when dealing with situations alike. As a further result, we prove that our method demonstrates greater applicability and reliability when compared to other decision models.

Keywords: analytical hierarchy process, delphi method, multi-criteria decision maiking method, neutrosophic set theory, personnel recruitment

Procedia PDF Downloads 200

Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Talal Alsulaiman, Khaldoun Khashanah

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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

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Authors: Dayron Camilo Bermudez Mendoza

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Road transportation is a significant source of externalities, notably in terms of environmental degradation and the emission of pollutants. These emissions adversely affect public health, attributable to criteria pollutants like particulate matter (PM2.5 and PM10) and carbon monoxide (CO), and also contribute to climate change through the release of greenhouse gases, such as carbon dioxide (CO2). It is, therefore, crucial to quantify the emissions from mobile sources and develop a methodological framework for their economic valuation, aiding in the assessment of associated costs and informing policy decisions. The forthcoming congress will shed light on the externalities of transportation in Bogotá, showcasing methodologies and findings from the construction of emission inventories and their spatial analysis within the city. This research focuses on the economic valuation of emissions from mobile sources in Bogotá, employing methods like hedonic pricing and contingent valuation. Conducted within the urban confines of Bogotá, the study leverages demographic, transportation, and emission data sourced from the Mobility Survey, official emission inventories, and tailored estimates and measurements. The use of hedonic pricing and contingent valuation methodologies facilitates the estimation of the influence of transportation emissions on real estate values and gauges the willingness of Bogotá's residents to invest in reducing these emissions. The findings are anticipated to be instrumental in the formulation and execution of public policies aimed at emission reduction and air quality enhancement. In compiling the emission inventory, innovative data sources were identified to determine activity factors, including information from automotive diagnostic centers and used vehicle sales websites. The COPERT model was utilized to ascertain emission factors, requiring diverse inputs such as data from the national transit registry (RUNT), OpenStreetMap road network details, climatological data from the IDEAM portal, and Google API for speed analysis. Spatial disaggregation employed GIS tools and publicly available official spatial data. The development of the valuation methodology involved an exhaustive systematic review, utilizing platforms like the EVRI (Environmental Valuation Reference Inventory) portal and other relevant sources. The contingent valuation method was implemented via surveys in various public settings across the city, using a referendum-style approach for a sample of 400 residents. For the hedonic price valuation, an extensive database was developed, integrating data from several official sources and basing analyses on the per-square meter property values in each city block. The upcoming conference anticipates the presentation and publication of these results, embodying a multidisciplinary knowledge integration and culminating in a master's thesis.

Keywords: economic valuation, transport economics, pollutant emissions, urban transportation, sustainable mobility

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

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Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, Unmanned Aerial Vehicle (UAV), uniform, random, kriging

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Authors: Jin-Siang Shaw, Patricia Moya Caceres, Sheng-Xiang Xu

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This paper aims to integrate the particle swarm optimization (PSO) method with the adaptive fuzzy sliding mode controller (AFSMC) to achieve vibration attenuation in a piezoelectric actuator subject to base excitation. The piezoelectric actuator is a complicated system made of ferroelectric materials and its performance can be affected by nonlinear hysteresis loop and unknown system parameters and external disturbances. In this study, an adaptive fuzzy sliding mode controller is proposed for the vibration control of the system, because the fuzzy sliding mode controller is designed to tackle the unknown parameters and external disturbance of the system, and the adaptive algorithm is aimed for fine-tuning this controller for error converging purpose. Particle swarm optimization method is used in order to find the optimal controller parameters for the piezoelectric actuator. PSO starts with a population of random possible solutions, called particles. The particles move through the search space with dynamically adjusted speed and direction that change according to their historical behavior, allowing the values of the particles to quickly converge towards the best solutions for the proposed problem. In this paper, an initial set of controller parameters is applied to the piezoelectric actuator which is subject to resonant base excitation with large amplitude vibration. The resulting vibration suppression is about 50%. Then PSO is applied to search for an optimal controller in the neighborhood of this initial controller. The performance of the optimal fuzzy sliding mode controller found by PSO indeed improves up to 97.8% vibration attenuation. Finally, adaptive version of fuzzy sliding mode controller is adopted for further improving vibration suppression. Simulation result verifies the performance of the adaptive controller with 99.98% vibration reduction. Namely the vibration of the piezoelectric actuator subject to resonant base excitation can be completely annihilated using this PSO based adaptive fuzzy sliding mode controller.

Keywords: adaptive fuzzy sliding mode controller, particle swarm optimization, piezoelectric actuator, vibration suppression

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Authors: Gherbi Khadidja

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In the Mediterranean region, water resources are limited and very unevenly distributed in space and time. The main objective of this project is the development of a wireless network for the management of water resources in northern Algeria, the Mitidja plain, which helps farmers to irrigate in the most optimized way and solve the problem of water shortage in the region. Therefore, we will develop an aid tool that can modernize and replace some traditional techniques, according to the real needs of the crops and according to the soil conditions as well as the climatic conditions (soil moisture, precipitation, characteristics of the unsaturated zone), These data are collected in real-time by sensors and analyzed by an algorithm and displayed on a mobile application and the website. The results are essential information and alerts with recommendations for action to farmers to ensure the sustainability of the agricultural sector under water shortage conditions. In the first part: We want to set up a wireless sensor network, for precise management of water resources, by presenting another type of equipment that allows us to measure the water content of the soil, such as the Watermark probe connected to the sensor via the acquisition card and an Arduino Uno, which allows collecting the captured data and then program them transmitted via a GSM module that will send these data to a web site and store them in a database for a later study. In a second part: We want to display the results on a website or a mobile application using the database to remotely manage our smart irrigation system, which allows the farmer to use this technology and offers the possibility to the growers to access remotely via wireless communication to see the field conditions and the irrigation operation, at home or at the office. The tool to be developed will be based on satellite imagery as regards land use and soil moisture. These tools will make it possible to follow the evolution of the needs of the cultures in time, but also to time, and also to predict the impact on water resources. According to the references consulted, if such a tool is used, it can reduce irrigation volumes by up to up to 40%, which represents more than 100 million m3 of savings per year for the Mitidja. This volume is equivalent to a medium-size dam.

Keywords: optimal irrigation, soil moisture, smart irrigation, water management

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Authors: Oscar E. Cariceo, Claudia V. Casal

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Machine learning offers a set of techniques to promote social work interventions and can lead to support decisions of practitioners in order to predict new behaviors based on data produced by the organizations, services agencies, users, clients or individuals. Machine learning techniques include a set of generalizable algorithms that are data-driven, which means that rules and solutions are derived by examining data, based on the patterns that are present within any data set. In other words, the goal of machine learning is teaching computers through 'examples', by training data to test specifics hypothesis and predict what would be a certain outcome, based on a current scenario and improve that experience. Machine learning can be classified into two general categories depending on the nature of the problem that this technique needs to tackle. First, supervised learning involves a dataset that is already known in terms of their output. Supervising learning problems are categorized, into regression problems, which involve a prediction from quantitative variables, using a continuous function; and classification problems, which seek predict results from discrete qualitative variables. For social work research, machine learning generates predictions as a key element to improving social interventions on complex social issues by providing better inference from data and establishing more precise estimated effects, for example in services that seek to improve their outcomes. This paper exposes the results of a classification algorithm to predict cyberbullying among adolescents. Data were retrieved from the National Polyvictimization Survey conducted by the government of Chile in 2017. A logistic regression model was created to predict if an adolescent would experience cyberbullying based on the interaction and behavior of gender, age, grade, type of school, and self-esteem sentiments. The model can predict with an accuracy of 59.8% if an adolescent will suffer cyberbullying. These results can help to promote programs to avoid cyberbullying at schools and improve evidence based practice.

Keywords: cyberbullying, evidence based practice, machine learning, social work research

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Authors: Aline Woine, Moïra Mikolajczak, Virginie Dardier, Isabelle Roskam

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Background information: Becoming a parent is said to be the happiest event one can ever experience in one’s life. This popular (and almost absolute) truth–which no reasonable and decent human being would ever dare question on pain of being singled out as a bad parent–contrasts with the nuances that reality offers. Indeed, while many parents do thrive in their parenting role, some others falter and become progressively overwhelmed by their parenting role, ineluctably caught in a spiral of exhaustion. Parental burnout (henceforth PB) sets in when parental demands (stressors) exceed parental resources. While it is now generally acknowledged that PB affects the parent’s behavior in terms of neglect and violence toward their offspring, little is known about the impact that the syndrome might have on the children’s internalized (anxious and depressive symptoms, somatic complaints, etc.) and/or externalized (irritability, violence, aggressiveness, conduct disorder, oppositional disorder, etc.) behaviors. Furthermore, at the time of writing, to our best knowledge, no research has yet tested the reverse effect, namely, that of the child's internalized and/or externalized behaviors on the onset and/or maintenance of parental burnout symptoms. Goals and hypotheses: The present pioneering research proposes to fill an important gap in the existing literature related to PB by investigating the bidirectional effect between PB and the child’s internalized and/or externalized behaviors. Relying on a cross-lagged longitudinal study with three waves of data collection (4 months apart), our study tests a transactional model with bidirectional and recursive relations between observed variables and at the three waves, as well as autoregressive paths and cross-sectional correlations. Methods: As we write this, wave-two data are being collected via Qualtrics, and we expect a final sample of about 600 participants composed of French-speaking (snowball sample) and English-speaking (Prolific sample) parents. Structural equation modeling is employed using Stata version 17. In order to retain as much statistical power as possible, we use all available data and therefore apply the maximum likelihood with a missing value (mlmv) as the method of estimation to compute the parameter estimates. To limit (in so far is possible) the shared method variance bias in the evaluation of the child’s behavior, the study relies on a multi-informant evaluation approach. Expected results: We expect our three-wave longitudinal study to show that PB symptoms (measured at T1) raise the occurrence/intensity of the child’s externalized and/or internalized behaviors (measured at T2 and T3). We further expect the child’s occurrence/intensity of externalized and/or internalized behaviors (measured at T1) to augment the risk for PB (measured at T2 and T3). Conclusion: Should our hypotheses be confirmed, our results will make an important contribution to the understanding of both PB and children’s behavioral issues, thereby opening interesting theoretical and clinical avenues.

Keywords: exhaustion, structural equation modeling, cross-lagged longitudinal study, violence and neglect, child-parent relationship

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Authors: Kathryn Rooney, Kaitlyn Eddy, Evan Landers, Weihui Li

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The international mortality rate for neonates and infants has been declining at a disproportionally low rate when compared to the overall decline in child mortality in recent decades. A significant portion of infant deaths could be prevented with the implementation of low-cost and easy to use physiological monitoring devices, by enabling early identification of symptoms before they progress into life-threatening illnesses. The oral diagnostic device discussed in this paper serves to continuously monitor the key vital signs of body temperature, respiratory rate, heart rate, and oxygen saturation. The device mimics an infant pacifier, designed to be easily tolerated by infants as well as orthodontically inert. The fundamental measurements are gathered via thermistors and a pulse oximeter, each encapsulated in medical-grade silicone and wired internally to a microcontroller chip. The chip then translates the raw measurements into physiological values via an internal algorithm, before outputting the data to a liquid crystal display screen and an Android application. Additionally, a biological sample collection chamber is incorporated into the internal portion of the device. The movement within the oral chamber created by sucking on the pacifier-like device pushes saliva through a small check valve in the distal end, where it is accumulated and stored. The collection chamber can be easily removed, making the sample readily available to be tested for various diseases and analytes. With the vital sign monitoring and sample collection offered by this device, abnormal fluctuations in physiological parameters can be identified and appropriate medical care can be sought. This device enables preventative diagnosis for infants who may otherwise have gone undiagnosed, due to the inaccessibility of healthcare that plagues vast numbers of underprivileged populations.

Keywords: neonate mortality, infant mortality, low-cost diagnostics, vital signs, saliva testing, preventative care

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Authors: Kana Matsuyanagi

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Anorexia Nervosa (AN) is a psychiatric disorder characterized by a relentless pursuit of thinness and strict restriction of food. The current therapeutic approaches for AN predominantly revolve around outpatient psychotherapies, which create significant financial barriers for the majority of affected patients, hindering their access to treatment. Nonetheless, AN exhibit one of the highest mortality and relapse rates among psychological disorders, underscoring the urgent need to provide patients with an affordable self-treatment tool, enabling those unable to access conventional medical intervention to address their condition autonomously. To this end, a neurofeedback software, termed RelaxNeuron, was developed with the objective of providing an economical and portable means to aid individuals in self-managing AN. Electroencephalography (EEG) was chosen as the preferred modality for RelaxNeuron, as it aligns with the study's goal of supplying a cost-effective and convenient solution for addressing AN. The primary aim of the software is to ameliorate the negative emotional responses towards food stimuli and the accompanying aberrant eye-tracking patterns observed in AN patient, ultimately alleviating the profound fear towards food an elemental symptom and, conceivably, the fundamental etiology of AN. The core functionality of RelaxNeuron hinges on the acquisition and analysis of EEG signals, alongside an electrocardiogram (ECG) signal, to infer the user's emotional state while viewing dynamic food-related imagery on the screen. Moreover, the software quantifies the user's performance in accurately tracking the moving food image. Subsequently, these two parameters undergo further processing in the subsequent algorithm, informing the delivery of either negative or positive feedback to the user. Preliminary test results have exhibited promising outcomes, suggesting the potential advantages of employing RelaxNeuron in the treatment of AN, as evidenced by its capacity to enhance emotional regulation and attentional processing through repetitive and persistent therapeutic interventions.

Keywords: Anorexia Nervosa, fear conditioning, neurofeedback, BCI

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Authors: Sukhvinder Kaur, Ashok Agarwal

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Background: For the first time in 2009, India was able to generate estimates of HIV incidence (the number of new HIV infections per year). Analysis of epidemic projections helped in revealing that the number of new annual HIV infections in India had declined by more than 50% during the last decade (GOI Ministry of Health and Family Welfare, 2010). Then, National AIDS Control Organisation (NACO) planned to scale up its efforts in generating projections through epidemiological analysis and modelling by taking recent available sources of evidence such as HIV Sentinel Surveillance (HSS), India Census data and other critical data sets. Recently, NACO generated current round of HIV estimates-2012 through globally recommended tool “Spectrum Software” and came out with the estimates for adult HIV prevalence, annual new infections, number of people living with HIV, AIDS-related deaths and treatment needs. State level prevalence and incidence projections produced were used to project consequences of the epidemic in spectrum. In presence of HIV estimates generated at state level in India by NACO, USIAD funded PIPPSE project under the leadership of NACO undertook the estimations and projections to district level using same Spectrum software. In 2011, adult HIV prevalence in one of the high prevalent States, Maharashtra was 0.42% ahead of the national average of 0.27%. Considering the heterogeneity of HIV epidemic between districts, two districts of Maharashtra – Thane and Mumbai were selected to estimate and project the number of People-Living-with-HIV/AIDS (PLHIV), HIV-prevalence among adults and annual new HIV infections till 2017. Methodology: Inputs in spectrum included demographic data from Census of India since 1980 and sample registration system, programmatic data on ‘Alive and on ART (adult and children)’,‘Mother-Baby pairs under PPTCT’ and ‘High Risk Group (HRG)-size mapping estimates’, surveillance data from various rounds of HSS, National Family Health Survey–III, Integrated Biological and Behavioural Assessment and Behavioural Sentinel Surveillance. Major Findings: Assuming current programmatic interventions in these districts, an estimated decrease of 12% points in Thane and 31% points in Mumbai among new infections in HRGs and migrants is observed from 2011 by 2017. Conclusions: Project also validated decrease in HIV new infection among one of the high risk groups-FSWs using program cohort data since 2012 to 2016. Though there is a decrease in HIV prevalence and new infections in Thane and Mumbai, further decrease is possible if appropriate programme response, strategies and interventions are envisaged for specific target groups based on this evidence. Moreover, evidence need to be validated by other estimation/modelling techniques; and evidence can be generated for other districts of the state, where HIV prevalence is high and reliable data sources are available, to understand the epidemic within the local context.

Keywords: HIV sentinel surveillance, high risk groups, projections, new infections

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Authors: A. Kachmazova, A. Shadiev, Y. Teterin, P. Yartcev

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Introduction: Biliary calculi disease (LCS) still occupies the leading position among urgent diseases of the abdominal cavity, manifesting itself from asymptomatic course to life-threatening states. Nowadays arsenal of diagnostic methods for choledocholithiasis is quite wide: ultrasound, hepatobiliscintigraphy (HBSG), magnetic resonance imaging (MRI), endoscopic retrograde cholangiography (ERCP). Among them, transabdominal ultrasound (TA ultrasound) is the most accessible and routine diagnostic method. Nowadays ERCG is the "gold" standard in diagnosis and one-stage treatment of biliary tract obstruction. However, transpapillary techniques are accompanied by serious postoperative complications (postmanipulative pancreatitis (3-5%), endoscopic papillosphincterotomy bleeding (2%), cholangitis (1%)), the lethality being 0.4%. GBSG and MRI are also quite informative methods in the diagnosis of choledocholithiasis. Small size of concrements, their localization in intrapancreatic and retroduodenal part of common bile duct significantly reduces informativity of all diagnostic methods described above, that demands additional studying of this problem. Materials and Methods: 890 patients with the diagnosis of cholelithiasis (calculous cholecystitis) were admitted to the Sklifosovsky Scientific Research Institute of Hospital Medicine in the period from August, 2020 to June, 2021. Of them 115 people with mechanical jaundice caused by concrements in bile ducts. Results: Final EUS diagnosis was made in all patients (100,0%). In all patients in whom choledocholithiasis diagnosis was revealed or confirmed after EUS, ERCP was performed urgently (within two days from the moment of its detection) as the X-ray operation room was provided; it confirmed the presence of concrements. All stones were removed by lithoextraction using Dormia basket. The postoperative period in these patients had no complications. Conclusions: EUS is the most informative and safe diagnostic method, which allows to detect choledocholithiasis in patients with discrepancies between clinical-laboratory and instrumental methods of diagnosis in shortest time, that in its turn will help to decide promptly on the further tactics of patient treatment. We consider it reasonable to include EUS in the diagnostic algorithm for choledocholithiasis. Disclosure: Nothing to disclose.

Keywords: endoscopic ultrasonography, choledocholithiasis, common bile duct, concrement, ERCP

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Authors: Ravi Ande, Mousumi Hazari

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One of the common geophysical techniques for mapping subsurface geo-electrical structures, extensive hydro-geological research, and engineering and environmental geophysics applications is the use of time domain electromagnetic (TDEM)/transient electromagnetic (TEM) soundings. A large transmitter loop for energising the ground and a small receiver loop or magnetometer for recording the transient voltage or magnetic field in the air or on the surface of the earth, with the receiver at the center of the loop or at any random point inside or outside the source loop, make up a large loop TEM system. In general, one can acquire data using one of the configurations with a large loop source, namely, with the receiver at the center point of the loop (central loop method), at an arbitrary in-loop point (in-loop method), coincident with the transmitter loop (coincidence-loop method), and at an arbitrary offset loop point (offset-loop method), respectively. Because of the mathematical simplicity associated with the expressions of EM fields, as compared to the in-loop and offset-loop systems, the central loop system (for ground surveys) and coincident loop system (for ground as well as airborne surveys) have been developed and used extensively for the exploration of mineral and geothermal resources, for mapping contaminated groundwater caused by hazardous waste and thickness of permafrost layer. Because a proper analytical expression for the TEM response over the layered earth model for the large loop TEM system does not exist, the forward problem used in this inversion scheme is first formulated in the frequency domain and then it is transformed in the time domain using Fourier cosine or sine transforms. Using the EMLCLLER algorithm, the forward computation is initially carried out in the frequency domain. As a result, the EMLCLLER modified the forward calculation scheme in NLSTCI to compute frequency domain answers before converting them to the time domain using Fourier Cosine and/or Sine transforms.

Keywords: time domain electromagnetic (TDEM), TEM system, geoelectrical sounding structure, Fourier cosine

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