Search results for: dimension reducing distribution load flow algorithm

Authors: Abdellahi Cheikh

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With the emergence of quantum computers with very powerful capabilities, the security of the exchange of shared keys between two interlocutors poses a big problem in terms of the rapid development of technologies such as computing power and computing speed. Therefore, the Diffie-Hellmann (DH) algorithm is more vulnerable than ever. No mechanism guarantees the security of the key exchange, so if an intermediary manages to intercept it, it is easy to intercept. In this regard, several studies have been conducted to improve the security of key exchange between two interlocutors, which has led to interesting results. The modification made on our model Diffie-Hellman-RSA-AES (DRA), which encrypts the information exchanged between two users using the three-encryption algorithms DH, RSA and AES, by using stenographic photos to hide the contents of the p, g and ClesAES values that are sent in an unencrypted state at the level of DRA model to calculate each user's public key. This work includes a comparative study between the DRA model and all existing solutions, as well as the modification made to this model, with an emphasis on the aspect of reliability in terms of security. This study presents a simulation to demonstrate the effectiveness of the modification made to the DRA model. The obtained results show that our model has a security advantage over the existing solution, so we made these changes to reinforce the security of the DRA model.

Keywords: Diffie-Hellmann, DRA, RSA, advanced encryption standard

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Authors: Amit S. Chauhan, S. Mandal

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The structural response of shear walls subjected to various types of loads is difficult to predict precisely. They are incorporated in buildings to resist lateral forces and support the gravity loads. The steel plate shear walls (SPSWs) are used as lateral load resisting systems for buildings and acts as an alternative to reinforced concrete shear walls (RCSWs). This paper compares the behavior of SPSW with the RCSW incorporated in a building frame having G+6 storey, located in Zone III, using the technique of Equivalent Static Method (ESM) as per Indian Standard Criteria For Earthquake Resistant Design of Structures IS 1893:2002. This paper intends to evaluate several parameters such as lateral displacement at tip, inter-storey drift, weight of steel and volume of concrete with the alteration of the shear wall with respect to different types viz., SPSW and RCSW. The strip model employed in this study is a widely accepted analytical tool for SPSW analysis. SPSW can be modelled as truss members by using a series of diagonal tension strips positioned at 45-degree angles. In this paper, by replacing the SPSWs with the tension strips, the G+6 building has been analyzed using STAAD.Pro V8i. Based on the present study, it can be concluded that structure with SPSWs is much better then structure with RCSWs.

Keywords: equivalent static method, inter-storey drift, lateral displacement, Steel plate shear wall, strip model

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Authors: Rabia Korkmaz Tan, Şebnem Bora

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The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies.

Keywords: parameter tuning, agent based modeling and simulation, metaheuristic algorithms, complex systems

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Authors: Raquel M. De sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

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Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of a higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses an artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of backpropagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this case iodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: artificial neural networks, biodiesel, iodine value, prediction

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

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Authors: Daneal Rorke, Gueguim Kana

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The shift towards renewable energy sources for biofuel production has received increasing attention. However, the use and pre-treatment of lignocellulosic material are inundated with the generation of fermentation inhibitors which severely impact the feasibility of bioprocesses. This study reports the profiling of all volatile compounds generated during microwave assisted chemical pre-treatment of sorghum leaves. Furthermore, the optimization of reducing sugar (RS) from microwave assisted acid pre-treatment of sorghum leaves was assessed and gave a coefficient of determination (R2) of 0.76, producing an optimal RS yield of 2.74 g FS/g substrate. The development of an intelligent model to predict volatile compound fractions gave R2 values of up to 0.93 for 21 volatile compounds. Sensitivity analysis revealed that furfural and phenol exhibited high sensitivity to acid concentration, alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity as well. These findings illustrate the potential of using an intelligent model to predict the volatile compound fraction profile of compounds generated during pre-treatment of sorghum leaves in order to establish a more robust and efficient pre-treatment regime for biofuel production.

Keywords: artificial neural networks, fermentation inhibitors, lignocellulosic pre-treatment, sorghum leaves

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Authors: Ali Abdrhman M. Ukasha

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Data security needed in data transmission, storage, and communication to ensure the security. The color image is decomposed into red, green, and blue channels. The blue and green channels are compressed using 3-levels discrete wavelet transform. The Arnold transform uses to changes the locations of red image channel pixels as image scrambling process. Then all these channels are encrypted separately using a key image that has same original size and is generating using private keys and modulo operations. Performing the X-OR and modulo operations between the encrypted channels images for image pixel values change purpose. The extracted contours of color image recovery can be obtained with accepted level of distortion using Canny edge detector. Experiments have demonstrated that proposed algorithm can fully encrypt 2D color image and completely reconstructed without any distortion. It has shown that the color image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: color image, wavelet transform, edge detector, Arnold transform, lossy image encryption

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Authors: Zakaria Hamdi, Mariyamni Awang

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For the past decades, CO₂ flooding has been used as a successful method for enhanced oil recovery (EOR). However, high mobility ratio and fingering effect are considered as important drawbacka of this process. Low temperature injection of CO₂ into high temperature reservoirs may improve the oil recovery, but simulating multiphase flow in the non-isothermal medium is difficult, and commercial simulators are very unstable in these conditions. Furthermore, to best of authors’ knowledge, no experimental work was done to verify the results of the simulations and to understand the pore-scale process. In this paper, we present results of investigations on injection of low temperature CO₂ into a high-pressure high-temperature micromodel with injection temperature range from 34 to 75 °F. Effect of temperature and saturation changes of different fluids are measured in each case. The results prove the proposed method. The injection of CO₂ at low temperatures increased the oil recovery in high temperature reservoirs significantly. Also, CO₂ rich phases available in the high temperature system can affect the oil recovery through the better sweep of the oil which is initially caused by penetration of LCO₂ inside the system. Furthermore, no unfavorable effect was detected using this method. Low temperature CO₂ is proposed to be used as early as secondary recovery.

Keywords: enhanced oil recovery, CO₂ flooding, micromodel studies, miscible flooding

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Authors: Candice Lemaitre

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During the 1990s, an alliance of international intergovernmental and non-governmental organizations proposed a set of regulatory norms designed to reduce corruption. Many governments in developing countries, such as Vietnam, enacted these global anti-corruption norms into their domestic law. This article draws on empirical research to understand why these anti-corruption norms have failed to reduce corruption in Vietnam and many other developing countries. Rather than investigating state compliance with global anti-corruption provisions, a topic that has already attracted considerable attention, this article aims to explore the comparatively under-researched area of business compliance. Based on data collected from semi-structured interviews with business managers in Vietnam and archival research, this article examines how businesses in Vietnam interpret and comply with global anti-corruption norms. It investigates why different types of companies in Vietnam engage with and respond to these norms in different ways. This article suggests that global anti-corruption norms have not been effective in reducing corruption in Vietnam because there is fragmentation in the way companies in Vietnam interpret and respond to these norms. This fragmentation results from differences in the epistemic (or interpretive) communities that companies draw upon to interpret global anti-corruption norms. This article uses discourse analysis to understand how the communities interpret global anti-corruption norms. This investigation aims to generate some predictive insights into how companies are likely to respond to anti-corruption regimes based on global anti-corruption norms.

Keywords: anti-corruption, business law, legal transfer, Vietnam

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Authors: Aman Berhe, Camille Guinaudeau, Claude Barras

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In order to facilitate information seeking in large collections of multimedia documents with long and progressive content (such as broadcast news or TV series), one can extract the semantic links that exist between semantically coherent parts of documents, i.e., scenes. The links can then create a coherent collection of scenes from which it is easier to perform content analysis, topic extraction, or information retrieval. In this paper, we focus on TV series structuring and propose two approaches for scene linking at different levels of granularity (episode and season): a fuzzy online clustering technique and a graph-based community detection algorithm. When evaluated on the two first seasons of the TV series Game of Thrones, we found that the fuzzy online clustering approach performed better compared to graph-based community detection at the episode level, while graph-based approaches show better performance at the season level.

Keywords: multimedia collection structuring, progressive content, scene linking, fuzzy clustering, community detection

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Authors: Tzu-Wen Liu, Yi-Feng Lin, Yu-Shao Chen

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CO_2 is believed to be partly responsible for changes to the global climates. Carbon capture and storage (CCS) is one way to reduce carbon dioxide emissions in the past. Recently, how to convert the captured CO_2 into fine chemicals gets lots of attention owing to reducing carbon dioxide emissions and providing greener feedstock for the chemicals industry. A variety of products can be manufactured from carbon dioxide and the most attractive products are cyclic carbonates. Therefore, the kind of catalyst plays an important role in cycloaddition of carbon dioxide to epoxides. Magnesium oxide can be an efficiency heterogeneous catalyst for the cycloaddition of carbon dioxide to epoxides because magnesium oxide has both acid and base active sites and can provide the adsorption of carbon dioxide, promoting ring-opening reaction. Spinning disk reactor (SDR) is one of the device of high-gravity technique and has successfully used for synthesis of nanoparticles by precipitation methods because of the high mass transfer rate. Synthesis of nanoparticles in SDR has advantages of low energy consumption and easy to scale up. The aim of this research is to synthesize magnesium hydroxide nanoparticles in SDR as precursors for magnesium oxide. Experimental results showed that the calcination temperature of magnesium hydroxide to magnesium oxide, and the pressure and temperature of cycloaddition reaction had significantly effect on the conversion and selectivity of the reaction.

Keywords: magnesium oxide, catalyst, cycloaddition, spinning disk reactor, carbon dioxide

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Authors: Emiliano Lustrissimi, Bonifacio Bianco, Sebastiano Caravaggi, Antonio Rosato

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A mathematical framework has been designed to enhance the configuration of an end-of-production-line (EOL) test bench. This system can be used to assess the performance of electric motors or axles intended for electric vehicles. The model has been developed to predict the behaviour of EOL test benches and electric motors/axles under various boundary conditions, eliminating the need for extensive physical testing and reducing the corresponding power consumption. The suggested model is versatile, capable of being utilized across various types of electric motors or axles, and adaptable to accommodate varying power ratings of electric motors or axles. The maximum performance to be guaranteed by the EMs according to the car maker's specifications are taken as inputs in the model. Then, the required performance of each main EOL test bench component is calculated, and the corresponding systems available on the market are selected based on manufacturers’ catalogues. In this study, an EOL test bench has been designed according to the proposed model outputs for testing a low-power (about 22 kW) electric axle. The performance of the designed EOL test bench has been measured and used to validate the proposed model and assess both the consistency of the constraints as well as the accuracy of predictions in terms of electric demands. The comparison between experimental and predicted data exhibited a reasonable agreement, allowing to demonstrate that, despite some discrepancies, the model gives an accurate representation of the EOL test benches' performance.

Keywords: electric motors, electric vehicles, end-of-production-line test bench, mathematical model, field tests

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Authors: Mona Elsalamawy, Ashraf Ragab Mohamed, Abdellatif Elsayed Abosen

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This paper aims to evaluate the effectiveness of different crystalline coating materials concerning of chloride ions penetration. The concrete ages at the coating installation and its moisture conditions were addressed; where, these two factors may play a dominant role for the effectiveness of the used materials. Rapid chloride ions penetration test (RCPT) was conducted at different ages and moisture conditions according to the relevant standard. In addition, the contaminated area and the penetration depth of the chloride ions were investigated immediately after the RCPT test using chemical identifier, 0.1 M silver nitrate AgNO₃ solution. Results have shown that, the very low chloride ions penetrability, for the studied crystallization materials, were investigated only with the old age concrete (G1). The significant reduction in chloride ions’ penetrability was illustrated after 7 days of installing the crystalline coating layers. Using imageJ is more reliable to describe the contaminated area of chloride ions, where the distribution of aggregate and heterogeneous of cement mortar was considered in the images analysis.

Keywords: chloride permeability, contaminated area, crystalline waterproofing materials, RCPT, XRD

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Authors: Christina E. Bannier, Milena Neubert

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This study examines the role of actual and perceived financial sophistication (i.e., financial literacy and confidence) for individuals’ wealth accumulation. Using survey data from the German SAVE initiative, we find strong gender- and education-related differences in the distribution of the two variables: Whereas financial literacy rises in formal education, confidence increases in education for men but decreases for women. As a consequence, highly-educated women become strongly underconfident, while men remain overconfident. We show that these differences influence wealth accumulation: The positive effect of financial literacy is stronger for women than for men and is increasing in women’s education but decreasing in men’s. For highly-educated men, however, overconfidence closes this gap by increasing wealth via stronger financial engagement. Interestingly, female underconfidence does not reduce current wealth levels though it weakens future-oriented financial engagement and may thus impair future wealth accumulation.

Keywords: financial literacy, financial sophistication, confidence, wealth, household finance, behavioral finance, gender, formal education

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Authors: Sayan Munshi, Subrajit Banerjee, Indrani Chakraborty

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Tourism is a prime sector in the economic development of many countries in particular the Indian sub-continent. Tourism is considered an integral pillar in the Make in India Program under the Government of India. The statistics of tourism in India had evolved from a past with the formation of History. The sector had shown dynamic changes in the statistics since 1980. With the evolving tourism along with destinations, this sector has been converted into the prime industry, as it not only impacts the destination but on the other hand supports the periphery of the destination. Tourism boost revenue and creates varied economic possibilities for the residents. Due to the influx of tourism in the cities, a load on the infrastructure and services can be observed, specifically in the Physical Infrastructure sectors. Due to the floating population in the designated tourism core of the Urban / Peri-Urban area, issues pertaining to Solid waste management and Transportation are highly observed. Thus, a need for capacity building arises for the infrastructure impacted by tourism, which may result in the upgradation of the lifestyle of the city and its permanent users. As tourism of a region has a dependency on the infrastructure, the paper here focuses on the relationship between tourism potential of a region and the infrastructural determinants of the city or region and hence to derive a structural equation supporting the relationship, further determine a coefficient and suggest the domain of in need of upgradation or retrofitting possibilities. The outcome of the paper is to suggest possible recommendations towards the formation of policies on an urban level to support the tourism potential of the region.

Keywords: urban planning, tourism planning, infrastructure, transportation, solid waste management

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Authors: Willie Eselebor

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Environmental governance is not new, but may consist of a series of actions taken to establish sanity and ensure sustainable environment. While there is a growing accord linking disaster risk reduction with the management of environment and natural resources, little is known about failure to act which constitute vulnerability and how improved governance reduces risk globally. The paper reviews emerging trends in the field of application of governance tools and approaches for reducing disaster risk. The Hyogo Framework for Action (HFA) enjoin all stakeholders to stimulate the sustainable use and management of ecosystems, which promote the implementation of integrated environmental and natural resource planning that incorporate disaster risk reduction, including structural and non-structural measures, such as integrated management of fragile ecosystems. The methodology adopted is a case study of disaster-prone sites, prompting guided analysis on which hazards are traceable to environmental degradation, why a degraded environment reduces community resilience; how healthy ecosystems provide natural defense, and which opportunities exist to address gaps in reduction of disasters in Nigeria. The paper further analyses the interaction between disaster risk and environmental change. It is established that environmental governance remains a challenge; which implies that there is the need for a shift in traditional approaches to disaster risk management; exploring new initiatives and allowing environmental managers to be docketed as disaster risk managers in context, potentially opening up a window of dialogue on disaster risk management.

Keywords: disaster, ecosystem, environment, risk

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Authors: Fatiha Naaoui-Outini

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Purpose – The purpose of this paper is to shed light on how a Downstream Supply Chain facilitated the Customer Service Performance (BTB) by more collaborative practices between the different stakeholders in the chain. Methodology/approach – The paper developed a theoretical framework and conducted a qualitative exploratory study approach based on six semi-structured interviews with two international groups in the distribution sector with the aim of understanding and analyzing how companies have changed their supply chains to ensure optimal customer service. Findings/Implications – The study contributes to the Global Supply Chain Management and Collaboration literature by integrating the role of the downstream supply chain into research that may actually influence customer service performance on BTB. Our findings also provide firms with some guidelines on building successful downstream supply chain collaboration and a significant influence on customer service performance in BTB. Because of the exploratory nature of the study approach, the research results are limited to the data collected, and these preliminary findings require further confirmation.

Keywords: customer service performance (B2B), global supply chain, downstream supply collaboration, qualitative case study

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Authors: Yoshinori Wakatake

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Many defects after the release of software packages are caused due to omissions of sufficient test items in test specifications. Poor test specifications are detected by manual review, which imposes a high human load. The prevention of omissions depends on the end-user awareness of test specification writers. If test specifications were written while envisioning the behavior of end-users, the number of omissions in test items would be greatly reduced. The paper pays attention to the point that writers who can achieve it differ from those who cannot in not only the description richness but also their gaze information. It proposes a method to estimate the degree of user-awareness of writers through the analysis of their gaze information when writing test specifications. We conduct an experiment to obtain the gaze information of a writer of the test specifications. Test specifications are automatically classified using gaze information. In this method, a Random Forest model is constructed for the classification. The classification is highly accurate. By looking at the explanatory variables which turn out to be important variables, we know behavioral features to distinguish test specifications of high quality from others. It is confirmed they are pupil diameter size and the number and the duration of blinks. The paper also investigates test specifications automatically classified with gaze information to discuss features in their writing ways in each quality level. The proposed method enables us to automatically classify test specifications. It also prevents test item omissions, because it reveals writing features that test specifications of high quality should satisfy.

Keywords: blink, eye tracking, gaze information, pupil diameter, quality improvement, specification document, user-awareness

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: biometric characters, facial recognition, neural network, OpenCV

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Authors: A. Majczak, G. Baranski, K. Pietrykowski

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Due to the growing popularity of light aircraft, it has become necessary to develop aircraft engines for this type of construction. One of engine system, designed to increase efficiency and reduce weight, is the engine with opposed pistons. In such an engine, the combustion chamber is formed by two pistons moving in one cylinder. Therefore, this type of engines run in a two-stroke cycle, so they have many advantages such as high power and torque, high efficiency, or a favorable power-to-weight ratio. Tests of one of the available aircraft engines with opposing piston system fueled with diesel oil were carried out on an engine dynamometer equipped with an eddy current brake and the necessary measuring and testing equipment. In order to get to know the basic parameters of the engine, the tests were carried out under partial load conditions for the following torque values: 40, 60, 80, 100 Nm. The rotational speed was changed from 1600 to 2500 rpm. Measurements were also taken for designated points of propeller characteristics. During the tests, the engine torque, engine power, fuel consumption, intake manifold pressure, and oil pressure were recorded. On the basis of the measurements carried out for particular loads, the power curve, hourly and specific fuel consumption curves were determined. Characteristics of charge pressure as a function of rotational speed as well as power, torque, hourly and specific fuel consumption curves for propeller characteristics were also prepared. The obtained characteristics make it possible to select the optimal points of engine operation.

Keywords: aircraft, diesel, engine testing, opposed piston

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Authors: Wilmer Osvaldo Martinez, Manuel Dario Hernandez, Juan Manuel Julio

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We propose to assess the performance of k forecast procedures by exploring the distributions of forecast errors and error losses. We argue that non systematic forecast errors minimize when their distributions are symmetric and unimodal, and that forecast accuracy should be assessed through stochastic loss order rather than expected loss order, which is the way it is customarily performed in previous work. Moreover, since forecast performance evaluation can be understood as a one way analysis of variance, we propose to explore loss distributions under two circumstances; when a strict (but unknown) joint stochastic order exists among the losses of all forecast alternatives, and when such order happens among subsets of alternative procedures. In spite of the fact that loss stochastic order is stronger than loss moment order, our proposals are at least as powerful as competing tests, and are robust to the correlation, autocorrelation and heteroskedasticity settings they consider. In addition, since our proposals do not require samples of the same size, their scope is also wider, and provided that they test the whole loss distribution instead of just loss moments, they can also be used to study forecast distributions as well. We illustrate the usefulness of our proposals by evaluating a set of real world forecasts.

Keywords: forecast evaluation, stochastic order, multiple comparison, non parametric test

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Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

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Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounders. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, with an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make any assumption about the data generating process and the type of the variables. Experiments using both synthetic and real-world datasets show the effectiveness of the method.

Keywords: average causal effect, hidden confounding, bound estimation, generative adversarial learning

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Authors: Ziwei Huang, Jian Ge, Jie Shen, Jiantao Weng

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Retrofitting of existing buildings plays a critical role to achieve sustainable development. This is being considered as one of the approaches to achieving sustainability in the built environment. In order to evaluate the different air-conditioning systems effectiveness and user satisfaction of the existing building which had transformed into green building effectively and accurately. This article takes the green office building in Zhejiang province, China as an example, analyzing the energy consumption, occupant satisfaction and indoor environment quality (IEQ) from the perspective of the thermal environment. This building is special because it combines ground source heat pump system and Variable Refrigerant Flow (VRF) air-conditioning system. Results showed that the ground source heat pump system(EUIa≈25.6) consumes more energy than VRF(EUIb≈23.8). In terms of a satisfaction survey, the use of the VRF air-conditioning was more satisfactory in temperature. However, the ground source heat pump is more satisfied in air quality.

Keywords: post-occupancy evaluation, green office building, air-conditioning systems, ground source heat pump system

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Authors: Ayobami Adeyemo, Michael Chimonyo, Munyaradzi Marufu

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Gastrointestinal nematode (GNT) infection significantly hinder sustainable and profitable sheep production on rangelands. While vitamin E and protein supplementation have individually proven to improve host immunity to parasitism in lambs, to our knowledge, there is no information on the interaction of dietary vitamin E and protein supplementation on lamb growth and GIN faecal egg counts in naturally infected lambs. Therefore, the current study investigated the interaction of dietary protein and vitamin E supplementation on faecal egg counts (FEC) and growth performance of lambs. Twenty four Dohne Merino lambs aged 12 months were allocated equally to each of four treatment combinations, with six lambs in each treatment group for a period of eight weeks. Treatment one lambs received dietary protein and vitamin E (PE), treatment two lambs received dietary protein and no vitamin E (PNE), treatment three received dietary vitamin E and no protein (NPE), and treatment four received no dietary protein and vitamin E supplementation (NPNE). The lambs were allowed to graze on Pennisetum clandestinum contaminated with a heavy load of nematodes. Dietary protein supplementation increased (P < 0.01) average daily gain (ADG) and body condition scores (BCS). Dietary vitamin E supplementation had no effect (P > 0.05) on ADG and BCS. There was no interaction (P > 0.05) between dietary protein and vitamin E supplementation on ADG and BCS. Combined supplementation of dietary protein and vitamin E supplementation significantly reduced (P < 0.01) faecal egg counts and larval counts, respectively. Also, dietary protein and vitamin E supplementation reduced GNT faecal egg counts over the exposure period. The current findings support the hypothesis that the interaction of dietary protein and vitamin E supplementation reduced faecal egg counts and larval counts in lambs. This necessitates future findings on the interaction of dietary protein and vitamin E supplementation on blood associated profiles.

Keywords: gastrointestinal nematodes, nematode eggs, Haemonchus, Trichostrongylus

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Authors: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou

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In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks

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Authors: Michaela Chovancova, Pavel Niedoba

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For the treatment of acute and chronic lung diseases it is preferred to deliver medicaments by inhalation. The drug is delivered directly to tracheobronchial tree. This way allows the given medicament to get directly into the place of action and it makes rapid onset of action and maximum efficiency. The transport of aerosol particles in the particular part of the lung is influenced by their size, anatomy of the lungs, breathing pattern and airway resistance. This article deals with calculation of airway resistance in the lung model of Horsfield. It solves the problem of determination of the pressure losses in bifurcation and thus defines the pressure drop at a given location in the bronchial tree. The obtained data will be used as boundary conditions for transport of aerosol particles in a central part of bronchial tree realized by Computational Fluid Dynamics (CFD) approach. The results obtained from CFD simulation will allow us to provide information on the required particle size and optimal inhalation technique for particle transport into particular part of the lung.

Keywords: human lungs, bronchial tree, pressure losses, airways resistance, flow, breathing

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Authors: Uduak Umoh, Imo Eyoh, Emmauel Nyoho

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This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753.

Keywords: Machine Learning Algorithms , Interval Type-2 Fuzzy Logic, Fire Outbreak, Support Vector Machine, K-Nearest Neighbour, Principal Component Analysis

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Authors: Ilham Ihoume, Rachid Tadili, Nora Arbaoui

Abstract:

The use of solar energy in agricultural applications has gained significant at‎tention ‎‎in recent years as a sustainable and environmentally friendly alternative to ‎‎conventional energy sources. In particular, solar drying of crops has ‎been identified ‎‎as an effective method to preserve agricultural produce while ‎minimizing energy ‎‎consumption and reducing carbon emissions. In this context, the present study ‎‎aims to evaluate the thermo-economic and ecological ‎performance of a solar-electric hybrid greenhouse dryer designed for grape ‎drying. The proposed system ‎‎integrates solar collectors, an electric heater, ‎and a greenhouse structure to create a ‎‎controlled and energy-efficient environment for grape drying. The thermo-economic assessment involves the ‎analysis of the thermal performance, energy ‎‎consumption, and cost-effectiveness of the solar-electric hybrid greenhouse dryer. ‎‎On the other ‎hand, the ecological assessment focuses on the environmental impact ‎‎of the ‎system in terms of carbon emissions and sustainability. The findings of this ‎‎‎study are expected to contribute to the development of sustainable agricultural ‎‎practices and the promotion of renewable energy technologies in the ‎context of ‎‎food production. Moreover, the results may serve as a basis for the ‎design and ‎‎optimization of similar solar drying systems for other crops and ‎regions.‎

Keywords: solar energy, sustainability, agriculture, energy ‎‎analysis‎

Procedia PDF Downloads 56

Authors: Salah Abdulla Salih, Muhamad Sohrabi

Abstract:

This paper aims to study the species, morphology, habitats and geographical distribution of new records of lichens from the Chwarta districts Kurdistan Region of Iraq from September 2022 to June 2023. As a result of the survey in the different locations belonging to the district, found that there are eleven species of lichens are reported from Iraq for the first time. Of these, the taxa of Lecanora thallophila H. Magn is the first species of parasitic lichen reported from Iraq, including the newly recorded genus of lichen Acarospora A.Massal. The lichenized fungi are Acarospora bullata Anzi., Acarospora laqueata Stizenb., Acarospora umbilicata Bagl., Acrocordia gemmata (Ach.) A. Massal., Squamulea subsoluta (Nyl.) Arup, Søchting & Frödén., Coppinsiella ulcerosa (Coppins & P.James) S.Y.Kondr. & L.Lőkös., Myriolecis flowersiana (H. Magn.) Śliwa, Zhao Xin & Lumbsch., Megaspora rimisorediata Valadbeigi & A. Nordin sp. nov., Physcia subtilis Degel., Polycauliona polycarpa (Hoffm.) Frödén, Arup, & Søchting.

Keywords: apothecia, lichen species, new records, Kurdistan region

Procedia PDF Downloads 74

Authors: Madhu Babu Cherukuri, Tamoghna Ghosh

Abstract:

Factory supply chain management spends billions of dollars every year to procure and manage equipment spare parts. Due to technology -and processes changes some of these spares become obsolete/dead inventory. Factories have huge dead inventory worth millions of dollars accumulating over time. This is due to lack of a scientific methodology to identify them and send the inventory back to the suppliers on a timely basis. The standard approach followed across industries to deal with this is: if a part is not used for a set pre-defined period of time it is declared dead. This leads to accumulation of dead parts over time and these parts cannot be sold back to the suppliers as it is too late as per contract agreement. Our main idea is the time period for identifying a part as dead cannot be a fixed pre-defined duration across all parts. Rather, it should depend on various properties of the part like historical consumption pattern, type of part, how many machines it is being used in, whether it- is a preventive maintenance part etc. We have designed a predictive algorithm which predicts part obsolescence well in advance with reasonable accuracy and which can help save millions.

Keywords: obsolete inventory, machine learning, big data, supply chain analytics, dead inventory

Procedia PDF Downloads 315