Search results for: artificial pollution.

Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

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Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility and overhead & profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: Construction cost factors, neural networks, roadworks, Zambian Construction Industry.

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Authors: Solomon Neway Jida, Jean-Francois Hetet, Pascal Chesse

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Vehicular emission is the key source of air pollution in the urban environment. This includes both fine particles (PM_2.5) and coarse particulate matters (PM₁₀). However, particulate matter emissions from road traffic comprise emissions from exhaust tailpipe and emissions due to wear and tear of the vehicle part such as brake, tire and clutch and re-suspension of dust (non-exhaust emission). This study estimates the share of the two sources of pollutant particle emissions from on-roadside vehicles in the Addis Ababa municipality, Ethiopia. To calculate its share, two methods were applied; the exhaust-tailpipe emissions were calculated using the Europeans emission inventory Tier II method and Tier I for the non-exhaust emissions (like vehicle tire wear, brake, and road surface wear). The results show that of the total traffic-related particulate emissions in the city, 63% emitted from vehicle exhaust and the remaining 37% from non-exhaust sources. The annual roads transport exhaust emission shares around 2394 tons of particles from all vehicle categories. However, from the total yearly non-exhaust particulate matter emissions’ contribution, tire and brake wear shared around 65% and 35% emanated by road-surface wear. Furthermore, vehicle tire and brake wear were responsible for annual 584.8 tons of coarse particles (PM₁₀) and 314.4 tons of fine particle matter (PM_2.5) emissions in the city whereas surface wear emissions were responsible for around 313.7 tons of PM₁₀ and 169.9 tons of PM_2.5 pollutant emissions in the city. This suggests that non-exhaust sources might be as significant as exhaust sources and have a considerable contribution to the impact on air quality.

Keywords: Addis Ababa, automotive emission, emission estimation, particulate matters.

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Authors: Hsiao-Tien Pao, Yi-Ying Li, Hsin-Chia Fu

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This paper addressed the impacts of energy consumption, economic growth, financial development, and population size on environmental degradation using grey relational analysis (GRA) for China, where foreign direct investment (FDI) inflows is the proxy variable for financial development. The more recent historical data during the period 2004–2011 are used, because the use of very old data for data analysis may not be suitable for rapidly developing countries. The results of the GRA indicate that the linkage effects of energy consumption–emissions and GDP–emissions are ranked first and second, respectively. These reveal that energy consumption and economic growth are strongly correlated with emissions. Higher economic growth requires more energy consumption and increasing environmental pollution. Likewise, more efficient energy use needs a higher level of economic development. Therefore, policies to improve energy efficiency and create a low-carbon economy can reduce emissions without hurting economic growth. The finding of FDI–emissions linkage is ranked third. This indicates that China do not apply weak environmental regulations to attract inward FDI. Furthermore, China’s government in attracting inward FDI should strengthen environmental policy. The finding of population–emissions linkage effect is ranked fourth, implying that population size does not directly affect CO₂ emissions, even though China has the world’s largest population, and Chinese people are very economical use of energy-related products. Overall, the energy conservation, improving efficiency, managing demand, and financial development, which aim at curtailing waste of energy, reducing both energy consumption and emissions, and without loss of the country’s competitiveness, can be adopted for developing economies. The GRA is one of the best way to use a lower data to build a dynamic analysis model.

Keywords: Grey relational analysis, foreign direct investment, CO2 emissions, China.

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Authors: Ioannis P. Panagopoulos, Christos C. Pavlatos, George K. Papakonstantinou

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Conventional approaches in the implementation of logic programming applications on embedded systems are solely of software nature. As a consequence, a compiler is needed that transforms the initial declarative logic program to its equivalent procedural one, to be programmed to the microprocessor. This approach increases the complexity of the final implementation and reduces the overall system's performance. On the contrary, presenting hardware implementations which are only capable of supporting logic programs prevents their use in applications where logic programs need to be intertwined with traditional procedural ones, for a specific application. We exploit HW/SW codesign methods to present a microprocessor, capable of supporting hybrid applications using both programming approaches. We take advantage of the close relationship between attribute grammar (AG) evaluation and knowledge engineering methods to present a programmable hardware parser that performs logic derivations and combine it with an extension of a conventional RISC microprocessor that performs the unification process to report the success or failure of those derivations. The extended RISC microprocessor is still capable of executing conventional procedural programs, thus hybrid applications can be implemented. The presented implementation is programmable, supports the execution of hybrid applications, increases the performance of logic derivations (experimental analysis yields an approximate 1000% increase in performance) and reduces the complexity of the final implemented code. The proposed hardware design is supported by a proposed extended C-language called C-AG.

Keywords: Attribute Grammars, Logic Programming, RISC microprocessor.

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling. The research proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling. The paper concludes the challenges and improvement directions for Deep Reinforcement Learning-based resource scheduling algorithms.

Keywords: Resource scheduling, deep reinforcement learning, distributed system, artificial intelligence.

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Authors: Farnaz Amin Salehi, David Edward.Cotton, Mohammad Ali Abdoli, Kambiz Rezapour

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The daily increase of organic waste materials resulting from different activities in the country is one of the main factors for the pollution of environment. Today, with regard to the low level of the output of using traditional methods, the high cost of disposal waste materials and environmental pollutions, the use of modern methods such as anaerobic digestion for the production of biogas has been prevailing. The collected biogas from the process of anaerobic digestion, as a renewable energy source similar to natural gas but with a less methane and heating value is usable. Today, with the help of technologies of filtration and proper preparation, access to biogas with features fully similar to natural gas has become possible. At present biogas is one of the main sources of supplying electrical and thermal energy and also an appropriate option to be used in four stroke engine, diesel engine, sterling engine, gas turbine, gas micro turbine and fuel cell to produce electricity. The use of biogas for different reasons which returns to socio-economic and environmental advantages has been noticed in CHP for the production of energy in the world. The production of biogas from the technology of anaerobic digestion and its application in CHP power plants in Iran can not only supply part of the energy demands in the country, but it can materialize moving in line with the sustainable development. In this article, the necessity of the development of CHP plants with biogas fuels in the country will be dealt based on studies performed from the economic, environmental and social aspects. Also to prove the importance of the establishment of these kinds of power plants from the economic point of view, necessary calculations has been done as a case study for a CHP power plant with a biogas fuel.

Keywords: Anaerobic Digestion, Biogas, CHP, Organic Wastes

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Authors: Roy R. Gu, Mahesh Sahu, Xianggui Zhao

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Contour filter strips planted with perennial vegetation can be used to improve surface and ground water quality by reducing pollutant, such as NO3-N, and sediment outflow from cropland to a river or lake. Meanwhile, the filter strips of perennial grass with biofuel potentials also have economic benefits of producing ethanol. In this study, The Soil and Water Assessment Tool (SWAT) model was applied to the Walnut Creek Watershed to examine the effectiveness of contour strips in reducing NO3-N outflows from crop fields to the river or lake. Required input data include watershed topography, slope, soil type, land-use, management practices in the watershed and climate parameters (precipitation, maximum/minimum air temperature, solar radiation, wind speed and relative humidity). Numerical experiments were conducted to identify potential subbasins in the watershed that have high water quality impact, and to examine the effects of strip size and location on NO3-N reduction in the subbasins under various meteorological conditions (dry, average and wet). Variable sizes of contour strips (10%, 20%, 30% and 50%, respectively, of a subbasin area) planted with perennial switchgrass were selected for simulating the effects of strip size and location on stream water quality. Simulation results showed that a filter strip having 10%-50% of the subbasin area could lead to 55%- 90% NO3-N reduction in the subbasin during an average rainfall year. Strips occupying 10-20% of the subbasin area were found to be more efficient in reducing NO3-N when placed along the contour than that when placed along the river. The results of this study can assist in cost-benefit analysis and decision-making in best water resources management practices for environmental protection.

Keywords: modeling, SWAT, water quality, NO3-N, watershed.

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Authors: Muhammad Anjum Aqueel, Zaighum Abbas, Mubasshir Sohail, Muhammad Abubakar, Hafiz Khurram Shurjeel, Abu Bakar Muhammad Raza, Muhammad Afzal, Sami Ullah

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Keeping in view the increasing demand, queen of Apis mellifera L. (Hymenoptera: Apidae) was reared artificially in this experiment at varying diets including royal jelly. Larval duration, pupal duration, weight, and size of pupae were evaluated at different diets including royal jelly. Queen larvae were raised by Doo Little grafting method. Four different diets were mixed with royal jelly and applied to larvae. Fructose, sugar, yeast, and honey were provided to rearing queen larvae along with same amount of royal jelly. Larval and pupal duration were longest (6.15 and 7.5 days, respectively) at yeast and shortest on honey (5.05 and 7.02 days, respectively). Heavier and bigger pupae were recorded on yeast (168.14 mg and 1.76 cm, respectively) followed by diets having sugar and honey. Due to production of heavier and bigger pupae, yeast was considered as best artificial diet for the growing queen larvae. So, in the second part of experiment, different amounts of yeast were provided to growing larvae along with fixed amount (0.5 g) of royal jelly. Survival rates of the larvae and queen bee were 70% and 40% in the 4-g food, 86.7% and 53.3% in the 6-g food, and 76.7% and 50% in the 8-g food. Weight of adult queen bee (1.459±0.191 g) and the number of ovarioles (41.7±21.3) were highest at 8 g of food. Results of this study are helpful for bee-keepers in producing fitter queen bees.

Keywords: Apis melifera L., dietary effect, survival and development, honey bee queen.

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: Image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization.

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Authors: Reihaneh Montazeri Shatouri, Rosmini Omar, Kunio Igusa

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Although automotive industry has brought different beneficiaries to human life, it is being pointed out as one of the major cause of global air pollution which resulted in climate change, smog, green house gases (GHGs), and human diseases by many reasons. Since auto industry is one of the largest consumers of fossil fuels, the realization of green innovations is becoming a crucial choice to meet the challenges towards sustainable development. Recently, many auto manufacturers have embarked on green technology initiatives to gain a competitive advantage in the global market; however, innovative manufacturing systems and technologies can enhance operational performance only if the human resource management is in place to elicit the motivation of the employees and develop their organizational expertise. No organization can perform at peak levels unless each employee is committed to the company goals and works as an effective team member. Strategic human resource practices are the primary means by which firms can shape the skills, attitudes, and behavior of individuals to align with the business strategic objectives. This study investigates on the comprehensive approach of multiple advanced technology innovations and human resource management at Toyota Motor Corporation as the market leader of full hybrid technology in the automotive industry. Then, HRM framework of the company is described and three sets of human resource practices that support the innovation-oriented HR system, presented. Finally, a conceptual framework for innovativeness in green technology in automotive industry by applying a deliberate strategic HR management system and knowledge management with the intervening factors of organizational culture, knowledge application and knowledge sharing is proposed.

Keywords: Automotive Industry, Green Technology, Innovation, Strategic Human Resource Management

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Authors: Fardin Boustani , Mohammah Hosein Hojati

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The Beshar River is one aquatic ecosystem,which is affected by pollutants. This study was conducted to evaluate the effects of human activities on the water quality of the Beshar river. This river is approximately 190 km in length and situated at the geographical positions of 51° 20' to 51° 48' E and 30° 18' to 30° 52' N it is one of the most important aquatic ecosystems of Kohkiloye and Boyerahmad province next to the city of Yasuj in southern Iran. The Beshar river has been contaminated by industrial, agricultural and other activities in this region such as factories, hospitals, agricultural farms, urban surface runoff and effluent of wastewater treatment plants. In order to evaluate the effects of these pollutants on the quality of the Beshar river, five monitoring stations were selected along its course. The first station is located upstream of Yasuj near the Dehnow village; stations 2 to 4 are located east, south and west of city; and the 5th station is located downstream of Yasuj. Several water quality parameters were sampled. These include pH, dissolved oxygen, biological oxygen demand (BOD), temperature, conductivity, turbidity, total dissolved solids and discharge or flow measurements. Water samples from the five stations were collected and analysed to determine the following physicochemical parameters: EC, pH, T.D.S, T.H, No2, DO, BOD5, COD during 2008 to 2009. The study shows that the BOD5 value of station 1 is at a minimum (1.5 ppm) and increases downstream from stations 2 to 4 to a maximum (7.2 ppm), and then decreases at station 5. The DO values of station 1 is a maximum (9.55 ppm), decreases downstream to stations 2 - 4 which are at a minimum (3.4 ppm), before increasing at station 5. The amount of BOD and TDS are highest at the 4th station and the amount of DO is lowest at this station, marking the 4th station as more highly polluted than the other stations. The physicochemical parameters improve at the 5th station due to pollutant degradation and dilution. Finally the point and nonpoint pollutant sources of Beshar river were determined and compared to the monitoring results.

Keywords: Beshar river, physicochemical parameter, waterpollution, Yasuj

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Authors: B. Sadepovich Maikanov, Z. Shabanbayevich Adilbekov, R. Husainovna Mustafina, L. Tyulegenovna Auteleyeva

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This study involves detailed information about contaminants of honey in the Republic of Kazakhstan. The requirements of the technical regulation ‘Requirements to safety of honey and bee products’ and GOST 19792-2001 were taken into account in this research. Contamination of honey by antibiotics wqs determined by the IEA (immune-enzyme analysis), Ridder analyzer and Tecna produced test systems. Voltammetry (TaLab device) was used to define contamination by salts of heavy metals and gamma-beta spectrometry, ‘Progress BG’ system, with preliminary ashing of the sample of honey was used to define radioactive contamination. This article pointed out that residues of chloramphenicol were detected in 24% of investigated products, in 22% of them –streptomycin, in 7.3% - sulfanilamide, in 2.4% - tylosin, and in 12% - combined contamination was noted. Geographically, the greatest degree of contamination of honey with antibiotics occurs in the Northern Kazakhstan – 54.4%, and Southern Kazakhstan - 50%, and the lowest in Central and Eastern Kazakhstan with 30% and 25%, respectively. Generally, pollution by heavy metals is within acceptable limits, but the contamination from lead is highest in the Akmola region. The level of radioactive cesium and strontium is also within acceptable concentrations. The highest radioactivity in terms of cesium was observed in the East Kazakhstan region - 49.00±10 Bq/kg, in Akmola, North Kazakhstan and Almaty - 12.00±5, 11.05±3 and 19.0±8 Bq/kg, respectively, while the norm is 100 Bq/kg. In terms of strontium, the radioactivity in the East Kazakhstan region is 25.03±15 Bq/kg, while in Akmola, North Kazakhstan and Almaty regions it is 12.00±3, 10.2±4 and 1.0±2 Bq/kg, respectively, with the norm of 80 Bq/kg. This accumulation is mainly associated with the environmental degradation, feeding and treating of bees. Moreover, in the process of collecting nectar, external substances can penetrate honey. Overall, this research determines factors and reasons of honey contamination.

Keywords: Antibiotics, contamination of honey, honey, radionuclides.

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Authors: Ebeed A. Saleh, Kadry M. Sadek, Safaa H. Ghorbal

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Due to the determination of the pollution status of fresh resources in the Egyptian territorial waters is very important for public health; this study was carried out to reveal the levels of heavy metals in the shellfish and their environment and its relation to the highly developed industrial activities in those areas. A total of 100 shellfish samples from the Rosetta, Edku, El-Maadiya, Abo-Kir and El-Max coasts [10 crustaceans (shrimp) and 10 mollusks (oysters)] were randomly collected from each coast. Additionally, 10 samples from both the water and the sediment were collected from each coast. Each collected sample was analyzed for cadmium, chromium, copper, lead and zinc residues using a Perkin Elmer atomic absorption spectrophotometer (AAS). The results showed that the levels of heavy metals were higher in the water and sediment from Abo-Kir. The heavy metal levels decreased successively for the Rosetta, Edku, El-Maadiya, and El-Max coasts, and the concentrations of heavy metals, except copper and zinc, in shellfish exhibited the same pattern. For the concentration of heavy metals in shellfish tissue, the highest was zinc and the concentrations decreased successively for copper, lead, chromium and cadmium for all coasts, except the Abo-Kir coast, where the chromium level was highest and the other metals decreased successively for zinc, copper, lead and cadmium. In Rosetta, chromium was higher only in the mollusks, while the level of this metal was lower in the crustaceans; this trend was observed at the Edku, El-Maadiya and El-Max coasts as well. Herein, we discuss the importance of such contamination for public health and the sources of shellfish contamination with heavy metals. We suggest measures to minimize and prevent these pollutants in the aquatic environment and, furthermore, how to protect humans from excessive intake.

Keywords: Atomic absorption, heavy metals, sediment, shellfish, water.

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Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

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The widespread popularity of mobile devices and the development of artificial intelligence (AI) have led to the widespread adoption of deep learning (DL) in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace, a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Additionally, we propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. Using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We conduct an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace outperformed FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: Mobile computing, deep learning apps, sensitive information, static analysis.

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Authors: Thea Freese

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Environmental legislation to protect North and Baltic Sea areas from harmful vessel-source emissions has received increased political attention in recent years. Legislative measures are expected to show positive effects on the health of the marine environment and society. At the same time, compliance might increase the costs to industry and have effects on freight rates and volumes shipped with potential negative repercussions on the environment. Building on an exploratory sequential mixed methods approach, this research project will study the economic effects of maritime environmental legislation in two phases. In Phase I, exploratory in-depth interviews were conducted with 12 experts from various stakeholder groups aiming at identifying variables influencing the relationship between environmental legislation, freight rates and volumes shipped. Influencing factors like compliance, enforcement and modal shift were identified and studied. Phase II will comprise of a quantitative study conducted with the aim of verifying the theory build in Phase I and quantifying economic effects of rules on shipping pollution. Research in this field might inform policy-makers about determinants of behaviour of ship operators in the face of the law and might further the development of a comprehensive legal system for marine environmental protection. At the present stage of research, first tentative results from the qualitative phase may be examined and open research questions to be addressed in the quantitative phase as well as possible research designs for phase II may be discussed. Input from other researchers will be highly valuable at this point.

Keywords: Clean shipping operations, compliance, maritime environmental legislation, maritime law and economics, mixed methods research, North and Baltic Sea area.

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Authors: Al-Shabatat, M. Ahmad, Abbas, M., Ismail, H. Nizam

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Achievement motivation is believed to promote giftedness attracting people to invest in many programs to adopt gifted students providing them with challenging activities. Intellectual giftedness is founded on the fluid intelligence and extends to more specific abilities through the growth and inputs from the achievement motivation. Acknowledging the roles played by the motivation in the development of giftedness leads to an effective nurturing of gifted individuals. However, no study has investigated the direct and indirect effects of the achievement motivation and fluid intelligence on intellectual giftedness. Thus, this study investigated the contribution of motivation factors to giftedness development by conducting tests of fluid intelligence using Cattell Culture Fair Test (CCFT) and analytical abilities using culture reduced test items covering problem solving, pattern recognition, audio-logic, audio-matrices, and artificial language, and self report questionnaire for the motivational factors. A number of 180 highscoring students were selected using CCFT from a leading university in Malaysia. Structural equation modeling was employed using Amos V.16 to determine the direct and indirect effects of achievement motivation factors (self confidence, success, perseverance, competition, autonomy, responsibility, ambition, and locus of control) on the intellectual giftedness. The findings showed that the hypothesized model fitted the data, supporting the model postulates and showed significant and strong direct and indirect effects of the motivation and fluid intelligence on the intellectual giftedness.

Keywords: Achievement motivation, Intellectual Giftedness, Fluid Intelligence, Analytical Giftedness, CCFT, Structural EquationModeling.

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Authors: Manal Mhada, BrahimEzzahiri, Ouafae Benlhabib

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Seventy-nine accessions, including two local wild species (Chenopodium album and C. murale) and several cultivated quinoa lines developed through recurrent selection in Morocco were screened for their resistance against Peronospora farinose, the causal agent of downy mildew disease. The method of artificial inoculation on detached healthy leaves taken from the middle stage of the plant was used. Screened accessions showed different levels of quantitative resistance to downy mildew as they were scored through the calculation of their area under disease progress curve and their two resistance components, the incubation period and the latent period. Significant differences were found between accessions regarding the three criteria (Incubation Period, Latent Period and Area Under Diseases Progress Curve). Accessions M2a and S938/1 were ranked resistant as they showed the longest Incubation Period (7 days) and Latent Period (12 days) and the lowest area under diseases progress curve (4). Therefore, M24 is the most susceptible accession as it has presented the highest area under diseases progress curve (34.5) and the shortest Incubation Period (1 day) and Latent Period (3 days). In parallel to this evaluation approach, the accession resistance was confirmed under the field conditions through natural infection by using the tree-leaf method. The high correlation found between detached leaf inoculation method and field screening under natural infection allows us to use this laboratory technique with sureness in further selection works.

Keywords: Detached leaf inoculation, Downy mildew, Field screening, Quinoa.

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Authors: Alireza Mortezaei, Kimia Mortezaei

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Recordings from recent earthquakes have provided evidence that ground motions in the near field of a rupturing fault differ from ordinary ground motions, as they can contain a large energy, or “directivity" pulse. This pulse can cause considerable damage during an earthquake, especially to structures with natural periods close to those of the pulse. Failures of modern engineered structures observed within the near-fault region in recent earthquakes have revealed the vulnerability of existing RC buildings against pulse-type ground motions. This may be due to the fact that these modern structures had been designed primarily using the design spectra of available standards, which have been developed using stochastic processes with relatively long duration that characterizes more distant ground motions. Many recently designed and constructed buildings may therefore require strengthening in order to perform well when subjected to near-fault ground motions. Fiber Reinforced Polymers are considered to be a viable alternative, due to their relatively easy and quick installation, low life cycle costs and zero maintenance requirements. The objective of this paper is to investigate the adequacy of Artificial Neural Networks (ANN) to determine the three dimensional dynamic response of FRP strengthened RC buildings under the near-fault ground motions. For this purpose, one ANN model is proposed to estimate the base shear force, base bending moments and roof displacement of buildings in two directions. A training set of 168 and a validation set of 21 buildings are produced from FEA analysis results of the dynamic response of RC buildings under the near-fault earthquakes. It is demonstrated that the neural network based approach is highly successful in determining the response.

Keywords: Seismic evaluation, FRP, neural network, near-fault ground motion

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Authors: Hachaichi Mohamed Nour El-Islem, Baouni Tahar

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Environmental sustainability rather than a trans-disciplinary and a scientific issue, is the main problem that characterizes all modern cities nowadays. In developing countries, this concern is expressed in a plethora of critical urban ills: traffic congestion, air pollution, noise, urban decay, increase in energy consumption and CO₂ emissions which blemish cities’ landscape and might threaten citizens’ health and welfare. As in the same manner as developing world cities, the rapid growth of Algiers’ human population and increasing in city scale phenomena lead eventually to increase in daily trips, energy consumption and CO₂ emissions. In addition, the lack of proper and sustainable planning of the city’s infrastructure is one of the most relevant issues from which Algiers suffers. The aim of this contribution is to estimate the carbon deficit of the City of Algiers, Algeria, using the Ecological Footprint Model (carbon footprint). In order to achieve this goal, the amount of CO₂ from fuel combustion has been calculated and aggregated into five sectors (agriculture, industry, residential, tertiary and transportation); as well, Algiers’ biocapacity (CO₂ uptake land) has been calculated to determine the ecological overshoot. This study shows that Algiers’ transport system is not sustainable and is generating more than 50% of Algiers total carbon footprint which cannot be sequestered by the local forest land. The aim of this research is to show that the Carbon Footprint Assessment might be a relevant indicator to design sustainable strategies/policies striving to reduce CO₂ by setting in motion the energy consumption in the transportation sector and reducing the use of fossil fuels as the main energy input.

Keywords: Biocapacity, carbon footprint, ecological footprint assessment, energy consumption.

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Authors: Guclu Insel, Efe Gumuslu, Gulten Yuksek, Nilay Sayi Ucar, Emine Ubay Cokgor, Tugba Olmez Hanci, Didem Okutman Tas, Fatos Germirli Babuna, Derya Firat Ertem, Okmen Yildirim, Ozge Erturan, Betul Kirci

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In order to eliminate water scarcity problems, effective precautions must be taken. Growing competition for water is increasingly forcing facilities to tackle their own water scarcity problems. At this point, application of wastewater reclamation and reuse results in considerable economic advantageous. In this study, an enamel coating facility, which is one of the high water consumed facilities, is evaluated in terms of its wastewater reuse potential. Wastewater reclamation and reuse can be defined as one of the best available techniques for this sector. Hence, process and pollution profiles together with detailed characterization of segregated wastewater sources are appraised in a way to find out the recoverable effluent streams arising from enamel coating operations. Daily, 170 m³ of process water is required and 160 m³ of wastewater is generated. The segregated streams generated by two enamel coating processes are characterized in terms of conventional parameters. Relatively clean segregated wastewater streams (reusable wastewaters) are separately collected and experimental treatability studies are conducted on it. The results reflected that the reusable wastewater fraction has an approximate amount of 110 m³/day that accounts for 68% of the total wastewaters. The need for treatment applicable on reusable wastewaters is determined by considering water quality requirements of various operations and characterization of reusable wastewater streams. Ultra-filtration (UF), Nano-filtration (NF) and Reverse Osmosis (RO) membranes are subsequently applied on reusable effluent fraction. Adequate organic matter removal is not obtained with the mentioned treatment sequence.

Keywords: enamel coating, membrane, reuse, wastewater

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Authors: First G.M. Karthik, Second Ramachandra.V.Pujeri, Dr.

Abstract:

Generalized Center String (GCS) problem are generalized from Common Approximate Substring problem and Common substring problems. GCS are known to be NP-hard allowing the problems lies in the explosion of potential candidates. Finding longest center string without concerning the sequence that may not contain any motifs is not known in advance in any particular biological gene process. GCS solved by frequent pattern-mining techniques and known to be fixed parameter tractable based on the fixed input sequence length and symbol set size. Efficient method known as Bpriori algorithms can solve GCS with reasonable time/space complexities. Bpriori 2 and Bpriori 3-2 algorithm are been proposed of any length and any positions of all their instances in input sequences. In this paper, we reduced the time/space complexity of Bpriori algorithm by Constrained Based Frequent Pattern mining (CBFP) technique which integrates the idea of Constraint Based Mining and FP-tree mining. CBFP mining technique solves the GCS problem works for all center string of any length, but also for the positions of all their mutated copies of input sequence. CBFP mining technique construct TRIE like with FP tree to represent the mutated copies of center string of any length, along with constraints to restraint growth of the consensus tree. The complexity analysis for Constrained Based FP mining technique and Bpriori algorithm is done based on the worst case and average case approach. Algorithm's correctness compared with the Bpriori algorithm using artificial data is shown.

Keywords: Constraint Based Mining, FP tree, Data mining, GCS problem, CBFP mining technique.

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Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

Abstract:

Farm management and operations will drastically change due to access to real-time data, real-time forecasting and tracking of physical items in combination with Internet of Things (IoT) developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm decision-making process does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyze on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue and environmental impact. Evolutionary Computing (EC) can be very effective in finding the optimal combination of sets of some objects and finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and EC in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management and its uptake has become a continuing trend.

Keywords: Big data, evolutionary computing, cloud, precision technologies

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Authors: Anthony Wachs, Guillaume Vinay, Gilles Ferrer, Jacques Kouakou, Calin Dan, Laurence Girolami

Abstract:

An original Direct Numerical Simulation (DNS) method to tackle the problem of particulate flows at moderate to high concentration and finite Reynolds number is presented. Our method is built on the framework established by Glowinski and his coworkers [1] in the sense that we use their Distributed Lagrange Multiplier/Fictitious Domain (DLM/FD) formulation and their operator-splitting idea but differs in the treatment of particle collisions. The novelty of our contribution relies on replacing the simple artificial repulsive force based collision model usually employed in the literature by an efficient Discrete Element Method (DEM) granular solver. The use of our DEM solver enables us to consider particles of arbitrary shape (at least convex) and to account for actual contacts, in the sense that particles actually touch each other, in contrast with the simple repulsive force based collision model. We recently upgraded our serial code, GRIFF 1 [2], to full MPI capabilities. Our new code, PeliGRIFF 2, is developed under the framework of the full MPI open source platform PELICANS [3]. The new MPI capabilities of PeliGRIFF open new perspectives in the study of particulate flows and significantly increase the number of particles that can be considered in a full DNS approach: O(100000) in 2D and O(10000) in 3D. Results on the 2D/3D sedimentation/fluidization of isometric polygonal/polyedral particles with collisions are presented.

Keywords: Particulate flow, distributed lagrange multiplier/fictitious domain method, discrete element method, polygonal shape, sedimentation, distributed computing, MPI

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

Abstract:

During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: Artificial intelligence, depression detection, facial emotion recognition, natural language processing, mental disorder.

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Authors: Pascal Mwenge, Tumisang Seodigeng

Abstract:

The combination of world population and the third industrial revolution led to high demand for fuels. On the other hand, the decrease of global fossil 8fuels deposits and the environmental air pollution caused by these fuels has compounded the challenges the world faces due to its need for energy. Therefore, new forms of environmentally friendly and renewable fuels such as biodiesel are needed. The primary analytical techniques for methanolysis yield monitoring have been chromatography and spectroscopy, these methods have been proven reliable but are more demanding, costly and do not provide real-time monitoring. In this work, the in situ monitoring of biodiesel from sunflower oil using FTIR (Fourier Transform Infrared) has been studied; the study was performed using EasyMax Mettler Toledo reactor equipped with a DiComp (Diamond) probe. The quantitative monitoring of methanolysis was performed by building a quantitative model with multivariate calibration using iC Quant module from iC IR 7.0 software. 15 samples of known concentrations were used for the modelling which were taken in duplicate for model calibration and cross-validation, data were pre-processed using mean centering and variance scale, spectrum math square root and solvent subtraction. These pre-processing methods improved the performance indexes from 7.98 to 0.0096, 11.2 to 3.41, 6.32 to 2.72, 0.9416 to 0.9999, RMSEC, RMSECV, RMSEP and R2Cum, respectively. The R² value of 1 (training), 0.9918 (test), 0.9946 (cross-validation) indicated the fitness of the model built. The model was tested against univariate model; small discrepancies were observed at low concentration due to unmodelled intermediates but were quite close at concentrations above 18%. The software eliminated the complexity of the Partial Least Square (PLS) chemometrics. It was concluded that the model obtained could be used to monitor methanol of sunflower oil at industrial and lab scale.

Keywords: Biodiesel, calibration, chemometrics, FTIR, methanolysis, multivariate analysis, transesterification.

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

Abstract:

Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: Climate change, coastal vulnerability index, global warming, sea level rise.

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Authors: Sara Zamani, Mojtaba Fazeli, Abdollah Rashidi Mehrabadi

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In a world worried about water resources with the shadow of drought and famine looming all around, the quality of water is as important as its quantity. The source of all concerns is the constant reduction of per capita quality water for different uses. Iran With an average annual precipitation of 250 mm compared to the 800 mm world average, Iran is considered a water scarce country and the disparity in the rainfall distribution, the limitations of renewable resources and the population concentration in the margins of desert and water scarce areas have intensified the problem. The shortage of per capita renewable freshwater and its poor quality in large areas of the country, which have saline, brackish or hard water resources, and the profusion of natural and artificial pollutant have caused the deterioration of water quality. Among methods of treatment and use of these waters one can refer to the application of membrane technologies, which have come into focus in recent years due to their great advantages. This process is quite efficient in eliminating multi-capacity ions; and due to the possibilities of production at different capacities, application as treatment process in points of use, and the need for less energy in comparison to Reverse Osmosis processes, it can revolutionize the water and wastewater sector in years to come. The article studied the different capacities of water resources in the Persian Gulf and Oman Sea watershed basins, and processes the possibility of using nanofiltration process to treat brackish and non-conventional waters in these basins.

Keywords: Membrane processes, saline waters, brackish waters, hard waters, zoning water quality in the Persian Gulf and the Oman Sea Watershed area, nanofiltration.

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Authors: P. Pimsee, C. Sablayrolles, P. de Caro, J. Guyomarch, N. Lesage, M. Montréjaud-Vignoles

Abstract:

The MIGR’HYCAR research project was initiated to provide decisional tools for risks connected to oil spill drifts in continental waters. These tools aim to serve in the decision-making process once oil spill pollution occurs and/or as reference tools to study scenarios of potential impacts of pollutions on a given site. This paper focuses on the study of the distribution of polycyclic aromatic hydrocarbons (PAHs) and derivatives from oil spill in water as function of environmental parameters. Eight petroleum oils covering a representative range of commercially available products were tested. 41 polycyclic aromatic hydrocarbons (PAHs) and derivates, among them 16 EPA priority pollutants were studied by dynamic tests at laboratory scale. The chemical profile of the water soluble fraction was different from the parent oil profile due to the various water solubility of oil components. Semi-volatile compounds (naphtalenes) constitute the major part of the water soluble fraction. A large variation in composition of the water soluble fraction was highlighted depending on oil type. Moreover, four environmental parameters (temperature, suspended solid quantity, salinity and oil: water surface ratio) were investigated with the Taguchi experimental design methodology. The results showed that oils are divided into three groups: the solubility of Domestic fuel and Jet A1 presented a high sensitivity to parameters studied, meaning they must be taken into account. For Gasoline (SP95-E10) and Diesel fuel, a medium sensitivity to parameters was observed. In fact, the four others oils have shown low sensitivity to parameters studied. Finally, three parameters were found to be significant towards the water soluble fraction.

Keywords: Monitoring, PAHs, SBSE, water soluble fraction, Taguchi experimental design.

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Authors: Yousef Swesi, Asia Elmeshergi, Abdelati Elalem, Walid Alfoghy

Abstract:

This paper involves a study of the heavy metal pollution of the soils around one of cement plants in Libya called Suk-Alkhameas and surrounding urban areas caused by cement kiln dust (CKD) emitted. Samples of soil was collected from sites at four directions around the cement factory at distances 250m, 1000m, and 3000m from the factory and at (0-10)cm deep in the soil. These samples are analyzed for Fe (iii), Zn(ii), and Pb (ii) as major pollutants. These values are compared with soils at 25 Km distances from the factory as a reference or control samples. The results show that the concentration of Fe ions in the surface soil was within the acceptable range of 1000ppm. However, for Zn and Pb ions the concentrations at the east and north sides of the factory were found six fold higher than the benchmark level. This high value was attributed to the wind which blows usually from south to north and from west to east. This work includes an investigation of the adsorption isotherms and adsorption efficiency of CKD as adsorbent of heavy metal ions (Fe (iii), Zn(ii), and Pb(ii)) from the polluted soils of Suk-Alkameas city. The investigation was conducted in batch and fixed bed column flow technique. The adsorption efficiency of the studied heavy metals ions removals onto CKD depends on the pH of the solution. The optimum pH values are found to be in the ranges of 8-10 and decreases at lower pH values. The removal efficiency of these heavy metals ions ranged from 93% for Pb, 94% for Zn, and 98% for Fe ions for 10 g.l^-1 adsorbent concentration. The maximum removal efficiency of these ions was achieved at 50-60 minutes contact times at which equilibrium is reached. Fixed bed column experimental measurements are also made to evaluate CKD as an adsorbent for the heavy metals. Results obtained are with good agreement with Langmuir and Drachsal assumption of multilayer formation on the adsorbent surface.

Keywords: Adsorption, Cement Kiln dust (CKD & CAC), Isotherms, Zn and Pb ions.

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

Abstract:

The detection and segmentation of mitochondria from fluorescence microscopy is crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. Although there exists a number of open-source software tools and artificial intelligence (AI) methods designed for analyzing mitochondrial images, the availability of only a few combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compactibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source Python and OpenCV library, the algorithms are implemented in three stages: pre-processing; image binarization; and coarse-to-fine segmentation. The proposed model is validated using the fluorescence mitochondrial dataset. Ground truth labels generated using Labkit were also used to evaluate the performance of our detection and segmentation model using precision, recall and rand index. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks concludes the paper.

Keywords: 2D, Binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation.

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