Search results for: variations

Authors: Nur-E-Jannat Tinu

Abstract:

Water is absolutely vital not only for the survival of human beings but also for plants, animals, and all other living organisms. Water bodies, such as lakes, rivers, ponds, and estuaries, are the source of water supply in domestic, industrial, agriculture, and aquaculture purposes. The Buriganga River flows through the south and west of Dhaka city. The water quality of this river has become a matter of concern due to anthropogenic intervention of vital pollutants such as industrial effluents, urban sewage, and solid wastes in this area. Buriganga River is at risk to contamination from untreated municipal wastes, industrial discharges, runoff from organic and inorganic fertilizers, pesticides, insecticides, and oil emission around the river. The residential and commercial establishments along the river discharge wastewater either directly into the river or through drains and canals into the river. However, several regulatory measures and policies have been enforced by the Government to protect the river Buriganga from pollution, in most cases to no affect. Water quality assessment reveals that the water is also not appropriate for irrigation purposes. The physical parameters (pH, TDS, EC, Temperature, DO, COD, BOD) indicated that the water is too poor to be useable for agricultural, drinking, or other purposes. Chemical concentrations showed significant seasonal variations with high-level concentrations during the monsoon season, presumably due to extreme seasonal surface runoff. A comparative study of Electrical Conductivity (EC) and Total Dissolved Solids (TDS) indicated a considerable increase over the last five years A change in trend was observed from 2020 June-July, probably due to monsoon and post-monsoon. EC values decreased from 775 to 665 mmho/cm during this period. DO increased significantly from the mid-post-monsoon months to the early monsoon period. The pH value of river water is strongly alkaline, ranging between 6.5 and 7.79. This indicates that ecological organic compounds cause the water to become alkaline after the monsoon and monsoon seasons. As the water pollution level is very high, an effective remediation and pollution control plan should be considered.

Keywords: precipitation, spatial distribution, effluent, remediation

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Authors: Manon Giraud, Francois-Damien Delapierre, Guenaelle Jasmin-Lebras, Cecile Feraudet-Tarisse, Stephanie Simon, Claude Fermon

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Early diagnosis or detection of harmful substances at low level is a growing field of high interest. The ideal test should be cheap, easy to use, quick, reliable, specific, and with very low detection limit. Combining the high specificity of antibodies-functionalized magnetic beads used to immune-capture biologic objects and the high sensitivity of a GMR-based sensors, it is possible to even detect these biologic objects one by one, such as a cancerous cell, a bacteria or a disease biomarker. The simplicity of the detection process makes its use possible even for untrained staff. Giant Magneto Resistance (GMR) is a recently discovered effect consisting in the electrical resistance modification of some conductive layers when exposed to a magnetic field. This effect allows the detection of very low variations of magnetic field (typically a few tens of nanoTesla). Magnetic nanobeads coated with antibodies targeting the analytes are mixed with a biological sample (blood, saliva) and incubated for 45 min. Then the mixture is injected in a very simple microfluidic chip and circulates above a GMR sensor that detects changes in the surrounding magnetic field. Magnetic particles do not create a field sufficient to be detected. Therefore, only the biological objects surrounded by several antibodies-functionalized magnetic beads (that have been captured by the complementary antigens) are detected when they move above the sensor. Proof of concept has been carried out on NS1 mouse cancerous cells diluted in PBS which have been bonded to magnetic 200nm particles. Signals were detected in cells-containing samples while none were recorded for negative controls. Binary response was hence assessed for this first biological model. The precise quantification of the analytes and its detection in highly diluted solution is the step now in progress.

Keywords: early diagnosis, giant magnetoresistance, lab-on-a-chip, submicron particle

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Authors: Javier Ascanio Villabona, Brayan Eduardo Tarazona Romero, Camilo Leonardo Sandoval Rodriguez, Arly Dario Rincon, Omar Lengerke Perez

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Efficient energy consumption in the housing sector in relation to refrigeration is a concern in the construction and rehabilitation of houses in tropical areas. Thermal comfort is aggravated by heat gain on the roof surface by heat gains. Thus, in the group of passive cooling techniques, one of the practices and technologies in solar control that provide improvements in comfortable conditions are thermal insulation or geometric changes of the roofs. On the other hand, methods with reflection and radiation are the methods used to decrease heat gain by facilitating the removal of excess heat inside a building to maintain a comfortable environment. Since the potential of these techniques varies in different climatic zones, their application in different zones should be examined. This research is based on the experimental study of a prototype of a roof radiator as a method of passive cooling in homes, which was developed through an experimental research methodology making measurements in a prototype built by means of the paradigm of appropriate technology, with the aim of establishing an initial behavior of the internal temperature resulting from the climate of the external environment. As a starting point, a selection matrix was made to identify the typologies of passive cooling systems to model the system and its subsequent implementation, establishing its constructive characteristics. Step followed by the measurement of the climatic variables (outside the prototype) and microclimatic variables (inside the prototype) to obtain a database to be analyzed. As a final result, the decrease in temperature that occurs inside the chamber with respect to the outside temperature was evidenced. likewise, a linearity in its behavior in relation to the variations of the climatic variables.

Keywords: appropriate technology, enveloping, energy efficiency, passive cooling

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Carlos Riascos, Peter Thomson

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Real-time hybrid simulation (RTHS) is a modern cyber-physical technique used for the experimental evaluation of complex systems, that treats the system components with predictable behavior as a numerical substructure and the components that are difficult to model as an experimental substructure. Therefore it is an attractive method for evaluation of the response of civil structures under earthquake, wind and anthropic loads. Another practical application of RTHS is the evaluation of control systems, as these devices are often nonlinear and their characterization is an important step in the design of controllers with the desired performance. In this paper, the response of three-story shear frame controlled by a tuned liquid column damper (TLCD) and subject to base excitation is considered. Both passive and semi-active control strategies were implemented and are compared. While the passive TLCD achieved a reduction of 50% in the acceleration response of the main structure in comparison with the structure without control, the semi-active TLCD achieved a reduction of 70%, and was robust to variations in the dynamic properties of the main structure. In addition, a RTHS was implemented with the main structure modeled as a linear, time-invariant (LTI) system through a state space representation and the TLCD, with both control strategies, was evaluated on a shake table that reproduced the displacement of the virtual structure. Current assessment measures for RTHS were used to quantify the performance with parameters such as generalized amplitude, equivalent time delay between the target and measured displacement of the shake table, and energy error using the measured force, and prove that the RTHS described in this paper is an accurate method for the experimental evaluation of structural control systems.

Keywords: structural control, hybrid simulation, tuned liquid column damper, semi-active sontrol strategy

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Authors: H. M. Zakir Hossain

Abstract:

Cenozoic mudstone samples, obtained from drilled cored and outcrop in northeastern Bengal Basin of Bangladesh were organic geochemically analyzed to identify vertical variations of organic facies, thermal maturity, hydrocarbon potential and depositional environments. Total organic carbon (TOC) content ranges from 0.11 to 1.56 wt% with an average of 0.43 wt%, indicating a good source rock potential. Total sulphur content is variable with values ranging from ~0.001 to 1.75 wt% with an average of 0.065 wt%. Rock-Eval S1 and S2 yields range from 0.03 to 0.14 mg HC/g rock and 0.01 to 0.66 mg HC/g rock, respectively. The hydrogen index values range from 2.71 to 56.09 mg HC/g TOC. These results revealed that the samples are dominated by type III kerogene. Tmax values of 426 to 453 °C and vitrinite reflectance of 0.51 to 0.66% indicate the organic matter is immature to mature. Saturated hydrocarbon ratios such as pristane, phytane, steranes, and hopanes, indicate mostly terrigenous organic matter with small influence of marine organic matter. Organic matter in the succession was accumulated in three different environmental conditions based on the integration of biomarker proxies. First phase (late Eocene to early Miocene): Deposition occurred entirely in seawater-dominated oxic conditions, with high inputs of land plants organic matter including angiosperms. Second phase (middle to late Miocene): Deposition occurred in freshwater-dominated anoxic conditions, with phytoplanktonic organic matter and a small influence of land plants. Third phase (late Miocene to Pleistocene): Deposition occurred in oxygen-poor freshwater conditions, with abundant input of planktonic organic matter and high influx of angiosperms. The lower part (middle Eocene to early Miocene) of the succession with moderate TOC contents and primarily terrestrial organic matter could have generated some condensates and oils in and around the study area.

Keywords: Bangladesh, geochemistry, hydrocarbon potential, mudstone

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Authors: Hailemariam Solomon, Taye Tadesse, Daniel Nadew, Firezer Girma

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Sorghum is an important crop in semi-arid regions and has shown resilience to drought stress. However, recurrent drought is affecting its productivity. Therefore, it is necessary to explore genes that contribute to drought stress adaptation to increase sorghum productivity. The aim of this study is to evaluate and determine the effect of root system traits, specifically root angle, on drought stress adaptation and grain yield performance in sorghum genotypes. A total of 428 sorghum genotypes from the Ethiopian breeding program were evaluated in three drought-stress environments. Field trials were conducted using a row-column design with three replications. Root system traits were phenotyped using a high-throughput phenotyping platform and analyzed using a row-column design with two replications. Data analysis was performed using R software and regression analysis. The study found significant variations in root system architecture among the sorghum genotypes. Non-stay-green genotypes had a grain yield ranging from 1.63 to 3.1 tons/ha, while stay-green genotypes had a grain yield ranging from 2.4 to 2.9 tons/ha. The analysis of root angle showed that non-stay-green genotypes had an angle ranging from 8.0 to 30.5 degrees, while stay-green genotypes had an angle ranging from 12.0 to 29.0 degrees. Improved varieties exhibited angles between 14.04 and 19.50 degrees. Positive and significant correlations were observed between leaf areas and shoot dry weight, as well as between leaf width and shoot dry weight. Negative correlations were observed between root angle and leaf area, as well as between root angle and root length. This research highlights the importance of root system architecture, particularly root angle traits, in enhancing grain yield production in drought-stressed conditions. It also establishes an association between root angle and grain yield traits for maximizing sorghum productivity.

Keywords: roor sysytem architecture, root angle, narrow root angle, wider root angle, drought

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Authors: Mrinal Kanti Bhowmik, Debanjana Debnath Jr., Debotosh Bhattacharjee

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A facial expression is undeniably the human manners. It is a significant channel for human communication and can be applied to extract emotional features accurately. People in pain often show variations in facial expressions that are readily observable to others. A core of actions is likely to occur or to increase in intensity when people are in pain. To illustrate the changes in the facial appearance, a system known as Facial Action Coding System (FACS) is pioneered by Ekman and Friesen for human observers. According to Prkachin and Solomon, a set of such actions carries the bulk of information about pain. Thus, the Prkachin and Solomon pain intensity (PSPI) metric is defined. So, it is very important to notice that facial expressions, being a behavioral source in communication media, provide an important opening into the issues of non-verbal communication in pain. People express their pain in many ways, and this pain behavior is the basis on which most inferences about pain are drawn in clinical and research settings. Hence, to understand the roles of different pain behaviors, it is essential to study the properties. For the past several years, the studies are concentrated on the properties of one specific form of pain behavior i.e. facial expression. This paper represents a comprehensive study on pain assessment that can model and estimate the intensity of pain that the patient is suffering. It also reviews the historical background of different pain assessment techniques in the context of painful expressions. Different approaches incorporate FACS from psychological views and a pain intensity score using the PSPI metric in pain estimation. This paper investigates in depth analysis of different approaches used in pain estimation and presents different observations found from each technique. It also offers a brief study on different distinguishing features of real and fake pain. Therefore, the necessity of the study lies in the emerging fields of painful face assessment in clinical settings.

Keywords: facial action coding system (FACS), pain, pain behavior, Prkachin and Solomon pain intensity (PSPI)

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Authors: Hema Lattupalli

Abstract:

Introduction: A bony bridge extends from the lateral mass of the atlas to postero medial margin of vertebral artery groove, termed as a posterior bridge of atlas or posterior ponticulus. The foramen formed by the bridge is called as arcuate foramen or retroarticulare superior. Another bony bridge sometimes extends laterally from lateral mass to posterior root of transverse foramen forming and additional groove for vertebral artery, above and behind foramen transversarium called Lateral bridge or ponticulus lateralis. When both posterior and lateral are present together it is called as Posterolateral ponticuli. Aim and Objectives: The aim of the present study is to detect the presence of such Bridge or Ponticuli called as Lateral, Posterior and Posterolateral reported by earlier investigators in atlas vertebrae. Material and Methods: The study was done on 100 Atlas vertebrae from the Department of Anatomy Narayana Medical College Nellore, and also from SVIMS Tirupati was collected over a period of 2 years. The parameters that were studied include the presence of ponticuli, complete and incomplete and right and left side ponticuli. They were observed for all these parameters and the results were documented and photographed. Results: Ponticuli were observed in 25 (25%) of atlas vertebrae. Posterior ponticuli were found in 16 (16%), Lateral in 01 (01%) and Posterolateral in 08(08%) of the atlas vertebrae. Complete ponticuli were present in 09 (09%) and incomplete ponticuli in 16 (16%) of the atlas vertebrae. Bilateral ponticuli were seen in 10 (10%) and unilateral ponticuli were seen in 15 (15%) of the atlas vertebrae. Right side ponticuli were seen in 04 (04%) and Left side ponticuli in 05 (05%) of the atlas vertebrae respectively. Interpretation and Conclusion: In the present study posterior complete ponticuli were said to be more than the lateral complete ponticuli. The presence of Bilateral Incomplete Posterior ponticuli is higher and also Atlantic ponticuli. The present study is to say that knowledge of normal anatomy and variations in the atlas vertebra is very much essential to the neurosurgeons giving a message that utmost care is needed to perform surgeries related to craniovertebral regions. This is additional information to the Anatomists, Neurosurgeons and Radiologist. This adds an extra page to the literature.

Keywords: atlas vertebra, ponticuli, posterior arch, arcuate foramen

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Authors: P. Serrão, E. Costa, A. Ribeiro, V. Infante

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The characteristic sonority of the Portuguese guitar is in great part what makes Fado so distinguishable from other traditional song styles. The Portuguese guitar is a pear-shaped plucked chordophone with six courses of double strings. This study compares the two types of plain strings available for Portuguese guitar and used by the musicians. One is stainless steel spring wire, the other is high carbon spring steel (music wire). Some musicians mention noticeable differences in sound quality between these two string materials, such as a little more brightness and sustain in the steel strings. Experimental tests were performed to characterize string tension at pitch; mechanical strength and tuning stability using the universal testing machine; dimensional control and chemical composition analysis using the scanning electron microscope. The string dynamical behaviour characterization experiments, including frequency response, inharmonicity, transient response, damping phenomena and were made in a monochord test set-up designed and built in-house. Damping factor was determined for the fundamental frequency. As musicians are able to detect very small damping differences, an accurate a characterization of the damping phenomena for all harmonics was necessary. With that purpose, another improved monochord was set and a new system identification methodology applied. Due to the complexity of this task several adjustments were necessary until obtaining good experimental data. In a few cases, dynamical tests were repeated to detect any evolution in damping parameters after break-in period when according to players experience a new string sounds gradually less dull until reaching the typically brilliant timbre. Finally, each set of strings was played on one guitar by a distinguished player and recorded. The recordings which include individual notes, scales, chords and a study piece, will be analysed to potentially characterize timbre variations.

Keywords: damping factor, music wire, portuguese guitar, string dynamics

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Authors: Emir Alaca, Hasbi Apaydin, Rohullah Rahmatullah, Necibe Fusun Oyman Serteller

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In recent years, Permanent Magnet Synchronous Motors (PMSMs) have found extensive applications in various industrial sectors, including electric vehicles, wind turbines, and robotics, due to their high performance and low losses. Accurate mathematical modeling of PMSMs is crucial for advanced studies in electric machines. To enhance the effectiveness of graduate-level education, incorporating virtual or real experiments becomes essential to reinforce acquired knowledge. Virtual laboratories have gained popularity as cost-effective alternatives to physical testing, mitigating the risks associated with electrical machine experiments. This study presents a MATLAB-based Graphical User Interface (GUI) for PMSMs. The GUI offers a visual interface that allows users to observe variations in motor outputs corresponding to different input parameters. It enables users to explore healthy motor conditions and the effects of short-circuit faults in the one-phase winding. Additionally, the interface includes menus through which users can access equivalent circuits related to the motor and gain hands-on experience with the mathematical equations used in synchronous motor calculations. The primary objective of this paper is to enhance the learning experience of graduate and doctoral students by providing a GUI-based approach in laboratory studies. This interactive platform empowers students to examine and analyze motor outputs by manipulating input parameters, facilitating a deeper understanding of PMSM operation and control.

Keywords: magnet synchronous motor, mathematical modelling, education tools, winding inter-turn fault

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Authors: Abou Bakr Merdji, Alaa Mhawish, Xiaofeng Xu, Chunsong Lu

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The characteristics of optical and microphysical properties of aerosols near deserts are analyzed using 11 AErosol RObotic NETwork (AERONET) sites located in 6 major desert areas (the Sahara, Arabia, Thar, Karakum, Taklamakan, and Gobi) between 1998 and 2021. The regional mean of Aerosol Optical Depth (AOD) (coarse AOD (CAOD)) are 0.44 (0.187), 0.38 (0.26), 0.35 (0.24), 0.23 (0.11), 0.20 (0.14), 0.10 (0.05) in the Thar, Arabian, Sahara, Karakum, Taklamakan and Gobi Deserts respectively, while an opposite for AE and Fine Mode Fraction (FMF). Higher extinctions are associated with larger particles (dust) over all the main desert regions. This is shown by the almost inversely proportional variations of AOD and CAOD compared with AE and FMF. Coarse particles contribute the most to the total AOD over the Sahara Desert compared to those in the other deserts all year round. Related to the seasonality of dust events, the maximum AOD (CAOD) generally appears in summer and spring, while the minimum is in winter. The mean values of absorbing AOD (AAOD), Absorbing AE (AAE), and the Single Scattering Albedo (SSA) for all sites ranged from 0.017 to 0.037, from 1.16 to 2.81 and from 0.844 to 0.944, respectively. Generally, the highest absorbing aerosol load are observed over the Thar, followed by the Karakum, the Sahara, the Gobi, and then the Taklamakan Deserts, while the largest absorbing particles are observed in the Sahara followed by Arabia, Thar, Karakum, Gobi, and the smallest over the Taklamakan Desert. Similar absorption qualities are observed over the Sahara, Arabia, Thar, and Karakum Deserts, with SSA values varying between 0.90 and 0.91, whereas the most and least absorbing particles are observed at the Taklamakan and the Gobi Deserts, respectively. The seasonal AAODs are distinctly different over the deserts, with parts of Sahara and Arabia, and the Dalanzadgad sites experiencing the maximum in summer, the Southern Sahara, Western Arabia, Jaipur, and Dushanbe in winter, while the Eastern Arabia and the Muztagh Ata in autumn. AAOD and SSA spectra are consistent with dust-dominated conditions that resulted from aerosol typing (dust and polluted dust) at most deserts, with a possible presence of other absorbing particles apart from dust at Arabia, the Taklamakan, and the Gobi Desert sites.

Keywords: sahara, AERONET, desert, dust belt, aerosols, optical properties

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Authors: James Ladzekpo

Abstract:

Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Vanessa Morgado, Ricardo Bettencourt da Silva, Carla Palma

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The environmental pollution by microplastics is well recognized. Microplastics were already detected in various matrices from distinct environmental compartments worldwide, some from remote areas. Various methodologies and techniques have been used to determine microplastic in such matrices, for instance, sediment samples from the ocean bottom. In order to determine microplastics in a sediment matrix, the sample is typically sieved through a 5 mm mesh, digested to remove the organic matter, and density separated to isolate microplastics from the denser part of the sediment. The physical analysis of microplastic consists of visual analysis under a stereomicroscope to determine particle size, colour, and shape. The chemical analysis is performed by an infrared spectrometer coupled to a microscope (micro-FTIR), allowing to the identification of the chemical composition of microplastic, i.e., the type of polymer. Creating legislation and policies to control and manage (micro)plastic pollution is essential to protect the environment, namely the coastal areas. The regulation is defined from the known relevance and trends of the pollution type. This work discusses the assessment of contamination trends of a 700 km² oceanic area affected by contamination heterogeneity, sampling representativeness, and the uncertainty of the analysis of collected samples. The methodology developed consists of objectively identifying meaningful variations of microplastic contamination by the Monte Carlo simulation of all uncertainty sources. This work allowed us to unequivocally conclude that the contamination level of the studied area did not vary significantly between two consecutive years (2018 and 2019) and that PET microplastics are the major type of polymer. The comparison of contamination levels was performed for a 99% confidence level. The developed know-how is crucial for the objective and binding determination of microplastic contamination in relevant environmental compartments.

Keywords: measurement uncertainty, micro-ATR-FTIR, microplastics, ocean contamination, sampling uncertainty

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Authors: Amit G. Dhorde

Abstract:

It is now well established that the global surface air temperatures have increased significantly during the period that followed the industrial revolution. One of the main predictions of climate change is that the occurrences of extreme weather events will increase in future. In many regions of the world, high-temperature extremes have already started occurring with rising frequency. The main objective of the present study is to understand spatial and temporal changes in days with heat and cold wave conditions over southern India. The study area includes the region of India that lies to the south of Tropic of Cancer. To fulfill the objective, daily maximum and minimum temperature data for 80 stations were collected for the period 1969-2006 from National Data Center of India Meteorological Department. After assessing the homogeneity of data, 62 stations were finally selected for the study. Heat and cold waves were classified as slight, moderate and severe based on the criteria given by Indias' meteorological department. For every year, numbers of days experiencing heat and cold wave conditions were computed. This data was analyzed with linear regression to find any existing trend. Further, the time period was divided into four decades to investigate the decadal frequency of the occurrence of heat and cold waves. The results revealed that the average annual temperature over southern India shows an increasing trend, which signifies warming over this area. Further, slight cold waves during winter season have been decreasing at the majority of the stations. The moderate cold waves also show a similar pattern at the majority of the stations. This is an indication of warming winters over the region. Besides this analysis, other extreme indices were also analyzed such as extremely hot days, hot days, very cold nights, cold nights, etc. This analysis revealed that nights are becoming warmer and days are getting warmer over some regions too.

Keywords: heat wave, cold wave, southern India, decadal frequency

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Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Kurias George, Ildefonso Cuesta Romeo, Clara Salueña Pérez, Jordi Sole Olle

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With the growing prevalence of wind energy there is a need, for modeling techniques to evaluate the impact of wind farms on meteorology and oceanography. This study presents an approach that utilizes the WRF (Weather Research and Forecasting )with that include a Wind Farm Parametrization model to simulate the dynamics around Parc Tramuntana project, a offshore wind farm to be located near the Gulf of Roses off the coast of Barcelona, Catalonia. The model incorporates parameterizations for wind turbines enabling a representation of the wind field and how it interacts with the infrastructure of the wind farm. Current results demonstrate that the model effectively captures variations in temeperature, pressure and in both wind speed and direction over time along with their resulting effects on power output from the wind farm. These findings are crucial for optimizing turbine placement and operation thus improving efficiency and sustainability of the wind farm. In addition to focusing on atmospheric interactions, this study delves into the wake effects within the turbines in the farm. A range of meteorological parameters were also considered to offer a comprehensive understanding of the farm's microclimate. The model was tested under different horizontal resolutions and farm layouts to scrutinize the wind farm's effects more closely. These experimental configurations allow for a nuanced understanding of how turbine wakes interact with each other and with the broader atmospheric and oceanic conditions. This modified approach serves as a potent tool for stakeholders in renewable energy, environmental protection, and marine spatial planning. environmental protection and marine spatial planning. It provides a range of information regarding the environmental and socio economic impacts of offshore wind energy projects.

Keywords: weather research and forecasting, wind turbine wake effects, environmental impact, wind farm parametrization, sustainability analysis

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Authors: Weichao Yu, Kai Wen, Weihe Huang, Yang Yang, Jing Gong

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Metal-loss corrosion is a major threat to the safety and integrity of gas pipelines as it may result in the burst failures which can cause severe consequences that may include enormous economic losses as well as the personnel casualties. Therefore, it is important to ensure the corroding pipeline integrity and efficiency, considering the value of wall thickness, which plays an important role in the failure probability of corroding pipeline. Actually, the wall thickness is controlled during pipe purchase stage. For example, the API_SPEC_5L standard regulates the allowable tolerance of the wall thickness from the specified value during the pipe purchase. The allowable wall thickness tolerance will be used to determine the wall thickness distribution characteristic such as the mean value, standard deviation and distribution. Taking the uncertainties of the input variables in the burst limit-state function into account, the reliability approach rather than the deterministic approach will be used to evaluate the failure probability. Moreover, the cost of pipe purchase will be influenced by the allowable wall thickness tolerance. More strict control of the wall thickness usually corresponds to a higher pipe purchase cost. Therefore changing the wall thickness tolerance will vary both the probability of a burst failure and the cost of the pipe. This paper describes an approach to optimize the wall thickness tolerance considering both the safety and economy of corroding pipelines. In this paper, the corrosion burst limit-state function in Annex O of CSAZ662-7 is employed to evaluate the failure probability using the Monte Carlo simulation technique. By changing the allowable wall thickness tolerance, the parameters of the wall thickness distribution in the limit-state function will be changed. Using the reliability approach, the corresponding variations in the burst failure probability will be shown. On the other hand, changing the wall thickness tolerance will lead to a change in cost in pipe purchase. Using the variation of the failure probability and pipe cost caused by changing wall thickness tolerance specification, the optimal allowable tolerance can be obtained, and used to define pipe purchase specifications.

Keywords: allowable tolerance, corroding pipeline segment, operation cost, production cost, reliability approach

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Authors: Sushma Tejwani, Shoruba Dinakaran, Kushal Kacha, K. Bhujang Shetty

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Introduction and aim: It is not unusual to find patients with glaucomatous damage and normal intraocular pressure, and to label a patient as Normal tension glaucoma (NTG) majority of clinicians depend on office Intraocular pressures (IOP) recordings; hence, the concern is that whether we are missing the late night or early morning spikes in this group of patients. Also, ischemia to the optic nerve is one of the presumed causes of damage in these patients, however demonstrating the same has been a challenge. The aim of this study was to evaluate IOP variations and patterns in a series of patients with open angles, glaucomatous discs or fields but normal office IOP, and in addition to identify ischemic factors for true NTG patients. Materials & Methods: This was an observational cross- sectional study from a tertiary care centre. The patients that underwent full day DVT from Jan 2012 to April 2014 were studied. All patients underwent IOP measurement on Goldmann applanation tonometry every 3 hours for 24 hours along with a recording of the blood pressure (BP). Further patients with normal IOP throughout the 24- hour period were evaluated with a cardiologist for echocardiography and carotid Doppler. Results: There were 47 patients and a maximum number of patients studied was in the age group of 50-70 years. A biphasic IOP peak was noted for almost all the patients. Out of the 47 patients, 2 were excluded from analysis as they were on treatment.20 patients (42%) were diagnosed on DVT to have an IOP spike and were then diagnosed as open angle glaucoma and another 25 (55%) were diagnosed to have normal tension glaucoma and were subsequently advised a carotid Doppler and a cardiologists consult. Another interesting finding was that 9 patients had a nocturnal dip in their BP and 3 were found to have carotid artery stenosis. Conclusion: A continuous 24-hour monitoring of the IOP and BP is a very useful albeit mildly cumbersome tool which provides a wealth of information in cases of glaucoma presenting with normal office pressures. It is of great value in differentiating between normal tension glaucoma patients & open angle glaucoma patients. It also helps in timely diagnosis & possible intervention due to referral to a cardiologist in cases of carotid artery stenosis.

Keywords: carotid artery disease in NTG, diurnal variation of IOP, ischemia in glaucoma, normal tension glaucoma

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Authors: E. Duffin, L. Brownlow

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Arson is a crime which can provide exceptional problems to forensic specialists. Its destructive nature makes evidence much harder to find, especially when used to cover up another crime. There is a consistent potential threat of arsonists seeking new and easier ways to set fires. Existing research in this field primarily focuses on the use of accelerants such as petrol, with less attention to other more accessible and harder to detect materials. This includes the growing speculation of potato and maize-based snacks being used as fire accelerants. It was hypothesized that all ‘crisp-type’ snacks in foil packaging had the potential to act as accelerants and would burn readily in the various experiments. To test this hypothesis, a series of small lab-based experiments were undertaken, igniting samples of the snacks. Factors such as ingredients, shape, packaging and calorific value were all taken into consideration. The time (in seconds) spent on fire by the individual snacks was recorded. It was found that all of the snacks tested burnt for statistically similar amounts of time with a p-value of 0.0157. This was followed with a large mock real-life scenario using packets of crisps on fire and car seats to investigate as to the possibility of these snacks being verifiable tools to the arsonist. Here, three full packets of crisps were selected based on variations in burning during the lab experiments. They were each lit with a lighter to initiate burning, then placed onto a car seat to be timed and observed with video cameras. In all three cases, the fire was significant and sustained by the 200-second mark. On the basis of this data, it was concluded that potato and maize-based snacks were viable accelerants of fire. They remain an effective method of starting fires whilst being cheap, accessible, non-suspicious and non-detectable. The results produced supported the hypothesis that all ‘crisp-type’ snacks in foil packaging (that had been tested) had the potential to act as accelerants and would burn readily in the various experiments. This study serves to raise awareness and provide a basis for research and prevention of arson regarding maize and potato-based snacks as fire accelerants.

Keywords: arson, crisps, fires, food

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: P. Caimmi, E. Bele, A. Abolfathi

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Lattice materials are cellular structures composed of a periodic network of beams. They offer high weight-specific mechanical properties and lend themselves to numerous weight-sensitive applications. The periodic internal structure responds to external vibrations through characteristic frequency bandgaps, making these materials suitable for the reduction of noise and vibration. However, the deviation from architectural homogeneity, due to, e.g., manufacturing imperfections, has a strong influence on the mechanical properties and vibration response of these materials. In this work, we present results on the influence of geometric imperfections on the vibration response of hexagonal lattices. Three classes of geometrical variables are used: the characteristics of the architecture (relative density, ligament length/cell size ratio), imperfection type (degree of non-periodicity, cracks, hard inclusions) and defect morphology (size, distribution). Test specimens with controlled size and distribution of imperfections are manufactured through selective laser sintering. The Frequency Response Functions (FRFs) in the form of accelerance are measured, and the modal shapes are captured through a high-speed camera. The finite element method is used to provide insights on the extension of these results to semi-infinite lattices. An updating procedure is conducted to increase the reliability of numerical simulation results compared to experimental measurements. This is achieved by updating the boundary conditions and material stiffness. Variations in FRFs of periodic structures due to changes in the relative density of the constituent unit cell are analysed. The effects of geometric imperfections on the dynamic response of periodic structures are investigated. The findings can be used to open up the opportunity for tailoring these lattice materials to achieve optimal amplitude attenuations at specific frequency ranges.

Keywords: lattice architectures, geometric imperfections, vibration attenuation, experimental modal analysis

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Authors: Yiyuan Tao

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Under the impact of global warming together with the intensification of human activities, the hydrological regimes may be altered, and the traditional stationary assumption was no longer satisfied. However, most of the current design standards of water infrastructures were still based on the hypothesis of stationarity, which may inevitably result in severe biases. Many critical impacts of climate on ecosystems, society, and the economy are controlled by extreme events rather than mean values. Therefore, it is of great significance to identify the non-stationarity of precipitation extremes and model the precipitation extremes in a nonstationary framework. The Wei River Basin (WRB), located in a continental monsoon climate zone in China, is selected as a case study in this study. Six extreme precipitation indices were employed to investigate the changing patterns and stationarity of precipitation extremes in the WRB. To identify if precipitation extremes are stationary, the Mann-Kendall trend test and the Pettitt test, which is used to examine the occurrence of abrupt changes are adopted in this study. Extreme precipitation indices series are fitted with non-stationary distributions that selected from six widely used distribution functions: Gumbel, lognormal, Weibull, gamma, generalized gamma and exponential distributions by means of the time-varying moments model generalized additive models for location, scale and shape (GAMLSS), where the distribution parameters are defined as a function of time. The results indicate that: (1) the trends were not significant for the whole WRB, but significant positive/negative trends were still observed in some stations, abrupt changes for consecutive wet days (CWD) mainly occurred in 1985, and the assumption of stationarity is invalid for some stations; (2) for these nonstationary extreme precipitation indices series with significant positive/negative trends, the GAMLSS models are able to capture well the temporal variations of the indices, and perform better than the stationary model. Finally, the differences between the quantiles of nonstationary and stationary models are analyzed, which highlight the importance of nonstationary modeling of precipitation extremes in the WRB.

Keywords: extreme precipitation, GAMLSSS, non-stationary, Wei River Basin

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Authors: Young-Tae Lee, Hee-Chang Lim

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

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

Procedia PDF Downloads 349

Authors: Etleva Hamzaraj, Eva Kica, Anila Paparisto, Pranvera Lazo

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Worldwide demographic developments of recent decades have been associated with negative environmental consequences. For this reason, there is a growing interest in assessing the state of natural ecosystems or assessing human impact on them. In this respect, this study aims to evaluate the change in water quality of the Mat River for a period of about ten years to highlight human impact. In one year, period of study, several biological and environmental parameters are determined to evaluate river water quality, and the data collected are compared with those of a similar study in 2007. Samples are collected every month in five stations evenly distributed along the river. Total coliform bacteria, the number of heterotrophic bacteria in water, and benthic macroinvertebrates are used as biological parameters of water quality. The most probable number index is used for evaluation of total coliform bacteria in water, while the number of heterotrophic bacteria is determined by counting colonies on plates with Plate Count Agar, cultivated with 0.1 ml sample after series dilutions. Benthic macroinvertebrates are analyzed by the number of individuals per taxa, the value of biotic index, EPT Richness Index value and tolerance value. Environmental parameters like pH, temperature, and electrical conductivity are measured onsite. As expected, the bacterial load was higher near urban areas, and the pollution increased with the course of the river. The maximum concentration of fecal coliforms was 1100 MPN/100 ml in summer and near the most urbanized area of the river. The data collected during this study show that after about ten years, there is a change in water quality of Mat River. According to a similar study carried out in 2007, the water of Mat River was of ‘excellent’ quality. But, according to this study, the water was classified as of ‘excellent’ quality only in one sampling site, near river source, while in all other stations was of ‘good’ quality. This result is based on biological and environmental parameters measured. The human impact on the quality of water of Mat River is more than evident.

Keywords: water quality, coliform bacteria, MPN index, benthic macroinvertebrates, biotic index

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Authors: Christian Fercher, Jiaul Islam, Simon R. Corrie

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Fluorescence-based immunodiagnostics are an emerging field in biosensor development and exhibit several advantages over traditional detection methods. While various affinity biosensors have been developed to generate a fluorescence signal upon sensing varying concentrations of analytes, reagentless, reversible, and continuous monitoring of complex biological samples remains challenging. Here, we aimed to genetically engineer biosensors based on single-chain antibody fragments (scFv) that are site-specifically labeled with environmentally sensitive fluorescent unnatural amino acids (UAA). A rational design approach resulted in quantifiable analyte-dependent changes in peak fluorescence emission wavelength and enabled antigen detection in vitro. Incorporation of a polarity indicator within the topological neighborhood of the antigen-binding interface generated a titratable wavelength blueshift with nanomolar detection limits. In order to ensure continuous analyte monitoring, scFv candidates with fast binding and dissociation kinetics were selected from a genetic library employing a high-throughput phage display and affinity screening approach. Initial rankings were further refined towards rapid dissociation kinetics using bio-layer interferometry (BLI) and surface plasmon resonance (SPR). The most promising candidates were expressed, purified to homogeneity, and tested for their potential to detect biomarkers in a continuous microfluidic-based assay. Variations of dissociation kinetics within an order of magnitude were achieved without compromising the specificity of the antibody fragments. This approach is generally applicable to numerous antibody/antigen combinations and currently awaits integration in a wide range of assay platforms for one-step protein quantification.

Keywords: antibody engineering, biosensor, phage display, unnatural amino acids

Procedia PDF Downloads 124

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

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

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

Procedia PDF Downloads 205

Authors: O. Benabdeljelil, A. Karioun, S. Amami, R. Rouger, M. Hamidine

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A model for preventing the risks related to climate conditions in the agricultural sector is presented. It will determine the yearly optimum premium to be paid by a producer in order to reach his required turnover. The model is based on both climatic stability and 'soft' responses of usually grown species to average climate variations at the same place and inside a safety ball which can be determined from past meteorological data. This allows the use of linear regression expression for dependence of production result in terms of driving meteorological parameters, the main ones of which are daily average sunlight, rainfall and temperature. By simple best parameter fit from the expert table drawn with professionals, optimal representation of yearly production is determined from records of previous years, and yearly payback is evaluated from minimum yearly produced turnover. The model also requires accurate pricing of commodity at N+1. Therefore, a pricing model is developed using 3 state variables, namely the spot price, the difference between the mean-term and the long-term forward price, and the long-term structure of the model. The use of historical data enables to calibrate the parameters of state variables, and allows the pricing of commodity. Application to beet sugar underlines pricer precision. Indeed, the percentage of accuracy between computed result and real world is 99,5%. Optimal premium is then deduced and gives the producer a useful bound for negotiating an offer by insurance companies to effectively protect its harvest. The application to beet production in French Oise department illustrates the reliability of present model with as low as 6% difference between predicted and real data. The model can be adapted to almost any agricultural field by changing state parameters and calibrating their associated coefficients.

Keywords: agriculture, production model, optimal price, meteorological factors, 3-factor model, parameter calibration, forward price

Procedia PDF Downloads 354

Authors: A. F. Agboola, B. R. O. Omidiwura, A. Oyeyemi, E. A. Iyayi, A. S. Adelani

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Dietary cholesterol has elicited the most public interest as it relates with coronary heart disease. Thus, humans have been paying more attention to health, thereby reducing consumption of cholesterol enriched food. Egg is considered as one of the major sources of human dietary cholesterol. However, an alternative way to reduce the potential cholesterolemic effect of eggs is to modify the fatty acid composition of the yolk. The effect of palm oil (PO), soybean oil (SO), sesame seed oil (SSO) and fish oil (FO) supplementation in the diets of layers on egg yolk fatty acid, cholesterol, egg production and egg quality parameters were evaluated in a 42-day feeding trial. One hundred and five Isa Brown laying hens of 34 weeks of age were randomly distributed into seven groups of five replicates and three birds per replicate in a completely randomized design. Seven corn-soybean basal diets (BD) were formulated: BD+No oil (T1), BD+1.5% PO (T2), BD+1.5% SO (T3), BD+1.5% SSO (T4), BD+1.5% FO (T5), BD+0.75% SO+0.75% FO (T6) and BD+0.75% SSO+0.75% FO (T7). Five eggs were randomly sampled at day 42 from each replicate to assay for the cholesterol, fatty acid profile of egg yolk and egg quality assessment. Results showed that there were no significant (P>0.05) differences observed in production performance, egg cholesterol and egg quality parameters except for yolk height, albumen height, yolk index, egg shape index, haugh unit, and yolk colour. There were no significant differences (P>0.05) observed in total cholesterol, high density lipoprotein and low density lipoprotein levels of egg yolk across the treatments. However, diets had effect (P<0.05) on TAG (triacylglycerol) and VLDL (very low density lipoprotein) of the egg yolk. The highest TAG (603.78 mg/dl) and VLDL values (120.76 mg/dl) were recorded in eggs of hens on T4 (1.5% sesame seed oil) and was similar to those on T3 (1.5% soybean oil), T5 (1.5% fish oil) and T6 (0.75% soybean oil + 0.75% fish oil). However, results revealed a significant (P<0.05) variations on eggs’ summation of polyunsaturated fatty acid (PUFA). In conclusion, it is suggested that dietary oils could be included in layers’ diets to produce designer eggs low in cholesterol and high in PUFA especially omega-3 fatty acids.

Keywords: dietary oils, egg cholesterol, egg fatty acid profile, egg quality parameters

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Authors: Nayab Kanwal, Noor Us Saher

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The management and utilization of food resources is becoming a big issue due to rapid urbanization, wastage and non-sustainable use of food, especially in developing countries. Therefore, the use of seafood as alternative sources is strongly promoted worldwide. Marine pollution strongly affects marine organisms, which ultimately decreases their export quality. The monitoring of contamination in marine organisms is a good indicator of the environmental quality as well as seafood quality. Monitoring the accumulation of chemical elements within various tissues of organisms has become a useful tool to survey current or chronic levels of heavy metal exposure within an environment. In this perspective, this study was carried out to compare the previous and current levels (Year 2012 and 2014) of heavy metals (Cd, Pb, Cr, Cu and Zn) in crabs marketed in Karachi and to estimate the toxicological risk associated with their intake. The accumulation of metals in marine organisms, both essential (Cu and Zn) and toxic (Pb, Cd and Cr), natural and anthropogenic, is an actual food safety issue. Significant (p>0.05) variations in metal concentrations were found in all crab species between the two years, with most of the metals showing high accumulation in 2012. For toxicological risk assessment, EWI (Estimated weekly intake), Target Hazard quotient (THQ) and cancer risk (CR) were also assessed and high EWI, Non- cancer risk (THQ < 1) showed that there is no serious threat associated with the consumption of shellfish species on Karachi coast. The Cancer risk showed the highest risk from Cd and Pb pollution if consumed in excess. We summarize key environmental health research on health effects associated with exposure to contaminated seafood. It could be concluded that considering the Pakistan coast, these edible species may be sensitive and vulnerable to the adverse effects of environmental contaminants; more attention should be paid to the Pb and Cd metal bioaccumulation and to toxicological risks to seafood and consumers.

Keywords: cancer risk, edible crabs, heavy metals pollution, risk assessment

Procedia PDF Downloads 355