Search results for: most likelihood amplitude estimation (MLQAE)

Authors: Rochelle Daley, Eric Garraway, Catherine Murphy

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Crime is a major problem in Jamaica as well as the high number of unsolved violent crimes. The introduction of forensic entomology in criminal investigations has the potential to decrease the number of unsolved violent crimes through the estimation of PMI (post-mortem interval) or time since death. Though it has great potential, forensic entomology requires data from insects specific to a geographical location to be credibly applied in legal investigations. It is a relatively new area of study in the Caribbean, with multiple pioneer research opportunities. Of critical importance in forensic entomology is the ability to identify the species of interest. Larvae are commonly collected at crime scenes and a means of rapid identification is crucial. Moreover, a low-cost method is critical in countries with limited budget available for crime fighting. Sarcophagids are one of the most important colonisers of a carcass however, they are difficult to distinguish using morphology due to their similarities, however, there is a lack of research on the larvae of this family. This research contributes to that, having identified the larvae of three species from the family Sarcophagidae: Peckia nicasia, Peckia chrysostoma and Blaesoxipha plinthopyga; important agents in flesh decomposition. Adults of Sarcophidae are also difficult to differentiate, often requiring study of the genitalia; the use of larvae in species identification is important in such cases. Adult Sarcophagids were attracted using bottle traps baited with pig liver. These adults larviposited and the larvae were collected and colonises (generation 2 and 3) reared at room temperature for morphological work (n=50). The posterior ends of the larvae from segments 9 or 10 were removed and mounted posterior end upwards to allow study using a light microscope at magnification X200 (posterior cavity and intersegmental spine bands) and X640 (anterior and posterior spiracle). The remaining sections of the larvae were cleared in 10 % KOH and the cephalopharyngeal skeleton dissected out and measured at different points. The cephalopharyngeal skeletons show observable differences in the shapes and sizes of the mouth hooks as well as the length of the ventral cornua. The most notable difference between species is in the general shape of the anal segments and the shape of the posterior spiracles. Intersegmental spine bands of these larvae become less pigmented and visible as the larvae change instars. Spine bands along with anterior spiracle are not recommended as features for species distinction. Larvae can potentially be used to distinguish Sarcophagids to the level of species, with observable differences in the anal segments and the cephalopharyngeal skeletons. However, this method of identification should be tested by comparing these morphological features with other Jamaican Sarcophagids to further support this conclusion.

Keywords: 3rd instar larval morphology, forensic entomology, Jamaica, Sarcophagidae

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Authors: Rosa Amalia Safitri, Ine van der Fels-Klerx, Henk Hogeveen

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Halal is a dietary law observed by people following Islamic faith. It is considered as a type of credence food quality which cannot be easily assured by consumers even upon and after consumption. Therefore, Halal certification takes place as a practical tool for the consumers to make an informed choice particularly in a non-Muslim majority country, including the Netherlands. Discrete choice experiment (DCE) was employed in this study for its ability to assess the importance of attributes attached to Halal beef in the Dutch market and to investigate consumer utilities. Furthermore, willingness to pay (WTP) for the desired Halal certification was estimated. Four most relevant attributes were selected, i.e., the slaughter method, traceability information, place of purchase, and Halal certification. Price was incorporated as an attribute to allow estimation of willingness to pay for Halal certification. There were 242 Muslim respondents who regularly consumed Halal beef completed the survey, from Dutch (53%) and non-Dutch consumers living in the Netherlands (47%). The vast majority of the respondents (95%) were within the age of 18-45 years old, with the largest group being student (43%) followed by employee (30%) and housewife (12%). Majority of the respondents (76%) had disposable monthly income less than € 2,500, while the rest earned more than € 2,500. The respondents assessed themselves of having good knowledge of the studied attributes, except for traceability information with 62% of the respondents considered themselves not knowledgeable. The findings indicated that slaughter method was valued as the most important attribute, followed by Halal certificate, place of purchase, price, and traceability information. This order of importance varied across sociodemographic variables, except for the slaughter method. Both Dutch and non-Dutch subgroups valued Halal certification as the third most important attributes. However, non-Dutch respondents valued it with higher importance (0,20) than their Dutch counterparts (0,16). For non-Dutch, the price was more important than Halal certification. The ideal product preferred by the consumers indicated the product serving the highest utilities for consumers, and characterized by beef obtained without pre-slaughtering stunning, with traceability info, available at Halal store, certified by an official certifier, and sold at 2.75 € per 500 gr. In general, an official Halal certifier was mostly preferred. However, consumers were not willing to pay for premium for any type of Halal certifiers, indicated by negative WTP of -0.73 €, -0.93 €, and -1,03€ for small, official, and international certifiers, respectively. This finding indicated that consumers tend to lose their utility when confronted with price. WTP estimates differ across socio-demographic variables with male and non-Dutch respondents had the lowest WTP. The unfamiliarity to traceability information might cause respondents to perceive it as the least important attribute. In the context of Halal certified meat, adding traceability information into meat packaging can serve two functions, first consumers can justify for themselves whether the processes comply with Halal requirements, for example, the use of pre-slaughtering stunning, and secondly to assure its safety. Therefore, integrating traceability info into meat packaging can help to make informed decision for both Halal status and food safety.

Keywords: consumer utilities, discrete choice experiments, Halal certification, willingness to pay

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Authors: J. A. N. Sandamali, R. P. Hewawasam, K. A. P. W. Jayatilaka, L. K. B. Mudduwa

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Introduction: Doxorubicin is widely used in the treatment of solid organ tumors and hematological malignancies, but the dose-dependent cardiotoxicity due to free radical formation compromises its clinical utility. Therapeutic strategies which enhance cellular endogenous defense systems have been identified as promising approaches to combat oxidative stress-associated conditions. Cinnamomum zeylanicum (Ceylon cinnamon) has a number antioxidant compounds, which can effectively scavenge reactive oxygen including superoxide anions, hydroxyl radicals and as well as other free radicals. Therefore, the objective of the study was to elucidate the most effective dose of Cinnamomum bark extract which ameliorates doxorubicin-induced cardiotoxicity. Materials and methods: Wistar rats were divided into seven groups of 10 animals in each. Group 1: normal control (distilled water, orally, for 14 days, 10 mL/kg saline, ip, after 16 hours fast on the 11th day); Group 2: doxorubicin control (distilled water, orally, for 14 days, 18 mg/kg doxorubicin, ip, after 16 hour fast on the 11th day); Groups 3-7: five doses of freeze dried aqueous bark extracts (0.125, 0.25, 0.5, 1.0, 2.0g/kg, orally, daily for 14 days, 18 mg/kg doxorubicin, ip, after 16 hours fast on the 11th day). Animals were sacrificed on the 15th day and blood was collected for the estimation of cardiac troponin I (cTnI), AST and LDH concentrations and myocardial tissues were collected for histopathological assessment of myocardial damage and irreversible changes were graded by developing a score. Results: cTnI concentration of groups 1-7 were 0, 161.9, 128.6, 95.9, 38, 19.41 & 12.36 pg/mL showing significant differences (p<0.05) between group 2 and groups 4-7. In groups 1-7, serum AST concentration were 26.82, 68.1, 37.18, 36.23, 26.8, 26.62 & 22.43U/L and LDH concentrations were 1166.13, 2428.84, 1658.35, 1474.34, 1277.58, 1110.21 & 974.40U/L and a significant difference (p<0.05) was observed between group 2 and groups 3-7. The maximum score for myocardial necrosis was observed in group 2. Parallel to the increase of the dosage of plant extract, a gradual reduction of the score for myocardial necrosis was observed in groups 3-7. Reversible histological changes such as vacuolation, congestion were observed in group 2 and all plant treated groups. Haemorrhages, inflammatory cell infiltrations, and interstitial oedema were observed in group 2, but absent in groups treated with higher doses of the plant extract. Discussion & Conclusion: According to the in vitro antioxidant assays performed, Cinnamomum zeylanicum (Ceylon cinnamon) bark possesses high amounts of polyphenolic substances and high antioxidant activity. The present study showed that Cinnamomum zeylanicum extract at 2.0 g/kg possesses the most significant cardioprotective effect against doxorubicin-induced cardiotoxicity. It can be postulated that pretreatment with Cinnamomum bark extract may replenish the cardiomyocytes with antioxidants that are needed for the defense against oxidative stress induced by doxorubicin.

Keywords: cardioprotection, Cinnamomum zeylanicum, doxorubicin, free radicals

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Authors: Muhammad Qasim, Laura Minet

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Victoria (BC), Canada, is a major cruise ship destination, attracting over 600,000 tourists annually. Residents of the James Bay neighborhood, home to the Ogden Point cruise terminal, have expressed concerns about the impacts of cruise ship activity on local traffic, air pollution, and safety compliance. This study evaluates the effects of cruise ship-induced traffic in James Bay, focusing on traffic flow intensification, density surges, changes in traffic mix, and speeding violations. To achieve these objectives, traffic data was collected in James Bay during two key periods: May, before the peak cruise season, and August, during full cruise operations. Three Miovision cameras captured the vehicular traffic mix at strategic entry points, while nine traffic counters monitored traffic distribution and speeding violations across the network. Traffic data indicated an average volume of 308 vehicles per hour during peak cruise times in May, compared to 116 vehicles per hour when no ships were in port. Preliminary analyses revealed a significant intensification of traffic flow during cruise ship "hoteling hours," with a volume increase of approximately 10% per cruise ship arrival. A notable 86% surge in taxi presence was observed on days with three cruise ships in port, indicating a substantial shift in traffic composition, particularly near the cruise terminal. The number of tourist buses escalated from zero in May to 32 in August, significantly altering traffic dynamics within the neighborhood. The period between 8 pm and 11 pm saw the most significant increases in traffic volume, especially when three ships were docked. Higher vehicle volumes were associated with a rise in speed violations, although this pattern was inconsistent across all areas. Speeding violations were more frequent on roads with lower traffic density, while roads with higher traffic density experienced fewer violations, due to reduced opportunities for speeding in congested conditions. PTV VISUM software was utilized for fuzzy distribution analysis and to visualize traffic distribution across the study area, including an assessment of the Level of Service on major roads during periods before and during the cruise ship season. This analysis identified the areas most affected by cruise ship-induced traffic, providing a detailed understanding of the impact on specific parts of the transportation network. These findings underscore the significant influence of cruise ship activity on traffic dynamics in Victoria, BC, particularly during peak periods when multiple ships are in port. The study highlights the need for targeted traffic management strategies to mitigate the adverse effects of increased traffic flow, changes in traffic mix, and speed violations, thereby enhancing road safety in the James Bay neighborhood. Further research will focus on detailed emissions estimation to fully understand the environmental impacts of cruise ship activity in Victoria.

Keywords: cruise ship tourism, air quality, traffic violations, transport dynamics, pollution

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Authors: Rajkumar Ghosh

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Earthquakes are natural disasters that pose a significant risk to human life and infrastructure. Effective earthquake mitigation measures require a thorough understanding of the dynamics of seismic occurrences, including thrust movement. Traditionally, estimating thrust movement has relied on typical techniques that may not capture the full complexity of these events. Therefore, investigating alternative approaches, such as incorporating out-of-sequence thrust movement data, could enhance earthquake mitigation strategies. This review aims to provide an overview of the applications of out-of-sequence thrust movement in earthquake mitigation. By examining existing research and studies, the objective is to understand how precise estimation of thrust movement can contribute to improving structural design, analyzing infrastructure risk, and developing early warning systems. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources, including GPS measurements, satellite imagery, and seismic recordings. By analyzing and synthesizing these diverse datasets, researchers can gain a more comprehensive understanding of thrust movement dynamics during seismic occurrences. The review identifies potential advantages of incorporating out-of-sequence data in earthquake mitigation techniques. These include improving the efficiency of structural design, enhancing infrastructure risk analysis, and developing more accurate early warning systems. By considering out-of-sequence thrust movement estimates, researchers and policymakers can make informed decisions to mitigate the impact of earthquakes. This study contributes to the field of seismic monitoring and earthquake risk assessment by highlighting the benefits of incorporating out-of-sequence thrust movement data. By broadening the scope of analysis beyond traditional techniques, researchers can enhance their knowledge of earthquake dynamics and improve the effectiveness of mitigation measures. The study collects data from various sources, including GPS measurements, satellite imagery, and seismic recordings. These datasets are then analyzed using appropriate statistical and computational techniques to estimate out-of-sequence thrust movement. The review integrates findings from multiple studies to provide a comprehensive assessment of the topic. The study concludes that incorporating out-of-sequence thrust movement data can significantly enhance earthquake mitigation measures. By utilizing diverse data sources, researchers and policymakers can gain a more comprehensive understanding of seismic dynamics and make informed decisions. However, challenges exist, such as data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and improve the accuracy of estimates, further research and advancements in methodology are recommended. Overall, this review serves as a valuable resource for researchers, engineers, and policymakers involved in earthquake mitigation, as it encourages the development of innovative strategies based on a better understanding of thrust movement dynamics.

Keywords: earthquake, out-of-sequence thrust, disaster, human life

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Authors: Monkiz Khasreen

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In the context of the global trend towards reducing new buildings carbon footprint, the design team is required to make early decisions that have a major influence on embodied and operational carbon. Sustainability strategies should be clear during early stages of building design process, as changes made later can be extremely costly. Life-Cycle Assessment (LCA) could be used as the vehicle to carry other tools and processes towards achieving the requested improvement. Although LCA is the ‘golden standard’ to evaluate buildings from 'cradle to grave', lack of details available on the concept design makes LCA very difficult, if not impossible, to be used as an estimation tool at early stages. Issues related to transparency and accessibility of information in the building industry are affecting the credibility of LCA studies. A verified database derived from LCA case studies is required to be accessible to researchers, design professionals, and decision makers in order to offer guidance on specific areas of significant impact. This database could be the build-up of data from multiple sources within a pool of research held in this context. One of the most important factors that affects the reliability of such data is the temporal factor as building materials, components, and systems are rapidly changing with the advancement of technology making production more efficient and less environmentally harmful. Recent LCA studies on different building functions, types, and structures are always needed to update databases derived from research and to form case bases for comparison studies. There is also a need to make these studies transparent and accessible to designers. The work in this paper sets out to address this need. This paper also presents life-cycle case study of a new-build educational building in England. The building utilised very current construction methods and technologies and is rated as BREEAM excellent. Carbon emissions of different life-cycle stages and different building materials and components were modelled. Scenario and sensitivity analyses were used to estimate the future of new educational buildings in England. The study attempts to form an indicator during the early design stages of similar buildings. Carbon dioxide emissions of this case study building, when normalised according to floor area, lie towards the lower end of the range of worldwide data reported in the literature. Sensitivity analysis shows that life cycle assessment results are highly sensitive to future assumptions made at the design stage, such as future changes in electricity generation structure over time, refurbishment processes and recycling. The analyses also prove that large savings in carbon dioxide emissions can result from very small changes at the design stage.

Keywords: architecture, building, carbon dioxide, construction, educational buildings, England, environmental impact, life-cycle assessment

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Authors: Coriolano Salvini

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The increasing utilization of renewable energy sources for electric power production calls for the introduction of energy storage systems to match the electric demand along the time. Although many countries are pursuing as a final goal a “decarbonized” electrical system, in the next decades the traditional fossil fuel fed power plant still will play a relevant role in fulfilling the electric demand. Presently, such plants provide grid ancillary services (frequency control, grid balance, reserve, etc.) by adapting the output power to the grid requirements. An interesting option is represented by the possibility to use traditional plants to improve the grid storage capabilities. The present paper is addressed to small-medium size systems suited for distributed energy storage. The proposed Energy Storage System (ESS) is based on a Compressed Air Energy Storage (CAES) integrated into a Gas-Steam Combined Cycle (GSCC) or a Gas Turbine based CHP plants. The systems can be incorporated in an ex novo built plant or added to an already existing one. To avoid any geological restriction related to the availability of natural compressed air reservoirs, artificial storage is addressed. During the charging phase, electric power is absorbed from the grid by an electric driven intercooled/aftercooled compressor. In the course of the discharge phase, the compressed stored air is sent to a heat transfer device fed by hot gas taken upstream the Heat Recovery Steam Generator (HRSG) and subsequently expanded for power production. To maximize the output power, a staged reheated expansion process is adopted. The specific power production related to the kilogram per second of exhaust gas used to heat the stored air is two/three times larger than that achieved if the gas were used to produce steam in the HRSG. As a result, a relevant power augmentation is attained with respect to normal GSCC plant operations without additional use of fuel. Therefore, the excess of output power can be considered “fuel free” and the storage system can be compared to “pure” ESSs such as electrochemical, pumped hydro or adiabatic CAES. Representative cases featured by different power absorption, production capability, and storage capacity have been taken into consideration. For each case, a technical optimization aimed at maximizing the storage efficiency has been carried out. On the basis of the resulting storage pressure and volume, number of compression and expansion stages, air heater arrangement and process quantities found for each case, a cost estimation of the storage systems has been performed. Storage efficiencies from 0.6 to 0.7 have been assessed. Capital costs in the range of 400-800 €/kW and 500-1000 €/kWh have been estimated. Such figures are similar or lower to those featuring alternative storage technologies.

Keywords: artificial air storage reservoir, compressed air energy storage (CAES), gas steam combined cycle (GSCC), techno-economic analysis

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Authors: Rahaf Mandura, Fatmah Abusharkh, Layan Kurdi, Rahaf Shigdar, Khadijah Alattas

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Introduction: Age-related macular degeneration (AMD) in older individuals are serious health issues that severely impact the quality of life of millions globally. In 2020, the fourth leading cause of blindness worldwide was AMD. The global prevalence of AMD is estimated to be around 8.7%. AMD is a progressive disease involving the macular region of the retina, and it has a complex pathophysiology. RPE cell dysfunction plays a crucial step in the pathway leading to irreversible degeneration of photoreceptors with yellowish lipid-rich, protein-containing drusen deposits accumulating between Bruch's membrane and the RPE. Furthermore, lipofuscinogenesis, drusogenesis, inflammation, and neovascularization are four main processes responsible for the formation of the two types of AMD: the wet (exudative, neovascular) and dry (non-exudative, geographic atrophy) types. We retrospectively evaluated the prevalence of AMD among patients visiting the retina clinic at King Abdulaziz University Hospital (Jeddah, Makkah Province, Saudi Arabia) to identify the commonly associated risk factors of AMD. Methods: The records of 3,067 individuals from 2017 to 2021 were reviewed. Of these, 1,935 satisfied the inclusion criteria and were included in this study. We excluded all patient below 18 years, and those who did not undergo fundus imaging or attend their booked appointments, follow-ups, treatments, and referrals were excluded. Results: The prevalence of AMD among the patients was 4%. The age of patients with AMD was significantly greater than those without AMD (72.4 ± 9.8 years vs. 57.2 ± 15.5 years; p < 0.001). Participants with a family history of AMD tended to have the disease more than those without such a history (85.7% vs. 45%; p = 0.043). Ex- and current smokers were more likely to have AMD than non-smokers (34% and 18.6% vs. 7.2%; p < 0.001). Patients with hypertension and those without type 1 diabetes were at a higher risk of developing AMD than those without hypertension (5.5% vs. 2.8%; p = 0.002) and those with type 1 diabetes (4.2% vs. 0.8%; p = 0.040). In contrast, sex, nationality, type 2 diabetes, and abnormal lipid profile were not significantly associated with AMD. Regarding the clinical characteristics of AMD cases, most cases (70.4%) were of the dry type and affected both eyes (77.2%). The disease duration was ≥5 years in 43.1% of the patients. The most frequent chronic diseases associated with AMD were type 2 diabetes (69.1%), hypertension (61.7%), and dyslipidemia (18.5%). Conclusion: In summary, our single tertiary center study showed that AMD is widely prevalent in Jeddah, Saudi Arabia (4%) and linked to a wide range of risk factors. Some of these are modifiable risk factors that can be adjusted to help reduce AMD occurrence. Furthermore, this study has shown the importance of screening and follow-up of family members of patients with AMD to promote early detection and intervention of AMD. We recommend conducting further research on AMD in Saudi Arabia. Concerning the study design, a community-based cross-sectional study would be more helpful for assessing the disease's prevalence. Finally, recruiting a larger sample size is required for more accurate estimation.

Keywords: age related macular degeneration, prevelence, risk factor, dry AMD

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Authors: Mohamed Habireche, Nacer E. Bacha, Mohamed Djeghdjough

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In the brick industry, smooth double roll crusher is used for medium and fine crushing of soft to medium hard material. Due to opposite inward rotation of the rolls, the feed material is nipped between the rolls and crushed by compression. They are subject to intense wear, known as three-body abrasion, due to the action of abrasive products. The production downtime affecting productivity stems from two sources: the bi-monthly rectification of the roll crushers and their replacement when they are completely worn out. Choosing the right material for the roll crushers should result in longer machine cycles, and reduced repair and maintenance costs. All roll crushers are imported from outside Algeria. This results in sometimes very long delivery times which handicap the brickyards, in particular in respecting delivery times and honored the orders made by customers. The aim of this work is to investigate the effect of alloying additions on microstructure and wear behavior of grey lamellar cast iron for smooth roll crushers in brick industry. The base gray iron was melted in an induction furnace with low frequency at a temperature of 1500 °C, in which return cast iron scrap, new cast iron ingot, and steel scrap were added to the melt to generate the desired composition. The chemical analysis of the bar samples was carried out using Emission Spectrometer Systems PV 8050 Series (Philips) except for the carbon, for which a carbon/sulphur analyser Elementrac CS-i was used. Unetched microstructure was used to evaluate the graphite flake morphology using the image comparison measurement method. At least five different fields were selected for quantitative estimation of phase constituents. The samples were observed under X100 magnification with a Zeiss Axiover T40 MAT optical microscope equipped with a digital camera. SEM microscope equipped with EDS was used to characterize the phases present in the microstructure. The hardness (750 kg load, 5mm diameter ball) was measured with a Brinell testing machine for both treated and as-solidified condition test pieces. The test bars were used for tensile strength and metallographic evaluations. Mechanical properties were evaluated using tensile specimens made as per ASTM E8 standards. Two specimens were tested for each alloy. From each rod, a test piece was made for the tensile test. The results showed that the quenched and tempered alloys had best wear resistance at 400 °C for alloyed grey cast iron (containing 0.62%Mn, 0.68%Cr, and 1.09% Cu) due to fine carbides in the tempered matrix. In quenched and tempered condition, increasing Cu content in cast irons improved its wear resistance moderately. Combined addition of Cu and Cr increases hardness and wear resistance for a quenched and tempered hypoeutectic grey cast iron.

Keywords: casting, cast iron, microstructure, heat treating

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Authors: Francesca Marsili

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The reliability assessment of existing structures with probabilistic methods is becoming an increasingly important and frequent engineering task. However probabilistic reliability methods are based on an exhaustive knowledge of the stochastic modeling of the variables involved in the assessment; at the moment standards for the modeling of variables are absent, representing an obstacle to the dissemination of probabilistic methods. The framework according to probability distribution functions (PDFs) are established is represented by the Bayesian statistics, which uses Bayes Theorem: a prior PDF for the considered parameter is established based on information derived from the design stage and qualitative judgments based on the engineer past experience; then, the prior model is updated with the results of investigation carried out on the considered structure, such as material testing, determination of action and structural properties. The application of Bayesian statistics arises two different kind of problems: 1. The results of the updating depend on the engineer previous experience; 2. The updating of the prior PDF can be performed only if the structure has been tested, and quantitative data that can be statistically manipulated have been collected; performing tests is always an expensive and time consuming operation; furthermore, if the considered structure is an ancient building, destructive tests could compromise its cultural value and therefore should be avoided. In order to solve those problems, an interesting research path is represented by investigating Artificial Intelligence (AI) techniques that can be useful for the automation of the modeling of variables and for the updating of material parameters without performing destructive tests. Among the others, one that raises particular attention in relation to the object of this study is constituted by Case-Based Reasoning (CBR). In this application, cases will be represented by existing buildings where material tests have already been carried out and an updated PDFs for the material mechanical parameters has been computed through a Bayesian analysis. Then each case will be composed by a qualitative description of the material under assessment and the posterior PDFs that describe its material properties. The problem that will be solved is the definition of PDFs for material parameters involved in the reliability assessment of the considered structure. A CBR system represent a good candi¬date in automating the modelling of variables because: 1. Engineers already draw an estimation of the material properties based on the experience collected during the assessment of similar structures, or based on similar cases collected in literature or in data-bases; 2. Material tests carried out on structure can be easily collected from laboratory database or from literature; 3. The system will provide the user of a reliable probabilistic description of the variables involved in the assessment that will also serve as a tool in support of the engineer’s qualitative judgments. Automated modeling of variables can help in spreading probabilistic reliability assessment of existing buildings in the common engineering practice, and target at the best intervention and further tests on the structure; CBR represents a technique which may help to achieve this.

Keywords: reliability assessment of existing buildings, Bayesian analysis, case-based reasoning, historical structures

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Authors: Jiujie Cai, Fengxia LI, Haibo Wang

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After years of research, offshore oil and gas development now are shifted to unconventional reservoirs, where multi-stage hydraulic fracturing technology has been widely used. However, the simulation of complex hydraulic fractures in tight reservoirs is faced with geological and engineering difficulties, such as large burial depths, sand-shale interbeds, and complex stress barriers. The objective of this work is to simulate the hydraulic fracture propagation in the tight sandstone matrix of the marine-continental transitional reservoirs, where the Shanxi Formation in Tianhuan syncline of the Dongsheng gas field was used as the research target. The characteristic parameters of the vertical rock samples with rich beddings were clarified through rock mechanics experiments. The influence of rock mechanical parameters, vertical stress difference of pay-zone and bedding layer, and fracturing parameters (such as injection rates, fracturing fluid viscosity, and number of perforation clusters within single stage) on fracture initiation and propagation were investigated. In this paper, a 3-D fracture propagation model was built to investigate the complex fracture propagation morphology by boundary element method, considering the strength of bonding surface between layers, vertical stress difference and fracturing parameters (such as injection rates, fluid volume and viscosity). The research results indicate that on the condition of vertical stress difference (3 MPa), the fracture height can break through and enter the upper interlayer when the thickness of the overlying bedding layer is 6-9 m, considering effect of the weak bonding surface between layers. The fracture propagates within the pay zone when overlying interlayer is greater than 13 m. Difference in fluid volume distribution between clusters could be more than 20% when the stress difference of each cluster in the segment exceeds 2MPa. Fracture cluster in high stress zones cannot initiate when the stress difference in the segment exceeds 5MPa. The simulation results of fracture height are much higher if the effect of weak bonding surface between layers is not involved. By increasing the injection rates, increasing fracturing fluid viscosity, and reducing the number of clusters within single stage can promote the fracture height propagation through layers. Optimizing the perforation position and reducing the number of perforations can promote the uniform expansion of fractures. Typical curves of fracture height estimation were established for the tight sandstone of the Lower Permian Shanxi Formation. The model results have good consistency with micro-seismic monitoring results of hydraulic fracturing in Well 1HF.

Keywords: fracture propagation, boundary element method, fracture height, offshore oil and gas, marine-continental transitional reservoirs, rock mechanics experiment

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Authors: Devansh Desai, Rahul Nigam

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Crop water stress (W) at a given growth stage starts to set in as moisture availability (M) to roots falls below 75% of maximum. It has been found that ratio of crop evapotranspiration (ET) and reference evapotranspiration (ET0) is an indicator of moisture adequacy and is strongly correlated with ‘M’ and ‘W’. The spatial variability of ET0 is generally less over an agricultural farm of 1-5 ha than ET, which depends on both surface and atmospheric conditions, while the former depends only on atmospheric conditions. Solutions from surface energy balance (SEB) and thermal infrared (TIR) remote sensing are now known to estimate latent heat flux of ET. In the present study, ET and moisture adequacy index (MAI) (=ET/ET0) have been estimated over two contrasting western India agricultural farms having rice-wheat system in semi-arid climate and arid grassland system, limited by moisture availability. High-resolution multi-band TIR sensing observations at 65m from ECOSTRESS (ECOsystemSpaceborne Thermal Radiometer Experiment on Space Station) instrument on-board International Space Station (ISS) were used in an analytical SEB model, STIC (Surface Temperature Initiated Closure) to estimate ET and MAI. The ancillary variables used in the ET modeling and MAI estimation were land surface albedo, NDVI from close-by LANDSAT data at 30m spatial resolution, ET0 product at 4km spatial resolution from INSAT 3D, meteorological forcing variables from short-range weather forecast on air temperature and relative humidity from NWP model. Farm-scale ET estimates at 65m spatial resolution were found to show low RMSE of 16.6% to 17.5% with R2 >0.8 from 18 datasets as compared to reported errors (25 – 30%) from coarser-scale ET at 1 to 8 km spatial resolution when compared to in situ measurements from eddy covariance systems. The MAI was found to show lower (<0.25) and higher (>0.5) magnitudes in the contrasting agricultural farms. The study showed the potential need of high-resolution high-repeat spaceborne multi-band TIR payloads alongwith optical payload in estimating farm-scale ET and MAI for estimating consumptive water use and water stress. A set of future high-resolution multi-band TIR sensors are planned on-board Indo-French TRISHNA, ESA’s LSTM, NASA’s SBG space-borne missions to address sustainable irrigation water management at farm-scale to improve crop water productivity. These will provide precise and fundamental variables of surface energy balance such as LST (Land Surface Temperature), surface emissivity, albedo and NDVI. A synchronization among these missions is needed in terms of observations, algorithms, product definitions, calibration-validation experiments and downstream applications to maximize the potential benefits.

Keywords: thermal remote sensing, land surface temperature, crop water stress, evapotranspiration

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

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

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

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

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

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

Procedia PDF Downloads 76

Authors: Robin Anderson, David Nisbet

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

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

Procedia PDF Downloads 128

Authors: Dayron Camilo Bermudez Mendoza

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

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

Procedia PDF Downloads 57

Authors: Sukhvinder Kaur, Ashok Agarwal

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

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

Procedia PDF Downloads 211

Authors: Raja Umer Sajjad, Chang Hee Lee

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Stormwater runoff is the leading contributor to pollution of receiving waters. In response, an efficient stormwater monitoring program is required to quantify and eventually reduce stormwater pollution. The overall goals of stormwater monitoring programs primarily include the identification of high-risk dischargers and the development of total maximum daily loads (TMDLs). The challenge in developing better monitoring program is to reduce the variability in flux estimates due to sampling errors; however, the success of monitoring program mainly depends on the accuracy of the estimates. Apart from sampling errors, manpower and budgetary constraints also influence the quality of the estimates. This study attempted to develop optimum stormwater monitoring design considering both cost and the quality of the estimated pollutants flux. Three years stormwater monitoring data (2012 – 2014) from a mix land use located within Geumhak watershed South Korea was evaluated. The regional climate is humid and precipitation is usually well distributed through the year. The investigation of a large number of water quality parameters is time-consuming and resource intensive. In order to identify a suite of easy-to-measure parameters to act as a surrogate, Principal Component Analysis (PCA) was applied. Means, standard deviations, coefficient of variation (CV) and other simple statistics were performed using multivariate statistical analysis software SPSS 22.0. The implication of sampling time on monitoring results, number of samples required during the storm event and impact of seasonal first flush were also identified. Based on the observations derived from the PCA biplot and the correlation matrix, total suspended solids (TSS) was identified as a potential surrogate for turbidity, total phosphorus and for heavy metals like lead, chromium, and copper whereas, Chemical Oxygen Demand (COD) was identified as surrogate for organic matter. The CV among different monitored water quality parameters were found higher (ranged from 3.8 to 15.5). It suggests that use of grab sampling design to estimate the mass emission rates in the study area can lead to errors due to large variability. TSS discharge load calculation error was found only 2 % with two different sample size approaches; i.e. 17 samples per storm event and equally distributed 6 samples per storm event. Both seasonal first flush and event first flush phenomena for most water quality parameters were observed in the study area. Samples taken at the initial stage of storm event generally overestimate the mass emissions; however, it was found that collecting a grab sample after initial hour of storm event more closely approximates the mean concentration of the event. It was concluded that site and regional climate specific interventions can be made to optimize the stormwater monitoring program in order to make it more effective and economical.

Keywords: first flush, pollutant load, stormwater monitoring, surrogate parameters

Procedia PDF Downloads 240

Authors: Tomola Obamuyi

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The banking system in Nigeria adopted innovative banking, with the aim of enhancing financial inclusion, and making financial services readily and cheaply available to majority of the people, and to contribute to the efficiency of the financial system. Some of the innovative services include: Automatic Teller Machines (ATMs), National Electronic Fund Transfer (NEFT), Point of Sale (PoS), internet (Web) banking, Mobile Money payment (MMO), Real-Time Gross Settlement (RTGS), agent banking, among others. The introduction of these payment systems is expected to increase bank efficiency and customers' satisfaction, culminating in better performance for the commercial banks. However, opinions differ on the possible effects of the various innovative payment systems on the performance of commercial banks in the country. Thus, this study empirically determines how commercial banks use innovation to gain competitive advantage in the specific context of Nigeria's finance and business. The study also analyses the effects of financial innovation on the performance of commercial banks, when different periods of analysis are considered. The study employed secondary data from 2009 to 2018, the period that witnessed aggressive innovation in the financial sector of the country. The Vector Autoregression (VAR) estimation technique forecasts the relative variance of each random innovation to the variables in the VAR, examine the effect of standard deviation shock to one of the innovations on current and future values of the impulse response and determine the causal relationship between the variables (VAR granger causality test). The study also employed the Multi-Criteria Decision Making (MCDM) to rank the innovations and the performance criteria of Return on Assets (ROA) and Return on Equity (ROE). The entropy method of MCDM was used to determine which of the performance criteria better reflect the contributions of the various innovations in the banking sector. On the other hand, the Range of Values (ROV) method was used to rank the contributions of the seven innovations to performance. The analysis was done based on medium term (five years) and long run (ten years) of innovations in the sector. The impulse response function derived from the VAR system indicated that the response of ROA to the values of cheques transaction, values of NEFT transactions, values of POS transactions was positive and significant in the periods of analysis. The paper also confirmed with entropy and range of value that, in the long run, both the CHEQUE and MMO performed best while NEFT was next in performance. The paper concluded that commercial banks would enhance their performance by continuously improving on the services provided through Cheques, National Electronic Fund Transfer and Point of Sale since these instruments have long run effects on their performance. This will increase the confidence of the populace and encourage more usage/patronage of these services. The banking sector will in turn experience better performance which will improve the economy of the country. Keywords: Bank performance, financial innovation, multi-criteria decision making, vector autoregression,

Keywords: Bank performance, financial innovation, multi-criteria decision making, vector autoregression

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Authors: Justyna P. Majewska, Szymon M. Truskolaski

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The growing importance of digitalized services in the so-called new economy, including the e-sports industry, can be observed recently. Various demographic or technological changes lead consumers to modify their needs, not regarding the services themselves but the method of their application (attracting customers, forms of payment, new content, etc.). In the case of leisure-related to competitive spectating activities, there is a growing need to participate in events whose content is not sports competitions but computer games challenge – e-sport. The literature in this area so far focuses on determining the number of e-sport fans with elements of a simple statistical description (mainly concerning demographic characteristics such as age, gender, place of residence). Meanwhile, the development of the industry is influenced by a combination of many different, intertwined demographic, personality and psychosocial characteristics of customers, as well as the characteristics of their environment. Therefore, there is a need for a deeper recognition of the determinants of the behavioral patterns upon selecting digitalized services by customers, which, in the absence of available large data sets, can be achieved by using econometric simulations – multi-agent modeling. The cognitive aim of the study is to reveal internal and external determinants of behavioral patterns of customers taking into account various variants of economic development (the pace of digitization and technological development, socio-demographic changes, etc.). In the paper, an agent-based model with heterogeneous agents (characteristics of customers themselves and their environment) was developed, which allowed identifying a three-stage development scenario: i) initial interest, ii) standardization, and iii) full professionalization. The probabilities regarding the transition process were estimated using the Method of Simulated Moments. The estimation of the agent-based model parameters and sensitivity analysis reveals crucial factors that have driven a rising trend in e-sport spectating and, in a wider perspective, the development of digitalized services. Among the psychosocial characteristics of customers, they are the level of familiarization with the rules of games as well as sports disciplines, active and passive participation history and individual perception of challenging activities. Environmental factors include general reception of games, number and level of recognition of community builders and the level of technological development of streaming as well as community building platforms. However, the crucial factor underlying the good predictive power of the model is the level of professionalization. While in the initial interest phase, the entry barriers for new customers are high. They decrease during the phase of standardization and increase again in the phase of full professionalization when new customers perceive participation history inaccessible. In this case, they are prone to switch to new methods of service application – in the case of e-sport vs. sports to new content and more modern methods of its delivery. In a wider context, the findings in the paper support the idea of a life cycle of services regarding methods of their application from “traditional” to digitalized.

Keywords: agent-based modeling, digitalized services, e-sport, spectators motives

Procedia PDF Downloads 172

Authors: Nejmeddine Ouhichi, Fethi Lachaal, Radhouane Hamdi, Olivier Grunberger

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Located in Cap Bon peninsula (Tunisia), the Lebna dam was built in 1987 to balance local water salt intrusion taking place in the coastal aquifer of Korba. The first intention was to reduce coastal groundwater over-pumping by supplying surface water to a large irrigation system. The unpredicted beneficial effect was recorded with the occurrence of a direct localized recharge to the coastal aquifer by leakage through the geological material of the southern bank of the lake. The hydrological balance of the reservoir dam gave an estimation of the annual leakage volume, but dynamic processes and sound quantification of recharge inputs are still required to understand the localized effect of the recharge in terms of piezometry and quality. Present work focused on simulating the recharge process to confirm the hypothesis, and established a sound quantification of the water supply to the coastal aquifer and extend it to multi-annual effects. A spatial frame of 30km² was used for modeling. Intensive outcrops and geophysical surveys based on 68 electrical resistivity soundings were used to characterize the aquifer 3D geometry and the limit of the Plio-quaternary geological material concerned by the underground flow paths. Permeabilities were determined using 17 pumping tests on wells and piezometers. Six seasonal piezometric surveys on 71 wells around southern reservoir dam banks were performed during the 2019-2021 period. Eight monitoring boreholes of high frequency (15min) piezometric data were used to examine dynamical aspects. Model boundary conditions were specified using the geophysics interpretations coupled with the piezometric maps. The dam-groundwater flow model was performed using Visual MODFLOW software. Firstly, permanent state calibration based on the first piezometric map of February 2019 was established to estimate the permanent flow related to the different reservoir levels. Secondly, piezometric data for the 2019-2021 period were used for transient state calibration and to confirm the robustness of the model. Preliminary results confirmed the temporal link between the reservoir level and the localized recharge flow with a strong threshold effect for levels below 16 m.a.s.l. The good agreement of computed flow through recharge cells on the southern banks and hydrological budget of the reservoir open the path to future simulation scenarios of the dilution plume imposed by the localized recharge. The dam reservoir-groundwater flow-model simulation results approve a potential for storage of up to 17mm/year in existing wells, under gravity-feed conditions during level increases on the reservoir into the three years of operation. The Lebna dam groundwater flow model characterized a spatiotemporal relation between groundwater and surface water.

Keywords: leakage, MODFLOW, saltwater intrusion, surface water-groundwater interaction

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Authors: Pulakesh Das, Mukunda Dev Behera, Manchiraju Sri Ramachandra Murthy

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Large numbers of people living in and around the Hindu Kush Himalaya (HKH) region, depends on this diverse mountainous region for ecosystem services. Following the global trend, this region also experiencing rapid population growth, and demand for timber and agriculture land. The eight countries sharing the HKH region have different forest resources utilization and conservation policies that exert varying forces in the forest ecosystem. This created a variable spatial as well altitudinal gradient in rate of deforestation and corresponding forest patch fragmentation. The quantitative relationship between fragmentation and demography has not been established before for HKH vis-à-vis along elevation gradient. This current study was carried out to attribute the overall and different nature in landscape fragmentations along the altitudinal gradient with the demography of each sharing countries. We have used the tree canopy cover data derived from Landsat data to analyze the deforestation and afforestation rate, and corresponding landscape fragmentation observed during 2000 – 2010. Area-weighted mean radius of gyration (AMN radius of gyration) was computed owing to its advantage as spatial indicator of fragmentation over non-spatial fragmentation indices. Using the subtraction method, the change in fragmentation was computed during 2000 – 2010. Using the tree canopy cover data as a surrogate of forest cover, highest forest loss was observed in Myanmar followed by China, India, Bangladesh, Nepal, Pakistan, Bhutan, and Afghanistan. However, the sequence of fragmentation was different after the maximum fragmentation observed in Myanmar followed by India, China, Bangladesh, and Bhutan; whereas increase in fragmentation was seen following the sequence of as Nepal, Pakistan, and Afghanistan. Using SRTM-derived DEM, we observed higher rate of fragmentation up to 2400m that corroborated with high human population for the year 2000 and 2010. To derive the nature of fragmentation along the altitudinal gradients, the Statistica software was used, where the user defined function was utilized for regression applying the Gauss-Newton estimation method with 50 iterations. We observed overall logarithmic decrease in fragmentation change (area-weighted mean radius of gyration), forest cover loss and population growth during 2000-2010 along the elevation gradient with very high R2 values (i.e., 0.889, 0.895, 0.944 respectively). The observed negative logarithmic function with the major contribution in the initial elevation gradients suggest to gap filling afforestation in the lower altitudes to enhance the forest patch connectivity. Our finding on the pattern of forest fragmentation and human population across the elevation gradient in HKH region will have policy level implication for different nations and would help in characterizing hotspots of change. Availability of free satellite derived data products on forest cover and DEM, grid-data on demography, and utility of geospatial tools helped in quick evaluation of the forest fragmentation vis-a-vis human impact pattern along the elevation gradient in HKH.

Keywords: area-weighted mean radius of gyration, fragmentation, human impact, tree canopy cover

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Authors: Oibar Martinez, Clara Oliver, Jose Miguel Miranda

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In this paper, we discuss the standard improvements which can be made to reduce the earth resistance in difficult terrains for optimum lightning protection, what are the practical limitations, and how the modeling can be refined for accurate diagnostics and ground resistance minimization. Ground resistance minimization can be made via three different approaches: burying vertical electrodes connected in parallel, burying horizontal conductive plates or meshes, or modifying the own terrain, either by changing the entire terrain material in a large volume or by adding earth-enhancing compounds. The use of vertical electrodes connected in parallel pose several practical limitations. In order to prevent loss of effectiveness, it is necessary to keep a minimum distance between each electrode, which is typically around five times larger than the electrode length. Otherwise, the overlapping of the local equipotential lines around each electrode reduces the efficiency of the configuration. The addition of parallel electrodes reduces the resistance and facilitates the measurement, but the basic parallel resistor formula of circuit theory will always underestimate the final resistance. Numerical simulation of equipotential lines around the electrodes overcomes this limitation. The resistance of a single electrode will always be proportional to the soil resistivity. The electrodes are usually installed with a backfilling material of high conductivity, which increases the effective diameter. However, the improvement is marginal, since the electrode diameter counts in the estimation of the ground resistance via a logarithmic function. Substances that are used for efficient chemical treatment must be environmentally friendly and must feature stability, high hygroscopicity, low corrosivity, and high electrical conductivity. A number of earth enhancement materials are commercially available. Many are comprised of carbon-based materials or clays like bentonite. These materials can also be used as backfilling materials to reduce the resistance of an electrode. Chemical treatment of soil has environmental issues. Some products contain copper sulfate or other copper-based compounds, which may not be environmentally friendly. Carbon-based compounds are relatively inexpensive and they do have very low resistivities, but they also feature corrosion issues. Typically, the carbon can corrode and destroy a copper electrode in around five years. These compounds also have potential environmental concerns. Some earthing enhancement materials contain cement, which, after installation acquire properties that are very close to concrete. This prevents the earthing enhancement material from leaching into the soil. After analyzing different configurations, we conclude that a buried conductive ring with vertical electrodes connected periodically should be the optimum baseline solution for the grounding of a large size structure installed on a large resistivity terrain. In order to show this, a practical example is explained here where we simulate the ground resistance of a conductive ring buried in a terrain with a resistivity in the range of 1 kOhm·m.

Keywords: grounding improvements, large scale scientific instrument, lightning risk assessment, lightning standards

Procedia PDF Downloads 139

Authors: Riadh Zorgati, Thomas Triboulet

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In quite diverse application areas such as astronomy, medical imaging, geophysics or nondestructive evaluation, many problems related to calibration, fitting or estimation of a large number of input parameters of a model from a small amount of output noisy data, can be cast as inverse problems. Due to noisy data corruption, insufficient data and model errors, most inverse problems are ill-posed in a Hadamard sense, i.e. existence, uniqueness and stability of the solution are not guaranteed. A wide class of inverse problems in physics relates to the Fredholm equation of the first kind. The ill-posedness of such inverse problem results, after discretization, in a very ill-conditioned linear system of equations, the condition number of the associated matrix can typically range from 109 to 1018. This condition number plays the role of an amplifier of uncertainties on data during inversion and then, renders the inverse problem difficult to handle numerically. Similar problems appear in other areas such as numerical optimization when using interior points algorithms for solving linear programs leads to face ill-conditioned systems of linear equations. Devising efficient solution approaches for such system of equations is therefore of great practical interest. Efficient iterative algorithms are proposed for solving a system of linear equations. The approach is based on a preconditioning of the initial matrix of the system with an approximation of a generalized inverse leading to a stochastic preconditioned matrix. This approach, valid for non-negative matrices, is first extended to hermitian, semi-definite positive matrices and then generalized to any complex rectangular matrices. The main results obtained are as follows: 1) We are able to build a generalized inverse of any complex rectangular matrix which satisfies the convergence condition requested in iterative algorithms for solving a system of linear equations. This completes the (short) list of generalized inverse having this property, after Kaczmarz and Cimmino matrices. Theoretical results on both the characterization of the type of generalized inverse obtained and the convergence are derived. 2) Thanks to its properties, this matrix can be efficiently used in different solving schemes as Richardson-Tanabe or preconditioned conjugate gradients. 3) By using Lp norms, we propose generalized Kaczmarz’s type matrices. We also show how Cimmino's matrix can be considered as a particular case consisting in choosing the Euclidian norm in an asymmetrical structure. 4) Regarding numerical results obtained on some pathological well-known test-cases (Hilbert, Nakasaka, …), some of the proposed algorithms are empirically shown to be more efficient on ill-conditioned problems and more robust to error propagation than the known classical techniques we have tested (Gauss, Moore-Penrose inverse, minimum residue, conjugate gradients, Kaczmarz, Cimmino). We end on a very early prospective application of our approach based on stochastic matrices aiming at computing some parameters (such as the extreme values, the mean, the variance, …) of the solution of a linear system prior to its resolution. Such an approach, if it were to be efficient, would be a source of information on the solution of a system of linear equations.

Keywords: conditioning, generalized inverse, linear system, norms, stochastic matrix

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Authors: Sunil Chandra, Himanshu Rawat, Vikas Gusain, Triparna Barman

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within reserved forests, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differenced normalized burnt ratio (dNBR) index approach that uses the burnt ratio values generated using the Short-Wave Infrared (SWIR) band and Near Infrared (NIR) bands of the Sentinel-2 image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel-2 bands. The training and testing data are generated from the Sentinel-2 data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated using spectral unmixing methods, which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: categorical data, log linear modeling, neural network, shifting cultivation

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Authors: Abha Gupta, Deepak K. Mishra

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Food security issue in India has surrounded over availability and accessibility of cereal which is regarded as the only food group to check hunger and improve nutrition. Significance of fruits, vegetables, meat and other food products have totally been neglected given the fact that they provide essential nutrients to the body. There is a need to shift the emphasis from cereal-based approach to a more diverse diet so that aim of achieving food security may change from just reducing hunger to an overall health. This paper attempts to analyse how far dietary diversity level has been achieved across different socio-economic groups in India. For this purpose, present paper sets objectives to determine (a) percentage share of different food groups to total food expenditure and consumption by background characteristics (b) source of and preference for all food items and, (c) diversity of diet across socio-economic groups. A cross sectional survey covering 304 households selected through proportional stratified random sampling was conducted in six villages of Aligarh district of Uttar Pradesh, India. Information on amount of food consumed, source of consumption and expenditure on food (74 food items grouped into 10 major food groups) was collected with a recall period of seven days. Per capita per day food consumption/expenditure was calculated through dividing consumption/expenditure by household size and number seven. Food variety score was estimated by giving 0 values to those food groups/items which had not been eaten and 1 to those which had been taken by households in last seven days. Addition of all food group/item score gave result of food variety score. Diversity of diet was computed using Herfindahl-Hirschman index. Findings of the paper show that cereal, milk, roots and tuber food groups contribute a major share in total consumption/expenditure. Consumption of these food groups vary across socio-economic groups whereas fruit, vegetables, meat and other food consumption remain low and same. Estimation of dietary diversity show higher concentration of diet due to higher consumption of cereals, milk, root and tuber products and dietary diversity slightly varies across background groups. Muslims, Scheduled caste, small farmers, lower income class, food insecure, below poverty line and labour families show higher concentration of diet as compared to their counterpart groups. These groups also evince lower mean intake of number of food item in a week due to poor economic constraints and resultant lower accessibility to number of expensive food items. Results advocate to make a shift from cereal based diet to dietary diversity which not only includes cereal and milk products but also nutrition rich food items such as fruits, vegetables, meat and other products. Integrating a dietary diversity approach in food security programmes of the country would help to achieve nutrition security as hidden hunger is widespread among the Indian population.

Keywords: dietary diversity, food Security, India, socio-economic groups

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Manish Saha, Manish Kumar Niranjan, Saket Asthana

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The K₀.₅Na₀.₅NbO₃ (KNN) system has emerged as one of the most promising lead-free piezoelectric over the years. In this work, we perform a comprehensive investigation of electronic structure, lattice dynamics and dielectric/ferroelectric properties of the room temperature phase of KNN by combining ab-initio DFT-based theoretical analysis and experimental characterization. We assign the symmetry labels to KNN vibrational modes and obtain ab-initio polarized Raman spectra, Infrared (IR) reflectivity, Born-effective charge tensors, oscillator strengths etc. The computed Raman spectrum is found to agree well with the experimental spectrum. In particular, the results suggest that the mode in the range ~840-870 cm-¹ reported in the experimental studies is longitudinal optical (LO) with A_1 symmetry. The Raman mode intensities are calculated for different light polarization set-ups, which suggests the observation of different symmetry modes in different polarization set-ups. The electronic structure of KNN is investigated, and an optical absorption spectrum is obtained. Further, the performances of DFT semi-local, metal-GGA and hybrid exchange-correlations (XC) functionals, in the estimation of KNN band gaps are investigated. The KNN bandgap computed using GGA-1/2 and HSE06 hybrid functional schemes are found to be in excellant agreement with the experimental value. The COHP, electron localization function and Bader charge analysis is also performed to deduce the nature of chemical bonding in the KNN. The solid-state reaction and hydrothermal methods are used to prepare the KNN ceramics, and the effects of grain size on the physical characteristics these ceramics are examined. A comprehensive study on the impact of different synthesis techniques on the structural, electrical, and photocatalytic properties of ferroelectric ceramics KNN. The KNN-S prepared by solid-state method have significantly larger grain size as compared to that for KNN-H prepared by hydrothermal method. Furthermore, the KNN-S is found to exhibit higher dielectric, piezoelectric and ferroelectric properties as compared to KNN-H. On the other hand, the increased photocatalytic activity is observed in KNN-H as compared to KNN-S. As compared to the hydrothermal synthesis, the solid-state synthesis causes an increase in the relative dielectric permittivity (ε^') from 2394 to 3286, remnant polarization (P_r) from 15.38 to 20.41 μC/cm^², planer electromechanical coupling factor (k_p) from 0.19 to 0.28 and piezoelectric coefficient (d_33) from 88 to 125 pC/N. The KNN-S ceramics are also found to have a lower leakage current density, and higher grain resistance than KNN-H ceramic. The enhanced photocatalytic activity of KNN-H is attributed to relatively smaller particle sizes. The KNN-S and KNN-H samples are found to have degradation efficiencies of RhB solution of 20% and 65%, respectively. The experimental study highlights the importance of synthesis methods and how these can be exploited to tailor the dielectric, piezoelectric and photocatalytic properties of KNN. Overall, our study provides several bench-mark important results on KNN that have not been reported so far.

Keywords: lead-free piezoelectric, Raman intensity spectrum, electronic structure, first-principles calculations, solid state synthesis, photocatalysis, hydrothermal synthesis

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Authors: Davit Pipoyan, Meline Beglaryan, Nicolò Merendino

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Mining industry is one of the priority sectors of Armenian economy. Along with the solution of some socio-economic development, it brings about numerous environmental problems, especially toxic element pollution, which largely influences the safety of agricultural products. In addition, accumulation of toxic elements in agricultural products, mainly in edible parts of plants represents a direct pathway for their penetration into the human food chain. In Armenia, the share of plant origin food in overall diet is significantly high, so estimation of dietary intakes of toxic trace elements via consumption of selected fruits and vegetables are of great importance for observing the underlying health risks. Therefore, the present study was aimed to assess dietary exposure of potentially toxic trace elements through the intake of locally grown fruits and vegetables in Akhtala community (Armenia), where not only mining industry is developed, but also cultivation of fruits and vegetables. Moreover, this investigation represents one of the very first attempts to estimate human dietary exposure of potentially toxic trace elements in the study area. Samples of some commonly grown fruits and vegetables (fig, cornel, raspberry, grape, apple, plum, maize, bean, potato, cucumber, onion, greens) were randomly collected from several home gardens located near mining areas in Akhtala community. The concentration of Cu, Mo, Ni, Cr, Pb, Zn, Hg, As and Cd in samples were determined by using an atomic absorption spectrophotometer (AAS). Precision and accuracy of analyses were guaranteed by repeated analysis of samples against NIST Standard Reference Materials. For a diet study, individual-based approach was used, so the consumption of selected fruits and vegetables was investigated through food frequency questionnaire (FFQ). Combining concentration data with contamination data, the estimated daily intakes (EDI) and cumulative daily intakes were assessed and compared with health-based guidance values (HBGVs). According to the determined concentrations of the studied trace elements in fruits and vegetables, it can be stressed that some trace elements (Cu, Ni, Pb, Zn) among the majority of samples exceeded maximum allowable limits set by international organizations. Meanwhile, others (Cr, Hg, As, Cd, Mo) either did not exceed these limits or still do not have established allowable limits. The obtained results indicated that only for Cu the EDI values exceeded dietary reference intake (0.01 mg/kg/Bw/day) for some investigated fruits and vegetables in decreasing order of potato > grape > bean > raspberry > fig > greens. In contrast to this, for combined consumption of selected fruits and vegetables estimated cumulative daily intakes exceeded reference doses in the following sequence: Zn > Cu > Ni > Mo > Pb. It may be concluded that habitual and combined consumption of the above mentioned fruits and vegetables can pose a health risk to the local population. Hence, further detailed studies are needed for the overall assessment of potential health implications taking into consideration adverse health effects posed by more than one toxic trace element.

Keywords: daily intake, dietary exposure, fruits, trace elements, vegetables

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